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Human Body Shape Completion With Implicit Shape and Flow Learning | Boyao Zhou, Di Meng, Jean-Sébastien Franco, Edmond Boyer | In this paper, we investigate how to complete human body shape models by combining shape and flow estimation given two consecutive depth images. Shape completion is a challenging task in computer vision that is highly under-constrained when considering partial depth observations. Besides model based strategies that exploit strong priors, and consequently struggle to preserve fine geometric details, learning based approaches build on weaker assumptions and can benefit from efficient implicit representations. We adopt such a representation and explore how the motion flow between two consecutive frames can contribute to the shape completion task. In order to effectively exploit the flow information, our architecture combines both estimations and implements two features for robustness: First, an all-to-all attention module that encodes the correlation between points in the same frame and between corresponding points in different frames; Second, a coarse-dense to fine-sparse strategy that balances the representation ability and the computational cost. Our experiments demonstrate that the flow actually benefits human body model completion. They also show that our method outperforms the state-of-the-art approaches for shape completion on 2 benchmarks, considering different human shapes, poses, and clothing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Human_Body_Shape_Completion_With_Implicit_Shape_and_Flow_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Human_Body_Shape_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Human_Body_Shape_Completion_With_Implicit_Shape_and_Flow_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Human_Body_Shape_Completion_With_Implicit_Shape_and_Flow_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Spider GAN: Leveraging Friendly Neighbors To Accelerate GAN Training | Siddarth Asokan, Chandra Sekhar Seelamantula | Training Generative adversarial networks (GANs) stably is a challenging task. The generator in GANs transform noise vectors, typically Gaussian distributed, into realistic data such as images. In this paper, we propose a novel approach for training GANs with images as inputs, but without enforcing any pairwise constraints. The intuition is that images are more structured than noise, which the generator can leverage to learn a more robust transformation. The process can be made efficient by identifying closely related datasets, or a "friendly neighborhood" of the target distribution, inspiring the moniker, Spider GAN. To define friendly neighborhoods leveraging proximity between datasets, we propose a new measure called the signed inception distance (SID), inspired by the polyharmonic kernel. We show that the Spider GAN formulation results in faster convergence, as the generator can discover correspondence even between seemingly unrelated datasets, for instance, between Tiny-ImageNet and CelebA faces. Further, we demonstrate cascading Spider GAN, where the output distribution from a pre-trained GAN generator is used as the input to the subsequent network. Effectively, transporting one distribution to another in a cascaded fashion until the target is learnt -- a new flavor of transfer learning. We demonstrate the efficacy of the Spider approach on DCGAN, conditional GAN, PGGAN, StyleGAN2 and StyleGAN3. The proposed approach achieves state-of-the-art Frechet inception distance (FID) values, with one-fifth of the training iterations, in comparison to their baseline counterparts on high-resolution small datasets such as MetFaces, Ukiyo-E Faces and AFHQ-Cats. | https://openaccess.thecvf.com/content/CVPR2023/papers/Asokan_Spider_GAN_Leveraging_Friendly_Neighbors_To_Accelerate_GAN_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Asokan_Spider_GAN_Leveraging_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.07613 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Asokan_Spider_GAN_Leveraging_Friendly_Neighbors_To_Accelerate_GAN_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Asokan_Spider_GAN_Leveraging_Friendly_Neighbors_To_Accelerate_GAN_Training_CVPR_2023_paper.html | CVPR 2023 | null |
CLIPPING: Distilling CLIP-Based Models With a Student Base for Video-Language Retrieval | Renjing Pei, Jianzhuang Liu, Weimian Li, Bin Shao, Songcen Xu, Peng Dai, Juwei Lu, Youliang Yan | Pre-training a vison-language model and then fine-tuning it on downstream tasks have become a popular paradigm. However, pre-trained vison-language models with the Transformer architecture usually take long inference time. Knowledge distillation has been an efficient technique to transfer the capability of a large model to a small one while maintaining the accuracy, which has achieved remarkable success in natural language processing. However, it faces many problems when applying KD to the multi-modality applications. In this paper, we propose a novel knowledge distillation method, named CLIPPING, where the plentiful knowledge of a large teacher model that has been fine-tuned for video-language tasks with the powerful pre-trained CLIP can be effectively transferred to a small student only at the fine-tuning stage. Especially, a new layer-wise alignment with the student as the base is proposed for knowledge distillation of the intermediate layers in CLIPPING, which enables the student's layers to be the bases of the teacher, and thus allows the student to fully absorb the knowledge of the teacher. CLIPPING with MobileViT-v2 as the vison encoder without any vison-language pre-training achieves 88.1%-95.3% of the performance of its teacher on three video-language retrieval benchmarks, with its vison encoder being 19.5x smaller. CLIPPING also significantly outperforms a state-of-the-art small baseline (ALL-in-one-B) on the MSR-VTT dataset, obtaining relatively 7.4% performance gain, with 29% fewer parameters and 86.9% fewer flops. Moreover, CLIPPING is comparable or even superior to many large pre-training models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pei_CLIPPING_Distilling_CLIP-Based_Models_With_a_Student_Base_for_Video-Language_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pei_CLIPPING_Distilling_CLIP-Based_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pei_CLIPPING_Distilling_CLIP-Based_Models_With_a_Student_Base_for_Video-Language_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pei_CLIPPING_Distilling_CLIP-Based_Models_With_a_Student_Base_for_Video-Language_CVPR_2023_paper.html | CVPR 2023 | null |
ScaleDet: A Scalable Multi-Dataset Object Detector | Yanbei Chen, Manchen Wang, Abhay Mittal, Zhenlin Xu, Paolo Favaro, Joseph Tighe, Davide Modolo | Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset detector (ScaleDet) that can scale up its generalization across datasets when increasing the number of training datasets. Unlike existing multi-dataset learners that mostly rely on manual relabelling efforts or sophisticated optimizations to unify labels across datasets, we introduce a simple yet scalable formulation to derive a unified semantic label space for multi-dataset training. ScaleDet is trained by visual-textual alignment to learn the label assignment with label semantic similarities across datasets. Once trained, ScaleDet can generalize well on any given upstream and downstream datasets with seen and unseen classes. We conduct extensive experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and 13 datasets from Object Detection in the Wild (ODinW) as downstream datasets. Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the same backbone. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_ScaleDet_A_Scalable_Multi-Dataset_Object_Detector_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_ScaleDet_A_Scalable_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_ScaleDet_A_Scalable_Multi-Dataset_Object_Detector_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_ScaleDet_A_Scalable_Multi-Dataset_Object_Detector_CVPR_2023_paper.html | CVPR 2023 | null |
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection | Hui Lv, Zhongqi Yue, Qianru Sun, Bin Luo, Zhen Cui, Hanwang Zhang | Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippet-level predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is notoriously known to suffer from many false alarms because the snippet-level detector is easily biased towards the abnormal snippets with simple context, confused by the normality with the same bias, and missing the anomaly with a different pattern. To this end, we propose a new MIL framework: Unbiased MIL (UMIL), to learn unbiased anomaly features that improve WSVAD. At each MIL training iteration, we use the current detector to divide the samples into two groups with different context biases: the most confident abnormal/normal snippets and the rest ambiguous ones. Then, by seeking the invariant features across the two sample groups, we can remove the variant context biases. Extensive experiments on benchmarks UCF-Crime and TAD demonstrate the effectiveness of our UMIL. Our code is provided at https://github.com/ktr-hubrt/UMIL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lv_Unbiased_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lv_Unbiased_Multiple_Instance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12369 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lv_Unbiased_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lv_Unbiased_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
BEVHeight: A Robust Framework for Vision-Based Roadside 3D Object Detection | Lei Yang, Kaicheng Yu, Tao Tang, Jun Li, Kun Yuan, Li Wang, Xinyu Zhang, Peng Chen | While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye view detection methods have inferior performances on roadside cameras. This is because these methods mainly focus on recovering the depth regarding the camera center, where the depth difference between the car and the ground quickly shrinks while the distance increases. In this paper, we propose a simple yet effective approach, dubbed BEVHeight, to address this issue. In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods. On popular 3D detection benchmarks of roadside cameras, our method surpasses all previous vision-centric methods by a significant margin. The code is available at https://github.com/ADLab-AutoDrive/BEVHeight. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_BEVHeight_A_Robust_Framework_for_Vision-Based_Roadside_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_BEVHeight_A_Robust_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08498 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_BEVHeight_A_Robust_Framework_for_Vision-Based_Roadside_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_BEVHeight_A_Robust_Framework_for_Vision-Based_Roadside_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Unbiased Volume Rendering of Neural Implicit Surfaces With Geometry Priors | Yongqiang Zhang, Zhipeng Hu, Haoqian Wu, Minda Zhao, Lincheng Li, Zhengxia Zou, Changjie Fan | Learning surface by neural implicit rendering has been a promising way for multi-view reconstruction in recent years. Existing neural surface reconstruction methods, such as NeuS and VolSDF, can produce reliable meshes from multi-view posed images. Although they build a bridge between volume rendering and Signed Distance Function (SDF), the accuracy is still limited. In this paper, we argue that this limited accuracy is due to the bias of their volume rendering strategies, especially when the viewing direction is close to be tangent to the surface. We revise and provide an additional condition for the unbiased volume rendering. Following this analysis, we propose a new rendering method by scaling the SDF field with the angle between the viewing direction and the surface normal vector. Experiments on simulated data indicate that our rendering method reduces the bias of SDF-based volume rendering. Moreover, there still exists non-negligible bias when the learnable standard deviation of SDF is large at early stage, which means that it is hard to supervise the rendered depth with depth priors. Alternatively we supervise zero-level set with surface points obtained from a pre-trained Multi-View Stereo network. We evaluate our method on the DTU dataset and show that it outperforms the state-of-the-arts neural implicit surface methods without mask supervision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Towards_Unbiased_Volume_Rendering_of_Neural_Implicit_Surfaces_With_Geometry_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Towards_Unbiased_Volume_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Towards_Unbiased_Volume_Rendering_of_Neural_Implicit_Surfaces_With_Geometry_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Towards_Unbiased_Volume_Rendering_of_Neural_Implicit_Surfaces_With_Geometry_CVPR_2023_paper.html | CVPR 2023 | null |
Modular Memorability: Tiered Representations for Video Memorability Prediction | Théo Dumont, Juan Segundo Hevia, Camilo L. Fosco | The question of how to best estimate the memorability of visual content is currently a source of debate in the memorability community. In this paper, we propose to explore how different key properties of images and videos affect their consolidation into memory. We analyze the impact of several features and develop a model that emulates the most important parts of a proposed "pathway to memory": a simple but effective way of representing the different hurdles that new visual content needs to surpass to stay in memory. This framework leads to the construction of our M3-S model, a novel memorability network that processes input videos in a modular fashion. Each module of the network emulates one of the four key steps of the pathway to memory: raw encoding, scene understanding, event understanding and memory consolidation. We find that the different representations learned by our modules are non-trivial and substantially different from each other. Additionally, we observe that certain representations tend to perform better at the task of memorability prediction than others, and we introduce an in-depth ablation study to support our results. Our proposed approach surpasses the state of the art on the two largest video memorability datasets and opens the door to new applications in the field. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dumont_Modular_Memorability_Tiered_Representations_for_Video_Memorability_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dumont_Modular_Memorability_Tiered_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dumont_Modular_Memorability_Tiered_Representations_for_Video_Memorability_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dumont_Modular_Memorability_Tiered_Representations_for_Video_Memorability_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly-Supervised Domain Adaptive Semantic Segmentation With Prototypical Contrastive Learning | Anurag Das, Yongqin Xian, Dengxin Dai, Bernt Schiele | There has been a lot of effort in improving the performance of unsupervised domain adaptation for semantic segmentation task, however there is still a huge gap in performance when compared with supervised learning. In this work, we propose a common framework to use different weak labels, e.g. image, point and coarse labels from target domain to reduce this performance gap. Specifically, we propose to learn better prototypes that are representative class features, by exploiting these weak labels. We use these improved prototypes for contrastive alignment of class features. In particular, we perform two different feature alignments, first, we align pixel features with prototypes within each domain and second, we align pixel features from source to prototype of target domain in an asymmetric way. This asymmetric alignment is beneficial as it preserves the target features during training, which is essential when weak labels are available from target domain. Our experiments on standard benchmarks shows that our framework achieves significant improvement compared to existing works and is able to reduce the performance gap with supervised learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Das_Weakly-Supervised_Domain_Adaptive_Semantic_Segmentation_With_Prototypical_Contrastive_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Das_Weakly-Supervised_Domain_Adaptive_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Das_Weakly-Supervised_Domain_Adaptive_Semantic_Segmentation_With_Prototypical_Contrastive_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Das_Weakly-Supervised_Domain_Adaptive_Semantic_Segmentation_With_Prototypical_Contrastive_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Language-Guided Music Recommendation for Video via Prompt Analogies | Daniel McKee, Justin Salamon, Josef Sivic, Bryan Russell | We propose a method to recommend music for an input video while allowing a user to guide music selection with free-form natural language. A key challenge of this problem setting is that existing music video datasets provide the needed (video, music) training pairs, but lack text descriptions of the music. This work addresses this challenge with the following three contributions. First, we propose a text-synthesis approach that relies on an analogy-based prompting procedure to generate natural language music descriptions from a large-scale language model (BLOOM-176B) given pre-trained music tagger outputs and a small number of human text descriptions. Second, we use these synthesized music descriptions to train a new trimodal model, which fuses text and video input representations to query music samples. For training, we introduce a text dropout regularization mechanism which we show is critical to model performance. Our model design allows for the retrieved music audio to agree with the two input modalities by matching visual style depicted in the video and musical genre, mood, or instrumentation described in the natural language query. Third, to evaluate our approach, we collect a testing dataset for our problem by annotating a subset of 4k clips from the YT8M-MusicVideo dataset with natural language music descriptions which we make publicly available. We show that our approach can match or exceed the performance of prior methods on video-to-music retrieval while significantly improving retrieval accuracy when using text guidance. | https://openaccess.thecvf.com/content/CVPR2023/papers/McKee_Language-Guided_Music_Recommendation_for_Video_via_Prompt_Analogies_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/McKee_Language-Guided_Music_Recommendation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/McKee_Language-Guided_Music_Recommendation_for_Video_via_Prompt_Analogies_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/McKee_Language-Guided_Music_Recommendation_for_Video_via_Prompt_Analogies_CVPR_2023_paper.html | CVPR 2023 | null |
Re2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization | Chen Zhao, Shuming Liu, Karttikeya Mangalam, Bernard Ghanem | Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content. Given limited GPU memory, training TAL end to end (i.e., from videos to predictions) on long videos is a significant challenge. Most methods can only train on pre-extracted features without optimizing them for the localization problem, consequently limiting localization performance. In this work, to extend the potential in TAL networks, we propose a novel end-to-end method Re2TAL, which rewires pretrained video backbones for reversible TAL. Re2TAL builds a backbone with reversible modules, where the input can be recovered from the output such that the bulky intermediate activations can be cleared from memory during training. Instead of designing one single type of reversible module, we propose a network rewiring mechanism, to transform any module with a residual connection to a reversible module without changing any parameters. This provides two benefits: (1) a large variety of reversible networks are easily obtained from existing and even future model designs, and (2) the reversible models require much less training effort as they reuse the pre-trained parameters of their original non-reversible versions. Re2TAL, only using the RGB modality, reaches 37.01% average mAP on ActivityNet-v1.3, a new state-of-the-art record, and mAP 64.9% at tIoU=0.5 on THUMOS-14, outperforming all other RGB-only methods. Code is available at https://github.com/coolbay/Re2TAL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Re2TAL_Rewiring_Pretrained_Video_Backbones_for_Reversible_Temporal_Action_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Re2TAL_Rewiring_Pretrained_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Re2TAL_Rewiring_Pretrained_Video_Backbones_for_Reversible_Temporal_Action_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Re2TAL_Rewiring_Pretrained_Video_Backbones_for_Reversible_Temporal_Action_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation | Yushun Tang, Ce Zhang, Heng Xu, Shuoshuo Chen, Jie Cheng, Luziwei Leng, Qinghai Guo, Zhihai He | Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradation problem of deep neural networks. We take inspiration from the biological plausibility learning where the neuron responses are tuned based on a local synapse-change procedure and activated by competitive lateral inhibition rules. Based on these feed-forward learning rules, we design a soft Hebbian learning process which provides an unsupervised and effective mechanism for online adaptation. We observe that the performance of this feed-forward Hebbian learning for fully test-time adaptation can be significantly improved by incorporating a feedback neuro-modulation layer. It is able to fine-tune the neuron responses based on the external feedback generated by the error back-propagation from the top inference layers. This leads to our proposed neuro-modulated Hebbian learning (NHL) method for fully test-time adaptation. With the unsupervised feed-forward soft Hebbian learning being combined with a learned neuro-modulator to capture feedback from external responses, the source model can be effectively adapted during the testing process. Experimental results on benchmark datasets demonstrate that our proposed method can significantly improve the adaptation performance of network models and outperforms existing state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Neuro-Modulated_Hebbian_Learning_for_Fully_Test-Time_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Neuro-Modulated_Hebbian_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00914 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Neuro-Modulated_Hebbian_Learning_for_Fully_Test-Time_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Neuro-Modulated_Hebbian_Learning_for_Fully_Test-Time_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
