Search is not available for this dataset
title
string | authors
string | abstract
string | pdf
string | supp
string | arXiv
string | bibtex
string | url
string | detail_url
string | tags
string | string |
|---|---|---|---|---|---|---|---|---|---|---|
Learning Rotation-Equivariant Features for Visual Correspondence | Jongmin Lee, Byungjin Kim, Seungwook Kim, Minsu Cho | Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effectively learns to obtain rotation-equivariant features and their orientations explicitly, without having to perform sophisticated data augmentations. The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension. Our group aligning technique achieves rotation-invariance without any collapse of the group dimension and thus eschews loss of discriminability. The proposed method is trained end-to-end in a self-supervised manner, where we use an orientation alignment loss for the orientation estimation and a contrastive descriptor loss for robust local descriptors to geometric/photometric variations. Our method demonstrates state-of-the-art matching accuracy among existing rotation-invariant descriptors under varying rotation and also shows competitive results when transferred to the task of keypoint matching and camera pose estimation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Learning_Rotation-Equivariant_Features_for_Visual_Correspondence_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Learning_Rotation-Equivariant_Features_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Learning_Rotation-Equivariant_Features_for_Visual_Correspondence_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Learning_Rotation-Equivariant_Features_for_Visual_Correspondence_CVPR_2023_paper.html | CVPR 2023 | null |
DexArt: Benchmarking Generalizable Dexterous Manipulation With Articulated Objects | Chen Bao, Helin Xu, Yuzhe Qin, Xiaolong Wang | To enable general-purpose robots, we will require the robot to operate daily articulated objects as humans do. Current robot manipulation has heavily relied on using a parallel gripper, which restricts the robot to a limited set of objects. On the other hand, operating with a multi-finger robot hand will allow better approximation to human behavior and enable the robot to operate on diverse articulated objects. To this end, we propose a new benchmark called DexArt, which involves Dexterous manipulation with Articulated objects in a physical simulator. In our benchmark, we define multiple complex manipulation tasks, and the robot hand will need to manipulate diverse articulated objects within each task. Our main focus is to evaluate the generalizability of the learned policy on unseen articulated objects. This is very challenging given the high degrees of freedom of both hands and objects. We use Reinforcement Learning with 3D representation learning to achieve generalization. Through extensive studies, we provide new insights into how 3D representation learning affects decision making in RL with 3D point cloud inputs. More details can be found at https://www.chenbao.tech/dexart/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bao_DexArt_Benchmarking_Generalizable_Dexterous_Manipulation_With_Articulated_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bao_DexArt_Benchmarking_Generalizable_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2305.05706 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_DexArt_Benchmarking_Generalizable_Dexterous_Manipulation_With_Articulated_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_DexArt_Benchmarking_Generalizable_Dexterous_Manipulation_With_Articulated_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection | Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, Ting Chen | Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multi-level information. In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework. First, to strengthen the constraints on anomalous data, we introduce a denoising procedure that allows the student network to learn more robust representations. From synthetically corrupted normal images, we train the student network to match the teacher network feature of the same images without corruption. Second, to fuse the multi-level S-T features adaptively, we train a segmentation network with rich supervision from synthetic anomaly masks, achieving a substantial performance improvement. Experiments on the industrial inspection benchmark dataset demonstrate that our method achieves state-of-the-art performance, 98.6% on image-level AUC, 75.8% on pixel-level average precision, and 76.4% on instance-level average precision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_DeSTSeg_Segmentation_Guided_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11317 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Rate Estimator and Unsupervised Learning for Efficient Distributed Image Analytics in Split-DNN Models | Nilesh Ahuja, Parual Datta, Bhavya Kanzariya, V. Srinivasa Somayazulu, Omesh Tickoo | Thanks to advances in computer vision and AI, there has been a large growth in the demand for cloud-based visual analytics in which images captured by a low-powered edge device are transmitted to the cloud for analytics. Use of conventional codecs (JPEG, MPEG, HEVC, etc.) for compressing such data introduces artifacts that can seriously degrade the performance of the downstream analytic tasks. Split-DNN computing has emerged as a paradigm to address such usages, in which a DNN is partitioned into a client-side portion and a server side portion. Low-complexity neural networks called 'bottleneck units' are introduced at the split point to transform the intermediate layer features into a lower-dimensional representation better suited for compression and transmission. Optimizing the pipeline for both compression and task-performance requires high-quality estimates of the information-theoretic rate of the intermediate features. Most works on compression for image analytics use heuristic approaches to estimate the rate, leading to suboptimal performance. We propose a high-quality 'neural rate-estimator' to address this gap. We interpret the lower-dimensional bottleneck output as a latent representation of the intermediate feature and cast the rate-distortion optimization problem as one of training an equivalent variational auto-encoder with an appropriate loss function. We show that this leads to improved rate-distortion outcomes. We further show that replacing supervised loss terms (such as cross-entropy loss) by distillation-based losses in a teacher-student framework allows for unsupervised training of bottleneck units without the need for explicit training labels. This makes our method very attractive for real world deployments where access to labeled training data is difficult or expensive. We demonstrate that our method outperforms several state-of-the-art methods by obtaining improved task accuracy at lower bitrates on image classification and semantic segmentation tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ahuja_Neural_Rate_Estimator_and_Unsupervised_Learning_for_Efficient_Distributed_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ahuja_Neural_Rate_Estimator_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ahuja_Neural_Rate_Estimator_and_Unsupervised_Learning_for_Efficient_Distributed_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ahuja_Neural_Rate_Estimator_and_Unsupervised_Learning_for_Efficient_Distributed_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Object Pop-Up: Can We Infer 3D Objects and Their Poses From Human Interactions Alone? | Ilya A. Petrov, Riccardo Marin, Julian Chibane, Gerard Pons-Moll | The intimate entanglement between objects affordances and human poses is of large interest, among others, for behavioural sciences, cognitive psychology, and Computer Vision communities. In recent years, the latter has developed several object-centric approaches: starting from items, learning pipelines synthesizing human poses and dynamics in a realistic way, satisfying both geometrical and functional expectations. However, the inverse perspective is significantly less explored: Can we infer 3D objects and their poses from human interactions alone? Our investigation follows this direction, showing that a generic 3D human point cloud is enough to pop up an unobserved object, even when the user is just imitating a functionality (e.g., looking through a binocular) without involving a tangible counterpart. We validate our method qualitatively and quantitatively, with synthetic data and sequences acquired for the task, showing applicability for XR/VR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Petrov_Object_Pop-Up_Can_We_Infer_3D_Objects_and_Their_Poses_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Petrov_Object_Pop-Up_Can_We_Infer_3D_Objects_and_Their_Poses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Petrov_Object_Pop-Up_Can_We_Infer_3D_Objects_and_Their_Poses_CVPR_2023_paper.html | CVPR 2023 | null |
VoP: Text-Video Co-Operative Prompt Tuning for Cross-Modal Retrieval | Siteng Huang, Biao Gong, Yulin Pan, Jianwen Jiang, Yiliang Lv, Yuyuan Li, Donglin Wang | Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by tuning the backbone with additional heavy modules, which not only brings huge computational burdens with much more parameters, but also leads to the knowledge forgetting from upstream models. In this work, we propose the VoP: Text-Video Co-operative Prompt Tuning for efficient tuning on the text-video retrieval task. The proposed VoP is an end-to-end framework with both video & text prompts introducing, which can be regarded as a powerful baseline with only 0.1% trainable parameters. Further, based on the spatio-temporal characteristics of videos, we develop three novel video prompt mechanisms to improve the performance with different scales of trainable parameters. The basic idea of the VoP enhancement is to model the frame position, frame context, and layer function with specific trainable prompts, respectively. Extensive experiments show that compared to full fine-tuning, the enhanced VoP achieves a 1.4% average R@1 gain across five text-video retrieval benchmarks with 6x less parameter overhead. The code will be available at https://github.com/bighuang624/VoP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_VoP_Text-Video_Co-Operative_Prompt_Tuning_for_Cross-Modal_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_VoP_Text-Video_Co-Operative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12764 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_VoP_Text-Video_Co-Operative_Prompt_Tuning_for_Cross-Modal_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_VoP_Text-Video_Co-Operative_Prompt_Tuning_for_Cross-Modal_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR | Aneeshan Sain, Ayan Kumar Bhunia, Subhadeep Koley, Pinaki Nath Chowdhury, Soumitri Chattopadhyay, Tao Xiang, Yi-Zhe Song | This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the art by 11%. This is not via complicated design though, but by addressing two critical issues facing the community (i) the gold standard triplet loss does not enforce holistic latent space geometry, and (ii) there are never enough sketches to train a high accuracy model. For the former, we propose a simple modification to the standard triplet loss, that explicitly enforces separation amongst photos/sketch instances. For the latter, we put forward a novel knowledge distillation module can leverage photo data for model training. Both modules are then plugged into a novel plug-n-playable training paradigm that allows for more stable training. More specifically, for (i) we employ an intra-modal triplet loss amongst sketches to bring sketches of the same instance closer from others, and one more amongst photos to push away different photo instances while bringing closer a structurally augmented version of the same photo (offering a gain of 4-6%). To tackle (ii), we first pre-train a teacher on the large set of unlabelled photos over the aforementioned intra-modal photo triplet loss. Then we distill the contextual similarity present amongst the instances in the teacher's embedding space to that in the student's embedding space, by matching the distribution over inter-feature distances of respective samples in both embedding spaces (delivering a further gain of 4-5%). Apart from outperforming prior arts significantly, our model also yields satisfactory results on generalising to new classes. Project page: https://aneeshan95.github.io/Sketch_PVT/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Sain_Exploiting_Unlabelled_Photos_for_Stronger_Fine-Grained_SBIR_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sain_Exploiting_Unlabelled_Photos_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13779 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sain_Exploiting_Unlabelled_Photos_for_Stronger_Fine-Grained_SBIR_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sain_Exploiting_Unlabelled_Photos_for_Stronger_Fine-Grained_SBIR_CVPR_2023_paper.html | CVPR 2023 | null |
You Do Not Need Additional Priors or Regularizers in Retinex-Based Low-Light Image Enhancement | Huiyuan Fu, Wenkai Zheng, Xiangyu Meng, Xin Wang, Chuanming Wang, Huadong Ma | Images captured in low-light conditions often suffer from significant quality degradation. Recent works have built a large variety of deep Retinex-based networks to enhance low-light images. The Retinex-based methods require decomposing the image into reflectance and illumination components, which is a highly ill-posed problem and there is no available ground truth. Previous works addressed this problem by imposing some additional priors or regularizers. However, finding an effective prior or regularizer that can be applied in various scenes is challenging, and the performance of the model suffers from too many additional constraints. We propose a contrastive learning method and a self-knowledge distillation method that allow training our Retinex-based model for Retinex decomposition without elaborate hand-crafted regularization functions. Rather than estimating reflectance and illuminance images and representing the final images as their element-wise products as in previous works, our regularizer-free Retinex decomposition and synthesis network (RFR) extracts reflectance and illuminance features and synthesizes them end-to-end. In addition, we propose a loss function for contrastive learning and a progressive learning strategy for self-knowledge distillation. Extensive experimental results demonstrate that our proposed methods can achieve superior performance compared with state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_You_Do_Not_Need_Additional_Priors_or_Regularizers_in_Retinex-Based_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_You_Do_Not_Need_Additional_Priors_or_Regularizers_in_Retinex-Based_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_You_Do_Not_Need_Additional_Priors_or_Regularizers_in_Retinex-Based_CVPR_2023_paper.html | CVPR 2023 | null |
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification | Meike Nauta, Jörg Schlötterer, Maurice van Keulen, Christin Seifert | Interpretable methods based on prototypical patches recognize various components in an image in order to explain their reasoning to humans. However, existing prototype-based methods can learn prototypes that are not in line with human visual perception, i.e., the same prototype can refer to different concepts in the real world, making interpretation not intuitive. Driven by the principle of explainability-by-design, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision. PIP-Net can be interpreted as a sparse scoring sheet where the presence of a prototypical part in an image adds evidence for a class. The model can also abstain from a decision for out-of-distribution data by saying "I haven't seen this before". We only use image-level labels and do not rely on any part annotations. PIP-Net is globally interpretable since the set of learned prototypes shows the entire reasoning of the model. A smaller local explanation locates the relevant prototypes in one image. We show that our prototypes correlate with ground-truth object parts, indicating that PIP-Net closes the "semantic gap" between latent space and pixel space. Hence, our PIP-Net with interpretable prototypes enables users to interpret the decision making process in an intuitive, faithful and semantically meaningful way. Code is available at https://github.com/M-Nauta/PIPNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nauta_PIP-Net_Patch-Based_Intuitive_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
SCADE: NeRFs from Space Carving With Ambiguity-Aware Depth Estimates | Mikaela Angelina Uy, Ricardo Martin-Brualla, Leonidas Guibas, Ke Li | Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple 2D input views. However, a well-known drawback of NeRFs is the less-than-ideal performance under a small number of views, due to insufficient constraints enforced by volumetric rendering. To address this issue, we introduce SCADE, a novel technique that improves NeRF reconstruction quality on sparse, unconstrained input views for in-the-wild indoor scenes. To constrain NeRF reconstruction, we leverage geometric priors in the form of per-view depth estimates produced with state-of-the-art monocular depth estimation models, which can generalize across scenes. A key challenge is that monocular depth estimation is an ill-posed problem, with inherent ambiguities. To handle this issue, we propose a new method that learns to predict, for each view, a continuous, multimodal distribution of depth estimates using conditional Implicit Maximum Likelihood Estimation (cIMLE). In order to disambiguate exploiting multiple views, we introduce an original space carving loss that guides the NeRF representation to fuse multiple hypothesized depth maps from each view and distill from them a common geometry that is consistent with all views. Experiments show that our approach enables higher fidelity novel view synthesis from sparse views. Our project page can be found at https://scade-spacecarving-nerfs.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Uy_SCADE_NeRFs_from_Space_Carving_With_Ambiguity-Aware_Depth_Estimates_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Uy_SCADE_NeRFs_from_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13582 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Uy_SCADE_NeRFs_from_Space_Carving_With_Ambiguity-Aware_Depth_Estimates_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Uy_SCADE_NeRFs_from_Space_Carving_With_Ambiguity-Aware_Depth_Estimates_CVPR_2023_paper.html | CVPR 2023 | null |
Re-Thinking Model Inversion Attacks Against Deep Neural Networks | Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, Ngai-Man Cheung | Model inversion (MI) attacks aim to infer and reconstruct private training data by abusing access to a model. MI attacks have raised concerns about the leaking of sensitive information (e.g. private face images used in training a face recognition system). Recently, several algorithms for MI have been proposed to improve the attack performance. In this work, we revisit MI, study two fundamental issues pertaining to all state-of-the-art (SOTA) MI algorithms, and propose solutions to these issues which lead to a significant boost in attack performance for all SOTA MI. In particular, our contributions are two-fold: 1) We analyze the optimization objective of SOTA MI algorithms, argue that the objective is sub-optimal for achieving MI, and propose an improved optimization objective that boosts attack performance significantly. 2) We analyze "MI overfitting", show that it would prevent reconstructed images from learning semantics of training data, and propose a novel "model augmentation" idea to overcome this issue. Our proposed solutions are simple and improve all SOTA MI attack accuracy significantly. E.g., in the standard CelebA benchmark, our solutions improve accuracy by 11.8% and achieve for the first time over 90% attack accuracy. Our findings demonstrate that there is a clear risk of leaking sensitive information from deep learning models. We urge serious consideration to be given to the privacy implications. Our code, demo, and models are available at https://ngoc-nguyen-0.github.io/re-thinking_model_inversion_attacks/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Nguyen_Re-Thinking_Model_Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nguyen_Re-Thinking_Model_Inversion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01669 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Re-Thinking_Model_Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Re-Thinking_Model_Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions | Dongshuo Yin, Yiran Yang, Zhechao Wang, Hongfeng Yu, Kaiwen Wei, Xian Sun | Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard technique for achieving state-of-the-art performance on computer vision benchmarks. However, fine-tuning the whole model with millions of parameters is inefficient as it requires storing a same-sized new model copy for each task. In this work, we propose LoRand, a method for fine-tuning large-scale vision models with a better trade-off between task performance and the number of trainable parameters. LoRand generates tiny adapter structures with low-rank synthesis while keeping the original backbone parameters fixed, resulting in high parameter sharing. To demonstrate LoRand's effectiveness, we implement extensive experiments on object detection, semantic segmentation, and instance segmentation tasks. By only training a small percentage (1% to 3%) of the pre-trained backbone parameters, LoRand achieves comparable performance to standard fine-tuning on COCO and ADE20K and outperforms fine-tuning in low-resource PASCAL VOC dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_1_VS_100_Parameter-Efficient_Low_Rank_Adapter_for_Dense_Predictions_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_1_VS_100_Parameter-Efficient_Low_Rank_Adapter_for_Dense_Predictions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_1_VS_100_Parameter-Efficient_Low_Rank_Adapter_for_Dense_Predictions_CVPR_2023_paper.html | CVPR 2023 | null |
