Search is not available for this dataset
title
string | authors
string | abstract
string | pdf
string | supp
string | arXiv
string | bibtex
string | url
string | detail_url
string | tags
string | string |
|---|---|---|---|---|---|---|---|---|---|---|
Adversarial Robustness via Random Projection Filters | Minjing Dong, Chang Xu | Deep Neural Networks show superior performance in various tasks but are vulnerable to adversarial attacks. Most defense techniques are devoted to the adversarial training strategies, however, it is difficult to achieve satisfactory robust performance only with traditional adversarial training. We mainly attribute it to that aggressive perturbations which lead to the loss increment can always be found via gradient ascent in white-box setting. Although some noises can be involved to prevent attacks from deriving precise gradients on inputs, there exist trade-offs between the defense capability and natural generalization. Taking advantage of the properties of random projection, we propose to replace part of convolutional filters with random projection filters, and theoretically explore the geometric representation preservation of proposed synthesized filters via Johnson-Lindenstrauss lemma. We conduct sufficient evaluation on multiple networks and datasets. The experimental results showcase the superiority of proposed random projection filters to state-of-the-art baselines. The code is available on https://github.com/UniSerj/Random-Projection-Filters. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_Adversarial_Robustness_via_Random_Projection_Filters_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_Adversarial_Robustness_via_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Adversarial_Robustness_via_Random_Projection_Filters_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Adversarial_Robustness_via_Random_Projection_Filters_CVPR_2023_paper.html | CVPR 2023 | null |
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution | Jaeill Kim, Suhyun Kang, Duhun Hwang, Jungwook Shin, Wonjong Rhee | Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of representation. However, manipulating such properties can be challenging in terms of implementational effectiveness and general applicability. To address these limitations, we propose to regularize von Neumann entropy (VNE) of representation. First, we demonstrate that the mathematical formulation of VNE is superior in effectively manipulating the eigenvalues of the representation autocorrelation matrix. Then, we demonstrate that it is widely applicable in improving state-of-the-art algorithms or popular benchmark algorithms by investigating domain-generalization, meta-learning, self-supervised learning, and generative models. In addition, we formally establish theoretical connections with rank, disentanglement, and isotropy of representation. Finally, we provide discussions on the dimension control of VNE and the relationship with Shannon entropy. Code is available at: https://github.com/jaeill/CVPR23-VNE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_VNE_An_Effective_Method_for_Improving_Deep_Representation_by_Manipulating_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_VNE_An_Effective_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01434 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_VNE_An_Effective_Method_for_Improving_Deep_Representation_by_Manipulating_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_VNE_An_Effective_Method_for_Improving_Deep_Representation_by_Manipulating_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Guided Diffusion Models | Vincent Tao Hu, David W. Zhang, Yuki M. Asano, Gertjan J. Burghouts, Cees G. M. Snoek | Diffusion models have demonstrated remarkable progress in image generation quality, especially when guidance is used to control the generative process. However, guidance requires a large amount of image-annotation pairs for training and is thus dependent on their availability and correctness. In this paper, we eliminate the need for such annotation by instead exploiting the flexibility of self-supervision signals to design a framework for self-guided diffusion models. By leveraging a feature extraction function and a self-annotation function, our method provides guidance signals at various image granularities: from the level of holistic images to object boxes and even segmentation masks. Our experiments on single-label and multi-label image datasets demonstrate that self-labeled guidance always outperforms diffusion models without guidance and may even surpass guidance based on ground-truth labels. When equipped with self-supervised box or mask proposals, our method further generates visually diverse yet semantically consistent images, without the need for any class, box, or segment label annotation. Self-guided diffusion is simple, flexible and expected to profit from deployment at scale. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Self-Guided_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Self-Guided_Diffusion_Models_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.06462 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Self-Guided_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Self-Guided_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation | Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Chen Cao, Jason Saragih, Michael Zollhöfer, Jessica Hodgins, Christoph Lassner | The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality. Both problems are highly challenging, because hair has complex geometry and appearance, as well as exhibits challenging motion. In this paper, we present a two-stage approach that models hair independently from the head to address these challenges in a data-driven manner. The first stage, state compression, learns a low-dimensional latent space of 3D hair states containing motion and appearance, via a novel autoencoder-as-a-tracker strategy. To better disentangle the hair and head in appearance learning, we employ multi-view hair segmentation masks in combination with a differentiable volumetric renderer. The second stage learns a novel hair dynamics model that performs temporal hair transfer based on the discovered latent codes. To enforce higher stability while driving our dynamics model, we employ the 3D point-cloud autoencoder from the compression stage for de-noising of the hair state. Our model outperforms the state of the art in novel view synthesis and is capable of creating novel hair animations without having to rely on hair observations as a driving signal | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_NeuWigs_A_Neural_Dynamic_Model_for_Volumetric_Hair_Capture_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_NeuWigs_A_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00613 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_NeuWigs_A_Neural_Dynamic_Model_for_Volumetric_Hair_Capture_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_NeuWigs_A_Neural_Dynamic_Model_for_Volumetric_Hair_Capture_and_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP2: Contrastive Language-Image-Point Pretraining From Real-World Point Cloud Data | Yihan Zeng, Chenhan Jiang, Jiageng Mao, Jianhua Han, Chaoqiang Ye, Qingqiu Huang, Dit-Yan Yeung, Zhen Yang, Xiaodan Liang, Hang Xu | Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks. However, due to the limited Text-3D data pairs, adapting the success of 2D Vision-Language Models (VLM) to the 3D space remains an open problem. Existing works that leverage VLM for 3D understanding generally resort to constructing intermediate 2D representations for the 3D data, but at the cost of losing 3D geometry information. To take a step toward open-world 3D vision understanding, we propose Contrastive Language-Image-Point Cloud Pretraining (CLIP^2) to directly learn the transferable 3D point cloud representation in realistic scenarios with a novel proxy alignment mechanism. Specifically, we exploit naturally-existed correspondences in 2D and 3D scenarios, and build well-aligned and instance-based text-image-point proxies from those complex scenarios. On top of that, we propose a cross-modal contrastive objective to learn semantic and instance-level aligned point cloud representation. Experimental results on both indoor and outdoor scenarios show that our learned 3D representation has great transfer ability in downstream tasks, including zero-shot and few-shot 3D recognition, which boosts the state-of-the-art methods by large margins. Furthermore, we provide analyses of the capability of different representations in real scenarios and present the optional ensemble scheme. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_CLIP2_Contrastive_Language-Image-Point_Pretraining_From_Real-World_Point_Cloud_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zeng_CLIP2_Contrastive_Language-Image-Point_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_CLIP2_Contrastive_Language-Image-Point_Pretraining_From_Real-World_Point_Cloud_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_CLIP2_Contrastive_Language-Image-Point_Pretraining_From_Real-World_Point_Cloud_Data_CVPR_2023_paper.html | CVPR 2023 | null |
HNeRV: A Hybrid Neural Representation for Videos | Hao Chen, Matthew Gwilliam, Ser-Nam Lim, Abhinav Shrivastava | Implicit neural representations store videos as neural networks and have performed well for vision tasks such as video compression and denoising. With frame index and/or positional index as input, implicit representations (NeRV, E-NeRV, etc.) reconstruct video frames from fixed and content-agnostic embeddings. Such embedding largely limits the regression capacity and internal generalization for video interpolation. In this paper, we propose a Hybrid Neural Representation for Videos (HNeRV), where learnable and content-adaptive embeddings act as decoder input. Besides the input embedding, we introduce a HNeRV block to make model parameters evenly distributed across the entire network, therefore higher layers (layers near the output) can have more capacity to store high-resolution content and video details. With content-adaptive embedding and re-designed model architecture, HNeRV outperforms implicit methods (NeRV, E-NeRV) in video regression task for both reconstruction quality and convergence speed, and shows better internal generalization. As a simple and efficient video representation, HNeRV also shows decoding advantages for speed, flexibility, and deployment, compared to traditional codecs (H.264, H.265) and learning-based compression methods. Finally, we explore the effectiveness of HNeRV on downstream tasks such as video compression and video inpainting. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_HNeRV_A_Hybrid_Neural_Representation_for_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_HNeRV_A_Hybrid_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02633 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_HNeRV_A_Hybrid_Neural_Representation_for_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_HNeRV_A_Hybrid_Neural_Representation_for_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Model-Agnostic Gender Debiased Image Captioning | Yusuke Hirota, Yuta Nakashima, Noa Garcia | Image captioning models are known to perpetuate and amplify harmful societal bias in the training set. In this work, we aim to mitigate such gender bias in image captioning models. While prior work has addressed this problem by forcing models to focus on people to reduce gender misclassification, it conversely generates gender-stereotypical words at the expense of predicting the correct gender. From this observation, we hypothesize that there are two types of gender bias affecting image captioning models: 1) bias that exploits context to predict gender, and 2) bias in the probability of generating certain (often stereotypical) words because of gender. To mitigate both types of gender biases, we propose a framework, called LIBRA, that learns from synthetically biased samples to decrease both types of biases, correcting gender misclassification and changing gender-stereotypical words to more neutral ones. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hirota_Model-Agnostic_Gender_Debiased_Image_Captioning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hirota_Model-Agnostic_Gender_Debiased_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03693 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hirota_Model-Agnostic_Gender_Debiased_Image_Captioning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hirota_Model-Agnostic_Gender_Debiased_Image_Captioning_CVPR_2023_paper.html | CVPR 2023 | null |
Local Implicit Ray Function for Generalizable Radiance Field Representation | Xin Huang, Qi Zhang, Ying Feng, Xiaoyu Li, Xuan Wang, Qing Wang | We propose LIRF (Local Implicit Ray Function), a generalizable neural rendering approach for novel view rendering. Current generalizable neural radiance fields (NeRF) methods sample a scene with a single ray per pixel and may therefore render blurred or aliased views when the input views and rendered views observe scene content at different resolutions. To solve this problem, we propose LIRF to aggregate the information from conical frustums to construct a ray. Given 3D positions within conical frustums, LIRF takes 3D coordinates and the features of conical frustums as inputs and predicts a local volumetric radiance field. Since the coordinates are continuous, LIRF renders high-quality novel views at a continuously-valued scale via volume rendering. Besides, we predict the visible weights for each input view via transformer-based feature matching to improve the performance in occluded areas. Experimental results on real-world scenes validate that our method outperforms state-of-the-art methods on novel view rendering of unseen scenes at arbitrary scales. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Local_Implicit_Ray_Function_for_Generalizable_Radiance_Field_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Local_Implicit_Ray_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12746 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Local_Implicit_Ray_Function_for_Generalizable_Radiance_Field_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Local_Implicit_Ray_Function_for_Generalizable_Radiance_Field_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
One-Shot High-Fidelity Talking-Head Synthesis With Deformable Neural Radiance Field | Weichuang Li, Longhao Zhang, Dong Wang, Bin Zhao, Zhigang Wang, Mulin Chen, Bang Zhang, Zhongjian Wang, Liefeng Bo, Xuelong Li | Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image. Most pioneering methods rely primarily on 2D representations and thus will inevitably suffer from face distortion when large head rotations are encountered. Recent works instead employ explicit 3D structural representations or implicit neural rendering to improve performance under large pose changes. Nevertheless, the fidelity of identity and expression is not so desirable, especially for novel-view synthesis. In this paper, we propose HiDe-NeRF, which achieves high-fidelity and free-view talking-head synthesis. Drawing on the recently proposed Deformable Neural Radiance Fields, HiDe-NeRF represents the 3D dynamic scene into a canonical appearance field and an implicit deformation field, where the former comprises the canonical source face and the latter models the driving pose and expression. In particular, we improve fidelity from two aspects: (i) to enhance identity expressiveness, we design a generalized appearance module that leverages multi-scale volume features to preserve face shape and details; (ii) to improve expression preciseness, we propose a lightweight deformation module that explicitly decouples the pose and expression to enable precise expression modeling. Extensive experiments demonstrate that our proposed approach can generate better results than previous works. Project page: https://www.waytron.net/hidenerf/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_One-Shot_High-Fidelity_Talking-Head_Synthesis_With_Deformable_Neural_Radiance_Field_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.05097 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_One-Shot_High-Fidelity_Talking-Head_Synthesis_With_Deformable_Neural_Radiance_Field_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_One-Shot_High-Fidelity_Talking-Head_Synthesis_With_Deformable_Neural_Radiance_Field_CVPR_2023_paper.html | CVPR 2023 | null |
FitMe: Deep Photorealistic 3D Morphable Model Avatars | Alexandros Lattas, Stylianos Moschoglou, Stylianos Ploumpis, Baris Gecer, Jiankang Deng, Stefanos Zafeiriou | In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images. The model consists of a multi-modal style-based generator, that captures facial appearance in terms of diffuse and specular reflectance, and a PCA-based shape model. We employ a fast differentiable rendering process that can be used in an optimization pipeline, while also achieving photorealistic facial shading. Our optimization process accurately captures both the facial reflectance and shape in high-detail, by exploiting the expressivity of the style-based latent representation and of our shape model. FitMe achieves state-of-the-art reflectance acquisition and identity preservation on single "in-the-wild" facial images, while it produces impressive scan-like results, when given multiple unconstrained facial images pertaining to the same identity. In contrast with recent implicit avatar reconstructions, FitMe requires only one minute and produces relightable mesh and texture-based avatars, that can be used by end-user applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lattas_FitMe_Deep_Photorealistic_3D_Morphable_Model_Avatars_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lattas_FitMe_Deep_Photorealistic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lattas_FitMe_Deep_Photorealistic_3D_Morphable_Model_Avatars_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lattas_FitMe_Deep_Photorealistic_3D_Morphable_Model_Avatars_CVPR_2023_paper.html | CVPR 2023 | null |
Dense Distinct Query for End-to-End Object Detection | Shilong Zhang, Xinjiang Wang, Jiaqi Wang, Jiangmiao Pang, Chengqi Lyu, Wenwei Zhang, Ping Luo, Kai Chen | One-to-one label assignment in object detection has successfully obviated the need of non-maximum suppression (NMS) as a postprocessing and makes the pipeline end-to-end. However, it triggers a new dilemma as the widely used sparse queries cannot guarantee a high recall, while dense queries inevitably bring more similar queries and encounters optimization difficulty. As both sparse and dense queries are problematic, then what are the expected queries in end-to-end object detection? This paper shows that the solution should be Dense Distinct Queries (DDQ). Concretely, we first lay dense queries like traditional detectors and then select distinct ones for one-to-one assignments. DDQ blends the advantages of traditional and recent end-to-end detectors and significantly improves the performance of various detectors including FCN, R-CNN, and DETRs. Most impressively, DDQ-DETR achieves 52.1 AP on MS-COCO dataset within 12 epochs using a ResNet-50 backbone, outperforming all existing detectors in the same setting. DDQ also shares the benefit of end-to-end detectors in crowded scenes and achieves 93.8 AP on CrowdHuman. We hope DDQ can inspire researchers to consider the complementarity between traditional methods and end-to-end detectors. The source code can be found at https://github.com/jshilong/DDQ. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Dense_Distinct_Query_for_End-to-End_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Dense_Distinct_Query_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12776 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Dense_Distinct_Query_for_End-to-End_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Dense_Distinct_Query_for_End-to-End_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