NeRFLight: Fast and Light Neural Radiance Fields Using a Shared Feature Grid | Fernando Rivas-Manzaneque, Jorge Sierra-Acosta, Adrian Penate-Sanchez, Francesc Moreno-Noguer, Angela Ribeiro | While original Neural Radiance Fields (NeRF) have shown impressive results in modeling the appearance of a scene with compact MLP architectures, they are not able to achieve real-time rendering. This has been recently addressed by either baking the outputs of NeRF into a data structure or arranging trainable parameters in an explicit feature grid. These strategies, however, significantly increase the memory footprint of the model which prevents their deployment on bandwidth-constrained applications. In this paper, we extend the grid-based approach to achieve real-time view synthesis at more than 150 FPS using a lightweight model. Our main contribution is a novel architecture in which the density field of NeRF-based representations is split into N regions and the density is modeled using N different decoders which reuse the same feature grid. This results in a smaller grid where each feature is located in more than one spatial position, forcing them to learn a compact representation that is valid for different parts of the scene. We further reduce the size of the final model by disposing of the features symmetrically on each region, which favors feature pruning after training while also allowing smooth gradient transitions between neighboring voxels. An exhaustive evaluation demonstrates that our method achieves real-time performance and quality metrics on a pair with state-of-the-art with an improvement of more than 2x in the FPS/MB ratio. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rivas-Manzaneque_NeRFLight_Fast_and_Light_Neural_Radiance_Fields_Using_a_Shared_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rivas-Manzaneque_NeRFLight_Fast_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rivas-Manzaneque_NeRFLight_Fast_and_Light_Neural_Radiance_Fields_Using_a_Shared_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rivas-Manzaneque_NeRFLight_Fast_and_Light_Neural_Radiance_Fields_Using_a_Shared_CVPR_2023_paper.html | CVPR 2023 | null |
MVImgNet: A Large-Scale Dataset of Multi-View Images | Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, Guanying Chen, Shuguang Cui, Xiaoguang Han | Being data-driven is one of the most iconic properties of deep learning algorithms. The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision. Pretraining on ImageNet to obtain rich universal representations has been manifested to benefit various 2D visual tasks, and becomes a standard in 2D vision. However, due to the laborious collection of real-world 3D data, there is yet no generic dataset serving as a counterpart of ImageNet in 3D vision, thus how such a dataset can impact the 3D community is unraveled. To remedy this defect, we introduce MVImgNet, a large-scale dataset of multi-view images, which is highly convenient to gain by shooting videos of real-world objects in human daily life. It contains 6.5 million frames from 219,188 videos crossing objects from 238 classes, with rich annotations of object masks, camera parameters, and point clouds. The multi-view attribute endows our dataset with 3D-aware signals, making it a soft bridge between 2D and 3D vision. We conduct pilot studies for probing the potential of MVImgNet on a variety of 3D and 2D visual tasks, including radiance field reconstruction, multi-view stereo, and view-consistent image understanding, where MVImgNet demonstrates promising performance, remaining lots of possibilities for future explorations. Besides, via dense reconstruction on MVImgNet, a 3D object point cloud dataset is derived, called MVPNet, covering 87,200 samples from 150 categories, with the class label on each point cloud. Experiments show that MVPNet can benefit the real-world 3D object classification while posing new challenges to point cloud understanding. MVImgNet and MVPNet will be publicly available, hoping to inspire the broader vision community. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_MVImgNet_A_Large-Scale_Dataset_of_Multi-View_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_MVImgNet_A_Large-Scale_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06042 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_MVImgNet_A_Large-Scale_Dataset_of_Multi-View_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_MVImgNet_A_Large-Scale_Dataset_of_Multi-View_Images_CVPR_2023_paper.html | CVPR 2023 | null |
LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models | Adrian Bulat, Georgios Tzimiropoulos | Soft prompt learning has recently emerged as one of the methods of choice for adapting V&L models to a downstream task using a few training examples. However, current methods significantly overfit the training data, suffering from large accuracy degradation when tested on unseen classes from the same domain. To this end, in this paper, we make the following 4 contributions: (1) To alleviate base class overfitting, we propose a novel Language-Aware Soft Prompting (LASP) learning method by means of a text-to-text cross-entropy loss that maximizes the probability of the learned prompts to be correctly classified with respect to pre-defined hand-crafted textual prompts. (2) To increase the representation capacity of the prompts, we propose grouped LASP where each group of prompts is optimized with respect to a separate subset of textual prompts. (3) We identify a visual-language misalignment introduced by prompt learning and LASP, and more importantly, propose a re-calibration mechanism to address it. (4) We show that LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available, further increasing the robustness of the learned prompts. Through evaluations on 11 datasets, we show that our approach (a) significantly outperforms all prior works on soft prompting, and (b) matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets. Code will be made available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bulat_LASP_Text-to-Text_Optimization_for_Language-Aware_Soft_Prompting_of_Vision__CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bulat_LASP_Text-to-Text_Optimization_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.01115 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bulat_LASP_Text-to-Text_Optimization_for_Language-Aware_Soft_Prompting_of_Vision__CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bulat_LASP_Text-to-Text_Optimization_for_Language-Aware_Soft_Prompting_of_Vision__CVPR_2023_paper.html | CVPR 2023 | null |
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization | Shichao Dong, Jin Wang, Renhe Ji, Jiajun Liang, Haoqiang Fan, Zheng Ge | In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by the unexpected learned identity representation on images. Termed as the Implicit Identity Leakage, this phenomenon has been qualitatively and quantitatively verified among various DNNs. Furthermore, based on such understanding, we propose a simple yet effective method named the ID-unaware Deepfake Detection Model to reduce the influence of this phenomenon. Extensive experimental results demonstrate that our method outperforms the state-of-the-art in both in-dataset and cross-dataset evaluation. The code is available at https://github.com/megvii-research/CADDM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_Implicit_Identity_Leakage_The_Stumbling_Block_to_Improving_Deepfake_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_Implicit_Identity_Leakage_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.14457 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Implicit_Identity_Leakage_The_Stumbling_Block_to_Improving_Deepfake_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Implicit_Identity_Leakage_The_Stumbling_Block_to_Improving_Deepfake_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Federated Visual Prompt in Null Space for MRI Reconstruction | Chun-Mei Feng, Bangjun Li, Xinxing Xu, Yong Liu, Huazhu Fu, Wangmeng Zuo | Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy. However, the data heterogeneity caused by different MRI protocols, insufficient local training data, and limited communication bandwidth inevitably impair global model convergence and updating. In this paper, we propose a new algorithm, FedPR, to learn federated visual prompts in the null space of global prompt for MRI reconstruction. FedPR is a new federated paradigm that adopts a powerful pre-trained model while only learning and communicating the prompts with few learnable parameters, thereby significantly reducing communication costs and achieving competitive performance on limited local data. Moreover, to deal with catastrophic forgetting caused by data heterogeneity, FedPR also updates efficient federated visual prompts that project the local prompts into an approximate null space of the global prompt, thereby suppressing the interference of gradients on the server performance. Extensive experiments on federated MRI show that FedPR significantly outperforms state-of-the-art FL algorithms with < 6% of communication costs when given the limited amount of local data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Learning_Federated_Visual_Prompt_in_Null_Space_for_MRI_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_Learning_Federated_Visual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Learning_Federated_Visual_Prompt_in_Null_Space_for_MRI_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Learning_Federated_Visual_Prompt_in_Null_Space_for_MRI_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
A New Benchmark: On the Utility of Synthetic Data With Blender for Bare Supervised Learning and Downstream Domain Adaptation | Hui Tang, Kui Jia | Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. However, exhaustive data annotation is impracticable for each task of all domains of interest, due to high labor costs and unguaranteed labeling accuracy. Besides, the uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist. All these nuisances may hinder the verification of typical theories and exposure to new findings. To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization. We in this work push forward along this line by doing profound and extensive research on bare supervised learning and downstream domain adaptation. Specifically, under the well-controlled, IID data setting enabled by 3D rendering, we systematically verify the typical, important learning insights, e.g., shortcut learning, and discover the new laws of various data regimes and network architectures in generalization. We further investigate the effect of image formation factors on generalization, e.g., object scale, material texture, illumination, camera viewpoint, and background in a 3D scene. Moreover, we use the simulation-to-reality adaptation as a downstream task for comparing the transferability between synthetic and real data when used for pre-training, which demonstrates that synthetic data pre-training is also promising to improve real test results. Lastly, to promote future research, we develop a new large-scale synthetic-to-real benchmark for image classification, termed S2RDA, which provides more significant challenges for transfer from simulation to reality. The code and datasets are available at https://github.com/huitangtang/On_the_Utility_of_Synthetic_Data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_A_New_Benchmark_On_the_Utility_of_Synthetic_Data_With_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_A_New_Benchmark_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09165 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_A_New_Benchmark_On_the_Utility_of_Synthetic_Data_With_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_A_New_Benchmark_On_the_Utility_of_Synthetic_Data_With_CVPR_2023_paper.html | CVPR 2023 | null |
Data-Driven Feature Tracking for Event Cameras | Nico Messikommer, Carter Fang, Mathias Gehrig, Davide Scaramuzza | Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generalize to different scenarios due to unmodeled effects. To tackle these deficiencies, we introduce the first data-driven feature tracker for event cameras, which leverages low-latency events to track features detected in a grayscale frame. We achieve robust performance via a novel frame attention module, which shares information across feature tracks. By directly transferring zero-shot from synthetic to real data, our data-driven tracker outperforms existing approaches in relative feature age by up to 120% while also achieving the lowest latency. This performance gap is further increased to 130% by adapting our tracker to real data with a novel self-supervision strategy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Messikommer_Data-Driven_Feature_Tracking_for_Event_Cameras_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Messikommer_Data-Driven_Feature_Tracking_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12826 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Messikommer_Data-Driven_Feature_Tracking_for_Event_Cameras_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Messikommer_Data-Driven_Feature_Tracking_for_Event_Cameras_CVPR_2023_paper.html | CVPR 2023 | null |
Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving | Lucas Nunes, Louis Wiesmann, Rodrigo Marcuzzi, Xieyuanli Chen, Jens Behley, Cyrill Stachniss | Semantic perception is a core building block in autonomous driving, since it provides information about the drivable space and location of other traffic participants. For learning-based perception, often a large amount of diverse training data is necessary to achieve high performance. Data labeling is usually a bottleneck for developing such methods, especially for dense prediction tasks, e.g., semantic segmentation or panoptic segmentation. For 3D LiDAR data, the annotation process demands even more effort than for images. Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data. This paper aims at taking an alternative path proposing a self-supervised representation learning method for 3D LiDAR data. Our approach exploits the vehicle motion to match objects across time viewed in different scans. We then train a model to maximize the point-wise feature similarities from points of the associated object in different scans, which enables to learn a consistent representation across time. The experimental results show that our approach performs better than previous state-of-the-art self-supervised representation learning methods when fine-tuning to different downstream tasks. We furthermore show that with only 10% of labeled data, a network pre-trained with our approach can achieve better performance than the same network trained from scratch with all labels for semantic segmentation on SemanticKITTI. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nunes_Temporal_Consistent_3D_LiDAR_Representation_Learning_for_Semantic_Perception_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nunes_Temporal_Consistent_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nunes_Temporal_Consistent_3D_LiDAR_Representation_Learning_for_Semantic_Perception_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nunes_Temporal_Consistent_3D_LiDAR_Representation_Learning_for_Semantic_Perception_in_CVPR_2023_paper.html | CVPR 2023 | null |
AutoAD: Movie Description in Context | Tengda Han, Max Bain, Arsha Nagrani, Gül Varol, Weidi Xie, Andrew Zisserman | The objective of this paper is an automatic Audio Description (AD) model that ingests movies and outputs AD in text form. Generating high-quality movie AD is challenging due to the dependency of the descriptions on context, and the limited amount of training data available. In this work, we leverage the power of pretrained foundation models, such as GPT and CLIP, and only train a mapping network that bridges the two models for visually-conditioned text generation. In order to obtain high-quality AD, we make the following four contributions: (i) we incorporate context from the movie clip, AD from previous clips, as well as the subtitles; (ii) we address the lack of training data by pretraining on large-scale datasets, where visual or contextual information is unavailable, e.g. text-only AD without movies or visual captioning datasets without context; (iii) we improve on the currently available AD datasets, by removing label noise in the MAD dataset, and adding character naming information; and (iv) we obtain strong results on the movie AD task compared with previous methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_AutoAD_Movie_Description_in_Context_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_AutoAD_Movie_Description_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16899 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_AutoAD_Movie_Description_in_Context_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_AutoAD_Movie_Description_in_Context_CVPR_2023_paper.html | CVPR 2023 | null |
DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation | Shuai Shen, Wenliang Zhao, Zibin Meng, Wanhua Li, Zheng Zhu, Jie Zhou, Jiwen Lu | Talking head synthesis is a promising approach for the video production industry. Recently, a lot of effort has been devoted in this research area to improve the generation quality or enhance the model generalization. However, there are few works able to address both issues simultaneously, which is essential for practical applications. To this end, in this paper, we turn attention to the emerging powerful Latent Diffusion Models, and model the Talking head generation as an audio-driven temporally coherent denoising process (DiffTalk). More specifically, instead of employing audio signals as the single driving factor, we investigate the control mechanism of the talking face, and incorporate reference face images and landmarks as conditions for personality-aware generalized synthesis. In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without any further fine-tuning. Additionally, our DiffTalk can be gracefully tailored for higher-resolution synthesis with negligible extra computational cost. Extensive experiments show that the proposed DiffTalk efficiently synthesizes high-fidelity audio-driven talking head videos for generalized novel identities. For more video results, please refer to https://sstzal.github.io/DiffTalk/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_DiffTalk_Crafting_Diffusion_Models_for_Generalized_Audio-Driven_Portraits_Animation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.03786 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_DiffTalk_Crafting_Diffusion_Models_for_Generalized_Audio-Driven_Portraits_Animation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_DiffTalk_Crafting_Diffusion_Models_for_Generalized_Audio-Driven_Portraits_Animation_CVPR_2023_paper.html | CVPR 2023 | null |
Autoregressive Visual Tracking | Xing Wei, Yifan Bai, Yongchao Zheng, Dahu Shi, Yihong Gong | We present ARTrack, an autoregressive framework for visual object tracking. ARTrack tackles tracking as a coordinate sequence interpretation task that estimates object trajectories progressively, where the current estimate is induced by previous states and in turn affects subsequences. This time-autoregressive approach models the sequential evolution of trajectories to keep tracing the object across frames, making it superior to existing template matching based trackers that only consider the per-frame localization accuracy. ARTrack is simple and direct, eliminating customized localization heads and post-processings. Despite its simplicity, ARTrack achieves state-of-the-art performance on prevailing benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
SceneComposer: Any-Level Semantic Image Synthesis | Yu Zeng, Zhe Lin, Jianming Zhang, Qing Liu, John Collomosse, Jason Kuen, Vishal M. Patel | We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes. More specifically, the input layout consists of one or more semantic regions with free-form text descriptions and adjustable precision levels, which can be set based on the desired controllability. The framework naturally reduces to text-to-image (T2I) at the lowest level with no shape information, and it becomes segmentation-to-image (S2I) at the highest level. By supporting the levels in-between, our framework is flexible in assisting users of different drawing expertise and at different stages of their creative workflow. We introduce several novel techniques to address the challenges coming with this new setup, including a pipeline for collecting training data; a precision-encoded mask pyramid and a text feature map representation to jointly encode precision level, semantics, and composition information; and a multi-scale guided diffusion model to synthesize images. To evaluate the proposed method, we collect a test dataset containing user-drawn layouts with diverse scenes and styles. Experimental results show that the proposed method can generate high-quality images following the layout at given precision, and compares favorably against existing methods. Project page https://zengxianyu.github.io/scenec/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_SceneComposer_Any-Level_Semantic_Image_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zeng_SceneComposer_Any-Level_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11742 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_SceneComposer_Any-Level_Semantic_Image_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_SceneComposer_Any-Level_Semantic_Image_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning | Cheng-Hao Tu, Zheda Mai, Wei-Lun Chao | Intermediate features of a pre-trained model have been shown informative for making accurate predictions on downstream tasks, even if the model backbone is frozen. The key challenge is how to utilize them, given the gigantic amount. We propose visual query tuning (VQT), a simple yet effective approach to aggregate intermediate features of Vision Transformers. Through introducing a handful of learnable "query" tokens to each layer, VQT leverages the inner workings of Transformers to "summarize" rich intermediate features of each layer, which can then be used to train the prediction heads of downstream tasks. As VQT keeps the intermediate features intact and only learns to combine them, it enjoys memory efficiency in training, compared to many other parameter-efficient fine-tuning approaches that learn to adapt features and need back-propagation through the entire backbone. This also suggests the complementary role between VQT and those approaches in transfer learning. Empirically, VQT consistently surpasses the state-of-the-art approach that utilizes intermediate features for transfer learning and outperforms full fine-tuning in many cases. Compared to parameter-efficient approaches that adapt features, VQT achieves much higher accuracy under memory constraints. Most importantly, VQT is compatible with these approaches to attain higher accuracy, making it a simple add-on to further boost transfer learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tu_Visual_Query_Tuning_Towards_Effective_Usage_of_Intermediate_Representations_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tu_Visual_Query_Tuning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03220 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_Visual_Query_Tuning_Towards_Effective_Usage_of_Intermediate_Representations_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_Visual_Query_Tuning_Towards_Effective_Usage_of_Intermediate_Representations_for_CVPR_2023_paper.html | CVPR 2023 | null |