ResFormer: Scaling ViTs With Multi-Resolution Training | Rui Tian, Zuxuan Wu, Qi Dai, Han Hu, Yu Qiao, Yu-Gang Jiang | Vision Transformers (ViTs) have achieved overwhelming success, yet they suffer from vulnerable resolution scalability, i.e., the performance drops drastically when presented with input resolutions that are unseen during training. We introduce, ResFormer, a framework that is built upon the seminal idea of multi-resolution training for improved performance on a wide spectrum of, mostly unseen, testing resolutions. In particular, ResFormer operates on replicated images of different resolutions and enforces a scale consistency loss to engage interactive information across different scales. More importantly, to alternate among varying resolutions effectively, especially novel ones in testing, we propose a global-local positional embedding strategy that changes smoothly conditioned on input sizes. We conduct extensive experiments for image classification on ImageNet. The results provide strong quantitative evidence that ResFormer has promising scaling abilities towards a wide range of resolutions. For instance, ResFormer- B-MR achieves a Top-1 accuracy of 75.86% and 81.72% when evaluated on relatively low and high resolutions respectively (i.e., 96 and 640), which are 48% and 7.49% better than DeiT-B. We also demonstrate, moreover, ResFormer is flexible and can be easily extended to semantic segmentation, object detection and video action recognition. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_ResFormer_Scaling_ViTs_With_Multi-Resolution_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_ResFormer_Scaling_ViTs_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00776 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_ResFormer_Scaling_ViTs_With_Multi-Resolution_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_ResFormer_Scaling_ViTs_With_Multi-Resolution_Training_CVPR_2023_paper.html | CVPR 2023 | null |
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model | Shengkun Tang, Yaqing Wang, Zhenglun Kong, Tianchi Zhang, Yao Li, Caiwen Ding, Yanzhi Wang, Yi Liang, Dongkuan Xu | Large-scale transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with increasing model size, resulting in slow inference speed and increased cost for severing. While some certain predictions benefit from the full complexity of the large-scale model, not all of input need the same amount of computation to conduct, potentially leading to computation resource waste. To handle this challenge, early exiting is proposed to adaptively allocate computational power in term of input complexity to improve inference efficiency. The existing early exiting strategies usually adopt output confidence based on intermediate layers as a proxy of input complexity to incur the decision of skipping following layers. However, such strategies cannot apply to encoder in the widely-used unified architecture with both encoder and decoder due to difficulty of output confidence estimation in the encoder. It is suboptimal in term of saving computation power to ignore the early exiting in encoder component. To handle this challenge, we propose a novel early exiting strategy for unified visual language models, which allows dynamically skip the layers in encoder and decoder simultaneously in term of input layer-wise similarities with multiple times of early exiting, namely MuE. By decomposing the image and text modalities in the encoder, MuE is flexible and can skip different layers in term of modalities, advancing the inference efficiency while minimizing performance drop. Experiments on the SNLI-VE and MS COCO datasets show that the proposed approach MuE can reduce inference time by up to 50% and 40% while maintaining 99% and 96% performance respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_You_Need_Multiple_Exiting_Dynamic_Early_Exiting_for_Accelerating_Unified_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.11152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_You_Need_Multiple_Exiting_Dynamic_Early_Exiting_for_Accelerating_Unified_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_You_Need_Multiple_Exiting_Dynamic_Early_Exiting_for_Accelerating_Unified_CVPR_2023_paper.html | CVPR 2023 | null |
CloSET: Modeling Clothed Humans on Continuous Surface With Explicit Template Decomposition | Hongwen Zhang, Siyou Lin, Ruizhi Shao, Yuxiang Zhang, Zerong Zheng, Han Huang, Yandong Guo, Yebin Liu | Creating animatable avatars from static scans requires the modeling of clothing deformations in different poses. Existing learning-based methods typically add pose-dependent deformations upon a minimally-clothed mesh template or a learned implicit template, which have limitations in capturing details or hinder end-to-end learning. In this paper, we revisit point-based solutions and propose to decompose explicit garment-related templates and then add pose-dependent wrinkles to them. In this way, the clothing deformations are disentangled such that the pose-dependent wrinkles can be better learned and applied to unseen poses. Additionally, to tackle the seam artifact issues in recent state-of-the-art point-based methods, we propose to learn point features on a body surface, which establishes a continuous and compact feature space to capture the fine-grained and pose-dependent clothing geometry. To facilitate the research in this field, we also introduce a high-quality scan dataset of humans in real-world clothing. Our approach is validated on two existing datasets and our newly introduced dataset, showing better clothing deformation results in unseen poses. The project page with code and dataset can be found at https://www.liuyebin.com/closet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_CloSET_Modeling_Clothed_Humans_on_Continuous_Surface_With_Explicit_Template_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_CloSET_Modeling_Clothed_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03167 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_CloSET_Modeling_Clothed_Humans_on_Continuous_Surface_With_Explicit_Template_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_CloSET_Modeling_Clothed_Humans_on_Continuous_Surface_With_Explicit_Template_CVPR_2023_paper.html | CVPR 2023 | null |
BUOL: A Bottom-Up Framework With Occupancy-Aware Lifting for Panoptic 3D Scene Reconstruction From a Single Image | Tao Chu, Pan Zhang, Qiong Liu, Jiaqi Wang | Understanding and modeling the 3D scene from a single image is a practical problem. A recent advance proposes a panoptic 3D scene reconstruction task that performs both 3D reconstruction and 3D panoptic segmentation from a single image. Although having made substantial progress, recent works only focus on top-down approaches that fill 2D instances into 3D voxels according to estimated depth, which hinders their performance by two ambiguities. (1) instance-channel ambiguity: The variable ids of instances in each scene lead to ambiguity during filling voxel channels with 2D information, confusing the following 3D refinement. (2) voxel-reconstruction ambiguity: 2D-to-3D lifting with estimated single view depth only propagates 2D information onto the surface of 3D regions, leading to ambiguity during the reconstruction of regions behind the frontal view surface. In this paper, we propose BUOL, a Bottom-Up framework with Occupancy-aware Lifting to address the two issues for panoptic 3D scene reconstruction from a single image. For instance-channel ambiguity, a bottom-up framework lifts 2D information to 3D voxels based on deterministic semantic assignments rather than arbitrary instance id assignments. The 3D voxels are then refined and grouped into 3D instances according to the predicted 2D instance centers. For voxel-reconstruction ambiguity, the estimated multi-plane occupancy is leveraged together with depth to fill the whole regions of things and stuff. Our method shows a tremendous performance advantage over state-of-the-art methods on synthetic dataset 3D-Front and real-world dataset Matterport3D, respectively. Code and models will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chu_BUOL_A_Bottom-Up_Framework_With_Occupancy-Aware_Lifting_for_Panoptic_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chu_BUOL_A_Bottom-Up_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chu_BUOL_A_Bottom-Up_Framework_With_Occupancy-Aware_Lifting_for_Panoptic_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chu_BUOL_A_Bottom-Up_Framework_With_Occupancy-Aware_Lifting_for_Panoptic_3D_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Video-Moment Retrieval and Step-Captioning | Abhay Zala, Jaemin Cho, Satwik Kottur, Xilun Chen, Barlas Oguz, Yashar Mehdad, Mohit Bansal | There is growing interest in searching for information from large video corpora. Prior works have studied relevant tasks, such as text-based video retrieval, moment retrieval, video summarization, and video captioning in isolation, without an end-to-end setup that can jointly search from video corpora and generate summaries. Such an end-to-end setup would allow for many interesting applications, e.g., a text-based search that finds a relevant video from a video corpus, extracts the most relevant moment from that video, and segments the moment into important steps with captions. To address this, we present the HiREST (HIerarchical REtrieval and STep-captioning) dataset and propose a new benchmark that covers hierarchical information retrieval and visual/textual stepwise summarization from an instructional video corpus. HiREST consists of 3.4K text-video pairs from an instructional video dataset, where 1.1K videos have annotations of moment spans relevant to text query and breakdown of each moment into key instruction steps with caption and timestamps (totaling 8.6K step captions). Our hierarchical benchmark consists of video retrieval, moment retrieval, and two novel moment segmentation and step captioning tasks. In moment segmentation, models break down a video moment into instruction steps and identify start-end boundaries. In step captioning, models generate a textual summary for each step. We also present starting point task-specific and end-to-end joint baseline models for our new benchmark. While the baseline models show some promising results, there still exists large room for future improvement by the community. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zala_Hierarchical_Video-Moment_Retrieval_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16406 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.html | CVPR 2023 | null |
PROB: Probabilistic Objectness for Open World Object Detection | Orr Zohar, Kuan-Chieh Wang, Serena Yeung | Open World Object Detection (OWOD) is a new and challenging computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. In addition to detecting and classifying seen/labeled objects, OWOD algorithms are expected to detect novel/unknown objects - which can be classified and incrementally learned. In standard OD, object proposals not overlapping with a labeled object are automatically classified as background. Therefore, simply applying OD methods to OWOD fails as unknown objects would be predicted as background. The challenge of detecting unknown objects stems from the lack of supervision in distinguishing unknown objects and background object proposals. Previous OWOD methods have attempted to overcome this issue by generating supervision using pseudo-labeling - however, unknown object detection has remained low. Probabilistic/generative models may provide a solution for this challenge. Herein, we introduce a novel probabilistic framework for objectness estimation, where we alternate between probability distribution estimation and objectness likelihood maximization of known objects in the embedded feature space - ultimately allowing us to estimate the objectness probability of different proposals. The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models, adapting them for the open-world setting. Comprehensive experiments on OWOD benchmarks show that PROB outperforms all existing OWOD methods in both unknown object detection ( 2x unknown recall) and known object detection ( mAP). Our code is available at https://github.com/orrzohar/PROB. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zohar_PROB_Probabilistic_Objectness_for_Open_World_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zohar_PROB_Probabilistic_Objectness_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.01424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zohar_PROB_Probabilistic_Objectness_for_Open_World_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zohar_PROB_Probabilistic_Objectness_for_Open_World_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
PD-Quant: Post-Training Quantization Based on Prediction Difference Metric | Jiawei Liu, Lin Niu, Zhihang Yuan, Dawei Yang, Xinggang Wang, Wenyu Liu | Post-training quantization (PTQ) is a neural network compression technique that converts a full-precision model into a quantized model using lower-precision data types. Although it can help reduce the size and computational cost of deep neural networks, it can also introduce quantization noise and reduce prediction accuracy, especially in extremely low-bit settings. How to determine the appropriate quantization parameters (e.g., scaling factors and rounding of weights) is the main problem facing now. Existing methods attempt to determine these parameters by minimize the distance between features before and after quantization, but such an approach only considers local information and may not result in the most optimal quantization parameters. We analyze this issue and propose PD-Quant, a method that addresses this limitation by considering global information. It determines the quantization parameters by using the information of differences between network prediction before and after quantization. In addition, PD-Quant can alleviate the overfitting problem in PTQ caused by the small number of calibration sets by adjusting the distribution of activations. Experiments show that PD-Quant leads to better quantization parameters and improves the prediction accuracy of quantized models, especially in low-bit settings. For example, PD-Quant pushes the accuracy of ResNet-18 up to 53.14% and RegNetX-600MF up to 40.67% in weight 2-bit activation 2-bit. The code is released at https://github.com/hustvl/PD-Quant. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_PD-Quant_Post-Training_Quantization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper.html | CVPR 2023 | null |
AUNet: Learning Relations Between Action Units for Face Forgery Detection | Weiming Bai, Yufan Liu, Zhipeng Zhang, Bing Li, Weiming Hu | Face forgery detection becomes increasingly crucial due to the serious security issues caused by face manipulation techniques. Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same domain. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen methods during training. Observing that face manipulation may alter the relation between different facial action units (AU), we propose the Action Units Relation Learning framework to improve the generality of forgery detection. In specific, it consists of the Action Units Relation Transformer (ART) and the Tampered AU Prediction (TAP). The ART constructs the relation between different AUs with AU-agnostic Branch and AU-specific Branch, which complement each other and work together to exploit forgery clues. In the Tampered AU Prediction, we tamper AU-related regions at the image level and develop challenging pseudo samples at the feature level. The model is then trained to predict the tampered AU regions with the generated location-specific supervision. Experimental results demonstrate that our method can achieve state-of-the-art performance in both the in-dataset and cross-dataset evaluations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bai_AUNet_Learning_Relations_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
SparseFusion: Distilling View-Conditioned Diffusion for 3D Reconstruction | Zhizhuo Zhou, Shubham Tulsiani | We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent advances in neural rendering and probabilistic image generation. Existing approaches typically build on neural rendering with re-projected features but fail to generate unseen regions or handle uncertainty under large viewpoint changes. Alternate methods treat this as a (probabilistic) 2D synthesis task, and while they can generate plausible 2D images, they do not infer a consistent underlying 3D. However, we find that this trade-off between 3D consistency and probabilistic image generation does not need to exist. In fact, we show that geometric consistency and generative inference can be complementary in a mode seeking behavior. By distilling a 3D consistent scene representation from a view-conditioned latent diffusion model, we are able to recover a plausible 3D representation whose renderings are both accurate and realistic. We evaluate our approach across 51 categories in the CO3D dataset and show that it outperforms existing methods, in both distortion and perception metrics, for sparse view novel view synthesis. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_SparseFusion_Distilling_View-Conditioned_Diffusion_for_3D_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_SparseFusion_Distilling_View-Conditioned_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00792 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_SparseFusion_Distilling_View-Conditioned_Diffusion_for_3D_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_SparseFusion_Distilling_View-Conditioned_Diffusion_for_3D_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
PolyFormer: Referring Image Segmentation As Sequential Polygon Generation | Jiang Liu, Hui Ding, Zhaowei Cai, Yuting Zhang, Ravi Kumar Satzoda, Vijay Mahadevan, R. Manmatha | In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the predicted polygons can be later converted into segmentation masks. This is enabled by a new sequence-to-sequence framework, Polygon Transformer (PolyFormer), which takes a sequence of image patches and text query tokens as input, and outputs a sequence of polygon vertices autoregressively. For more accurate geometric localization, we propose a regression-based decoder, which predicts the precise floating-point coordinates directly, without any coordinate quantization error. In the experiments, PolyFormer outperforms the prior art by a clear margin, e.g., 5.40% and 4.52% absolute improvements on the challenging RefCOCO+ and RefCOCOg datasets. It also shows strong generalization ability when evaluated on the referring video segmentation task without fine-tuning, e.g., achieving competitive 61.5% J&F on the Ref-DAVIS17 dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_PolyFormer_Referring_Image_Segmentation_As_Sequential_Polygon_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_PolyFormer_Referring_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.07387 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PolyFormer_Referring_Image_Segmentation_As_Sequential_Polygon_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PolyFormer_Referring_Image_Segmentation_As_Sequential_Polygon_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Seeing What You Miss: Vision-Language Pre-Training With Semantic Completion Learning | Yatai Ji, Rongcheng Tu, Jie Jiang, Weijie Kong, Chengfei Cai, Wenzhe Zhao, Hongfa Wang, Yujiu Yang, Wei Liu | Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-to-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in a limited cross-modal alignment ability of global representations. Therefore, in this paper, we propose a novel Semantic Completion Learning (SCL) task, complementary to existing masked modeling tasks, to facilitate global-to-local alignment. Specifically, the SCL task complements the missing semantics of masked data by capturing the corresponding information from the other modality, promoting learning more representative global features which have a great impact on the performance of downstream tasks. Moreover, we present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ji_Seeing_What_You_Miss_Vision-Language_Pre-Training_With_Semantic_Completion_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ji_Seeing_What_You_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Seeing_What_You_Miss_Vision-Language_Pre-Training_With_Semantic_Completion_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Seeing_What_You_Miss_Vision-Language_Pre-Training_With_Semantic_Completion_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Interactive Segmentation As Gaussion Process Classification | Minghao Zhou, Hong Wang, Qian Zhao, Yuexiang Li, Yawen Huang, Deyu Meng, Yefeng Zheng | Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. Albeit achieving promising performance, they do not fully and explicitly utilize and propagate the click information, inevitably leading to unsatisfactory segmentation results, even at clicked points. Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image. To solve this model, we utilize amortized variational inference to approximate the intractable GP posterior in a data-driven manner and then decouple the approximated GP posterior into double space forms for efficient sampling with linear complexity. Then, we correspondingly construct a GP classification framework, named GPCIS, which is integrated with the deep kernel learning mechanism for more flexibility. The main specificities of the proposed GPCIS lie in: 1) Under the explicit guidance of the derived GP posterior, the information contained in clicks can be finely propagated to the entire image and then boost the segmentation; 2) The accuracy of predictions at clicks has good theoretical support. These merits of GPCIS as well as its good generality and high efficiency are substantiated by comprehensive experiments on several benchmarks, as compared with representative methods both quantitatively and qualitatively. Codes will be released at https://github.com/zmhhmz/GPCIS_CVPR2023. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Interactive_Segmentation_As_Gaussion_Process_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Interactive_Segmentation_As_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Interactive_Segmentation_As_Gaussion_Process_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Interactive_Segmentation_As_Gaussion_Process_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