CLIPPO: Image-and-Language Understanding From Pixels Only | Michael Tschannen, Basil Mustafa, Neil Houlsby | Multimodal models are becoming increasingly effective, in part due to unified components, such as the Transformer architecture. However, multimodal models still often consist of many task- and modality-specific pieces and training procedures. For example, CLIP (Radford et al., 2021) trains independent text and image towers via a contrastive loss. We explore an additional unification: the use of a pure pixel-based model to perform image, text, and multimodal tasks. Our model is trained with contrastive loss alone, so we call it CLIP-Pixels Only (CLIPPO). CLIPPO uses a single encoder that processes both regular images and text rendered as images. CLIPPO performs image-based tasks such as retrieval and zero-shot image classification almost as well as CLIP-style models, with half the number of parameters and no text-specific tower or embedding. When trained jointly via image-text contrastive learning and next-sentence contrastive learning, CLIPPO can perform well on natural language understanding tasks, without any word-level loss (language modelling or masked language modelling), outperforming pixel-based prior work. Surprisingly, CLIPPO can obtain good accuracy in visual question answering, simply by rendering the question and image together. Finally, we exploit the fact that CLIPPO does not require a tokenizer to show that it can achieve strong performance on multilingual multimodal retrieval without modifications. Code and pretrained models are available at https://github.com/google-research/big_vision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tschannen_CLIPPO_Image-and-Language_Understanding_From_Pixels_Only_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tschannen_CLIPPO_Image-and-Language_Understanding_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08045 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tschannen_CLIPPO_Image-and-Language_Understanding_From_Pixels_Only_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tschannen_CLIPPO_Image-and-Language_Understanding_From_Pixels_Only_CVPR_2023_paper.html | CVPR 2023 | null |
Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting | Xiaogang Peng, Siyuan Mao, Zizhao Wu | Multi-person pose forecasting remains a challenging problem, especially in modeling fine-grained human body interaction in complex crowd scenarios. Existing methods typically represent the whole pose sequence as a temporal series, yet overlook interactive influences among people based on skeletal body parts. In this paper, we propose a novel Trajectory-Aware Body Interaction Transformer (TBIFormer) for multi-person pose forecasting via effectively modeling body part interactions. Specifically, we construct a Temporal Body Partition Module that transforms all the pose sequences into a Multi-Person Body-Part sequence to retain spatial and temporal information based on body semantics. Then, we devise a Social Body Interaction Self-Attention (SBI-MSA) module, utilizing the transformed sequence to learn body part dynamics for inter- and intra-individual interactions. Furthermore, different from prior Euclidean distance-based spatial encodings, we present a novel and efficient Trajectory-Aware Relative Position Encoding for SBI-MSA to offer discriminative spatial information and additional interactive clues. On both short- and long-term horizons, we empirically evaluate our framework on CMU-Mocap, MuPoTS-3D as well as synthesized datasets (6 10 persons), and demonstrate that our method greatly outperforms the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Peng_Trajectory-Aware_Body_Interaction_Transformer_for_Multi-Person_Pose_Forecasting_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.05095 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Trajectory-Aware_Body_Interaction_Transformer_for_Multi-Person_Pose_Forecasting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Trajectory-Aware_Body_Interaction_Transformer_for_Multi-Person_Pose_Forecasting_CVPR_2023_paper.html | CVPR 2023 | null |
Conditional Image-to-Video Generation With Latent Flow Diffusion Models | Haomiao Ni, Changhao Shi, Kai Li, Sharon X. Huang, Martin Renqiang Min | Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label like smile). The key challenge of the cI2V task lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given image and condition. In this paper, we propose an approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image. Compared to previous direct-synthesis-based works, our proposed LFDM can better synthesize spatial details and temporal motion by fully utilizing the spatial content of the given image and warping it in the latent space according to the generated temporally-coherent flow. The training of LFDM consists of two separate stages: (1) an unsupervised learning stage to train a latent flow auto-encoder for spatial content generation, including a flow predictor to estimate latent flow between pairs of video frames, and (2) a conditional learning stage to train a 3D-UNet-based diffusion model (DM) for temporal latent flow generation. Unlike previous DMs operating in pixel space or latent feature space that couples spatial and temporal information, the DM in our LFDM only needs to learn a low-dimensional latent flow space for motion generation, thus being more computationally efficient. We conduct comprehensive experiments on multiple datasets, where LFDM consistently outperforms prior arts. Furthermore, we show that LFDM can be easily adapted to new domains by simply finetuning the image decoder. Our code is available at https://github.com/nihaomiao/CVPR23_LFDM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ni_Conditional_Image-to-Video_Generation_With_Latent_Flow_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ni_Conditional_Image-to-Video_Generation_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.13744 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ni_Conditional_Image-to-Video_Generation_With_Latent_Flow_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ni_Conditional_Image-to-Video_Generation_With_Latent_Flow_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Virtual Sparse Convolution for Multimodal 3D Object Detection | Hai Wu, Chenglu Wen, Shaoshuai Shi, Xin Li, Cheng Wang | Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points generated from an image are very dense, introducing a huge amount of redundant computation during detection. Meanwhile, noises brought by inaccurate depth completion significantly degrade detection precision. This paper proposes a fast yet effective backbone, termed VirConvNet, based on a new operator VirConv (Virtual Sparse Convolution), for virtual-point-based 3D object detection. The VirConv consists of two key designs: (1) StVD (Stochastic Voxel Discard) and (2) NRConv (Noise-Resistant Submanifold Convolution). The StVD alleviates the computation problem by discarding large amounts of nearby redundant voxels. The NRConv tackles the noise problem by encoding voxel features in both 2D image and 3D LiDAR space. By integrating our VirConv, we first develop an efficient pipeline VirConv-L based on an early fusion design. Then, we build a high-precision pipeline VirConv-T based on a transformed refinement scheme. Finally, we develop a semi-supervised pipeline VirConv-S based on a pseudo-label framework. On the KITTI car 3D detection test leaderboard, our VirConv-L achieves 85% AP with a fast running speed of 56ms. Our VirConv-T and VirConv-S attains a high-precision of 86.3% and 87.2% AP, and currently rank 2nd and 1st, respectively. The code is available at https://github.com/hailanyi/VirConv. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Virtual_Sparse_Convolution_for_Multimodal_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Virtual_Sparse_Convolution_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02314 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Virtual_Sparse_Convolution_for_Multimodal_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Virtual_Sparse_Convolution_for_Multimodal_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
DETR With Additional Global Aggregation for Cross-Domain Weakly Supervised Object Detection | Zongheng Tang, Yifan Sun, Si Liu, Yi Yang | This paper presents a DETR-based method for cross-domain weakly supervised object detection (CDWSOD), aiming at adapting the detector from source to target domain through weak supervision. We think DETR has strong potential for CDWSOD due to an insight: the encoder and the decoder in DETR are both based on the attention mechanism and are thus capable of aggregating semantics across the entire image. The aggregation results, i.e., image-level predictions, can naturally exploit the weak supervision for domain alignment. Such motivated, we propose DETR with additional Global Aggregation (DETR-GA), a CDWSOD detector that simultaneously makes "instance-level + image-level" predictions and utilizes "strong + weak" supervisions. The key point of DETR-GA is very simple: for the encoder / decoder, we respectively add multiple class queries / a foreground query to aggregate the semantics into image-level predictions. Our query-based aggregation has two advantages. First, in the encoder, the weakly-supervised class queries are capable of roughly locating the corresponding positions and excluding the distraction from non-relevant regions. Second, through our design, the object queries and the foreground query in the decoder share consensus on the class semantics, therefore making the strong and weak supervision mutually benefit each other for domain alignment. Extensive experiments on four popular cross-domain benchmarks show that DETR-GA significantly improves CSWSOD and advances the states of the art (e.g., 29.0% --> 79.4% mAP on PASCAL VOC --> Clipart_all dataset). | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_DETR_With_Additional_Global_Aggregation_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.07082 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_DETR_With_Additional_Global_Aggregation_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_DETR_With_Additional_Global_Aggregation_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2023_paper.html | CVPR 2023 | null |
Divide and Adapt: Active Domain Adaptation via Customized Learning | Duojun Huang, Jichang Li, Weikai Chen, Junshi Huang, Zhenhua Chai, Guanbin Li | Active domain adaptation (ADA) aims to improve the model adaptation performance by incorporating the active learning (AL) techniques to label a maximally-informative subset of target samples. Conventional AL methods do not consider the existence of domain shift, and hence, fail to identify the truly valuable samples in the context of domain adaptation. To accommodate active learning and domain adaption, the two naturally different tasks, in a collaborative framework, we advocate that a customized learning strategy for the target data is the key to the success of ADA solutions. We present Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target instances into four categories with stratified transferable properties. With a novel data subdivision protocol based on uncertainty and domainness, DiaNA can accurately recognize the most gainful samples. While sending the informative instances for annotation, DiaNA employs tailored learning strategies for the remaining categories. Furthermore, we propose an informativeness score that unifies the data partitioning criteria. This enables the use of a Gaussian mixture model (GMM) to automatically sample unlabeled data into the proposed four categories. Thanks to the "divide-and-adapt" spirit, DiaNA can handle data with large variations of domain gap. In addition, we show that DiaNA can generalize to different domain adaptation settings, such as unsupervised domain adaptation (UDA), semi-supervised domain adaptation (SSDA), source-free domain adaptation (SFDA), etc. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Divide_and_Adapt_Active_Domain_Adaptation_via_Customized_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Divide_and_Adapt_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Divide_and_Adapt_Active_Domain_Adaptation_via_Customized_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Divide_and_Adapt_Active_Domain_Adaptation_via_Customized_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Universal Fake Image Detectors That Generalize Across Generative Models | Utkarsh Ojha, Yuheng Li, Yong Jae Lee | With generative models proliferating at a rapid rate, there is a growing need for general purpose fake image detectors. In this work, we first show that the existing paradigm, which consists of training a deep network for real-vs-fake classification, fails to detect fake images from newer breeds of generative models when trained to detect GAN fake images. Upon analysis, we find that the resulting classifier is asymmetrically tuned to detect patterns that make an image fake. The real class becomes a 'sink' class holding anything that is not fake, including generated images from models not accessible during training. Building upon this discovery, we propose to perform real-vs-fake classification without learning; i.e., using a feature space not explicitly trained to distinguish real from fake images. We use nearest neighbor and linear probing as instantiations of this idea. When given access to the feature space of a large pretrained vision-language model, the very simple baseline of nearest neighbor classification has surprisingly good generalization ability in detecting fake images from a wide variety of generative models; e.g., it improves upon the SoTA by +15.07 mAP and +25.90% acc when tested on unseen diffusion and autoregressive models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ojha_Towards_Universal_Fake_Image_Detectors_That_Generalize_Across_Generative_Models_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2302.10174 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ojha_Towards_Universal_Fake_Image_Detectors_That_Generalize_Across_Generative_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ojha_Towards_Universal_Fake_Image_Detectors_That_Generalize_Across_Generative_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Bridging the Performance Gaps of Joint Energy-Based Models | Xiulong Yang, Qing Su, Shihao Ji | Can we train a hybrid discriminative-generative model with a single network? This question has recently been answered in the affirmative, introducing the field of Joint Energy-based Model (JEM), which achieves high classification accuracy and image generation quality simultaneously. Despite recent advances, there remain two performance gaps: the accuracy gap to the standard softmax classifier, and the generation quality gap to state-of-the-art generative models. In this paper, we introduce a variety of training techniques to bridge the accuracy gap and the generation quality gap of JEM. 1) We incorporate a recently proposed sharpness-aware minimization (SAM) framework to train JEM, which promotes the energy landscape smoothness and the generalization of JEM. 2) We exclude data augmentation from the maximum likelihood estimate pipeline of JEM, and mitigate the negative impact of data augmentation to image generation quality. Extensive experiments on multiple datasets demonstrate our SADA-JEM achieves state-of-the-art performances and outperforms JEM in image classification, image generation, calibration, out-of-distribution detection and adversarial robustness by a notable margin. Our code is available at https://github.com/sndnyang/SADAJEM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Towards_Bridging_the_Performance_Gaps_of_Joint_Energy-Based_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Towards_Bridging_the_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.07959 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Towards_Bridging_the_Performance_Gaps_of_Joint_Energy-Based_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Towards_Bridging_the_Performance_Gaps_of_Joint_Energy-Based_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution | Yunfan Lu, Zipeng Wang, Minjie Liu, Hongjian Wang, Lin Wang | Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video super-resolution (VSR) task. In this paper, we make the first at tempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpass the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https://. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Learning_Spatial-Temporal_Implicit_Neural_Representations_for_Event-Guided_Video_Super-Resolution_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.13767 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Learning_Spatial-Temporal_Implicit_Neural_Representations_for_Event-Guided_Video_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Learning_Spatial-Temporal_Implicit_Neural_Representations_for_Event-Guided_Video_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation | Xu Zheng, Jinjing Zhu, Yexin Liu, Zidong Cao, Chong Fu, Lin Wang | The ability of scene understanding has sparked active research for panoramic image semantic segmentation. However, the performance is hampered by distortion of the equirectangular projection (ERP) and a lack of pixel-wise annotations. For this reason, some works treat the ERP and pinhole images equally and transfer knowledge from the pinhole to ERP images via unsupervised domain adaptation (UDA). However, they fail to handle the domain gaps caused by: 1) the inherent differences between camera sensors and captured scenes; 2) the distinct image formats (e.g., ERP and pinhole images). In this paper, we propose a novel yet flexible dual-path UDA framework, DPPASS, taking ERP and tangent projection (TP) images as inputs. To reduce the domain gaps, we propose cross-projection and intra-projection training. The cross-projection training includes tangent-wise feature contrastive training and prediction consistency training. That is, the former formulates the features with the same projection locations as positive examples and vice versa, for the models' awareness of distortion, while the latter ensures the consistency of cross-model predictions between the ERP and TP. Moreover, adversarial intra-projection training is proposed to reduce the inherent gap, between the features of the pinhole images and those of the ERP and TP images, respectively. Importantly, the TP path can be freely removed after training, leading to no additional inference cost. Extensive experiments on two benchmarks show that our DPPASS achieves +1.06% mIoU increment than the state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_Both_Style_and_Distortion_Matter_Dual-Path_Unsupervised_Domain_Adaptation_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_Both_Style_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14360 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Both_Style_and_Distortion_Matter_Dual-Path_Unsupervised_Domain_Adaptation_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Both_Style_and_Distortion_Matter_Dual-Path_Unsupervised_Domain_Adaptation_for_CVPR_2023_paper.html | CVPR 2023 | null |