MaPLe: Multi-Modal Prompt Learning | Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, Fahad Shahbaz Khan | Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text prompts and require careful selection of prompt templates to perform well. Inspired by the Natural Language Processing (NLP) literature, recent CLIP adaptation approaches learn prompts as the textual inputs to fine-tune CLIP for downstream tasks. We note that using prompting to adapt representations in a single branch of CLIP (language or vision) is sub-optimal since it does not allow the flexibility to dynamically adjust both representation spaces on a downstream task. In this work, we propose Multi-modal Prompt Learning (MaPLe) for both vision and language branches to improve alignment between the vision and language representations. Our design promotes strong coupling between the vision-language prompts to ensure mutual synergy and discourages learning independent uni-modal solutions. Further, we learn separate prompts across different early stages to progressively model the stage-wise feature relationships to allow rich context learning. We evaluate the effectiveness of our approach on three representative tasks of generalization to novel classes, new target datasets and unseen domain shifts. Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable performance and achieves an absolute gain of 3.45% on novel classes and 2.72% on overall harmonic-mean, averaged over 11 diverse image recognition datasets. Our code and pre-trained models are available at https://github.com/muzairkhattak/multimodal-prompt-learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Khattak_MaPLe_Multi-Modal_Prompt_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Khattak_MaPLe_Multi-Modal_Prompt_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.03117 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Khattak_MaPLe_Multi-Modal_Prompt_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Khattak_MaPLe_Multi-Modal_Prompt_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Domain Adaption With Pixel-Level Discriminator for Image-Aware Layout Generation | Chenchen Xu, Min Zhou, Tiezheng Ge, Yuning Jiang, Weiwei Xu | Layout is essential for graphic design and poster generation. Recently, applying deep learning models to generate layouts has attracted increasing attention. This paper focuses on using the GAN-based model conditioned on image contents to generate advertising poster graphic layouts, which requires an advertising poster layout dataset with paired product images and graphic layouts. However, the paired images and layouts in the existing dataset are collected by inpainting and annotating posters, respectively. There exists a domain gap between inpainted posters (source domain data) and clean product images (target domain data). Therefore, this paper combines unsupervised domain adaption techniques to design a GAN with a novel pixel-level discriminator (PD), called PDA-GAN, to generate graphic layouts according to image contents. The PD is connected to the shallow level feature map and computes the GAN loss for each input-image pixel. Both quantitative and qualitative evaluations demonstrate that PDA-GAN can achieve state-of-the-art performances and generate high-quality image-aware graphic layouts for advertising posters. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Unsupervised_Domain_Adaption_With_Pixel-Level_Discriminator_for_Image-Aware_Layout_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Unsupervised_Domain_Adaption_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.14377 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Unsupervised_Domain_Adaption_With_Pixel-Level_Discriminator_for_Image-Aware_Layout_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Unsupervised_Domain_Adaption_With_Pixel-Level_Discriminator_for_Image-Aware_Layout_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Compressing Volumetric Radiance Fields to 1 MB | Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Liefeng Bo | Approximating radiance fields with discretized volumetric grids is one of promising directions for improving NeRFs, represented by methods like DVGO, Plenoxels and TensoRF, which achieve super-fast training convergence and real-time rendering. However, these methods typically require a tremendous storage overhead, costing up to hundreds of megabytes of disk space and runtime memory for a single scene. We address this issue in this paper by introducing a simple yet effective framework, called vector quantized radiance fields (VQRF), for compressing these volume-grid-based radiance fields. We first present a robust and adaptive metric for estimating redundancy in grid models and performing voxel pruning by better exploring intermediate outputs of volumetric rendering. A trainable vector quantization is further proposed to improve the compactness of grid models. In combination with an efficient joint tuning strategy and post-processing, our method can achieve a compression ratio of 100x by reducing the overall model size to 1 MB with negligible loss on visual quality. Extensive experiments demonstrate that the proposed framework is capable of achieving unrivaled performance and well generalization across multiple methods with distinct volumetric structures, facilitating the wide use of volumetric radiance fields methods in real-world applications. Code is available at https://github.com/AlgoHunt/VQRF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Compressing_Volumetric_Radiance_Fields_to_1_MB_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Compressing_Volumetric_Radiance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16386 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Compressing_Volumetric_Radiance_Fields_to_1_MB_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Compressing_Volumetric_Radiance_Fields_to_1_MB_CVPR_2023_paper.html | CVPR 2023 | null |
Real-Time 6K Image Rescaling With Rate-Distortion Optimization | Chenyang Qi, Xin Yang, Ka Leong Cheng, Ying-Cong Chen, Qifeng Chen | The task of image rescaling aims at embedding an high-resolution (HR) image into a low-resolution (LR) one that can contain embedded information for HR image reconstruction. Existing image rescaling methods do not optimize the LR image file size and recent flow-based rescaling methods are not real-time yet for HR image reconstruction (e.g., 6K). To address these two challenges, we propose a novel framework (HyperThumbnail) for real-time 6K rate-distortion-aware image rescaling. Our HyperThumbnail first embeds an HR image into a JPEG LR image (thumbnail) by an encoder with our proposed learnable JPEG quantization module, which optimizes the file size of the embedding LR JPEG image. Then, an efficient decoder reconstructs a high-fidelity HR (6K) image from the LR one in real time. Extensive experiments demonstrate that our framework outperforms previous image rescaling baselines in both rate-distortion performance and is much faster than prior work in HR image reconstruction speed. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qi_Real-Time_6K_Image_Rescaling_With_Rate-Distortion_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qi_Real-Time_6K_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01064 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qi_Real-Time_6K_Image_Rescaling_With_Rate-Distortion_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qi_Real-Time_6K_Image_Rescaling_With_Rate-Distortion_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Gated Stereo: Joint Depth Estimation From Gated and Wide-Baseline Active Stereo Cues | Stefanie Walz, Mario Bijelic, Andrea Ramazzina, Amanpreet Walia, Fahim Mannan, Felix Heide | We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images. Using active and high dynamic range passive captures, Gated Stereo exploits multi-view cues alongside time-of-flight intensity cues from active gating. To this end, we propose a depth estimation method with a monocular and stereo depth prediction branch which are combined in a final fusion stage. Each block is supervised through a combination of supervised and gated self-supervision losses. To facilitate training and validation, we acquire a long-range synchronized gated stereo dataset for automotive scenarios. We find that the method achieves an improvement of more than 50 % MAE compared to the next best RGB stereo method, and 74 % MAE to existing monocular gated methods for distances up to 160 m. Our code, models and datasets are available here: https://light.princeton.edu/gatedstereo/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Walz_Gated_Stereo_Joint_Depth_Estimation_From_Gated_and_Wide-Baseline_Active_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Walz_Gated_Stereo_Joint_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Walz_Gated_Stereo_Joint_Depth_Estimation_From_Gated_and_Wide-Baseline_Active_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Walz_Gated_Stereo_Joint_Depth_Estimation_From_Gated_and_Wide-Baseline_Active_CVPR_2023_paper.html | CVPR 2023 | null |
Label Information Bottleneck for Label Enhancement | Qinghai Zheng, Jihua Zhu, Haoyu Tang | In this work, we focus on the challenging problem of Label Enhancement (LE), which aims to exactly recover label distributions from logical labels, and present a novel Label Information Bottleneck (LIB) method for LE. For the recovery process of label distributions, the label irrelevant information contained in the dataset may lead to unsatisfactory recovery performance. To address this limitation, we make efforts to excavate the essential label relevant information to improve the recovery performance. Our method formulates the LE problem as the following two joint processes: 1) learning the representation with the essential label relevant information, 2) recovering label distributions based on the learned representation. The label relevant information can be excavated based on the "bottleneck" formed by the learned representation. Significantly, both the label relevant information about the label assignments and the label relevant information about the label gaps can be explored in our method. Evaluation experiments conducted on several benchmark label distribution learning datasets verify the effectiveness and competitiveness of LIB. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Label_Information_Bottleneck_for_Label_Enhancement_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.06836 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Label_Information_Bottleneck_for_Label_Enhancement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Label_Information_Bottleneck_for_Label_Enhancement_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Modal Representation Learning With Text-Driven Soft Masks | Jaeyoo Park, Bohyung Han | We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. First, we generate diverse features for the image-text matching (ITM) task via soft-masking the regions in an image, which are most relevant to a certain word in the corresponding caption, instead of completely removing them. Since our framework relies only on image-caption pairs with no fine-grained annotations, we identify the relevant regions to each word by computing the word-conditional visual attention using multi-modal encoder. Second, we encourage the model to focus more on hard but diverse examples by proposing a focal loss for the image-text contrastive learning (ITC) objective, which alleviates the inherent limitations of overfitting and bias issues. Last, we perform multi-modal data augmentations for self-supervised learning via mining various examples by masking texts and rendering distortions on images. We show that the combination of these three innovations is effective for learning a pretrained model, leading to outstanding performance on multiple vision-language downstream tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Multi-Modal_Representation_Learning_With_Text-Driven_Soft_Masks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Multi-Modal_Representation_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00719 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Multi-Modal_Representation_Learning_With_Text-Driven_Soft_Masks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Multi-Modal_Representation_Learning_With_Text-Driven_Soft_Masks_CVPR_2023_paper.html | CVPR 2023 | null |
Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention | Sounak Mondal, Zhibo Yang, Seoyoung Ahn, Dimitris Samaras, Gregory Zelinsky, Minh Hoai | Predicting human gaze is important in Human-Computer Interaction (HCI). However, to practically serve HCI applications, gaze prediction models must be scalable, fast, and accurate in their spatial and temporal gaze predictions. Recent scanpath prediction models focus on goal-directed attention (search). Such models are limited in their application due to a common approach relying on trained target detectors for all possible objects, and the availability of human gaze data for their training (both not scalable). In response, we pose a new task called ZeroGaze, a new variant of zero-shot learning where gaze is predicted for never-before-searched objects, and we develop a novel model, Gazeformer, to solve the ZeroGaze problem. In contrast to existing methods using object detector modules, Gazeformer encodes the target using a natural language model, thus leveraging semantic similarities in scanpath prediction. We use a transformer-based encoder-decoder architecture because transformers are particularly useful for generating contextual representations. Gazeformer surpasses other models by a large margin (19% - 70%) on the ZeroGaze setting. It also outperforms existing target-detection models on standard gaze prediction for both target-present and target-absent search tasks. In addition to its improved performance, Gazeformer is more than five times faster than the state-of-the-art target-present visual search model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mondal_Gazeformer_Scalable_Effective_and_Fast_Prediction_of_Goal-Directed_Human_Attention_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mondal_Gazeformer_Scalable_Effective_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15274 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mondal_Gazeformer_Scalable_Effective_and_Fast_Prediction_of_Goal-Directed_Human_Attention_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mondal_Gazeformer_Scalable_Effective_and_Fast_Prediction_of_Goal-Directed_Human_Attention_CVPR_2023_paper.html | CVPR 2023 | null |
MammalNet: A Large-Scale Video Benchmark for Mammal Recognition and Behavior Understanding | Jun Chen, Ming Hu, Darren J. Coker, Michael L. Berumen, Blair Costelloe, Sara Beery, Anna Rohrbach, Mohamed Elhoseiny | Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function. Automatic recognition of animals and their behaviors is critical for capitalizing on the large unlabeled datasets generated by modern video devices and for accelerating monitoring efforts at scale. However, the development of automated recognition systems is currently hindered by a lack of appropriately labeled datasets. Existing video datasets 1) do not classify animals according to established biological taxonomies; 2) are too small to facilitate large-scale behavioral studies and are often limited to a single species; and 3) do not feature temporally localized annotations and therefore do not facilitate localization of targeted behaviors within longer video sequences. Thus, we propose MammalNet, a new large-scale animal behavior dataset with taxonomy-guided annotations of mammals and their common behaviors. MammalNet contains over 18K videos totaling 539 hours, which is 10 times larger than the largest existing animal behavior dataset. It covers 17 orders, 69 families, and 173 mammal categories for animal categorization and captures 12 high-level animal behaviors that received focus in previous animal behavior studies. We establish three benchmarks on MammalNet: standard animal and behavior recognition, compositional low-shot animal and behavior recognition, and behavior detection. Our dataset and code have been made available at: https://mammal-net.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_MammalNet_A_Large-Scale_Video_Benchmark_for_Mammal_Recognition_and_Behavior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_MammalNet_A_Large-Scale_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_MammalNet_A_Large-Scale_Video_Benchmark_for_Mammal_Recognition_and_Behavior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_MammalNet_A_Large-Scale_Video_Benchmark_for_Mammal_Recognition_and_Behavior_CVPR_2023_paper.html | CVPR 2023 | null |
Hand Avatar: Free-Pose Hand Animation and Rendering From Monocular Video | Xingyu Chen, Baoyuan Wang, Heung-Yeung Shum | We present HandAvatar, a novel representation for hand animation and rendering, which can generate smoothly compositional geometry and self-occlusion-aware texture. Specifically, we first develop a MANO-HD model as a high-resolution mesh topology to fit personalized hand shapes. Sequentially, we decompose hand geometry into per-bone rigid parts, and then re-compose paired geometry encodings to derive an across-part consistent occupancy field. As for texture modeling, we propose a self-occlusion-aware shading field (SelF). In SelF, drivable anchors are paved on the MANO-HD surface to record albedo information under a wide variety of hand poses. Moreover, directed soft occupancy is designed to describe the ray-to-surface relation, which is leveraged to generate an illumination field for the disentanglement of pose-independent albedo and pose-dependent illumination. Trained from monocular video data, our HandAvatar can perform free-pose hand animation and rendering while at the same time achieving superior appearance fidelity. We also demonstrate that HandAvatar provides a route for hand appearance editing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Hand_Avatar_Free-Pose_Hand_Animation_and_Rendering_From_Monocular_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Hand_Avatar_Free-Pose_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12782 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Hand_Avatar_Free-Pose_Hand_Animation_and_Rendering_From_Monocular_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Hand_Avatar_Free-Pose_Hand_Animation_and_Rendering_From_Monocular_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking the Correlation in Few-Shot Segmentation: A Buoys View | Yuan Wang, Rui Sun, Tianzhu Zhang | Few-shot segmentation (FSS) aims to segment novel objects in a given query image with only a few annotated support images. However, most previous best-performing methods, whether prototypical learning methods or affinity learning methods, neglect to alleviate false matches caused by their own pixel-level correlation. In this work, we rethink how to mitigate the false matches from the perspective of representative reference features (referred to as buoys), and propose a novel adaptive buoys correlation (ABC) network to rectify direct pairwise pixel-level correlation, including a buoys mining module and an adaptive correlation module. The proposed ABC enjoys several merits. First, to learn the buoys well without any correspondence supervision, we customize the buoys mining module according to the three characteristics of representativeness, task awareness and resilience. Second, the proposed adaptive correlation module is responsible for further endowing buoy-correlation-based pixel matching with an adaptive ability. Extensive experimental results with two different backbones on two challenging benchmarks demonstrate that our ABC, as a general plugin, achieves consistent improvements over several leading methods on both 1-shot and 5-shot settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Rethinking_the_Correlation_in_Few-Shot_Segmentation_A_Buoys_View_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Rethinking_the_Correlation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Rethinking_the_Correlation_in_Few-Shot_Segmentation_A_Buoys_View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Rethinking_the_Correlation_in_Few-Shot_Segmentation_A_Buoys_View_CVPR_2023_paper.html | CVPR 2023 | null |