Differentiable Shadow Mapping for Efficient Inverse Graphics | Markus Worchel, Marc Alexa | We show how shadows can be efficiently generated in differentiable rendering of triangle meshes. Our central observation is that pre-filtered shadow mapping, a technique for approximating shadows based on rendering from the perspective of a light, can be combined with existing differentiable rasterizers to yield differentiable visibility information. We demonstrate at several inverse graphics problems that differentiable shadow maps are orders of magnitude faster than differentiable light transport simulation with similar accuracy -- while differentiable rasterization without shadows often fails to converge. | https://openaccess.thecvf.com/content/CVPR2023/papers/Worchel_Differentiable_Shadow_Mapping_for_Efficient_Inverse_Graphics_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Worchel_Differentiable_Shadow_Mapping_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Worchel_Differentiable_Shadow_Mapping_for_Efficient_Inverse_Graphics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Worchel_Differentiable_Shadow_Mapping_for_Efficient_Inverse_Graphics_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Focus-Aware Positional Queries for Semantic Segmentation | Haoyu He, Jianfei Cai, Zizheng Pan, Jing Liu, Jing Zhang, Dacheng Tao, Bohan Zhuang | The DETR-like segmentors have underpinned the most recent breakthroughs in semantic segmentation, which end-to-end train a set of queries representing the class prototypes or target segments. Recently, masked attention is proposed to restrict each query to only attend to the foreground regions predicted by the preceding decoder block for easier optimization. Although promising, it relies on the learnable parameterized positional queries which tend to encode the dataset statistics, leading to inaccurate localization for distinct individual queries. In this paper, we propose a simple yet effective query design for semantic segmentation termed Dynamic Focus-aware Positional Queries (DFPQ), which dynamically generates positional queries conditioned on the cross-attention scores from the preceding decoder block and the positional encodings for the corresponding image features, simultaneously. Therefore, our DFPQ preserves rich localization information for the target segments and provides accurate and fine-grained positional priors. In addition, we propose to efficiently deal with high-resolution cross-attention by only aggregating the contextual tokens based on the low-resolution cross-attention scores to perform local relation aggregation. Extensive experiments on ADE20K and Cityscapes show that with the two modifications on Mask2former, our framework achieves SOTA performance and outperforms Mask2former by clear margins of 1.1%, 1.9%, and 1.1% single-scale mIoU with ResNet-50, Swin-T, and Swin-B backbones on the ADE20K validation set, respectively. Source code is available at https://github.com/ziplab/FASeg. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Dynamic_Focus-Aware_Positional_Queries_for_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Dynamic_Focus-Aware_Positional_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2204.01244 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Dynamic_Focus-Aware_Positional_Queries_for_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Dynamic_Focus-Aware_Positional_Queries_for_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
A Practical Stereo Depth System for Smart Glasses | Jialiang Wang, Daniel Scharstein, Akash Bapat, Kevin Blackburn-Matzen, Matthew Yu, Jonathan Lehman, Suhib Alsisan, Yanghan Wang, Sam Tsai, Jan-Michael Frahm, Zijian He, Peter Vajda, Michael F. Cohen, Matt Uyttendaele | We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback to monocular depth estimation when rectification is unreliable. The output of our depth sensing system is then used in a novel view generation pipeline to create 3D computational photography effects using point-of-view images captured by smart glasses. All these steps are executed on-device on the stringent compute budget of a mobile phone, and because we expect the users can use a wide range of smartphones, our design needs to be general and cannot be dependent on a particular hardware or ML accelerator such as a smartphone GPU. Although each of these steps is well studied, a description of a practical system is still lacking. For such a system, all these steps need to work in tandem with one another and fallback gracefully on failures within the system or less than ideal input data. We show how we handle unforeseen changes to calibration, e.g., due to heat, robustly support depth estimation in the wild, and still abide by the memory and latency constraints required for a smooth user experience. We show that our trained models are fast, and run in less than 1s on a six-year-old Samsung Galaxy S8 phone's CPU. Our models generalize well to unseen data and achieve good results on Middlebury and in-the-wild images captured from the smart glasses. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_A_Practical_Stereo_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.10551 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding and Constructing Latent Modality Structures in Multi-Modal Representation Learning | Qian Jiang, Changyou Chen, Han Zhao, Liqun Chen, Qing Ping, Son Dinh Tran, Yi Xu, Belinda Zeng, Trishul Chilimbi | Contrastive loss has been increasingly used in learning representations from multiple modalities. In the limit, the nature of the contrastive loss encourages modalities to exactly match each other in the latent space. Yet it remains an open question how the modality alignment affects the downstream task performance. In this paper, based on an information-theoretic argument, we first prove that exact modality alignment is sub-optimal in general for downstream prediction tasks. Hence we advocate that the key of better performance lies in meaningful latent modality structures instead of perfect modality alignment. To this end, we propose three general approaches to construct latent modality structures. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization. Extensive experiments are conducted on two popular multi-modal representation learning frameworks: the CLIP-based two-tower model and the ALBEF-based fusion model. We test our model on a variety of tasks including zero/few-shot image classification, image-text retrieval, visual question answering, visual reasoning, and visual entailment. Our method achieves consistent improvements over existing methods, demonstrating the effectiveness and generalizability of our proposed approach on latent modality structure regularization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_Understanding_and_Constructing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05952 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
PointConvFormer: Revenge of the Point-Based Convolution | Wenxuan Wu, Li Fuxin, Qi Shan | We introduce PointConvFormer, a novel building block for point cloud based deep network architectures. Inspired by generalization theory, PointConvFormer combines ideas from point convolution, where filter weights are only based on relative position, and Transformers which utilize feature-based attention. In PointConvFormer, attention computed from feature difference between points in the neighborhood is used to modify the convolutional weights at each point. Hence, we preserved the invariances from point convolution, whereas attention helps to select relevant points in the neighborhood for convolution. We experiment on both semantic segmentation and scene flow estimation tasks on point clouds with multiple datasets including ScanNet, SemanticKitti, FlyingThings3D and KITTI. Our results show that PointConvFormer substantially outperforms classic convolutions, regular transformers, and voxelized sparse convolution approaches with much smaller and faster networks. Visualizations show that PointConvFormer performs similarly to convolution on flat areas, whereas the neighborhood selection effect is stronger on object boundaries, showing that it has got the best of both worlds. The code will be available with the final version. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_PointConvFormer_Revenge_of_the_Point-Based_Convolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_PointConvFormer_Revenge_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.02879 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_PointConvFormer_Revenge_of_the_Point-Based_Convolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_PointConvFormer_Revenge_of_the_Point-Based_Convolution_CVPR_2023_paper.html | CVPR 2023 | null |
Instant Volumetric Head Avatars | Wojciech Zielonka, Timo Bolkart, Justus Thies | We present Instant Volumetric Head Avatars (INSTA), a novel approach for reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic neural radiance field based on neural graphics primitives embedded around a parametric face model. Our pipeline is trained on a single monocular RGB portrait video that observes the subject under different expressions and views. While state-of-the-art methods take up to several days to train an avatar, our method can reconstruct a digital avatar in less than 10 minutes on modern GPU hardware, which is orders of magnitude faster than previous solutions. In addition, it allows for the interactive rendering of novel poses and expressions. By leveraging the geometry prior of the underlying parametric face model, we demonstrate that INSTA extrapolates to unseen poses. In quantitative and qualitative studies on various subjects, INSTA outperforms state-of-the-art methods regarding rendering quality and training time. Project website: https://zielon.github.io/insta/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zielonka_Instant_Volumetric_Head_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.12499 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.html | CVPR 2023 | null |
HARP: Personalized Hand Reconstruction From a Monocular RGB Video | Korrawe Karunratanakul, Sergey Prokudin, Otmar Hilliges, Siyu Tang | We present HARP (HAnd Reconstruction and Personalization), a personalized hand avatar creation approach that takes a short monocular RGB video of a human hand as input and reconstructs a faithful hand avatar exhibiting a high-fidelity appearance and geometry. In contrast to the major trend of neural implicit representations, HARP models a hand with a mesh-based parametric hand model, a vertex displacement map, a normal map, and an albedo without any neural components. The explicit nature of our representation enables a truly scalable, robust, and efficient approach to hand avatar creation as validated by our experiments. HARP is optimized via gradient descent from a short sequence captured by a hand-held mobile phone and can be directly used in AR/VR applications with real-time rendering capability. To enable this, we carefully design and implement a shadow-aware differentiable rendering scheme that is robust to high degree articulations and self-shadowing regularly present in hand motions, as well as challenging lighting conditions. It also generalizes to unseen poses and novel viewpoints, producing photo-realistic renderings of hand animations. Furthermore, the learned HARP representation can be used for improving 3D hand pose estimation quality in challenging viewpoints. The key advantages of HARP are validated by the in-depth analyses on appearance reconstruction, novel view and novel pose synthesis, and 3D hand pose refinement. It is an AR/VR-ready personalized hand representation that shows superior fidelity and scalability. | https://openaccess.thecvf.com/content/CVPR2023/papers/Karunratanakul_HARP_Personalized_Hand_Reconstruction_From_a_Monocular_RGB_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Karunratanakul_HARP_Personalized_Hand_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09530 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Karunratanakul_HARP_Personalized_Hand_Reconstruction_From_a_Monocular_RGB_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Karunratanakul_HARP_Personalized_Hand_Reconstruction_From_a_Monocular_RGB_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Variational Distribution Learning for Unsupervised Text-to-Image Generation | Minsoo Kang, Doyup Lee, Jiseob Kim, Saehoon Kim, Bohyung Han | We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training. In this work, instead of simply generating pseudo-ground-truth sentences of training images using existing image captioning methods, we employ a pretrained CLIP model, which is capable of properly aligning embeddings of images and corresponding texts in a joint space and, consequently, works well on zero-shot recognition tasks. We optimize a text-to-image generation model by maximizing the data log-likelihood conditioned on pairs of image-text CLIP embeddings. To better align data in the two domains, we employ a principled way based on a variational inference, which efficiently estimates an approximate posterior of the hidden text embedding given an image and its CLIP feature. Experimental results validate that the proposed framework outperforms existing approaches by large margins under unsupervised and semi-supervised text-to-image generation settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Variational_Distribution_Learning_for_Unsupervised_Text-to-Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kang_Variational_Distribution_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16105 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Variational_Distribution_Learning_for_Unsupervised_Text-to-Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Variational_Distribution_Learning_for_Unsupervised_Text-to-Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
MetaMix: Towards Corruption-Robust Continual Learning With Temporally Self-Adaptive Data Transformation | Zhenyi Wang, Li Shen, Donglin Zhan, Qiuling Suo, Yanjun Zhu, Tiehang Duan, Mingchen Gao | Continual Learning (CL) has achieved rapid progress in recent years. However, it is still largely unknown how to determine whether a CL model is trustworthy and how to foster its trustworthiness. This work focuses on evaluating and improving the robustness to corruptions of existing CL models. Our empirical evaluation results show that existing state-of-the-art (SOTA) CL models are particularly vulnerable to various data corruptions during testing. To make them trustworthy and robust to corruptions deployed in safety-critical scenarios, we propose a meta-learning framework of self-adaptive data augmentation to tackle the corruption robustness in CL. The proposed framework, MetaMix, learns to augment and mix data, automatically transforming the new task data or memory data. It directly optimizes the generalization performance against data corruptions during training. To evaluate the corruption robustness of our proposed approach, we construct several CL corruption datasets with different levels of severity. We perform comprehensive experiments on both task- and class-continual learning. Extensive experiments demonstrate the effectiveness of our proposed method compared to SOTA baselines. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MetaMix_Towards_Corruption-Robust_Continual_Learning_With_Temporally_Self-Adaptive_Data_Transformation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_MetaMix_Towards_Corruption-Robust_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MetaMix_Towards_Corruption-Robust_Continual_Learning_With_Temporally_Self-Adaptive_Data_Transformation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MetaMix_Towards_Corruption-Robust_Continual_Learning_With_Temporally_Self-Adaptive_Data_Transformation_CVPR_2023_paper.html | CVPR 2023 | null |
Ultra-High Resolution Segmentation With Ultra-Rich Context: A Novel Benchmark | Deyi Ji, Feng Zhao, Hongtao Lu, Mingyuan Tao, Jieping Ye | With the increasing interest and rapid development of methods for Ultra-High Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of scenes with full fine-grained dense annotations is urgently needed to facilitate the field. To this end, the URUR dataset is introduced, in the meaning of Ultra-High Resolution dataset with Ultra-Rich Context. As the name suggests, URUR contains amounts of images with high enough resolution (3,008 images of size 5,120x5,120), a wide range of complex scenes (from 63 cities), rich-enough context (1 million instances with 8 categories) and fine-grained annotations (about 80 billion manually annotated pixels), which is far superior to all the existing UHR datasets including DeepGlobe, Inria Aerial, UDD, etc.. Moreover, we also propose WSDNet, a more efficient and effective framework for UHR segmentation especially with ultra-rich context. Specifically, multi-level Discrete Wavelet Transform (DWT) is naturally integrated to release computation burden while preserve more spatial details, along with a Wavelet Smooth Loss (WSL) to reconstruct original structured context and texture with a smooth constrain. Experiments on several UHR datasets demonstrate its state-of-the-art performance. The dataset is available at https://github.com/jankyee/URUR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ji_Ultra-High_Resolution_Segmentation_With_Ultra-Rich_Context_A_Novel_Benchmark_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Ultra-High_Resolution_Segmentation_With_Ultra-Rich_Context_A_Novel_Benchmark_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Ultra-High_Resolution_Segmentation_With_Ultra-Rich_Context_A_Novel_Benchmark_CVPR_2023_paper.html | CVPR 2023 | null |
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks | Samyak Jain, Sravanti Addepalli, Pawan Kumar Sahu, Priyam Dey, R. Venkatesh Babu | Generalization of Neural Networks is crucial for deploying them safely in the real world. Common training strategies to improve generalization involve the use of data augmentations, ensembling and model averaging. In this work, we first establish a surprisingly simple but strong benchmark for generalization which utilizes diverse augmentations within a training minibatch, and show that this can learn a more balanced distribution of features. Further, we propose Diversify-Aggregate-Repeat Training (DART) strategy that first trains diverse models using different augmentations (or domains) to explore the loss basin, and further Aggregates their weights to combine their expertise and obtain improved generalization. We find that Repeating the step of Aggregation throughout training improves the overall optimization trajectory and also ensures that the individual models have sufficiently low loss barrier to obtain improved generalization on combining them. We theoretically justify the proposed approach and show that it indeed generalizes better. In addition to improvements in In-Domain generalization, we demonstrate SOTA performance on the Domain Generalization benchmarks in the popular DomainBed framework as well. Our method is generic and can easily be integrated with several base training algorithms to achieve performance gains. Our code is available here: https://github.com/val-iisc/DART. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_DART_Diversify-Aggregate-Repeat_Training_Improves_Generalization_of_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jain_DART_Diversify-Aggregate-Repeat_Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14685 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_DART_Diversify-Aggregate-Repeat_Training_Improves_Generalization_of_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_DART_Diversify-Aggregate-Repeat_Training_Improves_Generalization_of_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
Cross-Domain Image Captioning With Discriminative Finetuning | Roberto Dessì, Michele Bevilacqua, Eleonora Gualdoni, Nathanaël Carraz Rakotonirina, Francesca Franzon, Marco Baroni | Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show that fine-tuning an out-of-the-box neural captioner with a self-supervised discriminative communication objective helps to recover a plain, visually descriptive language that is more informative about image contents. Given a target image, the system must learn to produce a description that enables an out-of-the-box text-conditioned image retriever to identify such image among a set of candidates. We experiment with the popular ClipCap captioner, also replicating the main results with BLIP. In terms of similarity to ground-truth human descriptions, the captions emerging from discriminative finetuning lag slightly behind those generated by the non-finetuned model, when the latter is trained and tested on the same caption dataset. However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human references more than those produced by the same captioner without finetuning. We further show that, on the Conceptual Captions dataset, discriminatively finetuned captions are more helpful than either vanilla ClipCap captions or ground-truth captions for human annotators tasked with an image discrimination task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dessi_Cross-Domain_Image_Captioning_With_Discriminative_Finetuning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dessi_Cross-Domain_Image_Captioning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dessi_Cross-Domain_Image_Captioning_With_Discriminative_Finetuning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dessi_Cross-Domain_Image_Captioning_With_Discriminative_Finetuning_CVPR_2023_paper.html | CVPR 2023 | null |