expOSE: Accurate Initialization-Free Projective Factorization Using Exponential Regularization | José Pedro Iglesias, Amanda Nilsson, Carl Olsson | Bundle adjustment is a key component in practically all available Structure from Motion systems. While it is crucial for achieving accurate reconstruction, convergence to the right solution hinges on good initialization. The recently introduced factorization-based pOSE methods formulate a surrogate for the bundle adjustment error without reliance on good initialization. In this paper, we show that pOSE has an undesirable penalization of large depths. To address this we propose expOSE which has an exponential regularization that is negligible for positive depths. To achieve efficient inference we use a quadratic approximation that allows an iterative solution with VarPro. Furthermore, we extend the method with radial distortion robustness by decomposing the Object Space Error into radial and tangential components. Experimental results confirm that the proposed method is robust to initialization and improves reconstruction quality compared to state-of-the-art methods even without bundle adjustment refinement. | https://openaccess.thecvf.com/content/CVPR2023/papers/Iglesias_expOSE_Accurate_Initialization-Free_Projective_Factorization_Using_Exponential_Regularization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Iglesias_expOSE_Accurate_Initialization-Free_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Iglesias_expOSE_Accurate_Initialization-Free_Projective_Factorization_Using_Exponential_Regularization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Iglesias_expOSE_Accurate_Initialization-Free_Projective_Factorization_Using_Exponential_Regularization_CVPR_2023_paper.html | CVPR 2023 | null |
OpenGait: Revisiting Gait Recognition Towards Better Practicality | Chao Fan, Junhao Liang, Chuanfu Shen, Saihui Hou, Yongzhen Huang, Shiqi Yu | Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly,we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, GaitBase. Experimentally, we comprehensively compare GaitBase with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://github.com/ShiqiYu/OpenGait. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fan_OpenGait_Revisiting_Gait_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.06597 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.html | CVPR 2023 | null |
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction | Zhen Wang, Shijie Zhou, Jeong Joon Park, Despoina Paschalidou, Suya You, Gordon Wetzstein, Leonidas Guibas, Achuta Kadambi | This work introduces alternating latent topologies (ALTO) for high-fidelity reconstruction of implicit 3D surfaces from noisy point clouds. Previous work identifies that the spatial arrangement of latent encodings is important to recover detail. One school of thought is to encode a latent vector for each point (point latents). Another school of thought is to project point latents into a grid (grid latents) which could be a voxel grid or triplane grid. Each school of thought has tradeoffs. Grid latents are coarse and lose high-frequency detail. In contrast, point latents preserve detail. However, point latents are more difficult to decode into a surface, and quality and runtime suffer. In this paper, we propose ALTO to sequentially alternate between geometric representations, before converging to an easy-to-decode latent. We find that this preserves spatial expressiveness and makes decoding lightweight. We validate ALTO on implicit 3D recovery and observe not only a performance improvement over the state-of-the-art, but a runtime improvement of 3-10x. Anonymized source code at https://visual.ee.ucla.edu/alto.htm/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_ALTO_Alternating_Latent_Topologies_for_Implicit_3D_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_ALTO_Alternating_Latent_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04096 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_ALTO_Alternating_Latent_Topologies_for_Implicit_3D_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_ALTO_Alternating_Latent_Topologies_for_Implicit_3D_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Debiased Representations via Conditional Attribute Interpolation | Yi-Kai Zhang, Qi-Wei Wang, De-Chuan Zhan, Han-Jia Ye | An image is usually described by more than one attribute like "shape" and "color". When a dataset is biased, i.e., most samples have attributes spuriously correlated with the target label, a Deep Neural Network (DNN) is prone to make predictions by the "unintended" attribute, especially if it is easier to learn. To improve the generalization ability when training on such a biased dataset, we propose a chi^2-model to learn debiased representations. First, we design a chi-shape pattern to match the training dynamics of a DNN and find Intermediate Attribute Samples (IASs) --- samples near the attribute decision boundaries, which indicate how the value of an attribute changes from one extreme to another. Then we rectify the representation with a chi-structured metric learning objective. Conditional interpolation among IASs eliminates the negative effect of peripheral attributes and facilitates retaining the intra-class compactness. Experiments show that chi^2-model learns debiased representation effectively and achieves remarkable improvements on various datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Learning_Debiased_Representations_via_Conditional_Attribute_Interpolation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Learning_Debiased_Representations_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_Debiased_Representations_via_Conditional_Attribute_Interpolation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_Debiased_Representations_via_Conditional_Attribute_Interpolation_CVPR_2023_paper.html | CVPR 2023 | null |
A Large-Scale Homography Benchmark | Daniel Barath, Dmytro Mishkin, Michal Polic, Wolfgang Förstner, Jiri Matas | We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The applications of the Pi3D dataset are diverse, e.g. training or evaluating monocular depth, surface normal estimation and image matching algorithms. The HEB dataset consists of 226 260 homographies and includes roughly 4M correspondences. The homographies link images that often undergo significant viewpoint and illumination changes. As applications of HEB, we perform a rigorous evaluation of a wide range of robust estimators and deep learning-based correspondence filtering methods, establishing the current state-of-the-art in robust homography estimation. We also evaluate the uncertainty of the SIFT orientations and scales w.r.t. the ground truth coming from the underlying homographies and provide codes for comparing uncertainty of custom detectors. | https://openaccess.thecvf.com/content/CVPR2023/papers/Barath_A_Large-Scale_Homography_Benchmark_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Barath_A_Large-Scale_Homography_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.09997 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Barath_A_Large-Scale_Homography_Benchmark_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Barath_A_Large-Scale_Homography_Benchmark_CVPR_2023_paper.html | CVPR 2023 | null |
Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery | Wenbin Li, Zhichen Fan, Jing Huo, Yang Gao | Novel class discovery (NCD) aims at learning a model that transfers the common knowledge from a class-disjoint labelled dataset to another unlabelled dataset and discovers new classes (clusters) within it. Many methods, as well as elaborate training pipelines and appropriate objectives, have been proposed and considerably boosted performance on NCD tasks. Despite all this, we find that the existing methods do not sufficiently take advantage of the essence of the NCD setting. To this end, in this paper, we propose to model both inter-class and intra-class constraints in NCD based on the symmetric Kullback-Leibler divergence (sKLD). Specifically, we propose an inter-class sKLD constraint to effectively exploit the disjoint relationship between labelled and unlabelled classes, enforcing the separability for different classes in the embedding space. In addition, we present an intra-class sKLD constraint to explicitly constrain the intra-relationship between a sample and its augmentations and ensure the stability of the training process at the same time. We conduct extensive experiments on the popular CIFAR10, CIFAR100 and ImageNet benchmarks and successfully demonstrate that our method can establish a new state of the art and can achieve significant performance improvements, e.g., 3.5%/3.7% clustering accuracy improvements on CIFAR100-50 dataset split under the task-aware/-agnostic evaluation protocol, over previous state-of-the-art methods. Code is available at https://github.com/FanZhichen/NCD-IIC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Modeling_Inter-Class_and_Intra-Class_Constraints_in_Novel_Class_Discovery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Modeling_Inter-Class_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.03591 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Modeling_Inter-Class_and_Intra-Class_Constraints_in_Novel_Class_Discovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Modeling_Inter-Class_and_Intra-Class_Constraints_in_Novel_Class_Discovery_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network | Zhicheng Zhang, Lijuan Wang, Jufeng Yang | Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://github.com/nku-zhichengzhang/WECL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Weakly_Supervised_Video_Emotion_Detection_and_Prediction_via_Cross-Modal_Temporal_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Weakly_Supervised_Video_Emotion_Detection_and_Prediction_via_Cross-Modal_Temporal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Weakly_Supervised_Video_Emotion_Detection_and_Prediction_via_Cross-Modal_Temporal_CVPR_2023_paper.html | CVPR 2023 | null |
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning | Kangning Liu, Weicheng Zhu, Yiqiu Shen, Sheng Liu, Narges Razavian, Krzysztof J. Geras, Carlos Fernandez-Granda | Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown promising results using contrastive self-supervised learning (CSSL), which learns to push apart representations corresponding to two different randomly-selected instances. Unfortunately, in real-world applications such as medical image classification, there is often class imbalance, so randomly-selected instances mostly belong to the same majority class, which precludes CSSL from learning inter-class differences. To address this issue, we propose a novel framework, Iterative Self-paced Supervised Contrastive Learning for MIL Representations (ItS2CLR), which improves the learned representation by exploiting instance-level pseudo labels derived from the bag-level labels. The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels. We evaluate ItS2CLR on three medical datasets, showing that it improves the quality of instance-level pseudo labels and representations, and outperforms existing MIL methods in terms of both bag and instance level accuracy. Code is available at https://github.com/Kangningthu/ItS2CLR | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Multiple_Instance_Learning_via_Iterative_Self-Paced_Supervised_Contrastive_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Multiple_Instance_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.09452 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Multiple_Instance_Learning_via_Iterative_Self-Paced_Supervised_Contrastive_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Multiple_Instance_Learning_via_Iterative_Self-Paced_Supervised_Contrastive_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Consistent View Synthesis With Pose-Guided Diffusion Models | Hung-Yu Tseng, Qinbo Li, Changil Kim, Suhib Alsisan, Jia-Bin Huang, Johannes Kopf | Novel view synthesis from a single image has been a cornerstone problem for many Virtual Reality applications that provide immersive experiences. However, most existing techniques can only synthesize novel views within a limited range of camera motion or fail to generate consistent and high-quality novel views under significant camera movement. In this work, we propose a pose-guided diffusion model to generate a consistent long-term video of novel views from a single image. We design an attention layer that uses epipolar lines as constraints to facilitate the association between different viewpoints. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed diffusion model against state-of-the-art transformer-based and GAN-based approaches. More qualitative results are available at https://poseguided-diffusion.github.io/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tseng_Consistent_View_Synthesis_With_Pose-Guided_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tseng_Consistent_View_Synthesis_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17598 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tseng_Consistent_View_Synthesis_With_Pose-Guided_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tseng_Consistent_View_Synthesis_With_Pose-Guided_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
MSMDFusion: Fusing LiDAR and Camera at Multiple Scales With Multi-Depth Seeds for 3D Object Detection | Yang Jiao, Zequn Jie, Shaoxiang Chen, Jingjing Chen, Lin Ma, Yu-Gang Jiang | Fusing LiDAR and camera information is essential for accurate and reliable 3D object detection in autonomous driving systems. This is challenging due to the difficulty of combining multi-granularity geometric and semantic features from two drastically different modalities. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as "seeds") into 3D space, and then incorporate 2D semantics via cross-modal interaction or fusion techniques. However, depth information is under-investigated in these approaches when lifting points into 3D space, thus 2D semantics can not be reliably fused with 3D points. Moreover, their multi-modal fusion strategy, which is implemented as concatenation or attention, either can not effectively fuse 2D and 3D information or is unable to perform fine-grained interactions in the voxel space. To this end, we propose a novel framework with better utilization of the depth information and fine-grained cross-modal interaction between LiDAR and camera, which consists of two important components. First, a Multi-Depth Unprojection (MDU) method is used to enhance the depth quality of the lifted points at each interaction level. Second, a Gated Modality-Aware Convolution (GMA-Conv) block is applied to modulate voxels involved with the camera modality in a fine-grained manner and then aggregate multi-modal features into a unified space. Together they provide the detection head with more comprehensive features from LiDAR and camera. On the nuScenes test benchmark, our proposed method, abbreviated as MSMDFusion, achieves state-of-the-art results on both 3D object detection and tracking tasks without using test-time-augmentation and ensemble techniques. The code is available at https://github.com/SxJyJay/MSMDFusion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiao_MSMDFusion_Fusing_LiDAR_and_Camera_at_Multiple_Scales_With_Multi-Depth_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiao_MSMDFusion_Fusing_LiDAR_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.03102 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiao_MSMDFusion_Fusing_LiDAR_and_Camera_at_Multiple_Scales_With_Multi-Depth_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiao_MSMDFusion_Fusing_LiDAR_and_Camera_at_Multiple_Scales_With_Multi-Depth_CVPR_2023_paper.html | CVPR 2023 | null |
Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baseline | Tiantian Geng, Teng Wang, Jinming Duan, Runmin Cong, Feng Zheng | Existing audio-visual event localization (AVE) handles manually trimmed videos with only a single instance in each of them. However, this setting is unrealistic as natural videos often contain numerous audio-visual events with different categories. To better adapt to real-life applications, in this paper we focus on the task of dense-localizing audio-visual events, which aims to jointly localize and recognize all audio-visual events occurring in an untrimmed video. The problem is challenging as it requires fine-grained audio-visual scene and context understanding. To tackle this problem, we introduce the first Untrimmed Audio-Visual (UnAV-100) dataset, which contains 10K untrimmed videos with over 30K audio-visual events. Each video has 2.8 audio-visual events on average, and the events are usually related to each other and might co-occur as in real-life scenes. Next, we formulate the task using a new learning-based framework, which is capable of fully integrating audio and visual modalities to localize audio-visual events with various lengths and capture dependencies between them in a single pass. Extensive experiments demonstrate the effectiveness of our method as well as the significance of multi-scale cross-modal perception and dependency modeling for this task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Geng_Dense-Localizing_Audio-Visual_Events_in_Untrimmed_Videos_A_Large-Scale_Benchmark_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Geng_Dense-Localizing_Audio-Visual_Events_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12930 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_Dense-Localizing_Audio-Visual_Events_in_Untrimmed_Videos_A_Large-Scale_Benchmark_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_Dense-Localizing_Audio-Visual_Events_in_Untrimmed_Videos_A_Large-Scale_Benchmark_and_CVPR_2023_paper.html | CVPR 2023 | null |
Weak-Shot Object Detection Through Mutual Knowledge Transfer | Xuanyi Du, Weitao Wan, Chong Sun, Chen Li | Weak-shot Object Detection methods exploit a fully-annotated source dataset to facilitate the detection performance on the target dataset which only contains image-level labels for novel categories. To bridge the gap between these two datasets, we aim to transfer the object knowledge between the source (S) and target (T) datasets in a bi-directional manner. We propose a novel Knowledge Transfer (KT) loss which simultaneously distills the knowledge of objectness and class entropy from a proposal generator trained on the S dataset to optimize a multiple instance learning module on the T dataset. By jointly optimizing the classification loss and the proposed KT loss, the multiple instance learning module effectively learns to classify object proposals into novel categories in the T dataset with the transferred knowledge from base categories in the S dataset. Noticing the predicted boxes on the T dataset can be regarded as an extension for the original annotations on the S dataset to refine the proposal generator in return, we further propose a novel Consistency Filtering (CF) method to reliably remove inaccurate pseudo labels by evaluating the stability of the multiple instance learning module upon noise injections. Via mutually transferring knowledge between the S and T datasets in an iterative manner, the detection performance on the target dataset is significantly improved. Extensive experiments on public benchmarks validate that the proposed method performs favourably against the state-of-the-art methods without increasing the model parameters or inference computational complexity. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Weak-Shot_Object_Detection_Through_Mutual_Knowledge_Transfer_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Weak-Shot_Object_Detection_Through_Mutual_Knowledge_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Weak-Shot_Object_Detection_Through_Mutual_Knowledge_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model | Gwanghyun Kim, Se Young Chun | Recent 3D generative models have achieved remarkable performance in synthesizing high resolution photorealistic images with view consistency and detailed 3D shapes, but training them for diverse domains is challenging since it requires massive training images and their camera distribution information. Text-guided domain adaptation methods have shown impressive performance on converting the 2D generative model on one domain into the models on other domains with different styles by leveraging the CLIP (Contrastive Language-Image Pre-training), rather than collecting massive datasets for those domains. However, one drawback of them is that the sample diversity in the original generative model is not well-preserved in the domain-adapted generative models due to the deterministic nature of the CLIP text encoder. Text-guided domain adaptation will be even more challenging for 3D generative models not only because of catastrophic diversity loss, but also because of inferior text-image correspondence and poor image quality. Here we propose DATID-3D, a domain adaptation method tailored for 3D generative models using text-to-image diffusion models that can synthesize diverse images per text prompt without collecting additional images and camera information for the target domain. Unlike 3D extensions of prior text-guided domain adaptation methods, our novel pipeline was able to fine-tune the state-of-the-art 3D generator of the source domain to synthesize high resolution, multi-view consistent images in text-guided targeted domains without additional data, outperforming the existing text-guided domain adaptation methods in diversity and text-image correspondence. Furthermore, we propose and demonstrate diverse 3D image manipulations such as one-shot instance-selected adaptation and single-view manipulated 3D reconstruction to fully enjoy diversity in text. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_DATID-3D_Diversity-Preserved_Domain_Adaptation_Using_Text-to-Image_Diffusion_for_3D_Generative_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_DATID-3D_Diversity-Preserved_Domain_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_DATID-3D_Diversity-Preserved_Domain_Adaptation_Using_Text-to-Image_Diffusion_for_3D_Generative_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_DATID-3D_Diversity-Preserved_Domain_Adaptation_Using_Text-to-Image_Diffusion_for_3D_Generative_CVPR_2023_paper.html | CVPR 2023 | null |