VindLU: A Recipe for Effective Video-and-Language Pretraining | Feng Cheng, Xizi Wang, Jie Lei, David Crandall, Mohit Bansal, Gedas Bertasius | The last several years have witnessed remarkable progress in video-and-language (VidL) understanding. However, most modern VidL approaches use complex and specialized model architectures and sophisticated pretraining protocols, making the reproducibility, analysis and comparisons of these frameworks difficult. Hence, instead of proposing yet another new VidL model, this paper conducts a thorough empirical study demystifying the most important factors in the VidL model design. Among the factors that we investigate are (i) the spatiotemporal architecture design, (ii) the multimodal fusion schemes, (iii) the pretraining objectives, (iv) the choice of pretraining data, (v) pretraining and finetuning protocols, and (vi) dataset and model scaling. Our empirical study reveals that the most important design factors include: temporal modeling, video-to-text multimodal fusion, masked modeling objectives, and joint training on images and videos. Using these empirical insights, we then develop a step-by-step recipe, dubbed VindLU, for effective VidL pretraining. Our final model trained using our recipe achieves comparable or better than state-of-the-art results on several VidL tasks without relying on external CLIP pretraining. In particular, on the text-to-video retrieval task, our approach obtains 61.2% on DiDeMo, and 55.0% on ActivityNet, outperforming current SOTA by 7.8% and 6.1% respectively. Furthermore, our model also obtains state-of-the-art video question-answering results on ActivityNet-QA, MSRVTT-QA, MSRVTT-MC and TVQA. Our code and pretrained models are publicly available at: https://github.com/klauscc/VindLU. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cheng_VindLU_A_Recipe_for_Effective_Video-and-Language_Pretraining_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cheng_VindLU_A_Recipe_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05051 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_VindLU_A_Recipe_for_Effective_Video-and-Language_Pretraining_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_VindLU_A_Recipe_for_Effective_Video-and-Language_Pretraining_CVPR_2023_paper.html | CVPR 2023 | null |
Scaling Language-Image Pre-Training via Masking | Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, Kaiming He | We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP. Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs given the same wall-clock time and contrast more samples per iteration with similar memory footprint. It leads to a favorable trade-off between accuracy and training time. In our experiments on 400 million image-text pairs, FLIP improves both accuracy and speed over the no-masking baseline. On a large diversity of downstream tasks, FLIP dominantly outperforms the CLIP counterparts trained on the same data. Facilitated by the speedup, we explore the scaling behavior of increasing the model size, data size, or training length, and report encouraging results and comparisons. We hope that our work will foster future research on scaling vision-language learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Scaling_Language-Image_Pre-Training_via_Masking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Scaling_Language-Image_Pre-Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00794 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Scaling_Language-Image_Pre-Training_via_Masking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Scaling_Language-Image_Pre-Training_via_Masking_CVPR_2023_paper.html | CVPR 2023 | null |
OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis | Hongyi Xu, Guoxian Song, Zihang Jiang, Jianfeng Zhang, Yichun Shi, Jing Liu, Wanchun Ma, Jiashi Feng, Linjie Luo | We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses. To achieve such high level of disentangled control, we first explicitly define a novel semantic signed distance function (SDF) around a head geometry (FLAME) conditioned on the control parameters. This semantic SDF allows us to build a differentiable volumetric correspondence map from the observation space to a disentangled canonical space from all the control parameters. We then leverage the 3D-aware GAN framework (EG3D) to synthesize detailed shape and appearance of 3D full heads in the canonical space, followed by a volume rendering step guided by the volumetric correspondence map to output into the observation space. To ensure the control accuracy on the synthesized head shapes and expressions, we introduce a geometry prior loss to conform to head SDF and a control loss to conform to the expression code. Further, we enhance the temporal realism with dynamic details conditioned upon varying expressions and joint poses. Our model can synthesize more preferable identity-preserved 3D heads with compelling dynamic details compared to the state-of-the-art methods both qualitatively and quantitatively. We also provide an ablation study to justify many of our system design choices. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_OmniAvatar_Geometry-Guided_Controllable_3D_Head_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_OmniAvatar_Geometry-Guided_Controllable_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15539 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_OmniAvatar_Geometry-Guided_Controllable_3D_Head_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_OmniAvatar_Geometry-Guided_Controllable_3D_Head_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
DiffRF: Rendering-Guided 3D Radiance Field Diffusion | Norman Müller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Matthias Nießner | We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising diffusion probabilistic models. While existing diffusion-based methods operate on images, latent codes, or point cloud data, we are the first to directly generate volumetric radiance fields. To this end, we propose a 3D denoising model which directly operates on an explicit voxel grid representation. However, as radiance fields generated from a set of posed images can be ambiguous and contain artifacts, obtaining ground truth radiance field samples is non-trivial. We address this challenge by pairing the denoising formulation with a rendering loss, enabling our model to learn a deviated prior that favours good image quality instead of trying to replicate fitting errors like floating artifacts. In contrast to 2D-diffusion models, our model learns multi-view consistent priors, enabling free-view synthesis and accurate shape generation. Compared to 3D GANs, our diffusion-based approach naturally enables conditional generation like masked completion or single-view 3D synthesis at inference time. | https://openaccess.thecvf.com/content/CVPR2023/papers/Muller_DiffRF_Rendering-Guided_3D_Radiance_Field_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Muller_DiffRF_Rendering-Guided_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Muller_DiffRF_Rendering-Guided_3D_Radiance_Field_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Muller_DiffRF_Rendering-Guided_3D_Radiance_Field_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
DNF: Decouple and Feedback Network for Seeing in the Dark | Xin Jin, Ling-Hao Han, Zhen Li, Chun-Le Guo, Zhi Chai, Chongyi Li | The exclusive properties of RAW data have shown great potential for low-light image enhancement. Nevertheless, the performance is bottlenecked by the inherent limitations of existing architectures in both single-stage and multi-stage methods. Mixed mapping across two different domains, noise-to-clean and RAW-to-sRGB, misleads the single-stage methods due to the domain ambiguity. The multi-stage methods propagate the information merely through the resulting image of each stage, neglecting the abundant features in the lossy image-level dataflow. In this paper, we probe a generalized solution to these bottlenecks and propose a Decouple aNd Feedback framework, abbreviated as DNF. To mitigate the domain ambiguity, domainspecific subtasks are decoupled, along with fully utilizing the unique properties in RAW and sRGB domains. The feature propagation across stages with a feedback mechanism avoids the information loss caused by image-level dataflow. The two key insights of our method resolve the inherent limitations of RAW data-based low-light image enhancement satisfactorily, empowering our method to outperform the previous state-of-the-art method by a large margin with only 19% parameters, achieving 0.97dB and 1.30dB PSNR improvements on the Sony and Fuji subsets of SID. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_DNF_Decouple_and_Feedback_Network_for_Seeing_in_the_Dark_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_DNF_Decouple_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_DNF_Decouple_and_Feedback_Network_for_Seeing_in_the_Dark_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_DNF_Decouple_and_Feedback_Network_for_Seeing_in_the_Dark_CVPR_2023_paper.html | CVPR 2023 | null |
SUDS: Scalable Urban Dynamic Scenes | Haithem Turki, Jason Y. Zhang, Francesco Ferroni, Deva Ramanan | We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons are that such methods (a) tend to scale linearly with the number of moving objects and input videos because a separate model is built for each and (b) tend to require supervision via 3D bounding boxes and panoptic labels, obtained manually or via category-specific models. As a step towards truly open-world reconstructions of dynamic cities, we introduce two key innovations: (a) we factorize the scene into three separate hash table data structures to efficiently encode static, dynamic, and far-field radiance fields, and (b) we make use of unlabeled target signals consisting of RGB images, sparse LiDAR, off-the-shelf self-supervised 2D descriptors, and most importantly, 2D optical flow. Operationalizing such inputs via photometric, geometric, and feature-metric reconstruction losses enables SUDS to decompose dynamic scenes into the static background, individual objects, and their motions. When combined with our multi-branch table representation, such reconstructions can be scaled to tens of thousands of objects across 1.2 million frames from 1700 videos spanning geospatial footprints of hundreds of kilometers, (to our knowledge) the largest dynamic NeRF built to date. We present qualitative initial results on a variety of tasks enabled by our representations, including novel-view synthesis of dynamic urban scenes, unsupervised 3D instance segmentation, and unsupervised 3D cuboid detection. To compare to prior work, we also evaluate on KITTI and Virtual KITTI 2, surpassing state-of-the-art methods that rely on ground truth 3D bounding box annotations while being 10x quicker to train. | https://openaccess.thecvf.com/content/CVPR2023/papers/Turki_SUDS_Scalable_Urban_Dynamic_Scenes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Turki_SUDS_Scalable_Urban_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14536 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Turki_SUDS_Scalable_Urban_Dynamic_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Turki_SUDS_Scalable_Urban_Dynamic_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
Deformable Mesh Transformer for 3D Human Mesh Recovery | Yusuke Yoshiyasu | We present Deformable mesh transFormer (DeFormer), a novel vertex-based approach to monocular 3D human mesh recovery. DeFormer iteratively fits a body mesh model to an input image via a mesh alignment feedback loop formed within a transformer decoder that is equipped with efficient body mesh driven attention modules: 1) body sparse self-attention and 2) deformable mesh cross attention. As a result, DeFormer can effectively exploit high-resolution image feature maps and a dense mesh model which were computationally expensive to deal with in previous approaches using the standard transformer attention. Experimental results show that DeFormer achieves state-of-the-art performances on the Human3.6M and 3DPW benchmarks. Ablation study is also conducted to show the effectiveness of the DeFormer model designs for leveraging multi-scale feature maps. Code is available at https://github.com/yusukey03012/DeFormer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yoshiyasu_Deformable_Mesh_Transformer_for_3D_Human_Mesh_Recovery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yoshiyasu_Deformable_Mesh_Transformer_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yoshiyasu_Deformable_Mesh_Transformer_for_3D_Human_Mesh_Recovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yoshiyasu_Deformable_Mesh_Transformer_for_3D_Human_Mesh_Recovery_CVPR_2023_paper.html | CVPR 2023 | null |
Vita-CLIP: Video and Text Adaptive CLIP via Multimodal Prompting | Syed Talal Wasim, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah | Adopting contrastive image-text pretrained models like CLIP towards video classification has gained attention due to its cost-effectiveness and competitive performance. However, recent works in this area face a trade-off. Finetuning the pretrained model to achieve strong supervised performance results in low zero-shot generalization. Similarly, freezing the backbone to retain zero-shot capability causes significant drop in supervised accuracy. Because of this, recent works in literature typically train separate models for supervised and zero-shot action recognition. In this work, we propose a multimodal prompt learning scheme that works to balance the supervised and zero-shot performance under a single unified training. Our prompting approach on the vision side caters for three aspects: 1) Global video-level prompts to model the data distribution; 2) Local frame-level prompts to provide per-frame discriminative conditioning; and 3) a summary prompt to extract a condensed video representation. Additionally, we define a prompting scheme on the text side to augment the textual context. Through this prompting scheme, we can achieve state-of-the-art zero-shot performance on Kinetics-600, HMDB51 and UCF101 while remaining competitive in the supervised setting. By keeping the pretrained backbone frozen, we optimize a much lower number of parameters and retain the existing general representation which helps achieve the strong zero-shot performance. Our codes and models will be publicly released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wasim_Vita-CLIP_Video_and_Text_Adaptive_CLIP_via_Multimodal_Prompting_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wasim_Vita-CLIP_Video_and_Text_Adaptive_CLIP_via_Multimodal_Prompting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wasim_Vita-CLIP_Video_and_Text_Adaptive_CLIP_via_Multimodal_Prompting_CVPR_2023_paper.html | CVPR 2023 | null |
HS-Pose: Hybrid Scope Feature Extraction for Category-Level Object Pose Estimation | Linfang Zheng, Chen Wang, Yinghan Sun, Esha Dasgupta, Hua Chen, Aleš Leonardis, Wei Zhang, Hyung Jin Chang | In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC) based methods have been widely used to extract local geometric features, but they have limitations for complex shaped objects and are sensitive to noise. Moreover, the scale and translation invariant properties of 3D-GC restrict the perception of an object's size and translation information. In this paper, we propose a simple network structure, the HS-layer, which extends 3D-GC to extract hybrid scope latent features from point cloud data for category-level object pose estimation tasks. The proposed HS-layer: 1) is able to perceive local-global geometric structure and global information, 2) is robust to noise, and 3) can encode size and translation information. Our experiments show that the simple replacement of the 3D-GC layer with the proposed HS-layer on the baseline method (GPV-Pose) achieves a significant improvement, with the performance increased by 14.5% on 5d2cm metric and 10.3% on IoU75. Our method outperforms the state-of-the-art methods by a large margin (8.3% on 5d2cm, 6.9% on IoU75) on REAL275 dataset and runs in real-time (50 FPS). | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_HS-Pose_Hybrid_Scope_Feature_Extraction_for_Category-Level_Object_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_HS-Pose_Hybrid_Scope_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_HS-Pose_Hybrid_Scope_Feature_Extraction_for_Category-Level_Object_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_HS-Pose_Hybrid_Scope_Feature_Extraction_for_Category-Level_Object_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-World | Yulu Gan, Mingjie Pan, Rongyu Zhang, Zijian Ling, Lingran Zhao, Jiaming Liu, Shanghang Zhang | When facing changing environments in the real world, the lightweight model on client devices suffer from severe performance drop under distribution shifts. The main limitations of existing device model lie in: (1) unable to update due to the computation limit of the device, (2) limited generalization ability of the lightweight model. Meanwhile, recent large models have shown strong generalization capability on cloud while they can not be deployed on client devices due to the poor computation constraint. To enable the device model to deal with changing environments, we propose a new learning paradigm of Cloud-Device Collaborative Continual Adaptation. To encourage collaboration between cloud and device and improve the generalization of device model, we propose an Uncertainty-based Visual Prompt Adapted (U-VPA) teacher-student model in such paradigm. Specifically, we first design the Uncertainty Guided Sampling (UGS) to screen out challenging data continuously and transmit the most out-of-distribution samples from the device to the cloud. To further transfer the generalization capability of the large model on the cloud to the device model, we propose a Visual Prompt Learning Strategy with Uncertainty guided updating (VPLU) to specifically deal with the selected samples with more distribution shifts. Then, we transmit the visual prompts to the device and concatenate them with the incoming data to pull the device testing distribution closer to the cloud training distribution. We conduct extensive experiments on two object detection datasets with continually changing environments. Our proposed U-VPA teacher-student framework outperforms previous state-of-the-art test time adaptation and device-cloud collaboration methods. The code and datasets will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pan_Cloud-Device_Collaborative_Adaptation_to_Continual_Changing_Environments_in_the_Real-World_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pan_Cloud-Device_Collaborative_Adaptation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00972 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Cloud-Device_Collaborative_Adaptation_to_Continual_Changing_Environments_in_the_Real-World_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Cloud-Device_Collaborative_Adaptation_to_Continual_Changing_Environments_in_the_Real-World_CVPR_2023_paper.html | CVPR 2023 | null |
Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching Between Parts and Words | Chuan Tang, Xi Yang, Bojian Wu, Zhizhong Han, Yi Chang | Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at https://github.com/JLUtangchuan/Parts2Words. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Parts2Words_Learning_Joint_Embedding_of_Point_Clouds_and_Texts_by_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2107.01872 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Parts2Words_Learning_Joint_Embedding_of_Point_Clouds_and_Texts_by_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Parts2Words_Learning_Joint_Embedding_of_Point_Clouds_and_Texts_by_CVPR_2023_paper.html | CVPR 2023 | null |
Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization | Huan Ren, Wenfei Yang, Tianzhu Zhang, Yongdong Zhang | Weakly-supervised temporal action localization aims to localize and recognize actions in untrimmed videos with only video-level category labels during training. Without instance-level annotations, most existing methods follow the Segment-based Multiple Instance Learning (S-MIL) framework, where the predictions of segments are supervised by the labels of videos. However, the objective for acquiring segment-level scores during training is not consistent with the target for acquiring proposal-level scores during testing, leading to suboptimal results. To deal with this problem, we propose a novel Proposal-based Multiple Instance Learning (P-MIL) framework that directly classifies the candidate proposals in both the training and testing stages, which includes three key designs: 1) a surrounding contrastive feature extraction module to suppress the discriminative short proposals by considering the surrounding contrastive information, 2) a proposal completeness evaluation module to inhibit the low-quality proposals with the guidance of the completeness pseudo labels, and 3) an instance-level rank consistency loss to achieve robust detection by leveraging the complementarity of RGB and FLOW modalities. Extensive experimental results on two challenging benchmarks including THUMOS14 and ActivityNet demonstrate the superior performance of our method. Our code is available at github.com/OpenSpaceAI/CVPR2023_P-MIL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_Proposal-Based_Multiple_Instance_Learning_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Proposal-Based_Multiple_Instance_Learning_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Proposal-Based_Multiple_Instance_Learning_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
LayoutDM: Transformer-Based Diffusion Model for Layout Generation | Shang Chai, Liansheng Zhuang, Fengying Yan | Automatic layout generation that can synthesize high-quality layouts is an important tool for graphic design in many applications. Though existing methods based on generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) have progressed, they still leave much room for improving the quality and diversity of the results. Inspired by the recent success of diffusion models in generating high-quality images, this paper explores their potential for conditional layout generation and proposes Transformer-based Layout Diffusion Model (LayoutDM) by instantiating the conditional denoising diffusion probabilistic model (DDPM) with a purely transformer-based architecture. Instead of using convolutional neural networks, a transformer-based conditional Layout Denoiser is proposed to learn the reverse diffusion process to generate samples from noised layout data. Benefitting from both transformer and DDPM, our LayoutDM is of desired properties such as high-quality generation, strong sample diversity, faithful distribution coverage, and stationary training in comparison to GANs and VAEs. Quantitative and qualitative experimental results show that our method outperforms state-of-the-art generative models in terms of quality and diversity. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chai_LayoutDM_Transformer-Based_Diffusion_Model_for_Layout_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chai_LayoutDM_Transformer-Based_Diffusion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.02567 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chai_LayoutDM_Transformer-Based_Diffusion_Model_for_Layout_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chai_LayoutDM_Transformer-Based_Diffusion_Model_for_Layout_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