Accelerating Vision-Language Pretraining With Free Language Modeling | Teng Wang, Yixiao Ge, Feng Zheng, Ran Cheng, Ying Shan, Xiaohu Qie, Ping Luo | The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training time, especially on large-scale web datasets. An essential obstacle to training efficiency lies in the entangled prediction rate (percentage of tokens for reconstruction) and corruption rate (percentage of corrupted tokens) in masked language modeling (MLM), that is, a proper corruption rate is achieved at the cost of a large portion of output tokens being excluded from prediction loss. To accelerate the convergence of VLP, we propose a new pretraining task, namely, free language modeling (FLM), that enables a 100% prediction rate with arbitrary corruption rates. FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted. FLM-trained models are encouraged to learn better and faster given the same GPU time by exploiting bidirectional contexts more flexibly. Extensive experiments show FLM could achieve an impressive 2.5x pretraining time reduction in comparison to the MLM-based methods, while keeping competitive performance on both vision-language understanding and generation tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Accelerating_Vision-Language_Pretraining_With_Free_Language_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Accelerating_Vision-Language_Pretraining_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14038 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Accelerating_Vision-Language_Pretraining_With_Free_Language_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Accelerating_Vision-Language_Pretraining_With_Free_Language_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Mask Correction for Click-Based Interactive Image Segmentation | Fei Du, Jianlong Yuan, Zhibin Wang, Fan Wang | The goal of click-based interactive image segmentation is to extract target masks with the input of positive/negative clicks. Every time a new click is placed, existing methods run the whole segmentation network to obtain a corrected mask, which is inefficient since several clicks may be needed to reach satisfactory accuracy. To this end, we propose an efficient method to correct the mask with a lightweight mask correction network. The whole network remains a low computational cost from the second click, even if we have a large backbone. However, a simple correction network with limited capacity is not likely to achieve comparable performance with a classic segmentation network. Thus, we propose a click-guided self-attention module and a click-guided correlation module to effectively exploits the click information to boost performance. First, several templates are selected based on the semantic similarity with click features. Then the self-attention module propagates the template information to other pixels, while the correlation module directly uses the templates to obtain target outlines. With the efficient architecture and two click-guided modules, our method shows preferable performance and efficiency compared to existing methods. The code will be released at https://github.com/feiaxyt/EMC-Click. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Efficient_Mask_Correction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields | Yu Chen, Gim Hee Lee | Recent works such as BARF and GARF can bundle adjust camera poses with neural radiance fields (NeRF) which is based on coordinate-MLPs. Despite the impressive results, these methods cannot be applied to Generalizable NeRFs (GeNeRFs) which require image feature extractions that are often based on more complicated 3D CNN or transformer architectures. In this work, we first analyze the difficulties of jointly optimizing camera poses with GeNeRFs, and then further propose our DBARF to tackle these issues. Our DBARF which bundle adjusts camera poses by taking a cost feature map as an implicit cost function can be jointly trained with GeNeRFs in a self-supervised manner. Unlike BARF and its follow-up works, which can only be applied to per-scene optimized NeRFs and need accurate initial camera poses with the exception of forward-facing scenes, our method can generalize across scenes and does not require any good initialization. Experiments show the effectiveness and generalization ability of our DBARF when evaluated on real-world datasets. Our code is available at https://aibluefisher.github.io/dbarf. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_DBARF_Deep_Bundle-Adjusting_Generalizable_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_DBARF_Deep_Bundle-Adjusting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14478 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DBARF_Deep_Bundle-Adjusting_Generalizable_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DBARF_Deep_Bundle-Adjusting_Generalizable_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
EvShutter: Transforming Events for Unconstrained Rolling Shutter Correction | Julius Erbach, Stepan Tulyakov, Patricia Vitoria, Alfredo Bochicchio, Yuanyou Li | Widely used Rolling Shutter (RS) CMOS sensors capture high resolution images at the expense of introducing distortions and artifacts in the presence of motion. In such situations, RS distortion correction algorithms are critical. Recent methods rely on a constant velocity assumption and require multiple frames to predict the dense displacement field. In this work, we introduce a new method, called Eventful Shutter (EvShutter), that corrects RS artifacts using a single RGB image and event information with high temporal resolution. The method firstly removes blur using a novel flow-based deblurring module and then compensates RS using a double encoder hourglass network. In contrast to previous methods, it does not rely on a constant velocity assumption and uses a simple architecture thanks to an event transformation dedicated to RS, called Filter and Flip (FnF), that transforms input events to encode only the changes between GS and RS images. To evaluate the proposed method and facilitate future research, we collect the first dataset with real events and high-quality RS images with optional blur, called RS-ERGB. We generate the RS images from GS images using a newly proposed simulator based on adaptive interpolation. The simulator permits the use of inexpensive cameras with long exposure to capture high-quality GS images. We show that on this realistic dataset the proposed method outperforms the state-of-the-art image- and event-based methods by 9.16 dB and 0.75 dB respectively in terms of PSNR and an improvement of 23% and 21% in LPIPS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Erbach_EvShutter_Transforming_Events_for_Unconstrained_Rolling_Shutter_Correction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Erbach_EvShutter_Transforming_Events_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Erbach_EvShutter_Transforming_Events_for_Unconstrained_Rolling_Shutter_Correction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Erbach_EvShutter_Transforming_Events_for_Unconstrained_Rolling_Shutter_Correction_CVPR_2023_paper.html | CVPR 2023 | null |
Graphics Capsule: Learning Hierarchical 3D Face Representations From 2D Images | Chang Yu, Xiangyu Zhu, Xiaomei Zhang, Zhaoxiang Zhang, Zhen Lei | The function of constructing the hierarchy of objects is important to the visual process of the human brain. Previous studies have successfully adopted capsule networks to decompose the digits and faces into parts in an unsupervised manner to investigate the similar perception mechanism of neural networks. However, their descriptions are restricted to the 2D space, limiting their capacities to imitate the intrinsic 3D perception ability of humans. In this paper, we propose an Inverse Graphics Capsule Network (IGC-Net) to learn the hierarchical 3D face representations from large-scale unlabeled images. The core of IGC-Net is a new type of capsule, named graphics capsule, which represents 3D primitives with interpretable parameters in computer graphics (CG), including depth, albedo, and 3D pose. Specifically, IGC-Net first decomposes the objects into a set of semantic-consistent part-level descriptions and then assembles them into object-level descriptions to build the hierarchy. The learned graphics capsules reveal how the neural networks, oriented at visual perception, understand faces as a hierarchy of 3D models. Besides, the discovered parts can be deployed to the unsupervised face segmentation task to evaluate the semantic consistency of our method. Moreover, the part-level descriptions with explicit physical meanings provide insight into the face analysis that originally runs in a black box, such as the importance of shape and texture for face recognition. Experiments on CelebA, BP4D, and Multi-PIE demonstrate the characteristics of our IGC-Net. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Graphics_Capsule_Learning_Hierarchical_3D_Face_Representations_From_2D_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Graphics_Capsule_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10896 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Graphics_Capsule_Learning_Hierarchical_3D_Face_Representations_From_2D_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Graphics_Capsule_Learning_Hierarchical_3D_Face_Representations_From_2D_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries | Yuanwen Yue, Theodora Kontogianni, Konrad Schindler, Francis Engelmann | We address 2D floorplan reconstruction from 3D scans. Existing approaches typically employ heuristically designed multi-stage pipelines. Instead, we formulate floorplan reconstruction as a single-stage structured prediction task: find a variable-size set of polygons, which in turn are variable-length sequences of ordered vertices. To solve it we develop a novel Transformer architecture that generates polygons of multiple rooms in parallel, in a holistic manner without hand-crafted intermediate stages. The model features two-level queries for polygons and corners, and includes polygon matching to make the network end-to-end trainable. Our method achieves a new state-of-the-art for two challenging datasets, Structured3D and SceneCAD, along with significantly faster inference than previous methods. Moreover, it can readily be extended to predict additional information, i.e., semantic room types and architectural elements like doors and windows. Our code and models are available at: https://github.com/ywyue/RoomFormer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yue_Connecting_the_Dots_Floorplan_Reconstruction_Using_Two-Level_Queries_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yue_Connecting_the_Dots_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15658 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yue_Connecting_the_Dots_Floorplan_Reconstruction_Using_Two-Level_Queries_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yue_Connecting_the_Dots_Floorplan_Reconstruction_Using_Two-Level_Queries_CVPR_2023_paper.html | CVPR 2023 | null |
Analyzing and Diagnosing Pose Estimation With Attributions | Qiyuan He, Linlin Yang, Kerui Gu, Qiuxia Lin, Angela Yao | We present Pose Integrated Gradient (PoseIG), the first interpretability technique designed for pose estimation. We extend the concept of integrated gradients for pose estimation to generate pixel-level attribution maps. To enable comparison across different pose frameworks, we unify different pose outputs into a common output space, along with a likelihood approximation function for gradient back-propagation. To complement the qualitative insight from the attribution maps, we propose three indices for quantitative analysis. With these tools, we systematically compare different pose estimation frameworks to understand the impacts of network design, backbone and auxiliary tasks. Our analysis reveals an interesting shortcut of the knuckles (MCP joints) for hand pose estimation and an under-explored inversion error for keypoints in body pose estimation. Project page: https://qy-h00.github.io/poseig/. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Analyzing_and_Diagnosing_Pose_Estimation_With_Attributions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Analyzing_and_Diagnosing_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Analyzing_and_Diagnosing_Pose_Estimation_With_Attributions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Analyzing_and_Diagnosing_Pose_Estimation_With_Attributions_CVPR_2023_paper.html | CVPR 2023 | null |
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection | Chang Liu, Weiming Zhang, Xiangru Lin, Wei Zhang, Xiao Tan, Junyu Han, Xiaomao Li, Errui Ding, Jingdong Wang | With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scores cannot properly represent the localization quality. (2) Assignment ambiguity that samples are matched with improper labels in pseudo-label assignment, as the strategy is misguided by missed objects and inaccurate pseudo boxes. To tackle these problems, we propose a Ambiguity-Resistant Semi-supervised Learning (ARSL) for one-stage detectors. Specifically, to alleviate the selection ambiguity, Joint-Confidence Estimation (JCE) is proposed to jointly quantifies the classification and localization quality of pseudo labels. As for the assignment ambiguity, Task-Separation Assignment (TSA) is introduced to assign labels based on pixel-level predictions rather than unreliable pseudo boxes. It employs a 'divide-and-conquer' strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity. Comprehensive experiments demonstrate that ARSL effectively mitigates the ambiguities and achieves state-of-the-art SSOD performance on MS COCO and PASCAL VOC. Codes can be found at https://github.com/PaddlePaddle/PaddleDetection. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14960 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Scalable, Detailed and Mask-Free Universal Photometric Stereo | Satoshi Ikehata | In this paper, we introduce SDM-UniPS, a groundbreaking Scalable, Detailed, Mask-free, and Universal Photometric Stereo network. Our approach can recover astonishingly intricate surface normal maps, rivaling the quality of 3D scanners, even when images are captured under unknown, spatially-varying lighting conditions in uncontrolled environments. We have extended previous universal photometric stereo networks to extract spatial-light features, utilizing all available information in high-resolution input images and accounting for non-local interactions among surface points. Moreover, we present a new synthetic training dataset that encompasses a diverse range of shapes, materials, and illumination scenarios found in real-world scenes. Through extensive evaluation, we demonstrate that our method not only surpasses calibrated, lighting-specific techniques on public benchmarks, but also excels with a significantly smaller number of input images even without object masks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ikehata_Scalable_Detailed_and_Mask-Free_Universal_Photometric_Stereo_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ikehata_Scalable_Detailed_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15724 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ikehata_Scalable_Detailed_and_Mask-Free_Universal_Photometric_Stereo_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ikehata_Scalable_Detailed_and_Mask-Free_Universal_Photometric_Stereo_CVPR_2023_paper.html | CVPR 2023 | null |
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting | Gen Li, Jie Ji, Minghai Qin, Wei Niu, Bin Ren, Fatemeh Afghah, Linke Guo, Xiaolong Ma | As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has become a new trend in the modern video delivery system. By dividing videos into chunks and overfitting each chunk with a super-resolution model, the server encodes videos before transmitting them to the clients, thus achieving better video quality and transmission efficiency. However, a large number of chunks are expected to ensure good overfitting quality, which substantially increases the storage and consumes more bandwidth resources for data transmission. On the other hand, decreasing the number of chunks through training optimization techniques usually requires high model capacity, which significantly slows down execution speed. To reconcile such, we propose a novel method for high-quality and efficient video resolution upscaling tasks, which leverages the spatial-temporal information to accurately divide video into chunks, thus keeping the number of chunks as well as the model size to a minimum. Additionally, we advance our method into a single overfitting model by a data-aware joint training technique, which further reduces the storage requirement with negligible quality drop. We deploy our proposed overfitting models on an off-the-shelf mobile phone, and experimental results show that our method achieves real-time video super-resolution with high video quality. Compared with the state-of-the-art, our method achieves 28 fps streaming speed with 41.60 PSNR, which is 14 times faster and 2.29 dB better in the live video resolution upscaling tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Towards_High-Quality_and_Efficient_Video_Super-Resolution_via_Spatial-Temporal_Data_Overfitting_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08331 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Towards_High-Quality_and_Efficient_Video_Super-Resolution_via_Spatial-Temporal_Data_Overfitting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Towards_High-Quality_and_Efficient_Video_Super-Resolution_via_Spatial-Temporal_Data_Overfitting_CVPR_2023_paper.html | CVPR 2023 | null |
Make-a-Story: Visual Memory Conditioned Consistent Story Generation | Tanzila Rahman, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, Shweta Mahajan, Leonid Sigal | There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches rely on conditional sentences that contain unambiguous descriptions of scenes and main actors in them. Therefore employing such models for more complex task of story visualization, where naturally references and co-references exist, and one requires to reason about when to maintain consistency of actors and backgrounds across frames/scenes, and when not to, based on story progression, remains a challenge. In this work, we address the aforementioned challenges and propose a novel autoregressive diffusion-based framework with a visual memory module that implicitly captures the actor and background context across the generated frames. Sentence-conditioned soft attention over the memories enables effective reference resolution and learns to maintain scene and actor consistency when needed. To validate the effectiveness of our approach, we extend the MUGEN dataset and introduce additional characters, backgrounds and referencing in multi-sentence storylines. Our experiments for story generation on the MUGEN, the PororoSV and the FlintstonesSV dataset show that our method not only outperforms prior state-of-the-art in generating frames with high visual quality, which are consistent with the story, but also models appropriate correspondences between the characters and the background. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rahman_Make-a-Story_Visual_Memory_Conditioned_Consistent_Story_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rahman_Make-a-Story_Visual_Memory_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rahman_Make-a-Story_Visual_Memory_Conditioned_Consistent_Story_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rahman_Make-a-Story_Visual_Memory_Conditioned_Consistent_Story_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
BiFormer: Vision Transformer With Bi-Level Routing Attention | Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, Rynson W.H. Lau | As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pairwise token interaction across all spatial locations is computed. A series of works attempt to alleviate this problem by introducing handcrafted and content-agnostic sparsity into attention, such as restricting the attention operation to be inside local windows, axial stripes, or dilated windows. In contrast to these approaches, we propose a novel dynamic sparse attention via bi-level routing to enable a more flexible allocation of computations with content awareness. Specifically, for a query, irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (i.e., routed regions). We provide a simple yet effective implementation of the proposed bi-level routing attention, which utilizes the sparsity to save both computation and memory while involving only GPU-friendly dense matrix multiplications. Built with the proposed bi-level routing attention, a new general vision transformer, named BiFormer, is then presented. As BiFormer attends to a small subset of relevant tokens in a query-adaptive manner without distraction from other irrelevant ones, it enjoys both good performance and high computational efficiency, especially in dense prediction tasks. Empirical results across several computer vision tasks such as image classification, object detection, and semantic segmentation verify the effectiveness of our design. Code is available at https://github.com/rayleizhu/BiFormer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_BiFormer_Vision_Transformer_With_Bi-Level_Routing_Attention_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_BiFormer_Vision_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08810 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_BiFormer_Vision_Transformer_With_Bi-Level_Routing_Attention_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_BiFormer_Vision_Transformer_With_Bi-Level_Routing_Attention_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Autoencoders Enable Efficient Knowledge Distillers | Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, Alan L. Yuille, Yuyin Zhou, Cihang Xie | This paper studies the potential of distilling knowledge from pre-trained models, especially Masked Autoencoders. Our approach is simple: in addition to optimizing the pixel reconstruction loss on masked inputs, we minimize the distance between the intermediate feature map of the teacher model and that of the student model. This design leads to a computationally efficient knowledge distillation framework, given 1) only a small visible subset of patches is used, and 2) the (cumbersome) teacher model only needs to be partially executed, i.e., forward propagate inputs through the first few layers, for obtaining intermediate feature maps. Compared to directly distilling fine-tuned models, distilling pre-trained models substantially improves downstream performance. For example, by distilling the knowledge from an MAE pre-trained ViT-L into a ViT-B, our method achieves 84.0% ImageNet top-1 accuracy, outperforming the baseline of directly distilling a fine-tuned ViT-L by 1.2%. More intriguingly, our method can robustly distill knowledge from teacher models even with extremely high masking ratios: e.g., with 95% masking ratio where merely TEN patches are visible during distillation, our ViT-B competitively attains a top-1 ImageNet accuracy of 83.6%; surprisingly, it can still secure 82.4% top-1 ImageNet accuracy by aggressively training with just FOUR visible patches (98% masking ratio). The code will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_Masked_Autoencoders_Enable_Efficient_Knowledge_Distillers_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2208.12256 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Masked_Autoencoders_Enable_Efficient_Knowledge_Distillers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Masked_Autoencoders_Enable_Efficient_Knowledge_Distillers_CVPR_2023_paper.html | CVPR 2023 | null |