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model | Dingkang Liang, Jiahao Xie, Zhikang Zou, Xiaoqing Ye, Wei Xu, Xiang Bai | Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsupervised framework for crowd counting, named CrowdCLIP. The core idea is built on two observations: 1) the recent contrastive pre-trained vision-language model (CLIP) has presented impressive performance on various downstream tasks; 2) there is a natural mapping between crowd patches and count text. To the best of our knowledge, CrowdCLIP is the first to investigate the vision-language knowledge to solve the counting problem. Specifically, in the training stage, we exploit the multi-modal ranking loss by constructing ranking text prompts to match the size-sorted crowd patches to guide the image encoder learning. In the testing stage, to deal with the diversity of image patches, we propose a simple yet effective progressive filtering strategy to first select the highly potential crowd patches and then map them into the language space with various counting intervals. Extensive experiments on five challenging datasets demonstrate that the proposed CrowdCLIP achieves superior performance compared to previous unsupervised state-of-the-art counting methods. Notably, CrowdCLIP even surpasses some popular fully-supervised methods under the cross-dataset setting. The source code will be available at https://github.com/dk-liang/CrowdCLIP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liang_CrowdCLIP_Unsupervised_Crowd_Counting_via_Vision-Language_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liang_CrowdCLIP_Unsupervised_Crowd_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04231 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_CrowdCLIP_Unsupervised_Crowd_Counting_via_Vision-Language_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_CrowdCLIP_Unsupervised_Crowd_Counting_via_Vision-Language_Model_CVPR_2023_paper.html | CVPR 2023 | null |
Toward Stable, Interpretable, and Lightweight Hyperspectral Super-Resolution | Wen-jin Guo, Weiying Xie, Kai Jiang, Yunsong Li, Jie Lei, Leyuan Fang | For real applications, existing HSI-SR methods are mostly not only limited to unstable performance under unknown scenarios but also suffer from high computation consumption. In this paper, we develop a new coordination optimization framework for stable, interpretable, and lightweight HSI-SR. Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework. The estimated degradation is applied to fusion as guidance for a degradation-aware HSI-SR. Under the framework, we establish an explicit degradation estimation method to tackle the indeterminacy and unstable performance driven by black-box simulation in previous methods. Considering the interpretability in fusion, we integrate spectral mixing prior to the fusion process, which can be easily realized by a tiny autoencoder, leading to a dramatic release of the computation burden. We then develop a partial fine-tune strategy in inference to reduce the computation cost further. Comprehensive experiments demonstrate the superiority of our method against state-of-the-art under synthetic and real datasets. For instance, we achieve a 2.3 dB promotion on PSNR with 120x model size reduction and 4300x FLOPs reduction under the CAVE dataset. Code is available in https://github.com/WenjinGuo/DAEM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Toward_Stable_Interpretable_and_Lightweight_Hyperspectral_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Toward_Stable_Interpretable_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Toward_Stable_Interpretable_and_Lightweight_Hyperspectral_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Toward_Stable_Interpretable_and_Lightweight_Hyperspectral_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Auto-Encoders Meet Generative Adversarial Networks and Beyond | Zhengcong Fei, Mingyuan Fan, Li Zhu, Junshi Huang, Xiaoming Wei, Xiaolin Wei | Masked Auto-Encoder (MAE) pretraining methods randomly mask image patches and then train a vision Transformer to reconstruct the original pixels based on the unmasked patches. While they demonstrates impressive performance for downstream vision tasks, it generally requires a large amount of training resource. In this paper, we introduce a novel Generative Adversarial Networks alike framework, referred to as GAN-MAE, where a generator is used to generate the masked patches according to the remaining visible patches, and a discriminator is employed to predict whether the patch is synthesized by the generator. We believe this capacity of distinguishing whether the image patch is predicted or original is benefit to representation learning. Another key point lies in that the parameters of the vision Transformer backbone in the generator and discriminator are shared. Extensive experiments demonstrate that adversarial training of GAN-MAE framework is more efficient and accordingly outperforms the standard MAE given the same model size, training data, and computation resource. The gains are substantially robust for different model sizes and datasets, in particular, a ViT-B model trained with GAN-MAE for 200 epochs outperforms the MAE with 1600 epochs on fine-tuning top-1 accuracy of ImageNet-1k with much less FLOPs. Besides, our approach also works well at transferring downstream tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fei_Masked_Auto-Encoders_Meet_Generative_Adversarial_Networks_and_Beyond_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fei_Masked_Auto-Encoders_Meet_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fei_Masked_Auto-Encoders_Meet_Generative_Adversarial_Networks_and_Beyond_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fei_Masked_Auto-Encoders_Meet_Generative_Adversarial_Networks_and_Beyond_CVPR_2023_paper.html | CVPR 2023 | null |
iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-Training for Visual Recognition | Yixuan Wei, Yue Cao, Zheng Zhang, Houwen Peng, Zhuliang Yao, Zhenda Xie, Han Hu, Baining Guo | This paper presents a method that effectively combines two prevalent visual recognition methods, i.e., image classification and contrastive language-image pre-training, dubbed iCLIP. Instead of naive multi-task learning that use two separate heads for each task, we fuse the two tasks in a deep fashion that adapts the image classification to share the same formula and the same model weights with the language-image pre-training. To further bridge these two tasks, we propose to enhance the category names in image classification tasks using external knowledge, such as their descriptions in dictionaries. Extensive experiments show that the proposed method combines the advantages of two tasks well: the strong discrimination ability in image classification tasks due to the clear and clean category labels, and the good zero-shot ability in CLIP tasks ascribed to the richer semantics in the text descriptions. In particular, it reaches 82.9% top-1 accuracy on IN-1K, and surpasses CLIPby 1.8%, with similar model size, on zero-shot recognition of Kornblith 12-dataset benchmark. The code and models are publicly available at https://github.com/weiyx16/iCLIP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_iCLIP_Bridging_Image_Classification_and_Contrastive_Language-Image_Pre-Training_for_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_iCLIP_Bridging_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_iCLIP_Bridging_Image_Classification_and_Contrastive_Language-Image_Pre-Training_for_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_iCLIP_Bridging_Image_Classification_and_Contrastive_Language-Image_Pre-Training_for_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Neural Volumetric Representations of Dynamic Humans in Minutes | Chen Geng, Sida Peng, Zhen Xu, Hujun Bao, Xiaowei Zhou | This paper addresses the challenge of efficiently reconstructing volumetric videos of dynamic humans from sparse multi-view videos. Some recent works represent a dynamic human as a canonical neural radiance field (NeRF) and a motion field, which are learned from input videos through differentiable rendering. But the per-scene optimization generally requires hours. Other generalizable NeRF models leverage learned prior from datasets to reduce the optimization time by only finetuning on new scenes at the cost of visual fidelity. In this paper, we propose a novel method for learning neural volumetric representations of dynamic humans in minutes with competitive visual quality. Specifically, we define a novel part-based voxelized human representation to better distribute the representational power of the network to different human parts. Furthermore, we propose a novel 2D motion parameterization scheme to increase the convergence rate of deformation field learning. Experiments demonstrate that our model can be learned 100 times faster than previous per-scene optimization methods while being competitive in the rendering quality. Training our model on a 512x512 video with 100 frames typically takes about 5 minutes on a single RTX 3090 GPU. The code is available on our project page: https://zju3dv.github.io/instant_nvr | https://openaccess.thecvf.com/content/CVPR2023/papers/Geng_Learning_Neural_Volumetric_Representations_of_Dynamic_Humans_in_Minutes_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2302.12237 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_Learning_Neural_Volumetric_Representations_of_Dynamic_Humans_in_Minutes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_Learning_Neural_Volumetric_Representations_of_Dynamic_Humans_in_Minutes_CVPR_2023_paper.html | CVPR 2023 | null |
Streaming Video Model | Yucheng Zhao, Chong Luo, Chuanxin Tang, Dongdong Chen, Noel Codella, Zheng-Jun Zha | Video understanding tasks have traditionally been modeled by two separate architectures, specially tailored for two distinct tasks. Sequence-based video tasks, such as action recognition, use a video backbone to directly extract spatiotemporal features, while frame-based video tasks, such as multiple object tracking (MOT), rely on single fixed-image backbone to extract spatial features. In contrast, we propose to unify video understanding tasks into one novel streaming video architecture, referred to as Streaming Vision Transformer (S-ViT). S-ViT first produces frame-level features with a memory-enabled temporally-aware spatial encoder to serve the frame-based video tasks. Then the frame features are input into a task-related temporal decoder to obtain spatiotemporal features for sequence-based tasks. The efficiency and efficacy of S-ViT is demonstrated by the state-of-the-art accuracy in the sequence-based action recognition task and the competitive advantage over conventional architecture in the frame-based MOT task. We believe that the concept of streaming video model and the implementation of S-ViT are solid steps towards a unified deep learning architecture for video understanding. Code will be available at https://github.com/yuzhms/Streaming-Video-Model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Streaming_Video_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Streaming_Video_Model_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17228 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Streaming_Video_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Streaming_Video_Model_CVPR_2023_paper.html | CVPR 2023 | null |
CapDet: Unifying Dense Captioning and Open-World Detection Pretraining | Yanxin Long, Youpeng Wen, Jianhua Han, Hang Xu, Pengzhen Ren, Wei Zhang, Shen Zhao, Xiaodan Liang | Benefiting from large-scale vision-language pre-training on image-text pairs, open-world detection methods have shown superior generalization ability under the zero-shot or few-shot detection settings. However, a pre-defined category space is still required during the inference stage of existing methods and only the objects belonging to that space will be predicted. To introduce a "real" open-world detector, in this paper, we propose a novel method named CapDet to either predict under a given category list or directly generate the category of predicted bounding boxes. Specifically, we unify the open-world detection and dense caption tasks into a single yet effective framework by introducing an additional dense captioning head to generate the region-grounded captions. Besides, adding the captioning task will in turn benefit the generalization of detection performance since the captioning dataset covers more concepts. Experiment results show that by unifying the dense caption task, our CapDet has obtained significant performance improvements (e.g., +2.1% mAP on LVIS rare classes) over the baseline method on LVIS (1203 classes). Besides, our CapDet also achieves state-of-the-art performance on dense captioning tasks, e.g., 15.44% mAP on VG V1.2 and 13.98% on the VG-COCO dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Long_CapDet_Unifying_Dense_Captioning_and_Open-World_Detection_Pretraining_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Long_CapDet_Unifying_Dense_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02489 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Long_CapDet_Unifying_Dense_Captioning_and_Open-World_Detection_Pretraining_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Long_CapDet_Unifying_Dense_Captioning_and_Open-World_Detection_Pretraining_CVPR_2023_paper.html | CVPR 2023 | null |
Bayesian Posterior Approximation With Stochastic Ensembles | Oleksandr Balabanov, Bernhard Mehlig, Hampus Linander | We introduce ensembles of stochastic neural networks to approximate the Bayesian posterior, combining stochastic methods such as dropout with deep ensembles. The stochastic ensembles are formulated as families of distributions and trained to approximate the Bayesian posterior with variational inference. We implement stochastic ensembles based on Monte Carlo dropout, DropConnect and a novel non-parametric version of dropout and evaluate them on a toy problem and CIFAR image classification. For both tasks, we test the quality of the posteriors directly against Hamiltonian Monte Carlo simulations. Our results show that stochastic ensembles provide more accurate posterior estimates than other popular baselines for Bayesian inference. | https://openaccess.thecvf.com/content/CVPR2023/papers/Balabanov_Bayesian_Posterior_Approximation_With_Stochastic_Ensembles_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Balabanov_Bayesian_Posterior_Approximation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08123 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Balabanov_Bayesian_Posterior_Approximation_With_Stochastic_Ensembles_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Balabanov_Bayesian_Posterior_Approximation_With_Stochastic_Ensembles_CVPR_2023_paper.html | CVPR 2023 | null |
RILS: Masked Visual Reconstruction in Language Semantic Space | Shusheng Yang, Yixiao Ge, Kun Yi, Dian Li, Ying Shan, Xiaohu Qie, Xinggang Wang | Both masked image modeling (MIM) and natural language supervision have facilitated the progress of transferable visual pre-training. In this work, we seek the synergy between two paradigms and study the emerging properties when MIM meets natural language supervision. To this end, we present a novel masked visual Reconstruction In Language semantic Space (RILS) pre-training framework, in which sentence representations, encoded by the text encoder, serve as prototypes to transform the vision-only signals into patch-sentence probabilities as semantically meaningful MIM reconstruction targets. The vision models can therefore capture useful components with structured information by predicting proper semantic of masked tokens. Better visual representations could, in turn, improve the text encoder via the image-text alignment objective, which is essential for the effective MIM target transformation. Extensive experimental results demonstrate that our method not only enjoys the best of previous MIM and CLIP but also achieves further improvements on various tasks due to their mutual benefits. RILS exhibits advanced transferability on downstream classification, detection, and segmentation, especially for low-shot regimes. Code is available at https://github.com/hustvl/RILS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_RILS_Masked_Visual_Reconstruction_in_Language_Semantic_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_RILS_Masked_Visual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.06958 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_RILS_Masked_Visual_Reconstruction_in_Language_Semantic_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_RILS_Masked_Visual_Reconstruction_in_Language_Semantic_Space_CVPR_2023_paper.html | CVPR 2023 | null |