HandNeRF: Neural Radiance Fields for Animatable Interacting Hands | Zhiyang Guo, Wengang Zhou, Min Wang, Li Li, Houqiang Li | We propose a novel framework to reconstruct accurate appearance and geometry with neural radiance fields (NeRF) for interacting hands, enabling the rendering of photo-realistic images and videos for gesture animation from arbitrary views. Given multi-view images of a single hand or interacting hands, an off-the-shelf skeleton estimator is first employed to parameterize the hand poses. Then we design a pose-driven deformation field to establish correspondence from those different poses to a shared canonical space, where a pose-disentangled NeRF for one hand is optimized. Such unified modeling efficiently complements the geometry and texture cues in rarely-observed areas for both hands. Meanwhile, we further leverage the pose priors to generate pseudo depth maps as guidance for occlusion-aware density learning. Moreover, a neural feature distillation method is proposed to achieve cross-domain alignment for color optimization. We conduct extensive experiments to verify the merits of our proposed HandNeRF and report a series of state-of-the-art results both qualitatively and quantitatively on the large-scale InterHand2.6M dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_HandNeRF_Neural_Radiance_Fields_for_Animatable_Interacting_Hands_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_HandNeRF_Neural_Radiance_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.13825 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_HandNeRF_Neural_Radiance_Fields_for_Animatable_Interacting_Hands_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_HandNeRF_Neural_Radiance_Fields_for_Animatable_Interacting_Hands_CVPR_2023_paper.html | CVPR 2023 | null |
ASPnet: Action Segmentation With Shared-Private Representation of Multiple Data Sources | Beatrice van Amsterdam, Abdolrahim Kadkhodamohammadi, Imanol Luengo, Danail Stoyanov | Most state-of-the-art methods for action segmentation are based on single input modalities or naive fusion of multiple data sources. However, effective fusion of complementary information can potentially strengthen segmentation models and make them more robust to sensor noise and more accurate with smaller training datasets. In order to improve multimodal representation learning for action segmentation, we propose to disentangle hidden features of a multi-stream segmentation model into modality-shared components, containing common information across data sources, and private components; we then use an attention bottleneck to capture long-range temporal dependencies in the data while preserving disentanglement in consecutive processing layers. Evaluation on 50salads, Breakfast and RARP45 datasets shows that our multimodal approach outperforms different data fusion baselines on both multiview and multimodal data sources, obtaining competitive or better results compared with the state-of-the-art. Our model is also more robust to additive sensor noise and can achieve performance on par with strong video baselines even with less training data. | https://openaccess.thecvf.com/content/CVPR2023/papers/van_Amsterdam_ASPnet_Action_Segmentation_With_Shared-Private_Representation_of_Multiple_Data_Sources_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/van_Amsterdam_ASPnet_Action_Segmentation_With_Shared-Private_Representation_of_Multiple_Data_Sources_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/van_Amsterdam_ASPnet_Action_Segmentation_With_Shared-Private_Representation_of_Multiple_Data_Sources_CVPR_2023_paper.html | CVPR 2023 | null |
Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts | Francesco Croce, Sylvestre-Alvise Rebuffi, Evan Shelhamer, Sven Gowal | Adversarial training is widely used to make classifiers robust to a specific threat or adversary, such as l_p-norm bounded perturbations of a given p-norm. However, existing methods for training classifiers robust to multiple threats require knowledge of all attacks during training and remain vulnerable to unseen distribution shifts. In this work, we describe how to obtain adversarially-robust model soups (i.e., linear combinations of parameters) that smoothly trade-off robustness to different l_p-norm bounded adversaries. We demonstrate that such soups allow us to control the type and level of robustness, and can achieve robustness to all threats without jointly training on all of them. In some cases, the resulting model soups are more robust to a given l_p-norm adversary than the constituent model specialized against that same adversary. Finally, we show that adversarially-robust model soups can be a viable tool to adapt to distribution shifts from a few examples. | https://openaccess.thecvf.com/content/CVPR2023/papers/Croce_Seasoning_Model_Soups_for_Robustness_to_Adversarial_and_Natural_Distribution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Croce_Seasoning_Model_Soups_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.10164 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Croce_Seasoning_Model_Soups_for_Robustness_to_Adversarial_and_Natural_Distribution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Croce_Seasoning_Model_Soups_for_Robustness_to_Adversarial_and_Natural_Distribution_CVPR_2023_paper.html | CVPR 2023 | null |
Introducing Competition To Boost the Transferability of Targeted Adversarial Examples Through Clean Feature Mixup | Junyoung Byun, Myung-Joon Kwon, Seungju Cho, Yoonji Kim, Changick Kim | Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications. These adversarial examples tend to be transferable between models, but targeted attacks still have lower attack success rates due to significant variations in decision boundaries. To enhance the transferability of targeted adversarial examples, we propose introducing competition into the optimization process. Our idea is to craft adversarial perturbations in the presence of two new types of competitor noises: adversarial perturbations towards different target classes and friendly perturbations towards the correct class. With these competitors, even if an adversarial example deceives a network to extract specific features leading to the target class, this disturbance can be suppressed by other competitors. Therefore, within this competition, adversarial examples should take different attack strategies by leveraging more diverse features to overwhelm their interference, leading to improving their transferability to different models. Considering the computational complexity, we efficiently simulate various interference from these two types of competitors in feature space by randomly mixing up stored clean features in the model inference and named this method Clean Feature Mixup (CFM). Our extensive experimental results on the ImageNet-Compatible and CIFAR-10 datasets show that the proposed method outperforms the existing baselines with a clear margin. Our code is available at https://github.com/dreamflake/CFM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Byun_Introducing_Competition_To_Boost_the_Transferability_of_Targeted_Adversarial_Examples_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Byun_Introducing_Competition_To_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Byun_Introducing_Competition_To_Boost_the_Transferability_of_Targeted_Adversarial_Examples_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Byun_Introducing_Competition_To_Boost_the_Transferability_of_Targeted_Adversarial_Examples_CVPR_2023_paper.html | CVPR 2023 | null |
Ingredient-Oriented Multi-Degradation Learning for Image Restoration | Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, Feng Zhao | Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have witnessed the flourish of various All-in-one methods, which handle multiple image degradations within a single model. In practice, however, few attempts have been made to excavate task correlations in that exploring the underlying fundamental ingredients of various image degradations, resulting in poor scalability as more tasks are involved. In this paper, we propose a novel perspective to delve into the degradation via an ingredients-oriented rather than previous task-oriented manner for scalable learning. Specifically, our method, named Ingredients-oriented Degradation Reformulation framework (IDR), consists of two stages, namely task-oriented knowledge collection and ingredients-oriented knowledge integration. In the first stage, we conduct ad hoc operations on different degradations according to the underlying physics principles, and establish the corresponding prior hubs for each type of degradation. While the second stage progressively reformulates the preceding task-oriented hubs into single ingredients-oriented hub via learnable Principal Component Analysis (PCA), and employs a dynamic routing mechanism for probabilistic unknown degradation removal. Extensive experiments on various image restoration tasks demonstrate the effectiveness and scalability of our method. More importantly, our IDR exhibits the favorable generalization ability to unknown downstream tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Ingredient-Oriented_Multi-Degradation_Learning_for_Image_Restoration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Ingredient-Oriented_Multi-Degradation_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Ingredient-Oriented_Multi-Degradation_Learning_for_Image_Restoration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Ingredient-Oriented_Multi-Degradation_Learning_for_Image_Restoration_CVPR_2023_paper.html | CVPR 2023 | null |
How To Prevent the Continuous Damage of Noises To Model Training? | Xiaotian Yu, Yang Jiang, Tianqi Shi, Zunlei Feng, Yuexuan Wang, Mingli Song, Li Sun | Deep learning with noisy labels is challenging and inevitable in many circumstances. Existing methods reduce the impact of noise samples by reducing loss weights of uncertain samples or by filtering out potential noise samples, which highly rely on the model's superior discriminative power for identifying noise samples. However, in the training stage, the trainee model is imperfect will miss many noise samples, which cause continuous damage to the model training. Consequently, there is a large performance gap between existing anti-noise models trained with noisy samples and models trained with clean samples. In this paper, we put forward a Gradient Switching Strategy (GSS) to prevent the continuous damage of noise samples to the classifier. Theoretical analysis shows that the damage comes from the misleading gradient direction computed from the noise samples. The trainee model will deviate from the correct optimization direction under the influence of the accumulated misleading gradient of noise samples. To address this problem, the proposed GSS alleviates the damage by switching the current gradient direction of each sample to a new direction selected from a gradient direction pool, which contains all-class gradient directions with different probabilities. During training, the trainee model is optimized along switched gradient directions generated by GSS, which assigns higher probabilities to potential principal directions for high-confidence samples. Conversely, uncertain samples have a relatively uniform probability distribution for all gradient directions, which can cancel out the misleading gradient directions. Extensive experiments show that a model trained with GSS can achieve comparable performance with a model trained with clean data. Moreover, the proposed GSS is pluggable for existing frameworks for noisy-label learning. This work can provide a new perspective for future noisy-label learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_How_To_Prevent_the_Continuous_Damage_of_Noises_To_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_How_To_Prevent_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_How_To_Prevent_the_Continuous_Damage_of_Noises_To_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_How_To_Prevent_the_Continuous_Damage_of_Noises_To_Model_CVPR_2023_paper.html | CVPR 2023 | null |
A Whac-a-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others | Zhiheng Li, Ivan Evtimov, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu, Mark Ibrahim | Machine learning models have been found to learn shortcuts---unintended decision rules that are unable to generalize---undermining models' reliability. Previous works address this problem under the tenuous assumption that only a single shortcut exists in the training data. Real-world images are rife with multiple visual cues from background to texture. Key to advancing the reliability of vision systems is understanding whether existing methods can overcome multiple shortcuts or struggle in a Whac-A-Mole game, i.e., where mitigating one shortcut amplifies reliance on others. To address this shortcoming, we propose two benchmarks: 1) UrbanCars, a dataset with precisely controlled spurious cues, and 2) ImageNet-W, an evaluation set based on ImageNet for watermark, a shortcut we discovered affects nearly every modern vision model. Along with texture and background, ImageNet-W allows us to study multiple shortcuts emerging from training on natural images. We find computer vision models, including large foundation models---regardless of training set, architecture, and supervision---struggle when multiple shortcuts are present. Even methods explicitly designed to combat shortcuts struggle in a Whac-A-Mole dilemma. To tackle this challenge, we propose Last Layer Ensemble, a simple-yet-effective method to mitigate multiple shortcuts without Whac-A-Mole behavior. Our results surface multi-shortcut mitigation as an overlooked challenge critical to advancing the reliability of vision systems. The datasets and code are released: https://github.com/facebookresearch/Whac-A-Mole. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_A_Whac-a-Mole_Dilemma_Shortcuts_Come_in_Multiples_Where_Mitigating_One_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_A_Whac-a-Mole_Dilemma_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_A_Whac-a-Mole_Dilemma_Shortcuts_Come_in_Multiples_Where_Mitigating_One_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_A_Whac-a-Mole_Dilemma_Shortcuts_Come_in_Multiples_Where_Mitigating_One_CVPR_2023_paper.html | CVPR 2023 | null |
Skinned Motion Retargeting With Residual Perception of Motion Semantics & Geometry | Jiaxu Zhang, Junwu Weng, Di Kang, Fang Zhao, Shaoli Huang, Xuefei Zhe, Linchao Bao, Ying Shan, Jue Wang, Zhigang Tu | A good motion retargeting cannot be reached without reasonable consideration of source-target differences on both the skeleton and shape geometry levels. In this work, we propose a novel Residual RETargeting network (R2ET) structure, which relies on two neural modification modules, to adjust the source motions to fit the target skeletons and shapes progressively. In particular, a skeleton-aware module is introduced to preserve the source motion semantics. A shape-aware module is designed to perceive the geometries of target characters to reduce interpenetration and contact-missing. Driven by our explored distance-based losses that explicitly model the motion semantics and geometry, these two modules can learn residual motion modifications on the source motion to generate plausible retargeted motion in a single inference without post-processing. To balance these two modifications, we further present a balancing gate to conduct linear interpolation between them. Extensive experiments on the public dataset Mixamo demonstrate that our R2ET achieves the state-of-the-art performance, and provides a good balance between the preservation of motion semantics as well as the attenuation of interpenetration and contact-missing. Code is available at https://github.com/Kebii/R2ET. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Skinned_Motion_Retargeting_With_Residual_Perception_of_Motion_Semantics__CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Skinned_Motion_Retargeting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08658 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Skinned_Motion_Retargeting_With_Residual_Perception_of_Motion_Semantics__CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Skinned_Motion_Retargeting_With_Residual_Perception_of_Motion_Semantics__CVPR_2023_paper.html | CVPR 2023 | null |
Weakly-Supervised Single-View Image Relighting | Renjiao Yi, Chenyang Zhu, Kai Xu | We present a learning-based approach to relight a single image of Lambertian and low-frequency specular objects. Our method enables inserting objects from photographs into new scenes and relighting them under the new environment lighting, which is essential for AR applications. To relight the object, we solve both inverse rendering and re-rendering. To resolve the ill-posed inverse rendering, we propose a weakly-supervised method by a low-rank constraint. To facilitate the weakly-supervised training, we contribute Relit, a large-scale (750K images) dataset of videos with aligned objects under changing illuminations. For re-rendering, we propose a differentiable specular rendering layer to render low-frequency non-Lambertian materials under various illuminations of spherical harmonics. The whole pipeline is end-to-end and efficient, allowing for a mobile app implementation of AR object insertion. Extensive evaluations demonstrate that our method achieves state-of-the-art performance. Project page: https://renjiaoyi.github.io/relighting/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yi_Weakly-Supervised_Single-View_Image_Relighting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yi_Weakly-Supervised_Single-View_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13852 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_Weakly-Supervised_Single-View_Image_Relighting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_Weakly-Supervised_Single-View_Image_Relighting_CVPR_2023_paper.html | CVPR 2023 | null |
DualVector: Unsupervised Vector Font Synthesis With Dual-Part Representation | Ying-Tian Liu, Zhifei Zhang, Yuan-Chen Guo, Matthew Fisher, Zhaowen Wang, Song-Hai Zhang | Automatic generation of fonts can be an important aid to typeface design. Many current approaches regard glyphs as pixelated images, which present artifacts when scaling and inevitable quality losses after vectorization. On the other hand, existing vector font synthesis methods either fail to represent the shape concisely or require vector supervision during training. To push the quality of vector font synthesis to the next level, we propose a novel dual-part representation for vector glyphs, where each glyph is modeled as a collection of closed "positive" and "negative" path pairs. The glyph contour is then obtained by boolean operations on these paths. We first learn such a representation only from glyph images and devise a subsequent contour refinement step to align the contour with an image representation to further enhance details. Our method, named DualVector, outperforms state-of-the-art methods in vector font synthesis both quantitatively and qualitatively. Our synthesized vector fonts can be easily converted to common digital font formats like TrueType Font for practical use. The code is released at https://github.com/thuliu-yt16/dualvector. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_DualVector_Unsupervised_Vector_Font_Synthesis_With_Dual-Part_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_DualVector_Unsupervised_Vector_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_DualVector_Unsupervised_Vector_Font_Synthesis_With_Dual-Part_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_DualVector_Unsupervised_Vector_Font_Synthesis_With_Dual-Part_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Scale-Invariant Generator With Column-Row Entangled Pixel Synthesis | Thuan Hoang Nguyen, Thanh Van Le, Anh Tran | Any-scale image synthesis offers an efficient and scalable solution to synthesize photo-realistic images at any scale, even going beyond 2K resolution. However, existing GAN-based solutions depend excessively on convolutions and a hierarchical architecture, which introduce inconsistency and the "texture sticking" issue when scaling the output resolution. From another perspective, INR-based generators are scale-equivariant by design, but their huge memory footprint and slow inference hinder these networks from being adopted in large-scale or real-time systems. In this work, we propose Column-Row Entangled Pixel Synthesisthes (CREPS), a new generative model that is both efficient and scale-equivariant without using any spatial convolutions or coarse-to-fine design. To save memory footprint and make the system scalable, we employ a novel bi-line representation that decomposes layer-wise feature maps into separate "thick" column and row encodings. Experiments on standard datasets, including FFHQ, LSUN-Church, and MetFaces, confirm CREPS' ability to synthesize scale-consistent and alias-free images up to 4K resolution with proper training and inference speed. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nguyen_Efficient_Scale-Invariant_Generator_With_Column-Row_Entangled_Pixel_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nguyen_Efficient_Scale-Invariant_Generator_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14157 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Efficient_Scale-Invariant_Generator_With_Column-Row_Entangled_Pixel_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Efficient_Scale-Invariant_Generator_With_Column-Row_Entangled_Pixel_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