TinyMIM: An Empirical Study of Distilling MIM Pre-Trained Models | Sucheng Ren, Fangyun Wei, Zheng Zhang, Han Hu | Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_TinyMIM_An_Empirical_Study_of_Distilling_MIM_Pre-Trained_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ren_TinyMIM_An_Empirical_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.01296 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_TinyMIM_An_Empirical_Study_of_Distilling_MIM_Pre-Trained_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_TinyMIM_An_Empirical_Study_of_Distilling_MIM_Pre-Trained_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Persistent Nature: A Generative Model of Unbounded 3D Worlds | Lucy Chai, Richard Tucker, Zhengqi Li, Phillip Isola, Noah Snavely | Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions. We investigate the task of unconditionally synthesizing unbounded nature scenes, enabling arbitrarily large camera motion while maintaining a persistent 3D world model. Our scene representation consists of an extendable, planar scene layout grid, which can be rendered from arbitrary camera poses via a 3D decoder and volume rendering, and a panoramic skydome. Based on this representation, we learn a generative world model solely from single-view internet photos. Our method enables simulating long flights through 3D landscapes, while maintaining global scene consistency---for instance, returning to the starting point yields the same view of the scene. Our approach enables scene extrapolation beyond the fixed bounds of current 3D generative models, while also supporting a persistent, camera-independent world representation that stands in contrast to auto-regressive 3D prediction models. Our project page: https://chail.github.io/persistent-nature/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chai_Persistent_Nature_A_Generative_Model_of_Unbounded_3D_Worlds_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chai_Persistent_Nature_A_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13515 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chai_Persistent_Nature_A_Generative_Model_of_Unbounded_3D_Worlds_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chai_Persistent_Nature_A_Generative_Model_of_Unbounded_3D_Worlds_CVPR_2023_paper.html | CVPR 2023 | null |
OneFormer: One Transformer To Rule Universal Image Segmentation | Jitesh Jain, Jiachen Li, Mang Tik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi | Universal Image Segmentation is not a new concept.Past attempts to unify image segmentation include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures. However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance. Ideally, a truly universal framework should be trained only once and achieve SOTA performance across all three image segmentation tasks. To that end, we propose OneFormer, a universal image segmentation framework that unifies segmentation with a multi-task train-once design. We first propose a task-conditioned joint training strategy that enables training on ground truths of each domain (semantic, instance, and panoptic segmentation) within a single multi-task training process. Secondly, we introduce a task token to condition our model on the task at hand, making our model task-dynamic to support multi-task training and inference. Thirdly, we propose using a query-text contrastive loss during training to establish better inter-task and inter-class distinctions. Notably, our single OneFormer model outperforms specialized Mask2Former models across all three segmentation tasks on ADE20k, Cityscapes, and COCO, despite the latter being trained on each task individually. We believe OneFormer is a significant step towards making image segmentation more universal and accessible. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_OneFormer_One_Transformer_To_Rule_Universal_Image_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jain_OneFormer_One_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.06220 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_OneFormer_One_Transformer_To_Rule_Universal_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_OneFormer_One_Transformer_To_Rule_Universal_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Neural Memory Network for Low Latency Event Processing | Ryuhei Hamaguchi, Yasutaka Furukawa, Masaki Onishi, Ken Sakurada | This paper proposes a low latency neural network architecture for event-based dense prediction tasks. Conventional architectures encode entire scene contents at a fixed rate regardless of their temporal characteristics. Instead, the proposed network encodes contents at a proper temporal scale depending on its movement speed. We achieve this by constructing temporal hierarchy using stacked latent memories that operate at different rates. Given low latency event steams, the multi-level memories gradually extract dynamic to static scene contents by propagating information from the fast to the slow memory modules. The architecture not only reduces the redundancy of conventional architectures but also exploits long-term dependencies. Furthermore, an attention-based event representation efficiently encodes sparse event streams into the memory cells. We conduct extensive evaluations on three event-based dense prediction tasks, where the proposed approach outperforms the existing methods on accuracy and latency, while demonstrating effective event and image fusion capabilities. The code is available at https://hamarh.github.io/hmnet/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Hamaguchi_Hierarchical_Neural_Memory_Network_for_Low_Latency_Event_Processing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hamaguchi_Hierarchical_Neural_Memory_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hamaguchi_Hierarchical_Neural_Memory_Network_for_Low_Latency_Event_Processing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hamaguchi_Hierarchical_Neural_Memory_Network_for_Low_Latency_Event_Processing_CVPR_2023_paper.html | CVPR 2023 | null |
Finding Geometric Models by Clustering in the Consensus Space | Daniel Barath, Denys Rozumnyi, Ivan Eichhardt, Levente Hajder, Jiri Matas | We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems -- at least two orders of magnitude faster than the competitors on two-view motion estimation. Also, we propose a deterministic sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively densified neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects; and we also propose a way of using multiple homographies in global SfM algorithms. Source code: https://github.com/danini/clustering-in-consensus-space. | https://openaccess.thecvf.com/content/CVPR2023/papers/Barath_Finding_Geometric_Models_by_Clustering_in_the_Consensus_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Barath_Finding_Geometric_Models_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Barath_Finding_Geometric_Models_by_Clustering_in_the_Consensus_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Barath_Finding_Geometric_Models_by_Clustering_in_the_Consensus_Space_CVPR_2023_paper.html | CVPR 2023 | null |
Leapfrog Diffusion Model for Stochastic Trajectory Prediction | Weibo Mao, Chenxin Xu, Qi Zhu, Siheng Chen, Yanfeng Wang | To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have revealed their tremendous representation capacities in numerous generation tasks, showing potential for stochastic trajectory prediction. However, expensive time consumption prevents diffusion models from real-time prediction, since a large number of denoising steps are required to assure sufficient representation ability. To resolve the dilemma, we present LEapfrog Diffusion model (LED), a novel diffusion-based trajectory prediction model, which provides real-time, precise, and diverse predictions. The core of the proposed LED is to leverage a trainable leapfrog initializer to directly learn an expressive multi-modal distribution of future trajectories, which skips a large number of denoising steps, significantly accelerating inference speed. Moreover, the leapfrog initializer is trained to appropriately allocate correlated samples to provide a diversity of predicted future trajectories, significantly improving prediction performances. Extensive experiments on four real-world datasets, including NBA/NFL/SDD/ETH-UCY, show that LED consistently improves performance and achieves 23.7%/21.9% ADE/FDE improvement on NFL. The proposed LED also speeds up the inference 19.3/30.8/24.3/25.1 times compared to the standard diffusion model on NBA/NFL/SDD/ETH-UCY, satisfying real-time inference needs. Code is available at https://github.com/MediaBrain-SJTU/LED. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mao_Leapfrog_Diffusion_Model_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10895 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
DaFKD: Domain-Aware Federated Knowledge Distillation | Haozhao Wang, Yichen Li, Wenchao Xu, Ruixuan Li, Yufeng Zhan, Zhigang Zeng | Federated Distillation (FD) has recently attracted increasing attention for its efficiency in aggregating multiple diverse local models trained from statistically heterogeneous data of distributed clients. Existing FD methods generally treat these models equally by merely computing the average of their output soft predictions for some given input distillation sample, which does not take the diversity across all local models into account, thus leading to degraded performance of the aggregated model, especially when some local models learn little knowledge about the sample. In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models. Specifically, we employ a domain discriminator for each client, which is trained to identify the correlation factor between the sample and the corresponding domain. Then, to facilitate the training of the domain discriminator while saving communication costs, we propose sharing its partial parameters with the classification model. Extensive experiments on various datasets and settings show that the proposed method can improve the model accuracy by up to 6.02% compared to state-of-the-art baselines. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_DaFKD_Domain-Aware_Federated_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
GeoLayoutLM: Geometric Pre-Training for Visual Information Extraction | Chuwei Luo, Changxu Cheng, Qi Zheng, Cong Yao | Visual information extraction (VIE) plays an important role in Document Intelligence. Generally, it is divided into two tasks: semantic entity recognition (SER) and relation extraction (RE). Recently, pre-trained models for documents have achieved substantial progress in VIE, particularly in SER. However, most of the existing models learn the geometric representation in an implicit way, which has been found insufficient for the RE task since geometric information is especially crucial for RE. Moreover, we reveal another factor that limits the performance of RE lies in the objective gap between the pre-training phase and the fine-tuning phase for RE. To tackle these issues, we propose in this paper a multi-modal framework, named GeoLayoutLM, for VIE. GeoLayoutLM explicitly models the geometric relations in pre-training, which we call geometric pre-training. Geometric pre-training is achieved by three specially designed geometry-related pre-training tasks. Additionally, novel relation heads, which are pre-trained by the geometric pre-training tasks and fine-tuned for RE, are elaborately designed to enrich and enhance the feature representation. According to extensive experiments on standard VIE benchmarks, GeoLayoutLM achieves highly competitive scores in the SER task and significantly outperforms the previous state-of-the-arts for RE (e.g.,the F1 score of RE on FUNSD is boosted from 80.35% to 89.45%). | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.10759 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.html | CVPR 2023 | null |
Class-Incremental Exemplar Compression for Class-Incremental Learning | Zilin Luo, Yaoyao Liu, Bernt Schiele, Qianru Sun | Exemplar-based class-incremental learning (CIL) finetunes the model with all samples of new classes but few-shot exemplars of old classes in each incremental phase, where the "few-shot" abides by the limited memory budget. In this paper, we break this "few-shot" limit based on a simple yet surprisingly effective idea: compressing exemplars by downsampling non-discriminative pixels and saving "many-shot" compressed exemplars in the memory. Without needing any manual annotation, we achieve this compression by generating 0-1 masks on discriminative pixels from class activation maps (CAM). We propose an adaptive mask generation model called class-incremental masking (CIM) to explicitly resolve two difficulties of using CAM: 1) transforming the heatmaps of CAM to 0-1 masks with an arbitrary threshold leads to a trade-off between the coverage on discriminative pixels and the quantity of exemplars, as the total memory is fixed; and 2) optimal thresholds vary for different object classes, which is particularly obvious in the dynamic environment of CIL. We optimize the CIM model alternatively with the conventional CIL model through a bilevel optimization problem. We conduct extensive experiments on high-resolution CIL benchmarks including Food-101, ImageNet-100, and ImageNet-1000, and show that using the compressed exemplars by CIM can achieve a new state-of-the-art CIL accuracy, e.g., 4.8 percentage points higher than FOSTER on 10-Phase ImageNet-1000. Our code is available at https://github.com/xfflzl/CIM-CIL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Class-Incremental_Exemplar_Compression_for_Class-Incremental_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_Class-Incremental_Exemplar_Compression_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14042 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Class-Incremental_Exemplar_Compression_for_Class-Incremental_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Class-Incremental_Exemplar_Compression_for_Class-Incremental_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Boost Vision Transformer With GPU-Friendly Sparsity and Quantization | Chong Yu, Tao Chen, Zhongxue Gan, Jiayuan Fan | The transformer extends its success from the language to the vision domain. Because of the numerous stacked self-attention and cross-attention blocks in the transformer, which involve many high-dimensional tensor multiplication operations, the acceleration deployment of vision transformer on GPU hardware is challenging and also rarely studied. This paper thoroughly designs a compression scheme to maximally utilize the GPU-friendly 2:4 fine-grained structured sparsity and quantization. Specially, an original large model with dense weight parameters is first pruned into a sparse one by 2:4 structured pruning, which considers the GPU's acceleration of 2:4 structured sparse pattern with FP16 data type, then the floating-point sparse model is further quantized into a fixed-point one by sparse-distillation-aware quantization aware training, which considers GPU can provide an extra speedup of 2:4 sparse calculation with integer tensors. A mixed-strategy knowledge distillation is used during the pruning and quantization process. The proposed compression scheme is flexible to support supervised and unsupervised learning styles. Experiment results show GPUSQ-ViT scheme achieves state-of-the-art compression by reducing vision transformer models 6.4-12.7 times on model size and 30.3-62 times on FLOPs with negligible accuracy degradation on ImageNet classification, COCO detection and ADE20K segmentation benchmarking tasks. Moreover, GPUSQ-ViT can boost actual deployment performance by 1.39-1.79 times and 3.22-3.43 times of latency and throughput on A100 GPU, and 1.57-1.69 times and 2.11-2.51 times improvement of latency and throughput on AGX Orin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Boost_Vision_Transformer_With_GPU-Friendly_Sparsity_and_Quantization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Boost_Vision_Transformer_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Boost_Vision_Transformer_With_GPU-Friendly_Sparsity_and_Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Boost_Vision_Transformer_With_GPU-Friendly_Sparsity_and_Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
Spectral Bayesian Uncertainty for Image Super-Resolution | Tao Liu, Jun Cheng, Shan Tan | Recently deep learning techniques have significantly advanced image super-resolution (SR). Due to the black-box nature, quantifying reconstruction uncertainty is crucial when employing these deep SR networks. Previous approaches for SR uncertainty estimation mostly focus on capturing pixel-wise uncertainty in the spatial domain. SR uncertainty in the frequency domain which is highly related to image SR is seldom explored. In this paper, we propose to quantify spectral Bayesian uncertainty in image SR. To achieve this, a Dual-Domain Learning (DDL) framework is first proposed. Combined with Bayesian approaches, the DDL model is able to estimate spectral uncertainty accurately, enabling a reliability assessment for high frequencies reasoning from the frequency domain perspective. Extensive experiments under non-ideal premises are conducted and demonstrate the effectiveness of the proposed spectral uncertainty. Furthermore, we propose a novel Spectral Uncertainty based Decoupled Frequency (SUDF) training scheme for perceptual SR. Experimental results show the proposed SUDF can evidently boost perceptual quality of SR results without sacrificing much pixel accuracy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Spectral_Bayesian_Uncertainty_for_Image_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Spectral_Bayesian_Uncertainty_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Spectral_Bayesian_Uncertainty_for_Image_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Spectral_Bayesian_Uncertainty_for_Image_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Behind the Scenes: Density Fields for Single View Reconstruction | Felix Wimbauer, Nan Yang, Christian Rupprecht, Daniel Cremers | Inferring a meaningful geometric scene representation from a single image is a fundamental problem in computer vision. Approaches based on traditional depth map prediction can only reason about areas that are visible in the image. Currently, neural radiance fields (NeRFs) can capture true 3D including color, but are too complex to be generated from a single image. As an alternative, we propose to predict an implicit density field from a single image. It maps every location in the frustum of the image to volumetric density. By directly sampling color from the available views instead of storing color in the density field, our scene representation becomes significantly less complex compared to NeRFs, and a neural network can predict it in a single forward pass. The network is trained through self-supervision from only video data. Our formulation allows volume rendering to perform both depth prediction and novel view synthesis. Through experiments, we show that our method is able to predict meaningful geometry for regions that are occluded in the input image. Additionally, we demonstrate the potential of our approach on three datasets for depth prediction and novel-view synthesis. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wimbauer_Behind_the_Scenes_Density_Fields_for_Single_View_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wimbauer_Behind_the_Scenes_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.07668 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wimbauer_Behind_the_Scenes_Density_Fields_for_Single_View_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wimbauer_Behind_the_Scenes_Density_Fields_for_Single_View_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
StyleGAN Salon: Multi-View Latent Optimization for Pose-Invariant Hairstyle Transfer | Sasikarn Khwanmuang, Pakkapon Phongthawee, Patsorn Sangkloy, Supasorn Suwajanakorn | Our paper seeks to transfer the hairstyle of a reference image to an input photo for virtual hair try-on. We target a variety of challenges scenarios, such as transforming a long hairstyle with bangs to a pixie cut, which requires removing the existing hair and inferring how the forehead would look, or transferring partially visible hair from a hat-wearing person in a different pose. Past solutions leverage StyleGAN for hallucinating any missing parts and producing a seamless face-hair composite through so-called GAN inversion or projection. However, there remains a challenge in controlling the hallucinations to accurately transfer hairstyle and preserve the face shape and identity of the input. To overcome this, we propose a multi-view optimization framework that uses "two different views" of reference composites to semantically guide occluded or ambiguous regions. Our optimization shares information between two poses, which allows us to produce high fidelity and realistic results from incomplete references. Our framework produces high-quality results and outperforms prior work in a user study that consists of significantly more challenging hair transfer scenarios than previously studied. Project page: https://stylegan-salon.github.io/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Khwanmuang_StyleGAN_Salon_Multi-View_Latent_Optimization_for_Pose-Invariant_Hairstyle_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Khwanmuang_StyleGAN_Salon_Multi-View_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02744 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Khwanmuang_StyleGAN_Salon_Multi-View_Latent_Optimization_for_Pose-Invariant_Hairstyle_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Khwanmuang_StyleGAN_Salon_Multi-View_Latent_Optimization_for_Pose-Invariant_Hairstyle_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