Decoupling Learning and Remembering: A Bilevel Memory Framework With Knowledge Projection for Task-Incremental Learning | Wenju Sun, Qingyong Li, Jing Zhang, Wen Wang, Yangli-ao Geng | The dilemma between plasticity and stability arises as a common challenge for incremental learning. In contrast, the human memory system is able to remedy this dilemma owing to its multi-level memory structure, which motivates us to propose a Bilevel Memory system with Knowledge Projection (BMKP) for incremental learning. BMKP decouples the functions of learning and knowledge remembering via a bilevel-memory design: a working memory responsible for adaptively model learning, to ensure plasticity; a long-term memory in charge of enduringly storing the knowledge incorporated within the learned model, to guarantee stability. However, an emerging issue is how to extract the learned knowledge from the working memory and assimilate it into the long-term memory. To approach this issue, we reveal that the model learned by the working memory are actually residing in a redundant high-dimensional space, and the knowledge incorporated in the model can have a quite compact representation under a group of pattern basis shared by all incremental learning tasks. Therefore, we propose a knowledge projection process to adapatively maintain the shared basis, with which the loosely organized model knowledge of working memory is projected into the compact representation to be remembered in the long-term memory. We evaluate BMKP on CIFAR-10, CIFAR-100, and Tiny-ImageNet. The experimental results show that BMKP achieves state-of-the-art performance with lower memory usage. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Decoupling_Learning_and_Remembering_A_Bilevel_Memory_Framework_With_Knowledge_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Decoupling_Learning_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Decoupling_Learning_and_Remembering_A_Bilevel_Memory_Framework_With_Knowledge_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Decoupling_Learning_and_Remembering_A_Bilevel_Memory_Framework_With_Knowledge_CVPR_2023_paper.html | CVPR 2023 | null |
R2Former: Unified Retrieval and Reranking Transformer for Place Recognition | Sijie Zhu, Linjie Yang, Chen Chen, Mubarak Shah, Xiaohui Shen, Heng Wang | Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC-based geometric verification for reranking. However, RANSAC only employs geometric information but ignores other possible information that could be useful for reranking, e.g. local feature correlations, and attention values. In this paper, we propose a unified place recognition framework that handles both retrieval and reranking with a novel transformer model, named R2Former. The proposed reranking module takes feature correlation, attention value, and xy coordinates into account, and learns to determine whether the image pair is from the same location. The whole pipeline is end-to-end trainable and the reranking module alone can also be adopted on other CNN or transformer backbones as a generic component. Remarkably, R2Former significantly outperforms state-of-the-art methods on major VPR datasets with much less inference time and memory consumption. It also achieves the state-of-the-art on the hold-out MSLS challenge set and could serve as a simple yet strong solution for real-world large-scale applications. Experiments also show vision transformer tokens are comparable and sometimes better than CNN local features on local matching. The code is released at https://github.com/Jeff-Zilence/R2Former. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_R2Former_Unified_Retrieval_and_Reranking_Transformer_for_Place_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_R2Former_Unified_Retrieval_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_R2Former_Unified_Retrieval_and_Reranking_Transformer_for_Place_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_R2Former_Unified_Retrieval_and_Reranking_Transformer_for_Place_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction | Donghao Zhou, Chunbin Gu, Junde Xu, Furui Liu, Qiong Wang, Guangyong Chen, Pheng-Ann Heng | In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to cells. In this paper, we model it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, subcellular structures vary considerably in size, which causes the multi-scale issue of SSP. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode can achieve state-of-the-art overall performance in SSP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_RepMode_Learning_to_Re-Parameterize_Diverse_Experts_for_Subcellular_Structure_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_RepMode_Learning_to_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.10066 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_RepMode_Learning_to_Re-Parameterize_Diverse_Experts_for_Subcellular_Structure_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_RepMode_Learning_to_Re-Parameterize_Diverse_Experts_for_Subcellular_Structure_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Symmetric Shape-Preserving Autoencoder for Unsupervised Real Scene Point Cloud Completion | Changfeng Ma, Yinuo Chen, Pengxiao Guo, Jie Guo, Chongjun Wang, Yanwen Guo | Unsupervised completion of real scene objects is of vital importance but still remains extremely challenging in preserving input shapes, predicting accurate results, and adapting to multi-category data. To solve these problems, we propose in this paper an Unsupervised Symmetric Shape-Preserving Autoencoding Network, termed USSPA, to predict complete point clouds of objects from real scenes. One of our main observations is that many natural and man-made objects exhibit significant symmetries. To accommodate this, we devise a symmetry learning module to learn from those objects and to preserve structural symmetries. Starting from an initial coarse predictor, our autoencoder refines the complete shape with a carefully designed upsampling refinement module. Besides the discriminative process on the latent space, the discriminators of our USSPA also take predicted point clouds as direct guidance, enabling more detailed shape prediction. Clearly different from previous methods which train each category separately, our USSPA can be adapted to the training of multi-category data in one pass through a classifier-guided discriminator, with consistent performance on single category. For more accurate evaluation, we contribute to the community a real scene dataset with paired CAD models as ground truth. Extensive experiments and comparisons demonstrate our superiority and generalization and show that our method achieves state-of-the-art performance on unsupervised completion of real scene objects. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_Symmetric_Shape-Preserving_Autoencoder_for_Unsupervised_Real_Scene_Point_Cloud_Completion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_Symmetric_Shape-Preserving_Autoencoder_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Symmetric_Shape-Preserving_Autoencoder_for_Unsupervised_Real_Scene_Point_Cloud_Completion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Symmetric_Shape-Preserving_Autoencoder_for_Unsupervised_Real_Scene_Point_Cloud_Completion_CVPR_2023_paper.html | CVPR 2023 | null |
Modality-Agnostic Debiasing for Single Domain Generalization | Sanqing Qu, Yingwei Pan, Guang Chen, Ting Yao, Changjun Jiang, Tao Mei | Deep neural networks (DNNs) usually fail to generalize well to outside of distribution (OOD) data, especially in the extreme case of single domain generalization (single-DG) that transfers DNNs from single domain to multiple unseen domains. Existing single-DG techniques commonly devise various data-augmentation algorithms, and remould the multi-source domain generalization methodology to learn domain-generalized (semantic) features. Nevertheless, these methods are typically modality-specific, thereby being only applicable to one single modality (e.g., image). In contrast, we target a versatile Modality-Agnostic Debiasing (MAD) framework for single-DG, that enables generalization for different modalities. Technically, MAD introduces a novel two-branch classifier: a biased-branch encourages the classifier to identify the domain-specific (superficial) features, and a general-branch captures domain-generalized features based on the knowledge from biased-branch. Our MAD is appealing in view that it is pluggable to most single-DG models. We validate the superiority of our MAD in a variety of single-DG scenarios with different modalities, including recognition on 1D texts, 2D images, 3D point clouds, and semantic segmentation on 2D images. More remarkably, for recognition on 3D point clouds and semantic segmentation on 2D images, MAD improves DSU by 2.82% and 1.5% in accuracy and mIOU. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qu_Modality-Agnostic_Debiasing_for_Single_Domain_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qu_Modality-Agnostic_Debiasing_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07123 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_Modality-Agnostic_Debiasing_for_Single_Domain_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_Modality-Agnostic_Debiasing_for_Single_Domain_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Difficulty-Based Sampling for Debiased Contrastive Representation Learning | Taeuk Jang, Xiaoqian Wang | Contrastive learning is a self-supervised representation learning method that achieves milestone performance in various classification tasks. However, due to its unsupervised fashion, it suffers from the false negative sample problem: randomly drawn negative samples that are assumed to have a different label but actually have the same label as the anchor. This deteriorates the performance of contrastive learning as it contradicts the motivation of contrasting semantically similar and dissimilar pairs. This raised the attention and the importance of finding legitimate negative samples, which should be addressed by distinguishing between 1) true vs. false negatives; 2) easy vs. hard negatives. However, previous works were limited to the statistical approach to handle false negative and hard negative samples with hyperparameters tuning. In this paper, we go beyond the statistical approach and explore the connection between hard negative samples and data bias. We introduce a novel debiased contrastive learning method to explore hard negatives by relative difficulty referencing the bias-amplifying counterpart. We propose triplet loss for training a biased encoder that focuses more on easy negative samples. We theoretically show that the triplet loss amplifies the bias in self-supervised representation learning. Finally, we empirically show the proposed method improves downstream classification performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jang_Difficulty-Based_Sampling_for_Debiased_Contrastive_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jang_Difficulty-Based_Sampling_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jang_Difficulty-Based_Sampling_for_Debiased_Contrastive_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jang_Difficulty-Based_Sampling_for_Debiased_Contrastive_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Motion Encoding for Self-Supervised Video Representation Learning | Xinyu Sun, Peihao Chen, Liangwei Chen, Changhao Li, Thomas H. Li, Mingkui Tan, Chuang Gan | How to learn discriminative video representation from unlabeled videos is challenging but crucial for video analysis. The latest attempts seek to learn a representation model by predicting the appearance contents in the masked regions. However, simply masking and recovering appearance contents may not be sufficient to model temporal clues as the appearance contents can be easily reconstructed from a single frame. To overcome this limitation, we present Masked Motion Encoding (MME), a new pre-training paradigm that reconstructs both appearance and motion information to explore temporal clues. In MME, we focus on addressing two critical challenges to improve the representation performance: 1) how to well represent the possible long-term motion across multiple frames; and 2) how to obtain fine-grained temporal clues from sparsely sampled videos. Motivated by the fact that human is able to recognize an action by tracking objects' position changes and shape changes, we propose to reconstruct a motion trajectory that represents these two kinds of change in the masked regions. Besides, given the sparse video input, we enforce the model to reconstruct dense motion trajectories in both spatial and temporal dimensions. Pre-trained with our MME paradigm, the model is able to anticipate long-term and fine-grained motion details. Code is available at https://github.com/XinyuSun/MME. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Masked_Motion_Encoding_for_Self-Supervised_Video_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Masked_Motion_Encoding_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.06096 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Masked_Motion_Encoding_for_Self-Supervised_Video_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Masked_Motion_Encoding_for_Self-Supervised_Video_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
CompletionFormer: Depth Completion With Convolutions and Vision Transformers | Youmin Zhang, Xianda Guo, Matteo Poggi, Zheng Zhu, Guan Huang, Stefano Mattoccia | Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth prediction. Despite the tremendous progress of deep-learning-based depth completion methods, the locality of the convolutional layer or graph model makes it hard for the network to model the long-range relationship between pixels. While recent fully Transformer-based architecture has reported encouraging results with the global receptive field, the performance and efficiency gaps to the well-developed CNN models still exist because of its deteriorative local feature details. This paper proposes a joint convolutional attention and Transformer block (JCAT), which deeply couples the convolutional attention layer and Vision Transformer into one block, as the basic unit to construct our depth completion model in a pyramidal structure. This hybrid architecture naturally benefits both the local connectivity of convolutions and the global context of the Transformer in one single model. As a result, our CompletionFormer outperforms state-of-the-art CNNs-based methods on the outdoor KITTI Depth Completion benchmark and indoor NYUv2 dataset, achieving significantly higher efficiency (nearly 1/3 FLOPs) compared to pure Transformer-based methods. Especially when the captured depth is highly sparse, the performance gap with other methods gets much larger. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_CompletionFormer_Depth_Completion_With_Convolutions_and_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_CompletionFormer_Depth_Completion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.13030 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_CompletionFormer_Depth_Completion_With_Convolutions_and_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_CompletionFormer_Depth_Completion_With_Convolutions_and_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Comprehensive and Delicate: An Efficient Transformer for Image Restoration | Haiyu Zhao, Yuanbiao Gou, Boyun Li, Dezhong Peng, Jiancheng Lv, Xi Peng | Vision Transformers have shown promising performance in image restoration, which usually conduct window- or channel-based attention to avoid intensive computations. Although the promising performance has been achieved, they go against the biggest success factor of Transformers to a certain extent by capturing the local instead of global dependency among pixels. In this paper, we propose a novel efficient image restoration Transformer that first captures the superpixel-wise global dependency, and then transfers it into each pixel. Such a coarse-to-fine paradigm is implemented through two neural blocks, i.e., condensed attention neural block (CA) and dual adaptive neural block (DA). In brief, CA employs feature aggregation, attention computation, and feature recovery to efficiently capture the global dependency at the superpixel level. To embrace the pixel-wise global dependency, DA takes a novel dual-way structure to adaptively encapsulate the globality from superpixels into pixels. Thanks to the two neural blocks, our method achieves comparable performance while taking only 6% FLOPs compared with SwinIR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Comprehensive_and_Delicate_An_Efficient_Transformer_for_Image_Restoration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Comprehensive_and_Delicate_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Comprehensive_and_Delicate_An_Efficient_Transformer_for_Image_Restoration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Comprehensive_and_Delicate_An_Efficient_Transformer_for_Image_Restoration_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Model Diagnosis | Jinqi Luo, Zhaoning Wang, Chen Henry Wu, Dong Huang, Fernando De la Torre | When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expensive, and prone to mistakes. The question we try to address is: can we evaluate the sensitivity of deep learning models to arbitrary visual attributes without an annotated test set? This paper argues the case that Zero-shot Model Diagnosis (ZOOM) is possible without the need for a test set nor labeling. To avoid the need for test sets, our system relies on a generative model and CLIP. The key idea is enabling the user to select a set of prompts (relevant to the problem) and our system will automatically search for semantic counterfactual images (i.e., synthesized images that flip the prediction in the case of a binary classifier) using the generative model. We evaluate several visual tasks (classification, key-point detection, and segmentation) in multiple visual domains to demonstrate the viability of our methodology. Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Zero-Shot_Model_Diagnosis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_Zero-Shot_Model_Diagnosis_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15441 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Zero-Shot_Model_Diagnosis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Zero-Shot_Model_Diagnosis_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Visual Grounding by Encouraging Consistent Gradient-Based Explanations | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Improving_Visual_Grounding_by_Encouraging_Consistent_Gradient-Based_Explanations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Improving_Visual_Grounding_by_Encouraging_Consistent_Gradient-Based_Explanations_CVPR_2023_paper.html | CVPR 2023 | null |
Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling | Zhanhao Hu, Wenda Chu, Xiaopei Zhu, Hui Zhang, Bo Zhang, Xiaolin Hu | Recent works have proposed to craft adversarial clothes for evading person detectors, while they are either only effective at limited viewing angles or very conspicuous to humans. We aim to craft adversarial texture for clothes based on 3D modeling, an idea that has been used to craft rigid adversarial objects such as a 3D-printed turtle. Unlike rigid objects, humans and clothes are non-rigid, leading to difficulties in physical realization. In order to craft natural-looking adversarial clothes that can evade person detectors at multiple viewing angles, we propose adversarial camouflage textures (AdvCaT) that resemble one kind of the typical textures of daily clothes, camouflage textures. We leverage the Voronoi diagram and Gumbel-softmax trick to parameterize the camouflage textures and optimize the parameters via 3D modeling. Moreover, we propose an efficient augmentation pipeline on 3D meshes combining topologically plausible projection (TopoProj) and Thin Plate Spline (TPS) to narrow the gap between digital and real-world objects. We printed the developed 3D texture pieces on fabric materials and tailored them into T-shirts and trousers. Experiments show high attack success rates of these clothes against multiple detectors. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Physically_Realizable_Natural-Looking_Clothing_Textures_Evade_Person_Detectors_via_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Physically_Realizable_Natural-Looking_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Physically_Realizable_Natural-Looking_Clothing_Textures_Evade_Person_Detectors_via_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Physically_Realizable_Natural-Looking_Clothing_Textures_Evade_Person_Detectors_via_3D_CVPR_2023_paper.html | CVPR 2023 | null |
ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal | Lanqing Guo, Chong Wang, Wenhan Yang, Siyu Huang, Yufei Wang, Hanspeter Pfister, Bihan Wen | Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB over SRD dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_ShadowDiffusion_When_Degradation_Prior_Meets_Diffusion_Model_for_Shadow_Removal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_ShadowDiffusion_When_Degradation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04711 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_ShadowDiffusion_When_Degradation_Prior_Meets_Diffusion_Model_for_Shadow_Removal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_ShadowDiffusion_When_Degradation_Prior_Meets_Diffusion_Model_for_Shadow_Removal_CVPR_2023_paper.html | CVPR 2023 | null |
FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction | Haoran Bai, Di Kang, Haoxian Zhang, Jinshan Pan, Linchao Bao | We present a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions. The dataset is derived from a large-scale face image dataset namely FFHQ, with the help of our fully automatic and robust UV-texture production pipeline. Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs. An elaborated UV-texture extraction, correction, and completion procedure is then applied to produce high-quality UV-maps from the normalized face images. Compared with existing UV-texture datasets, our dataset has more diverse and higher-quality texture maps. We further train a GAN-based texture decoder as the nonlinear texture basis for parametric fitting based 3D face reconstruction. Experiments show that our method improves the reconstruction accuracy over state-of-the-art approaches, and more importantly, produces high-quality texture maps that are ready for realistic renderings. The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-UV. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_FFHQ-UV_Normalized_Facial_UV-Texture_Dataset_for_3D_Face_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bai_FFHQ-UV_Normalized_Facial_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_FFHQ-UV_Normalized_Facial_UV-Texture_Dataset_for_3D_Face_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_FFHQ-UV_Normalized_Facial_UV-Texture_Dataset_for_3D_Face_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Pruning Parameterization With Bi-Level Optimization for Efficient Semantic Segmentation on the Edge | Changdi Yang, Pu Zhao, Yanyu Li, Wei Niu, Jiexiong Guan, Hao Tang, Minghai Qin, Bin Ren, Xue Lin, Yanzhi Wang | With the ever-increasing popularity of edge devices, it is necessary to implement real-time segmentation on the edge for autonomous driving and many other applications. Vision Transformers (ViTs) have shown considerably stronger results for many vision tasks. However, ViTs with the full-attention mechanism usually consume a large number of computational resources, leading to difficulties for real-time inference on edge devices. In this paper, we aim to derive ViTs with fewer computations and fast inference speed to facilitate the dense prediction of semantic segmentation on edge devices. To achieve this, we propose a pruning parameterization method to formulate the pruning problem of semantic segmentation. Then we adopt a bi-level optimization method to solve this problem with the help of implicit gradients. Our experimental results demonstrate that we can achieve 38.9 mIoU on ADE20K val with a speed of 56.5 FPS on Samsung S21, which is the highest mIoU under the same computation constraint with real-time inference. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Pruning_Parameterization_With_Bi-Level_Optimization_for_Efficient_Semantic_Segmentation_on_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Pruning_Parameterization_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Pruning_Parameterization_With_Bi-Level_Optimization_for_Efficient_Semantic_Segmentation_on_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Pruning_Parameterization_With_Bi-Level_Optimization_for_Efficient_Semantic_Segmentation_on_CVPR_2023_paper.html | CVPR 2023 | null |
Camouflaged Object Detection With Feature Decomposition and Edge Reconstruction | Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yulun Zhang, Zhenhua Guo, Xiu Li | Camouflaged object detection (COD) aims to address the tough issue of identifying camouflaged objects visually blended into the surrounding backgrounds. COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries. Existing approaches to this problem have developed various techniques to mimic the human visual system. Albeit effective in many cases, these methods still struggle when camouflaged objects are so deceptive to the vision system. In this paper, we propose the FEature Decomposition and Edge Reconstruction (FEDER) model for COD. The FEDER model addresses the intrinsic similarity of foreground and background by decomposing the features into different frequency bands using learnable wavelets. It then focuses on the most informative bands to mine subtle cues that differentiate foreground and background. To achieve this, a frequency attention module and a guidance-based feature aggregation module are developed. To combat the ambiguous boundary problem, we propose to learn an auxiliary edge reconstruction task alongside the COD task. We design an ordinary differential equation-inspired edge reconstruction module that generates exact edges. By learning the auxiliary task in conjunction with the COD task, the FEDER model can generate precise prediction maps with accurate object boundaries. Experiments show that our FEDER model significantly outperforms state-of-the-art methods with cheaper computational and memory costs. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Camouflaged_Object_Detection_With_Feature_Decomposition_and_Edge_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Camouflaged_Object_Detection_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Camouflaged_Object_Detection_With_Feature_Decomposition_and_Edge_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Camouflaged_Object_Detection_With_Feature_Decomposition_and_Edge_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
ALOFT: A Lightweight MLP-Like Architecture With Dynamic Low-Frequency Transform for Domain Generalization | Jintao Guo, Na Wang, Lei Qi, Yinghuan Shi | Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focus too much on local representations (e.g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability. Recently, several MLP-based methods have achieved promising results in supervised learning tasks by learning global interactions among different patches of the image. Inspired by this, in this paper, we first analyze the difference between CNN and MLP methods in DG and find that MLP methods exhibit a better generalization ability because they can better capture the global representations (e.g., structure) than CNN methods. Then, based on a recent lightweight MLP method, we obtain a strong baseline that outperforms most start-of-the-art CNN-based methods. The baseline can learn global structure representations with a filter to suppress structure-irrelevant information in the frequency space. Moreover, we propose a dynAmic LOw-Frequency spectrum Transform (ALOFT) that can perturb local texture features while preserving global structure features, thus enabling the filter to remove structure-irrelevant information sufficiently. Extensive experiments on four benchmarks have demonstrated that our method can achieve great performance improvement with a small number of parameters compared to SOTA CNN-based DG methods. Our code is available at https://github.com/lingeringlight/ALOFT/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_ALOFT_A_Lightweight_MLP-Like_Architecture_With_Dynamic_Low-Frequency_Transform_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_ALOFT_A_Lightweight_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11674 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_ALOFT_A_Lightweight_MLP-Like_Architecture_With_Dynamic_Low-Frequency_Transform_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_ALOFT_A_Lightweight_MLP-Like_Architecture_With_Dynamic_Low-Frequency_Transform_for_CVPR_2023_paper.html | CVPR 2023 | null |
NLOST: Non-Line-of-Sight Imaging With Transformer | Yue Li, Jiayong Peng, Juntian Ye, Yueyi Zhang, Feihu Xu, Zhiwei Xiong | Time-resolved non-line-of-sight (NLOS) imaging is based on the multi-bounce indirect reflections from the hidden objects for 3D sensing. Reconstruction from NLOS measurements remains challenging especially for complicated scenes. To boost the performance, we present NLOST, the first transformer-based neural network for NLOS reconstruction. Specifically, after extracting the shallow features with the assistance of physics-based priors, we design two spatial-temporal self attention encoders to explore both local and global correlations within 3D NLOS data by splitting or downsampling the features into different scales, respectively. Then, we design a spatial-temporal cross attention decoder to integrate local and global features in the token space of transformer, resulting in deep features with high representation capabilities. Finally, deep and shallow features are fused to reconstruct the 3D volume of hidden scenes. Extensive experimental results demonstrate the superior performance of the proposed method over existing solutions on both synthetic data and real-world data captured by different NLOS imaging systems. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_NLOST_Non-Line-of-Sight_Imaging_With_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_NLOST_Non-Line-of-Sight_Imaging_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_NLOST_Non-Line-of-Sight_Imaging_With_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_NLOST_Non-Line-of-Sight_Imaging_With_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Text-Visual Prompting for Efficient 2D Temporal Video Grounding | Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding | In this paper, we study the problem of temporal video grounding (TVG), which aims to predict the starting/ending time points of moments described by a text sentence within a long untrimmed video. Benefiting from fine-grained 3D visual features, the TVG techniques have achieved remarkable progress in recent years. However, the high complexity of 3D convolutional neural networks (CNNs) makes extracting dense 3D visual features time-consuming, which calls for intensive memory and computing resources. Towards efficient TVG, we propose a novel text-visual prompting (TVP) framework, which incorporates optimized perturbation patterns (that we call 'prompts') into both visual inputs and textual features of a TVG model. In sharp contrast to 3D CNNs, we show that TVP allows us to effectively co-train vision encoder and language encoder in a 2D TVG model and improves the performance of crossmodal feature fusion using only low-complexity sparse 2D visual features. Further, we propose a Temporal-Distance IoU (TDIoU) loss for efficient learning of TVG. Experiments on two benchmark datasets, Charades-STA and ActivityNet Captions datasets, empirically show that the proposed TVP significantly boosts the performance of 2D TVG (e.g., 9.79% improvement on Charades-STA and 30.77% improvement on ActivityNet Captions) and achieves 5x inference acceleration over TVG using 3D visual features. Codes are available at Open.Intel. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Text-Visual_Prompting_for_Efficient_2D_Temporal_Video_Grounding_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.04995 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Text-Visual_Prompting_for_Efficient_2D_Temporal_Video_Grounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Text-Visual_Prompting_for_Efficient_2D_Temporal_Video_Grounding_CVPR_2023_paper.html | CVPR 2023 | null |
SurfelNeRF: Neural Surfel Radiance Fields for Online Photorealistic Reconstruction of Indoor Scenes | Yiming Gao, Yan-Pei Cao, Ying Shan | Online reconstructing and rendering of large-scale indoor scenes is a long-standing challenge. SLAM-based methods can reconstruct 3D scene geometry progressively in real time but can not render photorealistic results. While NeRF-based methods produce promising novel view synthesis results, their long offline optimization time and lack of geometric constraints pose challenges to efficiently handling online input. Inspired by the complementary advantages of classical 3D reconstruction and NeRF, we thus investigate marrying explicit geometric representation with NeRF rendering to achieve efficient online reconstruction and high-quality rendering. We introduce SurfelNeRF, a variant of neural radiance field which employs a flexible and scalable neural surfel representation to store geometric attributes and extracted appearance features from input images. We further extend conventional surfel-based fusion scheme to progressively integrate incoming input frames into the reconstructed global neural scene representation. In addition, we propose a highly-efficient differentiable rasterization scheme for rendering neural surfel radiance fields, which helps SurfelNeRF achieve 10x speedups in training and inference time, respectively. Experimental results show that our method achieves the state-of-the-art 23.82 PSNR and 29.58 PSNR on ScanNet in feedforward inference and per-scene optimization settings, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_SurfelNeRF_Neural_Surfel_Radiance_Fields_for_Online_Photorealistic_Reconstruction_of_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_SurfelNeRF_Neural_Surfel_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08971 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_SurfelNeRF_Neural_Surfel_Radiance_Fields_for_Online_Photorealistic_Reconstruction_of_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_SurfelNeRF_Neural_Surfel_Radiance_Fields_for_Online_Photorealistic_Reconstruction_of_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Visual Representations via Language-Guided Sampling | Mohamed El Banani, Karan Desai, Justin Johnson | Although an object may appear in numerous contexts, we often describe it in a limited number of ways. Language allows us to abstract away visual variation to represent and communicate concepts. Building on this intuition, we propose an alternative approach to visual representation learning: using language similarity to sample semantically similar image pairs for contrastive learning. Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity instead of hand-crafted augmentations or learned clusters. Our approach also differs from image-text contrastive learning by relying on pre-trained language models to guide the learning rather than directly minimizing a cross-modal loss. Through a series of experiments, we show that language-guided learning yields better features than image-based and image-text representation learning approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Banani_Learning_Visual_Representations_via_Language-Guided_Sampling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Banani_Learning_Visual_Representations_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.12248 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Banani_Learning_Visual_Representations_via_Language-Guided_Sampling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Banani_Learning_Visual_Representations_via_Language-Guided_Sampling_CVPR_2023_paper.html | CVPR 2023 | null |
Logical Implications for Visual Question Answering Consistency | Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman | Despite considerable recent progress in Visual Question Answering (VQA) models, inconsistent or contradictory answers continue to cast doubt on their true reasoning capabilities. However, most proposed methods use indirect strategies or strong assumptions on pairs of questions and answers to enforce model consistency. Instead, we propose a novel strategy intended to improve model performance by directly reducing logical inconsistencies. To do this, we introduce a new consistency loss term that can be used by a wide range of the VQA models and which relies on knowing the logical relation between pairs of questions and answers. While such information is typically not available in VQA datasets, we propose to infer these logical relations using a dedicated language model and use these in our proposed consistency loss function. We conduct extensive experiments on the VQA Introspect and DME datasets and show that our method brings improvements to state-of-the-art VQA models while being robust across different architectures and settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tascon-Morales_Logical_Implications_for_Visual_Question_Answering_Consistency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tascon-Morales_Logical_Implications_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tascon-Morales_Logical_Implications_for_Visual_Question_Answering_Consistency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tascon-Morales_Logical_Implications_for_Visual_Question_Answering_Consistency_CVPR_2023_paper.html | CVPR 2023 | null |
NeUDF: Leaning Neural Unsigned Distance Fields With Volume Rendering | Yu-Tao Liu, Li Wang, Jie Yang, Weikai Chen, Xiaoxu Meng, Bo Yang, Lin Gao | Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering. However, existing methods based on signed distance function (SDF) are limited to closed surfaces, failing to reconstruct a wide range of real-world objects that contain open-surface structures. In this work, we introduce a new neural rendering framework, coded NeUDF, that can reconstruct surfaces with arbitrary topologies solely from multi-view supervision. To gain the flexibility of representing arbitrary surfaces, NeUDF leverages the unsigned distance function (UDF) as surface representation. While a naive extension of SDF-based neural renderer cannot scale to UDF, we propose two new formulations of weight function specially tailored for UDF-based volume rendering. Furthermore, to cope with open surface rendering, where the in/out test is no longer valid, we present a dedicated normal regularization strategy to resolve the surface orientation ambiguity. We extensively evaluate our method over a number of challenging datasets, including DTU, MGN, and Deep Fashion 3D. Experimental results demonstrate that NeUDF can significantly outperform the state-of-the-art method in the task of multi-view surface reconstruction, especially for the complex shapes with open boundaries. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_NeUDF_Leaning_Neural_Unsigned_Distance_Fields_With_Volume_Rendering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_NeUDF_Leaning_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_NeUDF_Leaning_Neural_Unsigned_Distance_Fields_With_Volume_Rendering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_NeUDF_Leaning_Neural_Unsigned_Distance_Fields_With_Volume_Rendering_CVPR_2023_paper.html | CVPR 2023 | null |
Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer | Hao Tang, Songhua Liu, Tianwei Lin, Shaoli Huang, Fu Li, Dongliang He, Xinchao Wang | Transformer-based models achieve favorable performance in artistic style transfer recently thanks to its global receptive field and powerful multi-head/layer attention operations. Nevertheless, the over-paramerized multi-layer structure increases parameters significantly and thus presents a heavy burden for training. Moreover, for the task of style transfer, vanilla Transformer that fuses content and style features by residual connections is prone to content-wise distortion. In this paper, we devise a novel Transformer model termed as Master specifically for style transfer. On the one hand, in the proposed model, different Transformer layers share a common group of parameters, which (1) reduces the total number of parameters, (2) leads to more robust training convergence, and (3) is readily to control the degree of stylization via tuning the number of stacked layers freely during inference. On the other hand, different from the vanilla version, we adopt a learnable scaling operation on content features before content-style feature interaction, which better preserves the original similarity between a pair of content features while ensuring the stylization quality. We also propose a novel meta learning scheme for the proposed model so that it can not only work in the typical setting of arbitrary style transfer, but also adaptable to the few-shot setting, by only fine-tuning the Transformer encoder layer in the few-shot stage for one specific style. Text-guided few-shot style transfer is firstly achieved with the proposed framework. Extensive experiments demonstrate the superiority of Master under both zero-shot and few-shot style transfer settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Master_Meta_Style_Transformer_for_Controllable_Zero-Shot_and_Few-Shot_Artistic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Master_Meta_Style_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11818 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Master_Meta_Style_Transformer_for_Controllable_Zero-Shot_and_Few-Shot_Artistic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Master_Meta_Style_Transformer_for_Controllable_Zero-Shot_and_Few-Shot_Artistic_CVPR_2023_paper.html | CVPR 2023 | null |
Affordance Diffusion: Synthesizing Hand-Object Interactions | Yufei Ye, Xueting Li, Abhinav Gupta, Shalini De Mello, Stan Birchfield, Jiaming Song, Shubham Tulsiani, Sifei Liu | Recent successes in image synthesis are powered by large-scale diffusion models. However, most methods are currently limited to either text- or image-conditioned generation for synthesizing an entire image, texture transfer or inserting objects into a user-specified region. In contrast, in this work we focus on synthesizing complex interactions (i.e., an articulated hand) with a given object. Given an RGB image of an object, we aim to hallucinate plausible images of a human hand interacting with it. We propose a two step generative approach that leverages a LayoutNet that samples an articulation-agnostic hand-object-interaction layout, and a ContentNet that synthesizes images of a hand grasping the object given the predicted layout. Both are built on top of a large-scale pretrained diffusion model to make use of its latent representation. Compared to baselines, the proposed method is shown to generalize better to novel objects and perform surprisingly well on out-of-distribution in-the-wild scenes. The resulting system allows us to predict descriptive affordance information, such as hand articulation and approaching orientation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_Affordance_Diffusion_Synthesizing_Hand-Object_Interactions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_Affordance_Diffusion_Synthesizing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12538 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Affordance_Diffusion_Synthesizing_Hand-Object_Interactions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Affordance_Diffusion_Synthesizing_Hand-Object_Interactions_CVPR_2023_paper.html | CVPR 2023 | null |
NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction From Multi-View Images | Yunfan Ye, Renjiao Yi, Zhirui Gao, Chenyang Zhu, Zhiping Cai, Kai Xu | We study the problem of reconstructing 3D feature curves of an object from a set of calibrated multi-view images. To do so, we learn a neural implicit field representing the density distribution of 3D edges which we refer to as Neural Edge Field (NEF). Inspired by NeRF, NEF is optimized with a view-based rendering loss where a 2D edge map is rendered at a given view and is compared to the ground-truth edge map extracted from the image of that view. The rendering-based differentiable optimization of NEF fully exploits 2D edge detection, without needing a supervision of 3D edges, a 3D geometric operator or cross-view edge correspondence. Several technical designs are devised to ensure learning a range-limited and view-independent NEF for robust edge extraction. The final parametric 3D curves are extracted from NEF with an iterative optimization method. On our benchmark with synthetic data, we demonstrate that NEF outperforms existing state-of-the-art methods on all metrics. Project page: https://yunfan1202.github.io/NEF/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_NEF_Neural_Edge_Fields_for_3D_Parametric_Curve_Reconstruction_From_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_NEF_Neural_Edge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07653 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_NEF_Neural_Edge_Fields_for_3D_Parametric_Curve_Reconstruction_From_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_NEF_Neural_Edge_Fields_for_3D_Parametric_Curve_Reconstruction_From_CVPR_2023_paper.html | CVPR 2023 | null |
Geometric Visual Similarity Learning in 3D Medical Image Self-Supervised Pre-Training | Yuting He, Guanyu Yang, Rongjun Ge, Yang Chen, Jean-Louis Coatrieux, Boyu Wang, Shuo Li | Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training, due to their sharing of numerous same semantic regions. However, the lack of the semantic prior in metrics and the semantic-independent variation in 3D medical images make it challenging to get a reliable measurement for the inter-image similarity, hindering the learning of consistent representation for same semantics. We investigate the challenging problem of this task, i.e., learning a consistent representation between images for a clustering effect of same semantic features. We propose a novel visual similarity learning paradigm, Geometric Visual Similarity Learning, which embeds the prior of topological invariance into the measurement of the inter-image similarity for consistent representation of semantic regions. To drive this paradigm, we further construct a novel geometric matching head, the Z-matching head, to collaboratively learn the global and local similarity of semantic regions, guiding the efficient representation learning for different scale-level inter-image semantic features. Our experiments demonstrate that the pre-training with our learning of inter-image similarity yields more powerful inner-scene, inter-scene, and global-local transferring ability on four challenging 3D medical image tasks. Our codes and pre-trained models will be publicly available in https://github.com/YutingHe-list/GVSL. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Geometric_Visual_Similarity_Learning_in_3D_Medical_Image_Self-Supervised_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Geometric_Visual_Similarity_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00874 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Geometric_Visual_Similarity_Learning_in_3D_Medical_Image_Self-Supervised_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Geometric_Visual_Similarity_Learning_in_3D_Medical_Image_Self-Supervised_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Artistic Image Aesthetics Assessment: A Large-Scale Dataset and a New Method | Ran Yi, Haoyuan Tian, Zhihao Gu, Yu-Kun Lai, Paul L. Rosin | Image aesthetics assessment (IAA) is a challenging task due to its highly subjective nature. Most of the current studies rely on large-scale datasets (e.g., AVA and AADB) to learn a general model for all kinds of photography images. However, little light has been shed on measuring the aesthetic quality of artistic images, and the existing datasets only contain relatively few artworks. Such a defect is a great obstacle to the aesthetic assessment of artistic images. To fill the gap in the field of artistic image aesthetics assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbrush Artistic Image Dataset (BAID), which consists of 60,337 artistic images covering various art forms, with more than 360,000 votes from online users. We then propose a new method, SAAN (Style-specific Art Assessment Network), which can effectively extract and utilize style-specific and generic aesthetic information to evaluate artistic images. Experiments demonstrate that our proposed approach outperforms existing IAA methods on the proposed BAID dataset according to quantitative comparisons. We believe the proposed dataset and method can serve as a foundation for future AIAA works and inspire more research in this field. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yi_Towards_Artistic_Image_Aesthetics_Assessment_A_Large-Scale_Dataset_and_a_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yi_Towards_Artistic_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15166 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_Towards_Artistic_Image_Aesthetics_Assessment_A_Large-Scale_Dataset_and_a_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_Towards_Artistic_Image_Aesthetics_Assessment_A_Large-Scale_Dataset_and_a_CVPR_2023_paper.html | CVPR 2023 | null |
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling With Informative-Preserved Reconstruction and Self-Distilled Consistency | Mingye Xu, Mutian Xu, Tong He, Wanli Ouyang, Yali Wang, Xiaoguang Han, Yu Qiao | Masked Modeling (MM) has demonstrated widespread success in various vision challenges, by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes remains an open problem due to the data sparsity and scene complexity. The conventional random masking paradigm used in 2D images often causes a high risk of ambiguity when recovering the masked region of 3D scenes. To this end, we propose a novel informative-preserved reconstruction, which explores local statistics to discover and preserve the representative structured points, effectively enhancing the pretext masking task for 3D scene understanding. Integrated with a progressive reconstruction manner, our method can concentrate on modeling regional geometry and enjoy less ambiguity for masked reconstruction. Besides, such scenes with progressive masking ratios can also serve to self-distill their intrinsic spatial consistency, requiring to learn the consistent representations from unmasked areas. By elegantly combining informative-preserved reconstruction on masked areas and consistency self-distillation from unmasked areas, a unified framework called MM-3DScene is yielded. We conduct comprehensive experiments on a host of downstream tasks. The consistent improvement (e.g., +6.1% [email protected] on object detection and +2.2% mIoU on semantic segmentation) demonstrates the superiority of our approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_MM-3DScene_3D_Scene_Understanding_by_Customizing_Masked_Modeling_With_Informative-Preserved_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_MM-3DScene_3D_Scene_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_MM-3DScene_3D_Scene_Understanding_by_Customizing_Masked_Modeling_With_Informative-Preserved_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_MM-3DScene_3D_Scene_Understanding_by_Customizing_Masked_Modeling_With_Informative-Preserved_CVPR_2023_paper.html | CVPR 2023 | null |
Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation | Narek Tumanyan, Michal Geyer, Shai Bagon, Tali Dekel | Large-scale text-to-image generative models have been a revolutionary breakthrough in the evolution of generative AI, synthesizing diverse images with highly complex visual concepts. However, a pivotal challenge in leveraging such models for real-world content creation is providing users with control over the generated content. In this paper, we present a new framework that takes text-to-image synthesis to the realm of image-to-image translation -- given a guidance image and a target text prompt as input, our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text, while preserving the semantic layout of the guidance image. Specifically, we observe and empirically demonstrate that fine-grained control over the generated structure can be achieved by manipulating spatial features and their self-attention inside the model. This results in a simple and effective approach, where features extracted from the guidance image are directly injected into the generation process of the translated image, requiring no training or fine-tuning. We demonstrate high-quality results on versatile text-guided image translation tasks, including translating sketches, rough drawings and animations into realistic images, changing the class and appearance of objects in a given image, and modifying global qualities such as lighting and color. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tumanyan_Plug-and-Play_Diffusion_Features_for_Text-Driven_Image-to-Image_Translation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.12572 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tumanyan_Plug-and-Play_Diffusion_Features_for_Text-Driven_Image-to-Image_Translation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tumanyan_Plug-and-Play_Diffusion_Features_for_Text-Driven_Image-to-Image_Translation_CVPR_2023_paper.html | CVPR 2023 | null |
Inverting the Imaging Process by Learning an Implicit Camera Model | Xin Huang, Qi Zhang, Ying Feng, Hongdong Li, Qing Wang | Representing visual signals with implicit coordinate-based neural networks, as an effective replacement of the traditional discrete signal representation, has gained considerable popularity in computer vision and graphics. In contrast to existing implicit neural representations which focus on modelling the scene only, this paper proposes a novel implicit camera model which represents the physical imaging process of a camera as a deep neural network. We demonstrate the power of this new implicit camera model on two inverse imaging tasks: i) generating all-in-focus photos, and ii) HDR imaging. Specifically, we devise an implicit blur generator and an implicit tone mapper to model the aperture and exposure of the camera's imaging process, respectively. Our implicit camera model is jointly learned together with implicit scene models under multi-focus stack and multi-exposure bracket supervision. We have demonstrated the effectiveness of our new model on large number of test images and videos, producing accurate and visually appealing all-in-focus and high dynamic range images. In principle, our new implicit neural camera model has the potential to benefit a wide array of other inverse imaging tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Inverting_the_Imaging_Process_by_Learning_an_Implicit_Camera_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Inverting_the_Imaging_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12748 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Inverting_the_Imaging_Process_by_Learning_an_Implicit_Camera_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Inverting_the_Imaging_Process_by_Learning_an_Implicit_Camera_Model_CVPR_2023_paper.html | CVPR 2023 | null |
Fast Contextual Scene Graph Generation With Unbiased Context Augmentation | Tianlei Jin, Fangtai Guo, Qiwei Meng, Shiqiang Zhu, Xiangming Xi, Wen Wang, Zonghao Mu, Wei Song | Scene graph generation (SGG) methods have historically suffered from long-tail bias and slow inference speed. In this paper, we notice that humans can analyze relationships between objects relying solely on context descriptions,and this abstract cognitive process may be guided by experience. For example, given descriptions of cup and table with their spatial locations, humans can speculate possible relationships < cup, on, table > or < table, near, cup >. Even without visual appearance information, some impossible predicates like flying in and looking at can be empirically excluded. Accordingly, we propose a contextual scene graph generation (C-SGG) method without using visual information and introduce a context augmentation method. We propose that slight perturbations in the position and size of objects do not essentially affect the relationship between objects. Therefore, at the context level, we can produce diverse context descriptions by using a context augmentation method based on the original dataset. These diverse context descriptions can be used for unbiased training of C-SGG to alleviate long-tail bias. In addition, we also introduce a context guided visual scene graph generation (CV-SGG) method, which leverages the C-SGG experience to guide vision to focus on possible predicates. Through extensive experiments on the publicly available dataset, C-SGG alleviates long-tail bias and omits the huge computation of visual feature extraction to realize real-time SGG. CV-SGG achieves a great trade-off between common predicates and tail predicates. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Fast_Contextual_Scene_Graph_Generation_With_Unbiased_Context_Augmentation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Fast_Contextual_Scene_Graph_Generation_With_Unbiased_Context_Augmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Fast_Contextual_Scene_Graph_Generation_With_Unbiased_Context_Augmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Less Is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation | Li Li, Hubert P. H. Shum, Toby P. Breckon | Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by LiDAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a 2.3x reduction in model parameters and 641x fewer multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More). | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Less_Is_More_Reducing_Task_and_Model_Complexity_for_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Less_Is_More_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11203 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Less_Is_More_Reducing_Task_and_Model_Complexity_for_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Less_Is_More_Reducing_Task_and_Model_Complexity_for_3D_CVPR_2023_paper.html | CVPR 2023 | null |
Re-Thinking Federated Active Learning Based on Inter-Class Diversity | SangMook Kim, Sangmin Bae, Hwanjun Song, Se-Young Yun | Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may have a significant amount of unlabeled instances. Among the various approaches to utilizing unlabeled data, a federated active learning framework has emerged as a promising solution. In the decentralized setting, there are two types of available query selector models, namely 'global' and 'local-only' models, but little literature discusses their performance dominance and its causes. In this work, we first demonstrate that the superiority of two selector models depends on the global and local inter-class diversity. Furthermore, we observe that the global and local-only models are the keys to resolving the imbalance of each side. Based on our findings, we propose LoGo, a FAL sampling strategy robust to varying local heterogeneity levels and global imbalance ratio, that integrates both models by two steps of active selection scheme. LoGo consistently outperforms six active learning strategies in the total number of 38 experimental settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Re-Thinking_Federated_Active_Learning_Based_on_Inter-Class_Diversity_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Re-Thinking_Federated_Active_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12317 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Re-Thinking_Federated_Active_Learning_Based_on_Inter-Class_Diversity_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Re-Thinking_Federated_Active_Learning_Based_on_Inter-Class_Diversity_CVPR_2023_paper.html | CVPR 2023 | null |