ReasonNet: End-to-End Driving With Temporal and Global Reasoning | Hao Shao, Letian Wang, Ruobing Chen, Steven L. Waslander, Hongsheng Li, Yu Liu | The large-scale deployment of autonomous vehicles is yet to come, and one of the major remaining challenges lies in urban dense traffic scenarios. In such cases, it remains challenging to predict the future evolution of the scene and future behaviors of objects, and to deal with rare adverse events such as the sudden appearance of occluded objects. In this paper, we present ReasonNet, a novel end-to-end driving framework that extensively exploits both temporal and global information of the driving scene. By reasoning on the temporal behavior of objects, our method can effectively process the interactions and relationships among features in different frames. Reasoning about the global information of the scene can also improve overall perception performance and benefit the detection of adverse events, especially the anticipation of potential danger from occluded objects. For comprehensive evaluation on occlusion events, we also release publicly a driving simulation benchmark DriveOcclusionSim consisting of diverse occlusion events. We conduct extensive experiments on multiple CARLA benchmarks, where our model outperforms all prior methods, ranking first on the sensor track of the public CARLA Leaderboard. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shao_ReasonNet_End-to-End_Driving_With_Temporal_and_Global_Reasoning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shao_ReasonNet_End-to-End_Driving_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_ReasonNet_End-to-End_Driving_With_Temporal_and_Global_Reasoning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_ReasonNet_End-to-End_Driving_With_Temporal_and_Global_Reasoning_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Situation Hyper-Graphs for Video Question Answering | Aisha Urooj, Hilde Kuehne, Bo Wu, Kim Chheu, Walid Bousselham, Chuang Gan, Niels Lobo, Mubarak Shah | Answering questions about complex situations in videos requires not only capturing of the presence of actors, objects, and their relations, but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs, and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a cross-entropy based VQA loss function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hyper-graphs helps the system to significantly improve its performance for novel challenges of video question answering task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Urooj_Learning_Situation_Hyper-Graphs_for_Video_Question_Answering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Urooj_Learning_Situation_Hyper-Graphs_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08682 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Urooj_Learning_Situation_Hyper-Graphs_for_Video_Question_Answering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Urooj_Learning_Situation_Hyper-Graphs_for_Video_Question_Answering_CVPR_2023_paper.html | CVPR 2023 | null |
H2ONet: Hand-Occlusion-and-Orientation-Aware Network for Real-Time 3D Hand Mesh Reconstruction | Hao Xu, Tianyu Wang, Xiao Tang, Chi-Wing Fu | Real-time 3D hand mesh reconstruction is challenging, especially when the hand is holding some object. Beyond the previous methods, we design H2ONet to fully exploit non-occluded information from multiple frames to boost the reconstruction quality. First, we decouple hand mesh reconstruction into two branches, one to exploit finger-level non-occluded information and the other to exploit global hand orientation, with lightweight structures to promote real-time inference. Second, we propose finger-level occlusion-aware feature fusion, leveraging predicted finger-level occlusion information as guidance to fuse finger-level information across time frames. Further, we design hand-level occlusion-aware feature fusion to fetch non-occluded information from nearby time frames. We conduct experiments on the Dex-YCB and HO3D-v2 datasets with challenging hand-object occlusion cases, manifesting that H2ONet is able to run in real-time and achieves state-of-the-art performance on both the hand mesh and pose precision. The code will be released on GitHub. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_H2ONet_Hand-Occlusion-and-Orientation-Aware_Network_for_Real-Time_3D_Hand_Mesh_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_H2ONet_Hand-Occlusion-and-Orientation-Aware_Network_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_H2ONet_Hand-Occlusion-and-Orientation-Aware_Network_for_Real-Time_3D_Hand_Mesh_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_H2ONet_Hand-Occlusion-and-Orientation-Aware_Network_for_Real-Time_3D_Hand_Mesh_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Interventional Bag Multi-Instance Learning on Whole-Slide Pathological Images | Tiancheng Lin, Zhimiao Yu, Hongyu Hu, Yi Xu, Chang-Wen Chen | Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images (WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL methods primarily focus on improving the feature extractor and aggregator. However, one deficiency of these methods is that the bag contextual prior may trick the model into capturing spurious correlations between bags and labels. This deficiency is a confounder that limits the performance of existing MIL methods. In this paper, we propose a novel scheme, Interventional Bag Multi-Instance Learning (IBMIL), to achieve deconfounded bag-level prediction. Unlike traditional likelihood-based strategies, the proposed scheme is based on the backdoor adjustment to achieve the interventional training, thus is capable of suppressing the bias caused by the bag contextual prior. Note that the principle of IBMIL is orthogonal to existing bag MIL methods. Therefore, IBMIL is able to bring consistent performance boosting to existing schemes, achieving new state-of-the-art performance. Code is available at https://github.com/HHHedo/IBMIL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Interventional_Bag_Multi-Instance_Learning_on_Whole-Slide_Pathological_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Interventional_Bag_Multi-Instance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06873 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Interventional_Bag_Multi-Instance_Learning_on_Whole-Slide_Pathological_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Interventional_Bag_Multi-Instance_Learning_on_Whole-Slide_Pathological_Images_CVPR_2023_paper.html | CVPR 2023 | null |
GazeNeRF: 3D-Aware Gaze Redirection With Neural Radiance Fields | Alessandro Ruzzi, Xiangwei Shi, Xi Wang, Gengyan Li, Shalini De Mello, Hyung Jin Chang, Xucong Zhang, Otmar Hilliges | We propose GazeNeRF, a 3D-aware method for the task of gaze redirection. Existing gaze redirection methods operate on 2D images and struggle to generate 3D consistent results. Instead, we build on the intuition that the face region and eye balls are separate 3D structures that move in a coordinated yet independent fashion. Our method leverages recent advancements in conditional image-based neural radiance fields and proposes a two-branch architecture that predicts volumetric features for the face and eye regions separately. Rigidly transforming the eye features via a 3D rotation matrix provides fine-grained control over the desired gaze angle. The final, redirected image is then attained via differentiable volume compositing. Our experiments show that this architecture outperforms naively conditioned NeRF baselines as well as previous state-of-the-art 2D gaze redirection methods in terms of redirection accuracy and identity preservation. Code and models will be released for research purposes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ruzzi_GazeNeRF_3D-Aware_Gaze_Redirection_With_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ruzzi_GazeNeRF_3D-Aware_Gaze_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04823 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ruzzi_GazeNeRF_3D-Aware_Gaze_Redirection_With_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ruzzi_GazeNeRF_3D-Aware_Gaze_Redirection_With_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
How Can Objects Help Action Recognition? | Xingyi Zhou, Anurag Arnab, Chen Sun, Cordelia Schmid | Current state-of-the-art video models process a video clip as a long sequence of spatio-temporal tokens. However, they do not explicitly model objects, their interactions across the video, and instead process all the tokens in the video. In this paper, we investigate how we can use knowledge of objects to design better video models, namely to process fewer tokens and to improve recognition accuracy. This is in contrast to prior works which either drop tokens at the cost of accuracy, or increase accuracy whilst also increasing the computation required. First, we propose an object-guided token sampling strategy that enables us to retain a small fraction of the input tokens with minimal impact on accuracy. And second, we propose an object-aware attention module that enriches our feature representation with object information and improves overall accuracy. Our resulting framework achieves better performance when using fewer tokens than strong baselines. In particular, we match our baseline with 30%, 40%, and 60% of the input tokens on SomethingElse, Something-something v2, and Epic-Kitchens, respectively. When we use our model to process the same number of tokens as our baseline, we improve by 0.6 to 4.2 points on these datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_How_Can_Objects_Help_Action_Recognition_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_How_Can_Objects_Help_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_How_Can_Objects_Help_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Realistic Saliency Guided Image Enhancement | S. Mahdi H. Miangoleh, Zoya Bylinskii, Eric Kee, Eli Shechtman, Yağiz Aksoy | Common editing operations performed by professional photographers include the cleanup operations: de-emphasizing distracting elements and enhancing subjects. These edits are challenging, requiring a delicate balance between manipulating the viewer's attention while maintaining photo realism. While recent approaches can boast successful examples of attention attenuation or amplification, most of them also suffer from frequent unrealistic edits. We propose a realism loss for saliency-guided image enhancement to maintain high realism across varying image types, while attenuating distractors and amplifying objects of interest. Evaluations with professional photographers confirm that we achieve the dual objective of realism and effectiveness, and outperform the recent approaches on their own datasets, while requiring a smaller memory footprint and runtime. We thus offer a viable solution for automating image enhancement and photo cleanup operations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Miangoleh_Realistic_Saliency_Guided_Image_Enhancement_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Miangoleh_Realistic_Saliency_Guided_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Miangoleh_Realistic_Saliency_Guided_Image_Enhancement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Miangoleh_Realistic_Saliency_Guided_Image_Enhancement_CVPR_2023_paper.html | CVPR 2023 | null |
SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments | Yudi Dai, Yitai Lin, Xiping Lin, Chenglu Wen, Lan Xu, Hongwei Yi, Siqi Shen, Yuexin Ma, Cheng Wang | We present SLOPER4D, a novel scene-aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild. Employing a head-mounted device integrated with a LiDAR and camera, we record 12 human subjects' activities over 10 diverse urban scenes from an egocentric view. Frame-wise annotations for 2D key points, 3D pose parameters, and global translations are provided, together with reconstructed scene point clouds. To obtain accurate 3D ground truth in such large dynamic scenes, we propose a joint optimization method to fit local SMPL meshes to the scene and fine-tune the camera calibration during dynamic motions frame by frame, resulting in plausible and scene-natural 3D human poses. Eventually, SLOPER4D consists of 15 sequences of human motions, each of which has a trajectory length of more than 200 meters (up to 1,300 meters) and covers an area of more than 200 square meters (up to 30,000 square meters), including more than 100k LiDAR frames, 300k video frames, and 500K IMU-based motion frames. With SLOPER4D, we provide a detailed and thorough analysis of two critical tasks, including camera-based 3D HPE and LiDAR-based 3D HPE in urban environments, and benchmark a new task, GHPE. The in-depth analysis demonstrates SLOPER4D poses significant challenges to existing methods and produces great research opportunities. The dataset and code are released at http://www.lidarhumanmotion.net/sloper4d/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dai_SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dai_SLOPER4D_A_Scene-Aware_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.09095 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dai_SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dai_SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization | Maxime Pietrantoni, Martin Humenberger, Torsten Sattler, Gabriela Csurka | Inspired by properties of semantic segmentation, in this paper we investigate how to leverage robust image segmentation in the context of privacy-preserving visual localization. We propose a new localization framework, SegLoc, that leverages image segmentation to create robust, compact, and privacy-preserving scene representations, i.e., 3D maps. We build upon the correspondence-supervised, fine-grained segmentation approach from Larsson et al (ICCV'19), making it more robust by learning a set of cluster labels with discriminative clustering, additional consistency regularization terms and we jointly learn a global image representation along with a dense local representation. In our localization pipeline, the former will be used for retrieving the most similar images, the latter to refine the retrieved poses by minimizing the label inconsistency between the 3D points of the map and their projection onto the query image. In various experiments, we show that our proposed representation allows to achieve (close-to) state-of-the-art pose estimation results while only using a compact 3D map that does not contain enough information about the original images for an attacker to reconstruct personal information. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pietrantoni_SegLoc_Learning_Segmentation-Based_Representations_for_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pietrantoni_SegLoc_Learning_Segmentation-Based_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pietrantoni_SegLoc_Learning_Segmentation-Based_Representations_for_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pietrantoni_SegLoc_Learning_Segmentation-Based_Representations_for_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Hierarchical Entropy Model for Learned Point Cloud Compression | Rui Song, Chunyang Fu, Shan Liu, Ge Li | Learning an accurate entropy model is a fundamental way to remove the redundancy in point cloud compression. Recently, the octree-based auto-regressive entropy model which adopts the self-attention mechanism to explore dependencies in a large-scale context is proved to be promising. However, heavy global attention computations and auto-regressive contexts are inefficient for practical applications. To improve the efficiency of the attention model, we propose a hierarchical attention structure that has a linear complexity to the context scale and maintains the global receptive field. Furthermore, we present a grouped context structure to address the serial decoding issue caused by the auto-regression while preserving the compression performance. Experiments demonstrate that the proposed entropy model achieves superior rate-distortion performance and significant decoding latency reduction compared with the state-of-the-art large-scale auto-regressive entropy model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Efficient_Hierarchical_Entropy_Model_for_Learned_Point_Cloud_Compression_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_Efficient_Hierarchical_Entropy_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Efficient_Hierarchical_Entropy_Model_for_Learned_Point_Cloud_Compression_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Efficient_Hierarchical_Entropy_Model_for_Learned_Point_Cloud_Compression_CVPR_2023_paper.html | CVPR 2023 | null |
RankMix: Data Augmentation for Weakly Supervised Learning of Classifying Whole Slide Images With Diverse Sizes and Imbalanced Categories | Yuan-Chih Chen, Chun-Shien Lu | Whole Slide Images (WSIs) are usually gigapixel in size and lack pixel-level annotations. The WSI datasets are also imbalanced in categories. These unique characteristics, significantly different from the ones in natural images, pose the challenge of classifying WSI images as a kind of weakly supervise learning problems. In this study, we propose, RankMix, a data augmentation method of mixing ranked features in a pair of WSIs. RankMix introduces the concepts of pseudo labeling and ranking in order to extract key WSI regions in contributing to the WSI classification task. A two-stage training is further proposed to boost stable training and model performance. To our knowledge, the study of weakly supervised learning from the perspective of data augmentation to deal with the WSI classification problem that suffers from lack of training data and imbalance of categories is relatively unexplored. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_RankMix_Data_Augmentation_for_Weakly_Supervised_Learning_of_Classifying_Whole_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_RankMix_Data_Augmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_RankMix_Data_Augmentation_for_Weakly_Supervised_Learning_of_Classifying_Whole_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_RankMix_Data_Augmentation_for_Weakly_Supervised_Learning_of_Classifying_Whole_CVPR_2023_paper.html | CVPR 2023 | null |
ActMAD: Activation Matching To Align Distributions for Test-Time-Training | Muhammad Jehanzeb Mirza, Pol Jané Soneira, Wei Lin, Mateusz Kozinski, Horst Possegger, Horst Bischof | Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform this adaptation via Activation Matching (ActMAD): We analyze activations of the model and align activation statistics of the OOD test data to those of the training data. In contrast to existing methods, which model the distribution of entire channels in the ultimate layer of the feature extractor, we model the distribution of each feature in multiple layers across the network. This results in a more fine-grained supervision and makes ActMAD attain state of the art performance on CIFAR-100C and Imagenet-C. ActMAD is also architecture- and task-agnostic, which lets us go beyond image classification, and score 15.4% improvement over previous approaches when evaluating a KITTI-trained object detector on KITTI-Fog. Our experiments highlight that ActMAD can be applied to online adaptation in realistic scenarios, requiring little data to attain its full performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mirza_ActMAD_Activation_Matching_To_Align_Distributions_for_Test-Time-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mirza_ActMAD_Activation_Matching_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12870 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mirza_ActMAD_Activation_Matching_To_Align_Distributions_for_Test-Time-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mirza_ActMAD_Activation_Matching_To_Align_Distributions_for_Test-Time-Training_CVPR_2023_paper.html | CVPR 2023 | null |
DKM: Dense Kernelized Feature Matching for Geometry Estimation | Johan Edstedt, Ioannis Athanasiadis, Mårten Wadenbäck, Michael Felsberg | Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense methods have previously shown inferior performance to their sparse and semi-sparse counterparts for estimation of two-view geometry. This changes with our novel dense method, which outperforms both dense and sparse methods on geometry estimation. The novelty is threefold: First, we propose a kernel regression global matcher. Secondly, we propose warp refinement through stacked feature maps and depthwise convolution kernels. Thirdly, we propose learning dense confidence through consistent depth and a balanced sampling approach for dense confidence maps. Through extensive experiments we confirm that our proposed dense method, Dense Kernelized Feature Matching, sets a new state-of-the-art on multiple geometry estimation benchmarks. In particular, we achieve an improvement on MegaDepth-1500 of +4.9 and +8.9 AUC@5 compared to the best previous sparse method and dense method respectively. Our code is provided at the following repository: https://github.com/Parskatt/DKM | https://openaccess.thecvf.com/content/CVPR2023/papers/Edstedt_DKM_Dense_Kernelized_Feature_Matching_for_Geometry_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Edstedt_DKM_Dense_Kernelized_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Edstedt_DKM_Dense_Kernelized_Feature_Matching_for_Geometry_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Edstedt_DKM_Dense_Kernelized_Feature_Matching_for_Geometry_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Image Cropping With Spatial-Aware Feature and Rank Consistency | Chao Wang, Li Niu, Bo Zhang, Liqing Zhang | Image cropping aims to find visually appealing crops in an image. Despite the great progress made by previous methods, they are weak in capturing the spatial relationship between crops and aesthetic elements (e.g., salient objects, semantic edges). Besides, due to the high annotation cost of labeled data, the potential of unlabeled data awaits to be excavated. To address the first issue, we propose spatial-aware feature to encode the spatial relationship between candidate crops and aesthetic elements, by feeding the concatenation of crop mask and selectively aggregated feature maps to a light-weighted encoder. To address the second issue, we train a pair-wise ranking classifier on labeled images and transfer such knowledge to unlabeled images to enforce rank consistency. Experimental results on the benchmark datasets show that our proposed method performs favorably against state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Image_Cropping_With_Spatial-Aware_Feature_and_Rank_Consistency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Image_Cropping_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Image_Cropping_With_Spatial-Aware_Feature_and_Rank_Consistency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Image_Cropping_With_Spatial-Aware_Feature_and_Rank_Consistency_CVPR_2023_paper.html | CVPR 2023 | null |
SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_SVGformer_Representation_Learning_for_Continuous_Vector_Graphics_Using_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_SVGformer_Representation_Learning_for_Continuous_Vector_Graphics_Using_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Structured 3D Features for Reconstructing Controllable Avatars | Enric Corona, Mihai Zanfir, Thiemo Alldieck, Eduard Gabriel Bazavan, Andrei Zanfir, Cristian Sminchisescu | We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, statistical human mesh surface. The 3D points have associated semantics and can move freely in 3D space. This allows for optimal coverage of the person of interest, beyond just the body shape, which in turn, additionally helps modeling accessories, hair, and loose clothing. Owing to this, we present a complete 3D transformer-based attention framework which, given a single image of a person in an unconstrained pose, generates an animatable 3D reconstruction with albedo and illumination decomposition, as a result of a single end-to-end model, trained semi-supervised, and with no additional postprocessing. We show that our S3F model surpasses the previous state-of-the-art on various tasks, including monocular 3D reconstruction, as well as albedo & shading estimation. Moreover, we show that the proposed methodology allows novel view synthesis, relighting, and re-posing the reconstruction, and can naturally be extended to handle multiple input images (e.g. different views of a person, or the same view, in different poses, in video). Finally, we demonstrate the editing capabilities of our model for 3D virtual try-on applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Corona_Structured_3D_Features_for_Reconstructing_Controllable_Avatars_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Corona_Structured_3D_Features_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06820 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Corona_Structured_3D_Features_for_Reconstructing_Controllable_Avatars_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Corona_Structured_3D_Features_for_Reconstructing_Controllable_Avatars_CVPR_2023_paper.html | CVPR 2023 | null |
Mask-Guided Matting in the Wild | Kwanyong Park, Sanghyun Woo, Seoung Wug Oh, In So Kweon, Joon-Young Lee | Mask-guided matting has shown great practicality compared to traditional trimap-based methods. The mask-guided approach takes an easily-obtainable coarse mask as guidance and produces an accurate alpha matte. To extend the success toward practical usage, we tackle mask-guided matting in the wild, which covers a wide range of categories in their complex context robustly. To this end, we propose a simple yet effective learning framework based on two core insights: 1) learning a generalized matting model that can better understand the given mask guidance and 2) leveraging weak supervision datasets (e.g., instance segmentation dataset) to alleviate the limited diversity and scale of existing matting datasets. Extensive experimental results on multiple benchmarks, consisting of a newly proposed synthetic benchmark (Composition-Wild) and existing natural datasets, demonstrate the superiority of the proposed method. Moreover, we provide appealing results on new practical applications (e.g., panoptic matting and mask-guided video matting), showing the great generality and potential of our model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Mask-Guided_Matting_in_the_Wild_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Mask-Guided_Matting_in_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Mask-Guided_Matting_in_the_Wild_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Mask-Guided_Matting_in_the_Wild_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Conceptional Contrastive Learning for Generalized Category Discovery | Nan Pu, Zhun Zhong, Nicu Sebe | Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. However, this manner largely ignores underlying relationships between instances of the same concepts (e.g., class, super-class, and sub-class), which results in inferior representation learning. In this paper, we propose a Dynamic Conceptional Contrastive Learning (DCCL) framework, which can effectively improve clustering accuracy by alternately estimating underlying visual conceptions and learning conceptional representation. In addition, we design a dynamic conception generation and update mechanism, which is able to ensure consistent conception learning and thus further facilitate the optimization of DCCL. Extensive experiments show that DCCL achieves new state-of-the-art performances on six generic and fine-grained visual recognition datasets, especially on fine-grained ones. For example, our method significantly surpasses the best competitor by 16.2% on the new classes for the CUB-200 dataset. Code is available at https://github.com/TPCD/DCCL | https://openaccess.thecvf.com/content/CVPR2023/papers/Pu_Dynamic_Conceptional_Contrastive_Learning_for_Generalized_Category_Discovery_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.17393 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pu_Dynamic_Conceptional_Contrastive_Learning_for_Generalized_Category_Discovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pu_Dynamic_Conceptional_Contrastive_Learning_for_Generalized_Category_Discovery_CVPR_2023_paper.html | CVPR 2023 | null |
Neumann Network With Recursive Kernels for Single Image Defocus Deblurring | Yuhui Quan, Zicong Wu, Hui Ji | Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant size variation. Motivated by the strong correlation among defocus kernels of different sizes and the blob-type structure of defocus kernels, we propose a learnable recursive kernel representation (RKR) for defocus kernels that expresses a defocus kernel by a linear combination of recursive, separable and positive atom kernels, leading to a compact yet effective and physics-encoded parametrization of the spatially-varying defocus blurring process. Afterwards, a physics-driven and efficient deep model with a cross-scale fusion structure is presented for SIDD, with inspirations from the truncated Neumann series for approximating the matrix inversion of the RKR-based blurring operator. In addition, a reblurring loss is proposed to regularize the RKR learning. Extensive experiments show that, our proposed approach significantly outperforms existing ones, with a model size comparable to that of the top methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Quan_Neumann_Network_With_Recursive_Kernels_for_Single_Image_Defocus_Deblurring_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Quan_Neumann_Network_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Quan_Neumann_Network_With_Recursive_Kernels_for_Single_Image_Defocus_Deblurring_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Quan_Neumann_Network_With_Recursive_Kernels_for_Single_Image_Defocus_Deblurring_CVPR_2023_paper.html | CVPR 2023 | null |
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm | Yichen Xie, Han Lu, Junchi Yan, Xiaokang Yang, Masayoshi Tomizuka, Wei Zhan | Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a popular paradigm in multiple computer vision tasks. Previous research has covered both the unsupervised pretraining and supervised finetuning in this paradigm, while little attention is paid to exploiting the annotation budget for finetuning. To fill in this gap, we formally define this new active finetuning task focusing on the selection of samples for annotation in the pretraining-finetuning paradigm. We propose a novel method called ActiveFT for active finetuning task to select a subset of data distributing similarly with the entire unlabeled pool and maintaining enough diversity by optimizing a parametric model in the continuous space. We prove that the Earth Mover's distance between the distributions of the selected subset and the entire data pool is also reduced in this process. Extensive experiments show the leading performance and high efficiency of ActiveFT superior to baselines on both image classification and semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Active_Finetuning_Exploiting_Annotation_Budget_in_the_Pretraining-Finetuning_Paradigm_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Active_Finetuning_Exploiting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14382 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Active_Finetuning_Exploiting_Annotation_Budget_in_the_Pretraining-Finetuning_Paradigm_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Active_Finetuning_Exploiting_Annotation_Budget_in_the_Pretraining-Finetuning_Paradigm_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Attribute and Class-Specific Representation Duet for Fine-Grained Fashion Analysis | Yang Jiao, Yan Gao, Jingjing Meng, Jin Shang, Yi Sun | Fashion representation learning involves the analysis and understanding of various visual elements at different granularities and the interactions among them. Existing works often learn fine-grained fashion representations at the attribute-level without considering their relationships and inter-dependencies across different classes. In this work, we propose to learn an attribute and class specific fashion representation duet to better model such attribute relationships and inter-dependencies by leveraging prior knowledge about the taxonomy of fashion attributes and classes. Through two sub-networks for the attributes and classes, respectively, our proposed an embedding network progressively learn and refine the visual representation of a fashion image to improve its robustness for fashion retrieval. A multi-granularity loss consisting of attribute-level and class-level losses is proposed to introduce appropriate inductive bias to learn across different granularities of the fashion representations. Experimental results on three benchmark datasets demonstrate the effectiveness of our method, which outperforms the state-of-the-art methods with a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiao_Learning_Attribute_and_Class-Specific_Representation_Duet_for_Fine-Grained_Fashion_Analysis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiao_Learning_Attribute_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiao_Learning_Attribute_and_Class-Specific_Representation_Duet_for_Fine-Grained_Fashion_Analysis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiao_Learning_Attribute_and_Class-Specific_Representation_Duet_for_Fine-Grained_Fashion_Analysis_CVPR_2023_paper.html | CVPR 2023 | null |
Pixels, Regions, and Objects: Multiple Enhancement for Salient Object Detection | Yi Wang, Ruili Wang, Xin Fan, Tianzhu Wang, Xiangjian He | Salient object detection (SOD) aims to mimic the human visual system (HVS) and cognition mechanisms to identify and segment salient objects. However, due to the complexity of these mechanisms, current methods are not perfect. Accuracy and robustness need to be further improved, particularly in complex scenes with multiple objects and background clutter. To address this issue, we propose a novel approach called Multiple Enhancement Network (MENet) that adopts the boundary sensibility, content integrity, iterative refinement, and frequency decomposition mechanisms of HVS. A multi-level hybrid loss is firstly designed to guide the network to learn pixel-level, region-level, and object-level features. A flexible multiscale feature enhancement module (ME-Module) is then designed to gradually aggregate and refine global or detailed features by changing the size order of the input feature sequence. An iterative training strategy is used to enhance boundary features and adaptive features in the dual-branch decoder of MENet. Comprehensive evaluations on six challenging benchmark datasets show that MENet achieves state-of-the-art results. Both the codes and results are publicly available at https://github.com/yiwangtz/MENet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Pixels_Regions_and_Objects_Multiple_Enhancement_for_Salient_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Pixels_Regions_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Pixels_Regions_and_Objects_Multiple_Enhancement_for_Salient_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Pixels_Regions_and_Objects_Multiple_Enhancement_for_Salient_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Leveraging Temporal Context in Low Representational Power Regimes | Camilo L. Fosco, SouYoung Jin, Emilie Josephs, Aude Oliva | Computer vision models are excellent at identifying and exploiting regularities in the world. However, it is computationally costly to learn these regularities from scratch. This presents a challenge for low-parameter models, like those running on edge devices (e.g. smartphones). Can the performance of models with low representational power be improved by supplementing training with additional information about these statistical regularities? We explore this in the domains of action recognition and action anticipation, leveraging the fact that actions are typically embedded in stereotypical sequences. We introduce the Event Transition Matrix (ETM), computed from action labels in an untrimmed video dataset, which captures the temporal context of a given action, operationalized as the likelihood that it was preceded or followed by each other action in the set. We show that including information from the ETM during training improves action recognition and anticipation performance on various egocentric video datasets. Through ablation and control studies, we show that the coherent sequence of information captured by our ETM is key to this effect, and we find that the benefit of this explicit representation of temporal context is most pronounced for smaller models. Code, matrices and models are available in our project page: https://camilofosco.com/etm_website. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fosco_Leveraging_Temporal_Context_in_Low_Representational_Power_Regimes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fosco_Leveraging_Temporal_Context_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fosco_Leveraging_Temporal_Context_in_Low_Representational_Power_Regimes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fosco_Leveraging_Temporal_Context_in_Low_Representational_Power_Regimes_CVPR_2023_paper.html | CVPR 2023 | null |
Guided Recommendation for Model Fine-Tuning | Hao Li, Charless Fowlkes, Hao Yang, Onkar Dabeer, Zhuowen Tu, Stefano Soatto | Model selection is essential for reducing the search cost of the best pre-trained model over a large-scale model zoo for a downstream task. After analyzing recent hand-designed model selection criteria with 400+ ImageNet pre-trained models and 40 downstream tasks, we find that they can fail due to invalid assumptions and intrinsic limitations. The prior knowledge on model capacity and dataset also can not be easily integrated into the existing criteria. To address these issues, we propose to convert model selection as a recommendation problem and to learn from the past training history. Specifically, we characterize the meta information of datasets and models as features, and use their transfer learning performance as the guided score. With thousands of historical training jobs, a recommendation system can be learned to predict the model selection score given the features of the dataset and the model as input. Our approach enables integrating existing model selection scores as additional features and scales with more historical data. We evaluate the prediction accuracy with 22 pre-trained models over 40 downstream tasks. With extensive evaluations, we show that the learned approach can outperform prior hand-designed model selection methods significantly when relevant training history is available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Guided_Recommendation_for_Model_Fine-Tuning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Guided_Recommendation_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Guided_Recommendation_for_Model_Fine-Tuning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Guided_Recommendation_for_Model_Fine-Tuning_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Image Training for Generalizable Deep Image Denoising | Haoyu Chen, Jinjin Gu, Yihao Liu, Salma Abdel Magid, Chao Dong, Qiong Wang, Hanspeter Pfister, Lei Zhu | When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method for image denoising, especially with the emergence of Transformer-based models that have achieved notable state-of-the-art results on various image tasks. However, deep learning-based methods often suffer from a lack of generalization ability. For example, deep models trained on Gaussian noise may perform poorly when tested on other noise distributions. To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training. Our method involves masking random pixels of the input image and reconstructing the missing information during training. We also mask out the features in the self-attention layers to avoid the impact of training-testing inconsistency. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios. Additionally, our interpretability analysis demonstrates the superiority of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Masked_Image_Training_for_Generalizable_Deep_Image_Denoising_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Masked_Image_Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13132 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Masked_Image_Training_for_Generalizable_Deep_Image_Denoising_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Masked_Image_Training_for_Generalizable_Deep_Image_Denoising_CVPR_2023_paper.html | CVPR 2023 | null |
In-Hand 3D Object Scanning From an RGB Sequence | Shreyas Hampali, Tomas Hodan, Luan Tran, Lingni Ma, Cem Keskin, Vincent Lepetit | We propose a method for in-hand 3D scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geometry and the appearance of the object, however, by contrast with most NeRF-based methods, we do not assume that the camera-object relative poses are known. Instead, we simultaneously optimize both the object shape and the pose trajectory. As direct optimization over all shape and pose parameters is prone to fail without coarse-level initialization, we propose an incremental approach that starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed. We reconstruct the object shape and track its poses independently within each segment, then merge all the segments before performing a global optimization. We show that our method is able to reconstruct the shape and color of both textured and challenging texture-less objects, outperforms classical methods that rely only on appearance features, and that its performance is close to recent methods that assume known camera poses. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hampali_In-Hand_3D_Object_Scanning_From_an_RGB_Sequence_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hampali_In-Hand_3D_Object_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16193 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hampali_In-Hand_3D_Object_Scanning_From_an_RGB_Sequence_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hampali_In-Hand_3D_Object_Scanning_From_an_RGB_Sequence_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Referring Image Segmentation With Global-Local Context Features | Seonghoon Yu, Paul Hongsuck Seo, Jeany Son | Referring image segmentation (RIS) aims to find a segmentation mask given a referring expression grounded to a region of the input image. Collecting labelled datasets for this task, however, is notoriously costly and labor-intensive. To overcome this issue, we propose a simple yet effective zero-shot referring image segmentation method by leveraging the pre-trained cross-modal knowledge from CLIP. In order to obtain segmentation masks grounded to the input text, we propose a mask-guided visual encoder that captures global and local contextual information of an input image. By utilizing instance masks obtained from off-the-shelf mask proposal techniques, our method is able to segment fine-detailed instance-level groundings. We also introduce a global-local text encoder where the global feature captures complex sentence-level semantics of the entire input expression while the local feature focuses on the target noun phrase extracted by a dependency parser. In our experiments, the proposed method outperforms several zero-shot baselines of the task and even the weakly supervised referring expression segmentation method with substantial margins. Our code is available at https://github.com/Seonghoon-Yu/Zero-shot-RIS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Zero-Shot_Referring_Image_Segmentation_With_Global-Local_Context_Features_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Zero-Shot_Referring_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17811 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Zero-Shot_Referring_Image_Segmentation_With_Global-Local_Context_Features_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Zero-Shot_Referring_Image_Segmentation_With_Global-Local_Context_Features_CVPR_2023_paper.html | CVPR 2023 | null |
SketchXAI: A First Look at Explainability for Human Sketches | Zhiyu Qu, Yulia Gryaditskaya, Ke Li, Kaiyue Pang, Tao Xiang, Yi-Zhe Song | This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a "human-centred" data form, represents a natural interface to study explainability. We focus on cultivating sketch-specific explainability designs. This starts by identifying strokes as a unique building block that offers a degree of flexibility in object construction and manipulation impossible in photos. Following this, we design a simple explainability-friendly sketch encoder that accommodates the intrinsic properties of strokes: shape, location, and order. We then move on to define the first ever XAI task for sketch, that of stroke location inversion SLI. Just as we have heat maps for photos, and correlation matrices for text, SLI offers an explainability angle to sketch in terms of asking a network how well it can recover stroke locations of an unseen sketch. We offer qualitative results for readers to interpret as snapshots of the SLI process in the paper, and as GIFs on the project page. A minor but interesting note is that thanks to its sketch-specific design, our sketch encoder also yields the best sketch recognition accuracy to date while having the smallest number of parameters. The code is available at https://sketchxai.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qu_SketchXAI_A_First_Look_at_Explainability_for_Human_Sketches_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qu_SketchXAI_A_First_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11744 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_SketchXAI_A_First_Look_at_Explainability_for_Human_Sketches_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_SketchXAI_A_First_Look_at_Explainability_for_Human_Sketches_CVPR_2023_paper.html | CVPR 2023 | null |