Resource-Efficient RGBD Aerial Tracking | Jinyu Yang, Shang Gao, Zhe Li, Feng Zheng, Aleš Leonardis | Aerial robots are now able to fly in complex environments, and drone-captured data gains lots of attention in object tracking. However, current research on aerial perception has mainly focused on limited categories, such as pedestrian or vehicle, and most scenes are captured in urban environments from a birds-eye view. Recently, UAVs equipped with depth cameras have been also deployed for more complex applications, while RGBD aerial tracking is still unexplored. Compared with traditional RGB object tracking, adding depth information can more effectively deal with more challenging scenes such as target and background interference. To this end, in this paper, we explore RGBD aerial tracking in an overhead space, which can greatly enlarge the development of drone-based visual perception. To boost the research, we first propose a large-scale benchmark for RGBD aerial tracking, containing 1,000 drone-captured RGBD videos with dense annotations. Then, as drone-based applications require for real-time processing with limited computational resources, we also propose an efficient RGBD tracker named EMT. Our tracker runs at over 100 fps on GPU, and 25 fps on the edge platform of NVidia Jetson NX Xavier, benefiting from its efficient multimodal fusion and feature matching. Extensive experiments show that our EMT achieves promising tracking performance. All resources are available at https://github.com/yjybuaa/RGBDAerialTracking. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Resource-Efficient_RGBD_Aerial_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Resource-Efficient_RGBD_Aerial_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Resource-Efficient_RGBD_Aerial_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Resource-Efficient_RGBD_Aerial_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Mutual Information-Based Temporal Difference Learning for Human Pose Estimation in Video | Runyang Feng, Yixing Gao, Xueqing Ma, Tze Ho Elden Tse, Hyung Jin Chang | Temporal modeling is crucial for multi-frame human pose estimation. Most existing methods directly employ optical flow or deformable convolution to predict full-spectrum motion fields, which might incur numerous irrelevant cues, such as a nearby person or background. Without further efforts to excavate meaningful motion priors, their results are suboptimal, especially in complicated spatiotemporal interactions. On the other hand, the temporal difference has the ability to encode representative motion information which can potentially be valuable for pose estimation but has not been fully exploited. In this paper, we present a novel multi-frame human pose estimation framework, which employs temporal differences across frames to model dynamic contexts and engages mutual information objectively to facilitate useful motion information disentanglement. To be specific, we design a multi-stage Temporal Difference Encoder that performs incremental cascaded learning conditioned on multi-stage feature difference sequences to derive informative motion representation. We further propose a Representation Disentanglement module from the mutual information perspective, which can grasp discriminative task-relevant motion signals by explicitly defining useful and noisy constituents of the raw motion features and minimizing their mutual information. These place us to rank No.1 in the Crowd Pose Estimation in Complex Events Challenge on benchmark dataset HiEve, and achieve state-of-the-art performance on three benchmarks PoseTrack2017, PoseTrack2018, and PoseTrack21. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Mutual_Information-Based_Temporal_Difference_Learning_for_Human_Pose_Estimation_in_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08475 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Mutual_Information-Based_Temporal_Difference_Learning_for_Human_Pose_Estimation_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Mutual_Information-Based_Temporal_Difference_Learning_for_Human_Pose_Estimation_in_CVPR_2023_paper.html | CVPR 2023 | null |
Bilateral Memory Consolidation for Continual Learning | Xing Nie, Shixiong Xu, Xiyan Liu, Gaofeng Meng, Chunlei Huo, Shiming Xiang | Humans are proficient at continuously acquiring and integrating new knowledge. By contrast, deep models forget catastrophically, especially when tackling highly long task sequences. Inspired by the way our brains constantly rewrite and consolidate past recollections, we propose a novel Bilateral Memory Consolidation (BiMeCo) framework that focuses on enhancing memory interaction capabilities. Specifically, BiMeCo explicitly decouples model parameters into short-term memory module and long-term memory module, responsible for representation ability of the model and generalization over all learned tasks, respectively. BiMeCo encourages dynamic interactions between two memory modules by knowledge distillation and momentum-based updating for forming generic knowledge to prevent forgetting. The proposed BiMeCo is parameter-efficient and can be integrated into existing methods seamlessly. Extensive experiments on challenging benchmarks show that BiMeCo significantly improves the performance of existing continual learning methods. For example, combined with the state-of-the-art method CwD, BiMeCo brings in significant gains of around 2% to 6% while using 2x fewer parameters on CIFAR-100 under ResNet-18. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nie_Bilateral_Memory_Consolidation_for_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nie_Bilateral_Memory_Consolidation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nie_Bilateral_Memory_Consolidation_for_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nie_Bilateral_Memory_Consolidation_for_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision | Xubo Liu, Egor Lakomkin, Konstantinos Vougioukas, Pingchuan Ma, Honglie Chen, Ruiming Xie, Morrie Doulaty, Niko Moritz, Jachym Kolar, Stavros Petridis, Maja Pantic, Christian Fuegen | Recently reported state-of-the-art results in visual speech recognition (VSR) often rely on increasingly large amounts of video data, while the publicly available transcribed video datasets are limited in size. In this paper, for the first time, we study the potential of leveraging synthetic visual data for VSR. Our method, termed SynthVSR, substantially improves the performance of VSR systems with synthetic lip movements. The key idea behind SynthVSR is to leverage a speech-driven lip animation model that generates lip movements conditioned on the input speech. The speech-driven lip animation model is trained on an unlabeled audio-visual dataset and could be further optimized towards a pre-trained VSR model when labeled videos are available. As plenty of transcribed acoustic data and face images are available, we are able to generate large-scale synthetic data using the proposed lip animation model for semi-supervised VSR training. We evaluate the performance of our approach on the largest public VSR benchmark - Lip Reading Sentences 3 (LRS3). SynthVSR achieves a WER of 43.3% with only 30 hours of real labeled data, outperforming off-the-shelf approaches using thousands of hours of video. The WER is further reduced to 27.9% when using all 438 hours of labeled data from LRS3, which is on par with the state-of-the-art self-supervised AV-HuBERT method. Furthermore, when combined with large-scale pseudo-labeled audio-visual data SynthVSR yields a new state-of-the-art VSR WER of 16.9% using publicly available data only, surpassing the recent state-of-the-art approaches trained with 29 times more non-public machine-transcribed video data (90,000 hours). Finally, we perform extensive ablation studies to understand the effect of each component in our proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_SynthVSR_Scaling_Up_Visual_Speech_Recognition_With_Synthetic_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_SynthVSR_Scaling_Up_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.17200 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SynthVSR_Scaling_Up_Visual_Speech_Recognition_With_Synthetic_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SynthVSR_Scaling_Up_Visual_Speech_Recognition_With_Synthetic_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
BiasBed - Rigorous Texture Bias Evaluation | Nikolai Kalischek, Rodrigo Caye Daudt, Torben Peters, Reinhard Furrer, Jan D. Wegner, Konrad Schindler | The well-documented presence of texture bias in modern convolutional neural networks has led to a plethora of algorithms that promote an emphasis on shape cues, often to support generalization to new domains. Yet, common datasets, benchmarks and general model selection strategies are missing, and there is no agreed, rigorous evaluation protocol. In this paper, we investigate difficulties and limitations when training networks with reduced texture bias. In particular, we also show that proper evaluation and meaningful comparisons between methods are not trivial. We introduce BiasBed, a testbed for texture- and style-biased training, including multiple datasets and a range of existing algorithms. It comes with an extensive evaluation protocol that includes rigorous hypothesis testing to gauge the significance of the results, despite the considerable training instability of some style bias methods. Our extensive experiments, shed new light on the need for careful, statistically founded evaluation protocols for style bias (and beyond). E.g., we find that some algorithms proposed in the literature do not significantly mitigate the impact of style bias at all. With the release of BiasBed, we hope to foster a common understanding of consistent and meaningful comparisons, and consequently faster progress towards learning methods free of texture bias. Code is available at https://github.com/D1noFuzi/BiasBed. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kalischek_BiasBed_-_Rigorous_Texture_Bias_Evaluation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kalischek_BiasBed_-_Rigorous_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kalischek_BiasBed_-_Rigorous_Texture_Bias_Evaluation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kalischek_BiasBed_-_Rigorous_Texture_Bias_Evaluation_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Category Human-Object Interaction Pre-Training via Language Modeling Framework | Sipeng Zheng, Boshen Xu, Qin Jin | Human-object interaction (HOI) has long been plagued by the conflict between limited supervised data and a vast number of possible interaction combinations in real life. Current methods trained from closed-set data predict HOIs as fixed-dimension logits, which restricts their scalability to open-set categories. To address this issue, we introduce OpenCat, a language modeling framework that reformulates HOI prediction as sequence generation. By converting HOI triplets into a token sequence through a serialization scheme, our model is able to exploit the open-set vocabulary of the language modeling framework to predict novel interaction classes with a high degree of freedom. In addition, inspired by the great success of vision-language pre-training, we collect a large amount of weakly-supervised data related to HOI from image-caption pairs, and devise several auxiliary proxy tasks, including soft relational matching and human-object relation prediction, to pre-train our model. Extensive experiments show that our OpenCat significantly boosts HOI performance, particularly on a broad range of rare and unseen categories. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_Open-Category_Human-Object_Interaction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.html | CVPR 2023 | null |
SFD2: Semantic-Guided Feature Detection and Description | Fei Xue, Ignas Budvytis, Roberto Cipolla | Visual localization is a fundamental task for various applications including autonomous driving and robotics. Prior methods focus on extracting large amounts of often redundant locally reliable features, resulting in limited efficiency and accuracy, especially in large-scale environments under challenging conditions. Instead, we propose to extract globally reliable features by implicitly embedding high-level semantics into both the detection and description processes. Specifically, our semantic-aware detector is able to detect keypoints from reliable regions (e.g. building, traffic lane) and suppress reliable areas (e.g. sky, car) implicitly instead of relying on explicit semantic labels. This boosts the accuracy of keypoint matching by reducing the number of features sensitive to appearance changes and avoiding the need of additional segmentation networks at test time. Moreover, our descriptors are augmented with semantics and have stronger discriminative ability, providing more inliers at test time. Particularly, experiments on long-term large-scale visual localization Aachen Day-Night and RobotCar-Seasons datasets demonstrate that our model outperforms previous local features and gives competitive accuracy to advanced matchers but is about 2 and 3 times faster when using 2k and 4k keypoints, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xue_SFD2_Semantic-Guided_Feature_Detection_and_Description_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xue_SFD2_Semantic-Guided_Feature_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.14845 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_SFD2_Semantic-Guided_Feature_Detection_and_Description_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_SFD2_Semantic-Guided_Feature_Detection_and_Description_CVPR_2023_paper.html | CVPR 2023 | null |
Search-Map-Search: A Frame Selection Paradigm for Action Recognition | Mingjun Zhao, Yakun Yu, Xiaoli Wang, Lei Yang, Di Niu | Despite the success of deep learning in video understanding tasks, processing every frame in a video is computationally expensive and often unnecessary in real-time applications. Frame selection aims to extract the most informative and representative frames to help a model better understand video content. Existing frame selection methods either individually sample frames based on per-frame importance prediction, without considering interaction among frames, or adopt reinforcement learning agents to find representative frames in succession, which are costly to train and may lead to potential stability issues. To overcome the limitations of existing methods, we propose a Search-Map-Search learning paradigm which combines the advantages of heuristic search and supervised learning to select the best combination of frames from a video as one entity. By combining search with learning, the proposed method can better capture frame interactions while incurring a low inference overhead. Specifically, we first propose a hierarchical search method conducted on each training video to search for the optimal combination of frames with the lowest error on the downstream task. A feature mapping function is then learned to map the frames of a video to the representation of its target optimal frame combination. During inference, another search is performed on an unseen video to select a combination of frames whose feature representation is close to the projected feature representation. Extensive experiments based on several action recognition benchmarks demonstrate that our frame selection method effectively improves performance of action recognition models, and significantly outperforms a number of competitive baselines. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Search-Map-Search_A_Frame_Selection_Paradigm_for_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Search-Map-Search_A_Frame_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Search-Map-Search_A_Frame_Selection_Paradigm_for_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Search-Map-Search_A_Frame_Selection_Paradigm_for_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction | Yi Xu, Armin Bazarjani, Hyung-gun Chi, Chiho Choi, Yun Fu | Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed sequences are complete while ignoring the potential for missing values caused by object occlusion, scope limitation, sensor failure, etc. This limitation inevitably hinders the accuracy of trajectory prediction. To address this issue, our paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously. Specifically, we introduce a novel Multi-Space Graph Neural Network (MS-GNN) that can extract spatial features from incomplete observations and leverage missing patterns. Additionally, we employ a Conditional VRNN with a specifically designed Temporal Decay (TD) module to capture temporal dependencies and temporal missing patterns in incomplete trajectories. The inclusion of the TD module allows for valuable information to be conveyed through the temporal flow. We also curate and benchmark three practical datasets for the joint problem of trajectory imputation and prediction. Extensive experiments verify the exceptional performance of our proposed method. As far as we know, this is the first work to address the lack of benchmarks and techniques for trajectory imputation and prediction in a unified manner. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Uncovering_the_Missing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16005 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not | Aneeshan Sain, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Subhadeep Koley, Tao Xiang, Yi-Zhe Song | In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability they seem to offer, but for the first time tailor it to benefit the sketch community. We put forward novel designs on how best to achieve this synergy, for both the category setting and the fine-grained setting ("all"). At the very core of our solution is a prompt learning setup. First we show just via factoring in sketch-specific prompts, we already have a category-level ZS-SBIR system that overshoots all prior arts, by a large margin (24.8%) - a great testimony on studying the CLIP and ZS-SBIR synergy. Moving onto the fine-grained setup is however trickier, and requires a deeper dive into this synergy. For that, we come up with two specific designs to tackle the fine-grained matching nature of the problem: (i) an additional regularisation loss to ensure the relative separation between sketches and photos is uniform across categories, which is not the case for the gold standard standalone triplet loss, and (ii) a clever patch shuffling technique to help establishing instance-level structural correspondences between sketch-photo pairs. With these designs, we again observe significant performance gains in the region of 26.9% over previous state-of-the-art. The take-home message, if any, is the proposed CLIP and prompt learning paradigm carries great promise in tackling other sketch-related tasks (not limited to ZS-SBIR) where data scarcity remains a great challenge. Project page: https://aneeshan95.github.io/Sketch_LVM/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sain_CLIP_for_All_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13440 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.html | CVPR 2023 | null |
FlexiViT: One Model for All Patch Sizes | Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic | Vision Transformers convert images to sequences by slicing them into patches. The size of these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher accuracy at greater computational cost, but changing the patch size typically requires retraining the model. In this paper, we demonstrate that simply randomizing the patch size at training time leads to a single set of weights that performs well across a wide range of patch sizes, making it possible to tailor the model to different compute budgets at deployment time. We extensively evaluate the resulting model, which we call FlexiViT, on a wide range of tasks, including classification, image-text retrieval, openworld detection, panoptic segmentation, and semantic segmentation, concluding that it usually matches, and sometimes outperforms, standard ViT models trained at a single patch size in an otherwise identical setup. Hence, FlexiViT training is a simple drop-in improvement for ViT that makes it easy to add compute-adaptive capabilities to most models relying on a ViT backbone architecture. Code and pretrained models are available at github.com/googleresearch/big_vision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Beyer_FlexiViT_One_Model_for_All_Patch_Sizes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Beyer_FlexiViT_One_Model_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08013 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Beyer_FlexiViT_One_Model_for_All_Patch_Sizes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Beyer_FlexiViT_One_Model_for_All_Patch_Sizes_CVPR_2023_paper.html | CVPR 2023 | null |
RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View Stereo | Changjiang Cai, Pan Ji, Qingan Yan, Yi Xu | This paper presents a learning-based method for multi-view depth estimation from posed images. Our core idea is a "learning-to-optimize" paradigm that iteratively indexes a plane-sweeping cost volume and regresses the depth map via a convolutional Gated Recurrent Unit (GRU). Since the cost volume plays a paramount role in encoding the multi-view geometry, we aim to improve its construction both at pixel- and frame- levels. At the pixel level, we propose to break the symmetry of the Siamese network (which is typically used in MVS to extract image features) by introducing a transformer block to the reference image (but not to the source images). Such an asymmetric volume allows the network to extract global features from the reference image to predict its depth map. Given potential inaccuracies in the poses between reference and source images, we propose to incorporate a residual pose network to correct the relative poses. This essentially rectifies the cost volume at the frame level. We conduct extensive experiments on real-world MVS datasets and show that our method achieves state-of-the-art performance in terms of both within-dataset evaluation and cross-dataset generalization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_RIAV-MVS_Recurrent-Indexing_an_Asymmetric_Volume_for_Multi-View_Stereo_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cai_RIAV-MVS_Recurrent-Indexing_an_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_RIAV-MVS_Recurrent-Indexing_an_Asymmetric_Volume_for_Multi-View_Stereo_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_RIAV-MVS_Recurrent-Indexing_an_Asymmetric_Volume_for_Multi-View_Stereo_CVPR_2023_paper.html | CVPR 2023 | null |
Structured Kernel Estimation for Photon-Limited Deconvolution | Yash Sanghvi, Zhiyuan Mao, Stanley H. Chan | Images taken in a low light condition with the presence of camera shake suffer from motion blur and photon shot noise. While state-of-the-art image restoration networks show promising results, they are largely limited to well-illuminated scenes and their performance drops significantly when photon shot noise is strong. In this paper, we propose a new blur estimation technique customized for photon-limited conditions. The proposed method employs a gradient-based backpropagation method to estimate the blur kernel. By modeling the blur kernel using a low-dimensional representation with the key points on the motion trajectory, we significantly reduce the search space and improve the regularity of the kernel estimation problem. When plugged into an iterative framework, our novel low-dimensional representation provides improved kernel estimates and hence significantly better deconvolution performance when compared to end-to-end trained neural networks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sanghvi_Structured_Kernel_Estimation_for_Photon-Limited_Deconvolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sanghvi_Structured_Kernel_Estimation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sanghvi_Structured_Kernel_Estimation_for_Photon-Limited_Deconvolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sanghvi_Structured_Kernel_Estimation_for_Photon-Limited_Deconvolution_CVPR_2023_paper.html | CVPR 2023 | null |
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection | Xincheng Yao, Ruoqi Li, Jing Zhang, Jun Sun, Chongyang Zhang | Most anomaly detection (AD) models are learned using only normal samples in an unsupervised way, which may result in ambiguous decision boundary and insufficient discriminability. In fact, a few anomaly samples are often available in real-world applications, the valuable knowledge of known anomalies should also be effectively exploited. However, utilizing a few known anomalies during training may cause another issue that the model may be biased by those known anomalies and fail to generalize to unseen anomalies. In this paper, we tackle supervised anomaly detection, i.e., we learn AD models using a few available anomalies with the objective to detect both the seen and unseen anomalies. We propose a novel explicit boundary guided semi-push-pull contrastive learning mechanism, which can enhance model's discriminability while mitigating the bias issue. Our approach is based on two core designs: First, we find an explicit and compact separating boundary as the guidance for further feature learning. As the boundary only relies on the normal feature distribution, the bias problem caused by a few known anomalies can be alleviated. Second, a boundary guided semi-push-pull loss is developed to only pull the normal features together while pushing the abnormal features apart from the separating boundary beyond a certain margin region. In this way, our model can form a more explicit and discriminative decision boundary to distinguish known and also unseen anomalies from normal samples more effectively. Code will be available at https://github.com/xcyao00/BGAD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Explicit_Boundary_Guided_Semi-Push-Pull_Contrastive_Learning_for_Supervised_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_Explicit_Boundary_Guided_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.01463 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Explicit_Boundary_Guided_Semi-Push-Pull_Contrastive_Learning_for_Supervised_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Explicit_Boundary_Guided_Semi-Push-Pull_Contrastive_Learning_for_Supervised_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
3D Video Loops From Asynchronous Input | Li Ma, Xiaoyu Li, Jing Liao, Pedro V. Sander | Looping videos are short video clips that can be looped endlessly without visible seams or artifacts. They provide a very attractive way to capture the dynamism of natural scenes. Existing methods have been mostly limited to 2D representations. In this paper, we take a step forward and propose a practical solution that enables an immersive experience on dynamic 3D looping scenes. The key challenge is to consider the per-view looping conditions from asynchronous input while maintaining view consistency for the 3D representation. We propose a novel sparse 3D video representation, namely Multi-Tile Video (MTV), which not only provides a view-consistent prior, but also greatly reduces memory usage, making the optimization of a 4D volume tractable. Then, we introduce a two-stage pipeline to construct the 3D looping MTV from completely asynchronous multi-view videos with no time overlap. A novel looping loss based on video temporal retargeting algorithms is adopted during the optimization to loop the 3D scene. Experiments of our framework have shown promise in successfully generating and rendering photorealistic 3D looping videos in real time even on mobile devices. The code, dataset, and live demos are available in https://limacv.github.io/VideoLoop3D_web/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_3D_Video_Loops_From_Asynchronous_Input_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_3D_Video_Loops_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05312 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_3D_Video_Loops_From_Asynchronous_Input_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_3D_Video_Loops_From_Asynchronous_Input_CVPR_2023_paper.html | CVPR 2023 | null |
Style Projected Clustering for Domain Generalized Semantic Segmentation | Wei Huang, Chang Chen, Yong Li, Jiacheng Li, Cheng Li, Fenglong Song, Youliang Yan, Zhiwei Xiong | Existing semantic segmentation methods improve generalization capability, by regularizing various images to a canonical feature space. While this process contributes to generalization, it weakens the representation inevitably. In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and stored as the bases of representation. Then, the generalization to unseen image styles is achieved by projecting features to this known space. Specifically, we realize the style projection as a weighted combination of stored bases, where the similarity distances are adopted as the weighting factors. Based on the same concept, we extend this process to the decision part of model and promote the generalization of semantic prediction. By measuring the similarity distances to semantic bases (i.e., prototypes), we replace the common deterministic prediction with semantic clustering. Comprehensive experiments demonstrate the advantage of proposed method to the state of the art, up to 3.6% mIoU improvement in average on unseen scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Style_Projected_Clustering_for_Domain_Generalized_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Style_Projected_Clustering_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Style_Projected_Clustering_for_Domain_Generalized_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Style_Projected_Clustering_for_Domain_Generalized_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
DIP: Dual Incongruity Perceiving Network for Sarcasm Detection | Changsong Wen, Guoli Jia, Jufeng Yang | Sarcasm indicates the literal meaning is contrary to the real attitude. Considering the popularity and complementarity of image-text data, we investigate the task of multi-modal sarcasm detection. Different from other multi-modal tasks, for the sarcastic data, there exists intrinsic incongruity between a pair of image and text as demonstrated in psychological theories. To tackle this issue, we propose a Dual Incongruity Perceiving (DIP) network consisting of two branches to mine the sarcastic information from factual and affective levels. For the factual aspect, we introduce a channel-wise reweighting strategy to obtain semantically discriminative embeddings, and leverage gaussian distribution to model the uncertain correlation caused by the incongruity. The distribution is generated from the latest data stored in the memory bank, which can adaptively model the difference of semantic similarity between sarcastic and non-sarcastic data. For the affective aspect, we utilize siamese layers with shared parameters to learn cross-modal sentiment information. Furthermore, we use the polarity value to construct a relation graph for the mini-batch, which forms the continuous contrastive loss to acquire affective embeddings. Extensive experiments demonstrate that our proposed method performs favorably against state-of-the-art approaches. Our code is released on https://github.com/downdric/MSD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wen_DIP_Dual_Incongruity_Perceiving_Network_for_Sarcasm_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_DIP_Dual_Incongruity_Perceiving_Network_for_Sarcasm_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_DIP_Dual_Incongruity_Perceiving_Network_for_Sarcasm_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Frame Interpolation Transformer and Uncertainty Guidance | Markus Plack, Karlis Martins Briedis, Abdelaziz Djelouah, Matthias B. Hullin, Markus Gross, Christopher Schroers | Video frame interpolation has seen important progress in recent years, thanks to developments in several directions. Some works leverage better optical flow methods with improved splatting strategies or additional cues from depth, while others have investigated alternative approaches through direct predictions or transformers. Still, the problem remains unsolved in more challenging conditions such as complex lighting or large motion. In this work, we are bridging the gap towards video production with a novel transformer-based interpolation network architecture capable of estimating the expected error together with the interpolated frame. This offers several advantages that are of key importance for frame interpolation usage: First, we obtained improved visual quality over several datasets. The improvement in terms of quality is also clearly demonstrated through a user study. Second, our method estimates error maps for the interpolated frame, which are essential for real-life applications on longer video sequences where problematic frames need to be flagged. Finally, for rendered content a partial rendering pass of the intermediate frame, guided by the predicted error, can be utilized during the interpolation to generate a new frame of superior quality. Through this error estimation, our method can produce even higher-quality intermediate frames using only a fraction of the time compared to a full rendering. | https://openaccess.thecvf.com/content/CVPR2023/papers/Plack_Frame_Interpolation_Transformer_and_Uncertainty_Guidance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Plack_Frame_Interpolation_Transformer_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Plack_Frame_Interpolation_Transformer_and_Uncertainty_Guidance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Plack_Frame_Interpolation_Transformer_and_Uncertainty_Guidance_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic Space | Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, Chang-Wen Chen | Scene graph generation (SGG) aims to abstract an image into a graph structure, by representing objects as graph nodes and their relations as labeled edges. However, two knotty obstacles limit the practicability of current SGG methods in real-world scenarios: 1) training SGG models requires time-consuming ground-truth annotations, and 2) the closed-set object categories make the SGG models limited in their ability to recognize novel objects outside of training corpora. To address these issues, we novelly exploit a powerful pre-trained visual-semantic space (VSS) to trigger language-supervised and open-vocabulary SGG in a simple yet effective manner. Specifically, cheap scene graph supervision data can be easily obtained by parsing image language descriptions into semantic graphs. Next, the noun phrases on such semantic graphs are directly grounded over image regions through region-word alignment in the pre-trained VSS. In this way, we enable open-vocabulary object detection by performing object category name grounding with a text prompt in this VSS. On the basis of visually-grounded objects, the relation representations are naturally built for relation recognition, pursuing open-vocabulary SGG. We validate our proposed approach with extensive experiments on the Visual Genome benchmark across various SGG scenarios (i.e., supervised / language-supervised, closed-set / open-vocabulary). Consistent superior performances are achieved compared with existing methods, demonstrating the potential of exploiting pre-trained VSS for SGG in more practical scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Learning_To_Generate_Language-Supervised_and_Open-Vocabulary_Scene_Graph_Using_Pre-Trained_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Learning_To_Generate_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_To_Generate_Language-Supervised_and_Open-Vocabulary_Scene_Graph_Using_Pre-Trained_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_To_Generate_Language-Supervised_and_Open-Vocabulary_Scene_Graph_Using_Pre-Trained_CVPR_2023_paper.html | CVPR 2023 | null |
VectorFloorSeg: Two-Stream Graph Attention Network for Vectorized Roughcast Floorplan Segmentation | Bingchen Yang, Haiyong Jiang, Hao Pan, Jun Xiao | Vector graphics (VG) are ubiquitous in industrial designs. In this paper, we address semantic segmentation of a typical VG, i.e., roughcast floorplans with bare wall structures, whose output can be directly used for further applications like interior furnishing and room space modeling. Previous semantic segmentation works mostly process well-decorated floorplans in raster images and usually yield aliased boundaries and outlier fragments in segmented rooms, due to pixel-level segmentation that ignores the regular elements (e.g. line segments) in vector floorplans. To overcome these issues, we propose to fully utilize the regular elements in vector floorplans for more integral segmentation. Our pipeline predicts room segmentation from vector floorplans by dually classifying line segments as room boundaries, and regions partitioned by line segments as room segments. To fully exploit the structural relationships between lines and regions, we use two-stream graph neural networks to process the line segments and partitioned regions respectively, and devise a novel modulated graph attention layer to fuse the heterogeneous information from one stream to the other. Extensive experiments show that by directly operating on vector floorplans, we outperform image-based methods in both mIoU and mAcc. In addition, we propose a new metric that captures room integrity and boundary regularity, which confirms that our method produces much more regular segmentations. Source code is available at https://github.com/DrZiji/VecFloorSeg | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_VectorFloorSeg_Two-Stream_Graph_Attention_Network_for_Vectorized_Roughcast_Floorplan_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_VectorFloorSeg_Two-Stream_Graph_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_VectorFloorSeg_Two-Stream_Graph_Attention_Network_for_Vectorized_Roughcast_Floorplan_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_VectorFloorSeg_Two-Stream_Graph_Attention_Network_for_Vectorized_Roughcast_Floorplan_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Preset for Color Style Transfer | Zhanghan Ke, Yuhao Liu, Lei Zhu, Nanxuan Zhao, Rynson W.H. Lau | In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed. Our method is based on two core designs. First, we propose Deterministic Neural Color Mapping (DNCM) to consistently operate on each pixel via an image-adaptive color mapping matrix, avoiding artifacts and supporting high-resolution inputs with a small memory footprint. Second, we develop a two-stage pipeline by dividing the task into color normalization and stylization, which allows efficient style switching by extracting color styles as presets and reusing them on normalized input images. Due to the unavailability of pairwise datasets, we describe how to train Neural Preset via a self-supervised strategy. Various advantages of Neural Preset over existing methods are demonstrated through comprehensive evaluations. Besides, we show that our trained model can naturally support multiple applications without fine-tuning, including low-light image enhancement, underwater image correction, image dehazing, and image harmonization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ke_Neural_Preset_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13511 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
DeCo: Decomposition and Reconstruction for Compositional Temporal Grounding via Coarse-To-Fine Contrastive Ranking | Lijin Yang, Quan Kong, Hsuan-Kung Yang, Wadim Kehl, Yoichi Sato, Norimasa Kobori | Understanding dense action in videos is a fundamental challenge towards the generalization of vision models. Several works show that compositionality is key to achieving generalization by combining known primitive elements, especially for handling novel composited structures. Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding. In recent works, composition is assumed to be learned from pairs of whole videos and language embeddings through large scale self-supervised pre-training. Alternatively, one can process the video and language into word-level primitive elements, and then only learn fine-grained semantic correspondences. Both approaches do not consider the granularity of the compositions, where different query granularity corresponds to different video segments. Therefore, a good compositional representation should be sensitive to different video and query granularity. We propose a method to learn a coarse-to-fine compositional representation by decomposing the original query sentence into different granular levels, and then learning the correct correspondences between the video and recombined queries through a contrastive ranking constraint. Additionally, we run temporal boundary prediction in a coarse-to-fine manner for precise grounding boundary detection. Experiments are performed on two datasets Charades-CG and ActivityNet-CG showing the superior compositional generalizability of our approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_DeCo_Decomposition_and_Reconstruction_for_Compositional_Temporal_Grounding_via_Coarse-To-Fine_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_DeCo_Decomposition_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_DeCo_Decomposition_and_Reconstruction_for_Compositional_Temporal_Grounding_via_Coarse-To-Fine_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_DeCo_Decomposition_and_Reconstruction_for_Compositional_Temporal_Grounding_via_Coarse-To-Fine_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Aggregated Network for Gait Recognition | Kang Ma, Ying Fu, Dezhi Zheng, Chunshui Cao, Xuecai Hu, Yongzhen Huang | Gait recognition is beneficial for a variety of applications, including video surveillance, crime scene investigation, and social security, to mention a few. However, gait recognition often suffers from multiple exterior factors in real scenes, such as carrying conditions, wearing overcoats, and diverse viewing angles. Recently, various deep learning-based gait recognition methods have achieved promising results, but they tend to extract one of the salient features using fixed-weighted convolutional networks, do not well consider the relationship within gait features in key regions, and ignore the aggregation of complete motion patterns. In this paper, we propose a new perspective that actual gait features include global motion patterns in multiple key regions, and each global motion pattern is composed of a series of local motion patterns. To this end, we propose a Dynamic Aggregation Network (DANet) to learn more discriminative gait features. Specifically, we create a dynamic attention mechanism between the features of neighboring pixels that not only adaptively focuses on key regions but also generates more expressive local motion patterns. In addition, we develop a self-attention mechanism to select representative local motion patterns and further learn robust global motion patterns. Extensive experiments on three popular public gait datasets, i.e., CASIA-B, OUMVLP, and Gait3D, demonstrate that the proposed method can provide substantial improvements over the current state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_Dynamic_Aggregated_Network_for_Gait_Recognition_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Dynamic_Aggregated_Network_for_Gait_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Dynamic_Aggregated_Network_for_Gait_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Wavelet Diffusion Models Are Fast and Scalable Image Generators | Hao Phung, Quan Dao, Anh Tran | Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances. However, their slow training and inference speed is a huge bottleneck, blocking them from being used in real-time applications. A recent DiffusionGAN method significantly decreases the models' running time by reducing the number of sampling steps from thousands to several, but their speeds still largely lag behind the GAN counterparts. This paper aims to reduce the speed gap by proposing a novel wavelet-based diffusion scheme. We extract low-and-high frequency components from both image and feature levels via wavelet decomposition and adaptively handle these components for faster processing while maintaining good generation quality. Furthermore, we propose to use a reconstruction term, which effectively boosts the model training convergence. Experimental results on CelebA-HQ, CIFAR-10, LSUN-Church, and STL-10 datasets prove our solution is a stepping-stone to offering real-time and high-fidelity diffusion models. Our code and pre-trained checkpoints are available at https://github.com/VinAIResearch/WaveDiff.git. | https://openaccess.thecvf.com/content/CVPR2023/papers/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Phung_Wavelet_Diffusion_Models_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.html | CVPR 2023 | null |
PA&DA: Jointly Sampling Path and Data for Consistent NAS | Shun Lu, Yu Hu, Longxing Yang, Zihao Sun, Jilin Mei, Jianchao Tan, Chengru Song | Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. However, several works have pointed out that the shared weights suffer from different gradient descent directions during training. And we further find that large gradient variance occurs during supernet training, which degrades the supernet ranking consistency. To mitigate this issue, we propose to explicitly minimize the gradient variance of the supernet training by jointly optimizing the sampling distributions of PAth and DAta (PA&DA). We theoretically derive the relationship between the gradient variance and the sampling distributions, and reveal that the optimal sampling probability is proportional to the normalized gradient norm of path and training data. Hence, we use the normalized gradient norm as the importance indicator for path and training data, and adopt an importance sampling strategy for the supernet training. Our method only requires negligible computation cost for optimizing the sampling distributions of path and data, but achieves lower gradient variance during supernet training and better generalization performance for the supernet, resulting in a more consistent NAS. We conduct comprehensive comparisons with other improved approaches in various search spaces. Results show that our method surpasses others with more reliable ranking performance and higher accuracy of searched architectures, showing the effectiveness of our method. Code is available at https://github.com/ShunLu91/PA-DA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_PADA_Jointly_Sampling_Path_and_Data_for_Consistent_NAS_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_PADA_Jointly_Sampling_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_PADA_Jointly_Sampling_Path_and_Data_for_Consistent_NAS_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_PADA_Jointly_Sampling_Path_and_Data_for_Consistent_NAS_CVPR_2023_paper.html | CVPR 2023 | null |