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild | Garrick Brazil, Abhinav Kumar, Julian Straub, Nikhila Ravi, Justin Johnson, Georgia Gkioxari | Recognizing scenes and objects in 3D from a single image is a longstanding goal of computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets and scalable solutions have led to unprecedented advances. In 3D, existing benchmarks are small in size and approaches specialize in few object categories and specific domains, e.g. urban driving scenes. Motivated by the success of 2D recognition, we revisit the task of 3D object detection by introducing a large benchmark, called Omni3D. Omni3D re-purposes and combines existing datasets resulting in 234k images annotated with more than 3 million instances and 98 categories. 3D detection at such scale is challenging due to variations in camera intrinsics and the rich diversity of scene and object types. We propose a model, called Cube R-CNN, designed to generalize across camera and scene types with a unified approach. We show that Cube R-CNN outperforms prior works on the larger Omni3D and existing benchmarks. Finally, we prove that Omni3D is a powerful dataset for 3D object recognition and show that it improves single-dataset performance and can accelerate learning on new smaller datasets via pre-training. | https://openaccess.thecvf.com/content/CVPR2023/papers/Brazil_Omni3D_A_Large_Benchmark_and_Model_for_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Brazil_Omni3D_A_Large_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.10660 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Brazil_Omni3D_A_Large_Benchmark_and_Model_for_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Brazil_Omni3D_A_Large_Benchmark_and_Model_for_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
OT-Filter: An Optimal Transport Filter for Learning With Noisy Labels | Chuanwen Feng, Yilong Ren, Xike Xie | The success of deep learning is largely attributed to the training over clean data. However, data is often coupled with noisy labels in practice. Learning with noisy labels is challenging because the performance of the deep neural networks (DNN) drastically degenerates, due to confirmation bias caused by the network memorization over noisy labels. To alleviate that, a recent prominent direction is on sample selection, which retrieves clean data samples from noisy samples, so as to enhance the model's robustness and tolerance to noisy labels. In this paper, we revamp the sample selection from the perspective of optimal transport theory and propose a novel method, called the OT-Filter. The OT-Filter provides geometrically meaningful distances and preserves distribution patterns to measure the data discrepancy, thus alleviating the confirmation bias. Extensive experiments on benchmarks, such as Clothing1M and ANIMAL-10N, show that the performance of the OT- Filter outperforms its counterparts. Meanwhile, results on benchmarks with synthetic labels, such as CIFAR-10/100, show the superiority of the OT-Filter in handling data labels of high noise. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_OT-Filter_An_Optimal_Transport_Filter_for_Learning_With_Noisy_Labels_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_OT-Filter_An_Optimal_Transport_Filter_for_Learning_With_Noisy_Labels_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_OT-Filter_An_Optimal_Transport_Filter_for_Learning_With_Noisy_Labels_CVPR_2023_paper.html | CVPR 2023 | null |
Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning | Sungmin Cha, Sungjun Cho, Dasol Hwang, Sunwon Hong, Moontae Lee, Taesup Moon | Batch Normalization (BN) and its variants has been extensively studied for neural nets in various computer vision tasks, but relatively little work has been dedicated to studying the effect of BN in continual learning. To that end, we develop a new update patch for BN, particularly tailored for the exemplar-based class-incremental learning (CIL). The main issue of BN in CIL is the imbalance of training data between current and past tasks in a mini-batch, which makes the empirical mean and variance as well as the learnable affine transformation parameters of BN heavily biased toward the current task --- contributing to the forgetting of past tasks. While one of the recent BN variants has been developed for "online" CIL, in which the training is done with a single epoch, we show that their method does not necessarily bring gains for "offline" CIL, in which a model is trained with multiple epochs on the imbalanced training data. The main reason for the ineffectiveness of their method lies in not fully addressing the data imbalance issue, especially in computing the gradients for learning the affine transformation parameters of BN. Accordingly, our new hyperparameter-free variant, dubbed as Task-Balanced BN (TBBN), is proposed to more correctly resolve the imbalance issue by making a horizontally-concatenated task-balanced batch using both reshape and repeat operations during training. Based on our experiments on class incremental learning of CIFAR-100, ImageNet-100, and five dissimilar task datasets, we demonstrate that our TBBN, which works exactly the same as the vanilla BN in the inference time, is easily applicable to most existing exemplar-based offline CIL algorithms and consistently outperforms other BN variants. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cha_Rebalancing_Batch_Normalization_for_Exemplar-Based_Class-Incremental_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cha_Rebalancing_Batch_Normalization_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2201.12559 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cha_Rebalancing_Batch_Normalization_for_Exemplar-Based_Class-Incremental_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cha_Rebalancing_Batch_Normalization_for_Exemplar-Based_Class-Incremental_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
OmniVidar: Omnidirectional Depth Estimation From Multi-Fisheye Images | Sheng Xie, Daochuan Wang, Yun-Hui Liu | Estimating depth from four large field of view (FoV) cameras has been a difficult and understudied problem. In this paper, we proposed a novel and simple system that can convert this difficult problem into easier binocular depth estimation. We name this system OmniVidar, as its results are similar to LiDAR, but rely only on vision. OmniVidar contains three components: (1) a new camera model to address the shortcomings of existing models, (2) a new multi-fisheye camera based epipolar rectification method for solving the image distortion and simplifying the depth estimation problem, (3) an improved binocular depth estimation network, which achieves a better balance between accuracy and efficiency. Unlike other omnidirectional stereo vision methods, OmniVidar does not contain any 3D convolution, so it can achieve higher resolution depth estimation at fast speed. Results demonstrate that OmniVidar outperforms all other methods in terms of accuracy and performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_OmniVidar_Omnidirectional_Depth_Estimation_From_Multi-Fisheye_Images_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_OmniVidar_Omnidirectional_Depth_Estimation_From_Multi-Fisheye_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_OmniVidar_Omnidirectional_Depth_Estimation_From_Multi-Fisheye_Images_CVPR_2023_paper.html | CVPR 2023 | null |
RWSC-Fusion: Region-Wise Style-Controlled Fusion Network for the Prohibited X-Ray Security Image Synthesis | Luwen Duan, Min Wu, Lijian Mao, Jun Yin, Jianping Xiong, Xi Li | Automatic prohibited item detection in security inspection X-ray images is necessary for transportation.The abundance and diversity of the X-ray security images with prohibited item, termed as prohibited X-ray security images, are essential for training the detection model. In order to solve the data insufficiency, we propose a RegionWise Style-Controlled Fusion (RWSC-Fusion) network, which superimposes the prohibited items onto the normal X-ray security images, to synthesize the prohibited X-ray security images. The proposed RWSC-Fusion innovates both network structure and loss functions to generate more realistic X-ray security images. Specifically, a RWSCFusion module is designed to enable the region-wise fusion by controlling the appearance of the overlapping region with novel modulation parameters. In addition, an EdgeAttention (EA) module is proposed to effectively improve the sharpness of the synthetic images. As for the unsupervised loss function, we propose the Luminance loss in Logarithmic form (LL) and Correlation loss of Saturation Difference (CSD), to optimize the fused X-ray security images in terms of luminance and saturation. We evaluate the authenticity and the training effect of the synthetic X-ray security images on private and public SIXray dataset. The results confirm that our synthetic images are reliable enough to augment the prohibited Xray security images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Duan_RWSC-Fusion_Region-Wise_Style-Controlled_Fusion_Network_for_the_Prohibited_X-Ray_Security_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Duan_RWSC-Fusion_Region-Wise_Style-Controlled_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Duan_RWSC-Fusion_Region-Wise_Style-Controlled_Fusion_Network_for_the_Prohibited_X-Ray_Security_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Duan_RWSC-Fusion_Region-Wise_Style-Controlled_Fusion_Network_for_the_Prohibited_X-Ray_Security_CVPR_2023_paper.html | CVPR 2023 | null |
Octree Guided Unoriented Surface Reconstruction | Chamin Hewa Koneputugodage, Yizhak Ben-Shabat, Stephen Gould | We address the problem of surface reconstruction from unoriented point clouds. Implicit neural representations (INRs) have become popular for this task, but when information relating to the inside versus outside of a shape is not available (such as shape occupancy, signed distances or surface normal orientation) optimization relies on heuristics and regularizers to recover the surface. These methods can be slow to converge and easily get stuck in local minima. We propose a two-step approach, OG-INR, where we (1) construct a discrete octree and label what is inside and outside (2) optimize for a continuous and high-fidelity shape using an INR that is initially guided by the octree's labelling. To solve for our labelling, we propose an energy function over the discrete structure and provide an efficient move-making algorithm that explores many possible labellings. Furthermore we show that we can easily inject knowledge into the discrete octree, providing a simple way to influence the result from the continuous INR. We evaluate the effectiveness of our approach on two unoriented surface reconstruction datasets and show competitive performance compared to other unoriented, and some oriented, methods. Our results show that the exploration by the move-making algorithm avoids many of the bad local minima reached by purely gradient descent optimized methods (see Figure 1). | https://openaccess.thecvf.com/content/CVPR2023/papers/Koneputugodage_Octree_Guided_Unoriented_Surface_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Koneputugodage_Octree_Guided_Unoriented_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Koneputugodage_Octree_Guided_Unoriented_Surface_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Koneputugodage_Octree_Guided_Unoriented_Surface_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Rigidity-Aware Detection for 6D Object Pose Estimation | Yang Hai, Rui Song, Jiaojiao Li, Mathieu Salzmann, Yinlin Hu | Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor initialization to the subsequent pose network. To address this, we propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid. This lets us introduce an approach to sampling positive object regions from the entire visible object area during training, instead of naively drawing samples from the bounding box center where the object might be occluded. As such, every visible object part can contribute to the final bounding box prediction, yielding better detection robustness. Key to the success of our approach is a visibility map, which we propose to build using a minimum barrier distance between every pixel in the bounding box and the box boundary. Our results on seven challenging 6D pose estimation datasets evidence that our method outperforms general detection frameworks by a large margin. Furthermore, combined with a pose regression network, we obtain state-of-the-art pose estimation results on the challenging BOP benchmark. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hai_Rigidity-Aware_Detection_for_6D_Object_Pose_Estimation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.12396 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hai_Rigidity-Aware_Detection_for_6D_Object_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hai_Rigidity-Aware_Detection_for_6D_Object_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
ToThePoint: Efficient Contrastive Learning of 3D Point Clouds via Recycling | Xinglin Li, Jiajing Chen, Jinhui Ouyang, Hanhui Deng, Senem Velipasalar, Di Wu | Recent years have witnessed significant developments in point cloud processing, including classification and segmentation. However, supervised learning approaches need a lot of well-labeled data for training, and annotation is labor- and time-intensive. Self-supervised learning, on the other hand, uses unlabeled data, and pre-trains a backbone with a pretext task to extract latent representations to be used with the downstream tasks. Compared to 2D images, self-supervised learning of 3D point clouds is under-explored. Existing models, for self-supervised learning of 3D point clouds, rely on a large number of data samples, and require significant amount of computational resources and training time. To address this issue, we propose a novel contrastive learning approach, referred to as ToThePoint. Different from traditional contrastive learning methods, which maximize agreement between features obtained from a pair of point clouds formed only with different types of augmentation, ToThePoint also maximizes the agreement between the permutation invariant features and features discarded after max pooling. We first perform self-supervised learning on the ShapeNet dataset, and then evaluate the performance of the network on different downstream tasks. In the downstream task experiments, performed on the ModelNet40, ModelNet40C, ScanobjectNN and ShapeNet-Part datasets, our proposed ToThePoint achieves competitive, if not better results compared to the state-of-the-art baselines, and does so with significantly less training time (200 times faster than baselines) | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_ToThePoint_Efficient_Contrastive_Learning_of_3D_Point_Clouds_via_Recycling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_ToThePoint_Efficient_Contrastive_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ToThePoint_Efficient_Contrastive_Learning_of_3D_Point_Clouds_via_Recycling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ToThePoint_Efficient_Contrastive_Learning_of_3D_Point_Clouds_via_Recycling_CVPR_2023_paper.html | CVPR 2023 | null |
Clover: Towards a Unified Video-Language Alignment and Fusion Model | Jingjia Huang, Yinan Li, Jiashi Feng, Xinglong Wu, Xiaoshuai Sun, Rongrong Ji | Building a universal video-language model for solving various video understanding tasks (e.g., text-video retrieval, video question answering) is an open challenge to the machine learning field. Towards this goal, most recent works build the model by stacking uni-modal and cross-modal feature encoders and train it with pair-wise contrastive pre-text tasks. Though offering attractive generality, the resulted models have to compromise between efficiency and performance. They mostly adopt different architectures to deal with different downstream tasks. We find this is because the pair-wise training cannot well align and fuse features from different modalities. We then introduce Clover--a Correlated Video-Language pre-training method--towards a universal video-language model for solving multiple video understanding tasks with neither performance nor efficiency compromise. It improves cross-modal feature alignment and fusion via a novel tri-modal alignment pre-training task. Additionally, we propose to enhance the tri-modal alignment via incorporating learning from semantic masked samples and a new pair-wise ranking loss. Clover establishes new state-of-the-arts on multiple downstream tasks, including three retrieval tasks for both zero-shot and fine-tuning settings, and eight video question answering tasks. Codes and pre-trained models will be released at https://github.com/LeeYN-43/Clover. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Clover_Towards_a_Unified_Video-Language_Alignment_and_Fusion_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Clover_Towards_a_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.07885 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Clover_Towards_a_Unified_Video-Language_Alignment_and_Fusion_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Clover_Towards_a_Unified_Video-Language_Alignment_and_Fusion_Model_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Monocular 3D Object Detection Using Multi-View Projection and Direction Consistency | Runzhou Tao, Wencheng Han, Zhongying Qiu, Cheng-Zhong Xu, Jianbing Shen | Monocular 3D object detection has become a mainstream approach in automatic driving for its easy application. A prominent advantage is that it does not need LiDAR point clouds during the inference. However, most current methods still rely on 3D point cloud data for labeling the ground truths used in the training phase. This inconsistency between the training and inference makes it hard to utilize the large-scale feedback data and increases the data collection expenses. To bridge this gap, we propose a new weakly supervised monocular 3D objection detection method, which can train the model with only 2D labels marked on images. To be specific, we explore three types of consistency in this task, i.e. the projection, multi-view and direction consistency, and design a weakly-supervised architecture based on these consistencies. Moreover, we propose a new 2D direction labeling method in this task to guide the model for accurate rotation direction prediction. Experiments show that our weakly-supervised method achieves comparable performance with some fully supervised methods. When used as a pre-training method, our model can significantly outperform the corresponding fully-supervised baseline with only 1/3 3D labels. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tao_Weakly_Supervised_Monocular_3D_Object_Detection_Using_Multi-View_Projection_and_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08686 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tao_Weakly_Supervised_Monocular_3D_Object_Detection_Using_Multi-View_Projection_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tao_Weakly_Supervised_Monocular_3D_Object_Detection_Using_Multi-View_Projection_and_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Learning From Images With a Joint-Embedding Predictive Architecture | Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas | This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction. | https://openaccess.thecvf.com/content/CVPR2023/papers/Assran_Self-Supervised_Learning_From_Images_With_a_Joint-Embedding_Predictive_Architecture_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Assran_Self-Supervised_Learning_From_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.08243 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Assran_Self-Supervised_Learning_From_Images_With_a_Joint-Embedding_Predictive_Architecture_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Assran_Self-Supervised_Learning_From_Images_With_a_Joint-Embedding_Predictive_Architecture_CVPR_2023_paper.html | CVPR 2023 | null |
EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding | Yanmin Wu, Xinhua Cheng, Renrui Zhang, Zesen Cheng, Jian Zhang | 3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues. However, existing methods either extract the sentence-level features coupling all words or focus more on object names, which would lose the word-level information or neglect other attributes. To alleviate these issues, we present EDA that Explicitly Decouples the textual attributes in a sentence and conducts Dense Alignment between such fine-grained language and point cloud objects. Specifically, we first propose a text decoupling module to produce textual features for every semantic component. Then, we design two losses to supervise the dense matching between two modalities: position alignment loss and semantic alignment loss. On top of that, we further introduce a new visual grounding task, locating objects without object names, which can thoroughly evaluate the model's dense alignment capacity. Through experiments, we achieve state-of-the-art performance on two widely-adopted 3D visual grounding datasets, ScanRefer and SR3D/NR3D, and obtain absolute leadership on our newly-proposed task. The source code is available at https://github.com/yanmin-wu/EDA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_EDA_Explicit_Text-Decoupling_and_Dense_Alignment_for_3D_Visual_Grounding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_EDA_Explicit_Text-Decoupling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.14941 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_EDA_Explicit_Text-Decoupling_and_Dense_Alignment_for_3D_Visual_Grounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_EDA_Explicit_Text-Decoupling_and_Dense_Alignment_for_3D_Visual_Grounding_CVPR_2023_paper.html | CVPR 2023 | null |
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