Sphere-Guided Training of Neural Implicit Surfaces | Andreea Dogaru, Andrei-Timotei Ardelean, Savva Ignatyev, Egor Zakharov, Evgeny Burnaev | In recent years, neural distance functions trained via volumetric ray marching have been widely adopted for multi-view 3D reconstruction. These methods, however, apply the ray marching procedure for the entire scene volume, leading to reduced sampling efficiency and, as a result, lower reconstruction quality in the areas of high-frequency details. In this work, we address this problem via joint training of the implicit function and our new coarse sphere-based surface reconstruction. We use the coarse representation to efficiently exclude the empty volume of the scene from the volumetric ray marching procedure without additional forward passes of the neural surface network, which leads to an increased fidelity of the reconstructions compared to the base systems. We evaluate our approach by incorporating it into the training procedures of several implicit surface modeling methods and observe uniform improvements across both synthetic and real-world datasets. Our codebase can be accessed via the project page. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dogaru_Sphere-Guided_Training_of_Neural_Implicit_Surfaces_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dogaru_Sphere-Guided_Training_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.15511 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dogaru_Sphere-Guided_Training_of_Neural_Implicit_Surfaces_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dogaru_Sphere-Guided_Training_of_Neural_Implicit_Surfaces_CVPR_2023_paper.html | CVPR 2023 | null |
3D Spatial Multimodal Knowledge Accumulation for Scene Graph Prediction in Point Cloud | Mingtao Feng, Haoran Hou, Liang Zhang, Zijie Wu, Yulan Guo, Ajmal Mian | In-depth understanding of a 3D scene not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them. However, since 3D scenes contain partially scanned objects with physical connections, dense placement, changing sizes, and a wide variety of challenging relationships, existing methods perform quite poorly with limited training samples. In this work, we find that the inherently hierarchical structures of physical space in 3D scenes aid in the automatic association of semantic and spatial arrangements, specifying clear patterns and leading to less ambiguous predictions. Thus, they well meet the challenges due to the rich variations within scene categories. To achieve this, we explicitly unify these structural cues of 3D physical spaces into deep neural networks to facilitate scene graph prediction. Specifically, we exploit an external knowledge base as a baseline to accumulate both contextualized visual content and textual facts to form a 3D spatial multimodal knowledge graph. Moreover, we propose a knowledge-enabled scene graph prediction module benefiting from the 3D spatial knowledge to effectively regularize semantic space of relationships. Extensive experiments demonstrate the superiority of the proposed method over current state-of-the-art competitors. Our code is available at https://github.com/HHrEtvP/SMKA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_3D_Spatial_Multimodal_Knowledge_Accumulation_for_Scene_Graph_Prediction_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_3D_Spatial_Multimodal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_3D_Spatial_Multimodal_Knowledge_Accumulation_for_Scene_Graph_Prediction_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_3D_Spatial_Multimodal_Knowledge_Accumulation_for_Scene_Graph_Prediction_in_CVPR_2023_paper.html | CVPR 2023 | null |
Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation | Guozhen Zhang, Yuhan Zhu, Haonan Wang, Youxin Chen, Gangshan Wu, Limin Wang | Effectively extracting inter-frame motion and appearance information is important for video frame interpolation (VFI). Previous works either extract both types of information in a mixed way or devise separate modules for each type of information, which lead to representation ambiguity and low efficiency. In this paper, we propose a new module to explicitly extract motion and appearance information via a unified operation. Specifically, we rethink the information process in inter-frame attention and reuse its attention map for both appearance feature enhancement and motion information extraction. Furthermore, for efficient VFI, our proposed module could be seamlessly integrated into a hybrid CNN and Transformer architecture. This hybrid pipeline can alleviate the computational complexity of inter-frame attention as well as preserve detailed low-level structure information. Experimental results demonstrate that, for both fixed- and arbitrary-timestep interpolation, our method achieves state-of-the-art performance on various datasets. Meanwhile, our approach enjoys a lighter computation overhead over models with close performance. The source code and models are available at https://github.com/MCG-NJU/EMA-VFI. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Extracting_Motion_and_Appearance_via_Inter-Frame_Attention_for_Efficient_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Extracting_Motion_and_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.00440 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Extracting_Motion_and_Appearance_via_Inter-Frame_Attention_for_Efficient_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Extracting_Motion_and_Appearance_via_Inter-Frame_Attention_for_Efficient_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Bias Mimicking: A Simple Sampling Approach for Bias Mitigation | Maan Qraitem, Kate Saenko, Bryan A. Plummer | Prior work has shown that Visual Recognition datasets frequently underrepresent bias groups B (e.g. Female) within class labels Y (e.g. Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Alternatively, data sampling baselines from the class imbalance literature (eg Undersampling, Upweighting), which can often be implemented in a single line of code and often have no hyperparameters, offer a cheaper and more efficient solution. However, these methods suffer from significant shortcomings. For example, Undersampling drops a significant part of the input distribution per epoch while Oversampling repeats samples, causing overfitting. To address these shortcomings, we introduce a new class-conditioned sampling method: Bias Mimicking. The method is based on the observation that if a class c bias distribution, i.e., P_D(B|Y=c) is mimicked across every c' != c, then Y and B are statistically independent. Using this notion, BM, through a novel training procedure, ensures that the model is exposed to the entire distribution per epoch without repeating samples. Consequently, Bias Mimicking improves underrepresented groups' accuracy of sampling methods by 3% over four benchmarks while maintaining and sometimes improving performance over nonsampling methods. Code: https://github.com/mqraitem/Bias-Mimicking | https://openaccess.thecvf.com/content/CVPR2023/papers/Qraitem_Bias_Mimicking_A_Simple_Sampling_Approach_for_Bias_Mitigation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qraitem_Bias_Mimicking_A_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.15605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qraitem_Bias_Mimicking_A_Simple_Sampling_Approach_for_Bias_Mitigation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qraitem_Bias_Mimicking_A_Simple_Sampling_Approach_for_Bias_Mitigation_CVPR_2023_paper.html | CVPR 2023 | null |
ViTs for SITS: Vision Transformers for Satellite Image Time Series | Michail Tarasiou, Erik Chavez, Stefanos Zafeiriou | In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the Vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes can be found at https://github.com/michaeltrs/DeepSatModels. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tarasiou_ViTs_for_SITS_Vision_Transformers_for_Satellite_Image_Time_Series_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tarasiou_ViTs_for_SITS_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.04944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tarasiou_ViTs_for_SITS_Vision_Transformers_for_Satellite_Image_Time_Series_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tarasiou_ViTs_for_SITS_Vision_Transformers_for_Satellite_Image_Time_Series_CVPR_2023_paper.html | CVPR 2023 | null |
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers | Yijiang Liu, Huanrui Yang, Zhen Dong, Kurt Keutzer, Li Du, Shanghang Zhang | The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision transformer activations hinders the effectiveness of previous post-training quantization methods, even with advanced quantizer designs. Instead of tuning the quantizer to better fit the complicated activation distribution, this paper proposes NoisyQuant, a quantizer-agnostic enhancement for the post-training activation quantization performance of vision transformers. We make a surprising theoretical discovery that for a given quantizer, adding a fixed Uniform noisy bias to the values being quantized can significantly reduce the quantization error under provable conditions. Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer. Extensive experiments show NoisyQuant largely improves the post-training quantization performance of vision transformer with minimal computation overhead. For instance, on linear uniform 6-bit activation quantization, NoisyQuant improves SOTA top-1 accuracy on ImageNet by up to 1.7%, 1.1% and 0.5% for ViT, DeiT, and Swin Transformer respectively, achieving on-par or even higher performance than previous nonlinear, mixed-precision quantization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_NoisyQuant_Noisy_Bias-Enhanced_Post-Training_Activation_Quantization_for_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_NoisyQuant_Noisy_Bias-Enhanced_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16056 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_NoisyQuant_Noisy_Bias-Enhanced_Post-Training_Activation_Quantization_for_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_NoisyQuant_Noisy_Bias-Enhanced_Post-Training_Activation_Quantization_for_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Supervised Stereo-Based 3D Object Detection via Cross-View Consensus | Wenhao Wu, Hau San Wong, Si Wu | Stereo-based 3D object detection, which aims at detecting 3D objects with stereo cameras, shows great potential in low-cost deployment compared to LiDAR-based methods and excellent performance compared to monocular-based algorithms. However, the impressive performance of stereo-based 3D object detection is at the huge cost of high-quality manual annotations, which are hardly attainable for any given scene. Semi-supervised learning, in which limited annotated data and numerous unannotated data are required to achieve a satisfactory model, is a promising method to address the problem of data deficiency. In this work, we propose to achieve semi-supervised learning for stereo-based 3D object detection through pseudo annotation generation from a temporal-aggregated teacher model, which temporally accumulates knowledge from a student model. To facilitate a more stable and accurate depth estimation, we introduce Temporal-Aggregation-Guided (TAG) disparity consistency, a cross-view disparity consistency constraint between the teacher model and the student model for robust and improved depth estimation. To mitigate noise in pseudo annotation generation, we propose a cross-view agreement strategy, in which pseudo annotations should attain high degree of agreements between 3D and 2D views, as well as between binocular views. We perform extensive experiments on the KITTI 3D dataset to demonstrate our proposed method's capability in leveraging a huge amount of unannotated stereo images to attain significantly improved detection results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Semi-Supervised_Stereo-Based_3D_Object_Detection_via_Cross-View_Consensus_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Semi-Supervised_Stereo-Based_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Semi-Supervised_Stereo-Based_3D_Object_Detection_via_Cross-View_Consensus_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Semi-Supervised_Stereo-Based_3D_Object_Detection_via_Cross-View_Consensus_CVPR_2023_paper.html | CVPR 2023 | null |
Minimizing Maximum Model Discrepancy for Transferable Black-Box Targeted Attacks | Anqi Zhao, Tong Chu, Yahao Liu, Wen Li, Jingjing Li, Lixin Duan | In this work, we study the black-box targeted attack problem from the model discrepancy perspective. On the theoretical side, we present a generalization error bound for black-box targeted attacks, which gives a rigorous theoretical analysis for guaranteeing the success of the attack. We reveal that the attack error on a target model mainly depends on empirical attack error on the substitute model and the maximum model discrepancy among substitute models. On the algorithmic side, we derive a new algorithm for black-box targeted attacks based on our theoretical analysis, in which we additionally minimize the maximum model discrepancy(M3D) of the substitute models when training the generator to generate adversarial examples. In this way, our model is capable of crafting highly transferable adversarial examples that are robust to the model variation, thus improving the success rate for attacking the black-box model. We conduct extensive experiments on the ImageNet dataset with different classification models, and our proposed approach outperforms existing state-of-the-art methods by a significant margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Minimizing_Maximum_Model_Discrepancy_for_Transferable_Black-Box_Targeted_Attacks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Minimizing_Maximum_Model_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09035 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Minimizing_Maximum_Model_Discrepancy_for_Transferable_Black-Box_Targeted_Attacks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Minimizing_Maximum_Model_Discrepancy_for_Transferable_Black-Box_Targeted_Attacks_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Loss Function by Minimizing the Detrimental Effect of Floating-Point Errors on Gradient-Based Attacks | Yunrui Yu, Cheng-Zhong Xu | Attackers can deceive neural networks by adding human imperceptive perturbations to their input data; this reveals the vulnerability and weak robustness of current deep-learning networks. Many attack techniques have been proposed to evaluate the model's robustness. Gradient-based attacks suffer from severely overestimating the robustness. This paper identifies that the relative error in calculated gradients caused by floating-point errors, including floating-point underflow and rounding errors, is a fundamental reason why gradient-based attacks fail to accurately assess the model's robustness. Although it is hard to eliminate the relative error in the gradients, we can control its effect on the gradient-based attacks. Correspondingly, we propose an efficient loss function by minimizing the detrimental impact of the floating-point errors on the attacks. Experimental results show that it is more efficient and reliable than other loss functions when examined across a wide range of defence mechanisms. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Efficient_Loss_Function_by_Minimizing_the_Detrimental_Effect_of_Floating-Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Efficient_Loss_Function_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Efficient_Loss_Function_by_Minimizing_the_Detrimental_Effect_of_Floating-Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Efficient_Loss_Function_by_Minimizing_the_Detrimental_Effect_of_Floating-Point_CVPR_2023_paper.html | CVPR 2023 | null |
BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields | Peng Wang, Lingzhe Zhao, Ruijie Ma, Peidong Liu | Neural Radiance Fields (NeRF) have received considerable attention recently, due to its impressive capability in photo-realistic 3D reconstruction and novel view synthesis, given a set of posed camera images. Earlier work usually assumes the input images are of good quality. However, image degradation (e.g. image motion blur in low-light conditions) can easily happen in real-world scenarios, which would further affect the rendering quality of NeRF. In this paper, we present a novel bundle adjusted deblur Neural Radiance Fields (BAD-NeRF), which can be robust to severe motion blurred images and inaccurate camera poses. Our approach models the physical image formation process of a motion blurred image, and jointly learns the parameters of NeRF and recovers the camera motion trajectories during exposure time. In experiments, we show that by directly modeling the real physical image formation process, BAD-NeRF achieves superior performance over prior works on both synthetic and real datasets. Code and data are available at https://github.com/WU-CVGL/BAD-NeRF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_BAD-NeRF_Bundle_Adjusted_Deblur_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_BAD-NeRF_Bundle_Adjusted_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_BAD-NeRF_Bundle_Adjusted_Deblur_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_BAD-NeRF_Bundle_Adjusted_Deblur_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Video Compression With Entropy-Constrained Neural Representations | Carlos Gomes, Roberto Azevedo, Christopher Schroers | Encoding videos as neural networks is a recently proposed approach that allows new forms of video processing. However, traditional techniques still outperform such neural video representation (NVR) methods for the task of video compression. This performance gap can be explained by the fact that current NVR methods: i) use architectures that do not efficiently obtain a compact representation of temporal and spatial information; and ii) minimize rate and distortion disjointly (first overfitting a network on a video and then using heuristic techniques such as post-training quantization or weight pruning to compress the model). We propose a novel convolutional architecture for video representation that better represents spatio-temporal information and a training strategy capable of jointly optimizing rate and distortion. All network and quantization parameters are jointly learned end-to-end, and the post-training operations used in previous works are unnecessary. We evaluate our method on the UVG dataset, achieving new state-of-the-art results for video compression with NVRs. Moreover, we deliver the first NVR-based video compression method that improves over the typically adopted HEVC benchmark (x265, disabled b-frames, "medium" preset), closing the gap to autoencoder-based video compression techniques. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gomes_Video_Compression_With_Entropy-Constrained_Neural_Representations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gomes_Video_Compression_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gomes_Video_Compression_With_Entropy-Constrained_Neural_Representations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gomes_Video_Compression_With_Entropy-Constrained_Neural_Representations_CVPR_2023_paper.html | CVPR 2023 | null |
Prompt, Generate, Then Cache: Cascade of Foundation Models Makes Strong Few-Shot Learners | Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, Hongsheng Li | Visual recognition in low-data regimes requires deep neural networks to learn generalized representations from limited training samples. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. We then question, if the more diverse pre-training knowledge can be cascaded to further assist few-shot representation learning. In this paper, we propose CaFo, a Cascade of Foundation models that incorporates diverse prior knowledge of various pre training paradigms for better few-shot learning. Our CaFo incorporates CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, DALL-E's vision generative knowledge, and GPT-3's language-generative knowledge. Specifically, CaFo works by 'Prompt, Generate, then Cache'. Firstly, we leverage GPT-3 to produce textual inputs for prompting CLIP with rich downstream linguistic semantics. Then, we generate synthetic images via DALL-E to expand the few-shot training data without any manpower. At last, we introduce a learnable cache model to adaptively blend the predictions from CLIP and DINO. By such col laboration, CaFo can fully unleash the potential of different pre-training methods and unify them to perform state-of the-art for few-shot classification. Code is available at https://github.com/ZrrSkywalker/CaFo. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Prompt_Generate_Then_Cache_Cascade_of_Foundation_Models_Makes_Strong_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Prompt_Generate_Then_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02151 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Prompt_Generate_Then_Cache_Cascade_of_Foundation_Models_Makes_Strong_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Prompt_Generate_Then_Cache_Cascade_of_Foundation_Models_Makes_Strong_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Random Projector: Accelerated Deep Image Prior | Taihui Li, Hengkang Wang, Zhong Zhuang, Ju Sun | Deep image prior (DIP) has shown great promise in tackling a variety of image restoration (IR) and general visual inverse problems, needing no training data. However, the resulting optimization process is often very slow, inevitably hindering DIP's practical usage for time-sensitive scenarios. In this paper, we focus on IR, and propose two crucial modifications to DIP that help achieve substantial speedup: 1) optimizing the DIP seed while freezing randomly-initialized network weights, and 2) reducing the network depth. In addition, we reintroduce explicit priors, such as sparse gradient prior---encoded by total-variation regularization, to preserve the DIP peak performance. We evaluate the proposed method on three IR tasks, including image denoising, image super-resolution, and image inpainting, against the original DIP and variants, as well as the competing metaDIP that uses meta-learning to learn good initializers with extra data. Our method is a clear winner in obtaining competitive restoration quality in a minimal amount of time. Our code is available at https://github.com/sun-umn/Deep-Random-Projector. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Deep_Random_Projector_Accelerated_Deep_Image_Prior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Deep_Random_Projector_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Deep_Random_Projector_Accelerated_Deep_Image_Prior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Deep_Random_Projector_Accelerated_Deep_Image_Prior_CVPR_2023_paper.html | CVPR 2023 | null |
Subsets and Splits