Enhanced Training of Query-Based Object Detection via Selective Query Recollection | Fangyi Chen, Han Zhang, Kai Hu, Yu-Kai Huang, Chenchen Zhu, Marios Savvides | This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the training process and attribute the overlooked phenomenon to two limitations: lack of training emphasis and cascading errors from decoding sequence. We design and present Selective Query Recollection (SQR), a simple and effective training strategy for query-based object detectors. It cumulatively collects intermediate queries as decoding stages go deeper and selectively forwards the queries to the downstream stages aside from the sequential structure. Such-wise, SQR places training emphasis on later stages and allows later stages to work with intermediate queries from earlier stages directly. SQR can be easily plugged into various query-based object detectors and significantly enhances their performance while leaving the inference pipeline unchanged. As a result, we apply SQR on Adamixer, DAB-DETR, and Deformable-DETR across various settings (backbone, number of queries, schedule) and consistently brings 1.4 2.8 AP improvement. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Enhanced_Training_of_Query-Based_Object_Detection_via_Selective_Query_Recollection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Enhanced_Training_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07593 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Enhanced_Training_of_Query-Based_Object_Detection_via_Selective_Query_Recollection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Enhanced_Training_of_Query-Based_Object_Detection_via_Selective_Query_Recollection_CVPR_2023_paper.html | CVPR 2023 | null |
AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning With Masked Autoencoders | Wele Gedara Chaminda Bandara, Naman Patel, Ali Gholami, Mehdi Nikkhah, Motilal Agrawal, Vishal M. Patel | Masked Autoencoders (MAEs) learn generalizable representations for image, text, audio, video, etc., by reconstructing masked input data from tokens of the visible data. Current MAE approaches for videos rely on random patch, tube, or frame based masking strategies to select these tokens. This paper proposes AdaMAE, an adaptive masking strategy for MAEs that is end-to-end trainable. Our adaptive masking strategy samples visible tokens based on the semantic context using an auxiliary sampling network. This network estimates a categorical distribution over spacetime-patch tokens. The tokens that increase the expected reconstruction error are rewarded and selected as visible tokens, motivated by the policy gradient algorithm in reinforcement learning. We show that AdaMAE samples more tokens from the high spatiotemporal information regions, thereby allowing us to mask 95% of tokens, resulting in lower memory requirements and faster pre-training. We conduct ablation studies on the Something-Something v2 (SSv2) dataset to demonstrate the efficacy of our adaptive sampling approach and report state-of-the-art results of 70.0% and 81.7% in top-1 accuracy on SSv2 and Kinetics-400 action classification datasets with a ViT-Base backbone and 800 pre-training epochs. Code and pre-trained models are available at: https://github.com/wgcban/adamae.git | https://openaccess.thecvf.com/content/CVPR2023/papers/Bandara_AdaMAE_Adaptive_Masking_for_Efficient_Spatiotemporal_Learning_With_Masked_Autoencoders_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bandara_AdaMAE_Adaptive_Masking_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.09120 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bandara_AdaMAE_Adaptive_Masking_for_Efficient_Spatiotemporal_Learning_With_Masked_Autoencoders_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bandara_AdaMAE_Adaptive_Masking_for_Efficient_Spatiotemporal_Learning_With_Masked_Autoencoders_CVPR_2023_paper.html | CVPR 2023 | null |
Detecting Human-Object Contact in Images | Yixin Chen, Sai Kumar Dwivedi, Michael J. Black, Dimitrios Tzionas | Humans constantly contact objects to move and perform tasks. Thus, detecting human-object contact is important for building human-centered artificial intelligence. However, there exists no robust method to detect contact between the body and the scene from an image, and there exists no dataset to learn such a detector. We fill this gap with HOT ("Human-Object conTact"), a new dataset of human-object contacts in images. To build HOT, we use two data sources: (1) We use the PROX dataset of 3D human meshes moving in 3D scenes, and automatically annotate 2D image areas for contact via 3D mesh proximity and projection. (2) We use the V-COCO, HAKE and Watch-n-Patch datasets, and ask trained annotators to draw polygons around the 2D image areas where contact takes place. We also annotate the involved body part of the human body. We use our HOT dataset to train a new contact detector, which takes a single color image as input, and outputs 2D contact heatmaps as well as the body-part labels that are in contact. This is a new and challenging task, that extends current foot-ground or hand-object contact detectors to the full generality of the whole body. The detector uses a part-attention branch to guide contact estimation through the context of the surrounding body parts and scene. We evaluate our detector extensively, and quantitative results show that our model outperforms baselines, and that all components contribute to better performance. Results on images from an online repository show reasonable detections and generalizability. Our HOT data and model are available for research at https://hot.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Detecting_Human-Object_Contact_in_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Detecting_Human-Object_Contact_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03373 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Detecting_Human-Object_Contact_in_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Detecting_Human-Object_Contact_in_Images_CVPR_2023_paper.html | CVPR 2023 | null |
PointClustering: Unsupervised Point Cloud Pre-Training Using Transformation Invariance in Clustering | Fuchen Long, Ting Yao, Zhaofan Qiu, Lusong Li, Tao Mei | Feature invariance under different data transformations, i.e., transformation invariance, can be regarded as a type of self-supervision for representation learning. In this paper, we present PointClustering, a new unsupervised representation learning scheme that leverages transformation invariance for point cloud pre-training. PointClustering formulates the pretext task as deep clustering and employs transformation invariance as an inductive bias, following the philosophy that common point cloud transformation will not change the geometric properties and semantics. Technically, PointClustering iteratively optimizes the feature clusters and backbone, and delves into the transformation invariance as learning regularization from two perspectives: point level and instance level. Point-level invariance learning maintains local geometric properties through gathering point features of one instance across transformations, while instance-level invariance learning further measures clusters over the entire dataset to explore semantics of instances. Our PointClustering is architecture-agnostic and readily applicable to MLP-based, CNN-based and Transformer-based backbones. We empirically demonstrate that the models pre-learnt on the ScanNet dataset by PointClustering provide superior performances on six benchmarks, across downstream tasks of classification and segmentation. More remarkably, PointClustering achieves an accuracy of 94.5% on ModelNet40 with Transformer backbone. Source code is available at https://github.com/FuchenUSTC/PointClustering. | https://openaccess.thecvf.com/content/CVPR2023/papers/Long_PointClustering_Unsupervised_Point_Cloud_Pre-Training_Using_Transformation_Invariance_in_Clustering_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Long_PointClustering_Unsupervised_Point_Cloud_Pre-Training_Using_Transformation_Invariance_in_Clustering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Long_PointClustering_Unsupervised_Point_Cloud_Pre-Training_Using_Transformation_Invariance_in_Clustering_CVPR_2023_paper.html | CVPR 2023 | null |
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution | Jiezhang Cao, Qin Wang, Yongqin Xian, Yawei Li, Bingbing Ni, Zhiming Pi, Kai Zhang, Yulun Zhang, Radu Timofte, Luc Van Gool | Learning continuous image representations is recently gaining popularity for image super-resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary scales from low-resolution inputs. Existing methods mostly ensemble nearby features to predict the new pixel at any queried coordinate in the SR image. Such a local ensemble suffers from some limitations: i) it has no learnable parameters and it neglects the similarity of the visual features; ii) it has a limited receptive field and cannot ensemble relevant features in a large field which are important in an image. To address these issues, this paper proposes a continuous implicit attention-in-attention network, called CiaoSR. We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features. Furthermore, we embed a scale-aware attention in this implicit attention network to exploit additional non-local information. Extensive experiments on benchmark datasets demonstrate CiaoSR significantly outperforms the existing single image SR methods with the same backbone. In addition, CiaoSR also achieves the state-of-the-art performance on the arbitrary-scale SR task. The effectiveness of the method is also demonstrated on the real-world SR setting. More importantly, CiaoSR can be flexibly integrated into any backbone to improve the SR performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_CiaoSR_Continuous_Implicit_Attention-in-Attention_Network_for_Arbitrary-Scale_Image_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_CiaoSR_Continuous_Implicit_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04362 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_CiaoSR_Continuous_Implicit_Attention-in-Attention_Network_for_Arbitrary-Scale_Image_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_CiaoSR_Continuous_Implicit_Attention-in-Attention_Network_for_Arbitrary-Scale_Image_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning | Yu Wang, Pengchong Qiao, Chang Liu, Guoli Song, Xiawu Zheng, Jie Chen | Recent advances in robust semi-supervised learning (SSL) typical filters out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initiative step to explore and propose a unified framework termed as OOD Semantic Pruning (OSP), aims at pruning OOD semantics out from the in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality transformation to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. Codes are available in the supplementary material. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Out-of-Distributed_Semantic_Pruning_for_Robust_Semi-Supervised_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Out-of-Distributed_Semantic_Pruning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Out-of-Distributed_Semantic_Pruning_for_Robust_Semi-Supervised_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Out-of-Distributed_Semantic_Pruning_for_Robust_Semi-Supervised_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
The Best Defense Is a Good Offense: Adversarial Augmentation Against Adversarial Attacks | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Frosio_The_Best_Defense_Is_a_Good_Offense_Adversarial_Augmentation_Against_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Frosio_The_Best_Defense_Is_a_Good_Offense_Adversarial_Augmentation_Against_CVPR_2023_paper.html | CVPR 2023 | null |
GaitGCI: Generative Counterfactual Intervention for Gait Recognition | Huanzhang Dou, Pengyi Zhang, Wei Su, Yunlong Yu, Yining Lin, Xi Li | Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognition, we propose a Generative Counterfactual Intervention framework, dubbed GaitGCI, consisting of Counterfactual Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution (DCDC). CIL leverages causal inference to alleviate the impact of confounders by maximizing the likelihood difference between factual/counterfactual attention. DCDC adaptively generates sample-wise factual/counterfactual attention to perceive the sample properties. With matrix decomposition and diversity constraint, DCDC guarantees the model's efficiency and effectiveness. Extensive experiments indicate that proposed GaitGCI: 1) could effectively focus on the discriminative and interpretable regions that reflect gait patterns; 2) is model-agnostic and could be plugged into existing models to improve performance with nearly no extra cost; 3) efficiently achieves state-of-the-art performance on arbitrary scenarios (in-the-lab and in-the-wild). | https://openaccess.thecvf.com/content/CVPR2023/papers/Dou_GaitGCI_Generative_Counterfactual_Intervention_for_Gait_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dou_GaitGCI_Generative_Counterfactual_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dou_GaitGCI_Generative_Counterfactual_Intervention_for_Gait_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dou_GaitGCI_Generative_Counterfactual_Intervention_for_Gait_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Constructing Deep Spiking Neural Networks From Artificial Neural Networks With Knowledge Distillation | Qi Xu, Yaxin Li, Jiangrong Shen, Jian K. Liu, Huajin Tang, Gang Pan | Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems. Although spiking based models are energy efficient by taking advantage of discrete spike signals, their performance is limited by current network structures and their training methods. As discrete signals, typical SNNs cannot apply the gradient descent rules directly into parameters adjustment as artificial neural networks (ANNs). Aiming at this limitation, here we propose a novel method of constructing deep SNN models with knowledge distillation (KD) that uses ANN as teacher model and SNN as student model. Through ANN-SNN joint training algorithm, the student SNN model can learn rich feature information from the teacher ANN model through the KD method, yet it avoids training SNN from scratch when communicating with non-differentiable spikes. Our method can not only build a more efficient deep spiking structure feasibly and reasonably, but use few time steps to train whole model compared to direct training or ANN to SNN methods. More importantly, it has a superb ability of noise immunity for various types of artificial noises and natural signals. The proposed novel method provides efficient ways to improve the performance of SNN through constructing deeper structures in a high-throughput fashion, with potential usage for light and efficient brain-inspired computing of practical scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Constructing_Deep_Spiking_Neural_Networks_From_Artificial_Neural_Networks_With_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.05627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Constructing_Deep_Spiking_Neural_Networks_From_Artificial_Neural_Networks_With_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Constructing_Deep_Spiking_Neural_Networks_From_Artificial_Neural_Networks_With_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding and Improving Visual Prompting: A Label-Mapping Perspective | Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zhang, Sijia Liu | We revisit and advance visual prompting (VP), an input prompting technique for vision tasks. VP can reprogram a fixed, pre-trained source model to accomplish downstream tasks in the target domain by simply incorporating universal prompts (in terms of input perturbation patterns) into downstream data points. Yet, it remains elusive why VP stays effective even given a ruleless label mapping (LM) between the source classes and the target classes. Inspired by the above, we ask: How is LM interrelated with VP? And how to exploit such a relationship to improve its accuracy on target tasks? We peer into the influence of LM on VP and provide an affirmative answer that a better 'quality' of LM (assessed by mapping precision and explanation) can consistently improve the effectiveness of VP. This is in contrast to the prior art where the factor of LM was missing. To optimize LM, we propose a new VP framework, termed ILM-VP (iterative label mapping-based visual prompting), which automatically re-maps the source labels to the target labels and progressively improves the target task accuracy of VP. Further, when using a contrastive language-image pretrained (CLIP) model, we propose to integrate an LM process to assist the text prompt selection of CLIP and to improve the target task accuracy. Extensive experiments demonstrate that our proposal significantly outperforms state-of-the-art VP methods. As highlighted below, we show that when reprogramming an ImageNet-pretrained ResNet-18 to 13 target tasks, our method outperforms baselines by a substantial margin, e.g., 7.9% and 6.7% accuracy improvements in transfer learning to the target Flowers102 and CIFAR100 datasets. Besides, our proposal on CLIP-based VP provides 13.7% and 7.1% accuracy improvements on Flowers102 and DTD respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Understanding_and_Improving_Visual_Prompting_A_Label-Mapping_Perspective_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Understanding_and_Improving_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11635 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Understanding_and_Improving_Visual_Prompting_A_Label-Mapping_Perspective_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Understanding_and_Improving_Visual_Prompting_A_Label-Mapping_Perspective_CVPR_2023_paper.html | CVPR 2023 | null |
Directional Connectivity-Based Segmentation of Medical Images | Ziyun Yang, Sina Farsiu | Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent segmentation via deep networks is incorporating pixel connectivity, a basic concept in digital topology, to model inter-pixel relationships. However, previous works on connectivity modeling have ignored the rich channel-wise directional information in the latent space. In this work, we demonstrate that effective disentanglement of directional sub-space from the shared latent space can significantly enhance the feature representation in the connectivity-based network. To this end, we propose a directional connectivity modeling scheme for segmentation that decouples, tracks, and utilizes the directional information across the network. Experiments on various public medical image segmentation benchmarks show the effectiveness of our model as compared to the state-of-the-art methods. Code is available at https://github.com/Zyun-Y/DconnNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Directional_Connectivity-Based_Segmentation_of_Medical_Images_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.00145 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Directional_Connectivity-Based_Segmentation_of_Medical_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Directional_Connectivity-Based_Segmentation_of_Medical_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Flexible Multi-Modal Document Models | Naoto Inoue, Kotaro Kikuchi, Edgar Simo-Serra, Mayu Otani, Kota Yamaguchi | Creative workflows for generating graphical documents involve complex inter-related tasks, such as aligning elements, choosing appropriate fonts, or employing aesthetically harmonious colors. In this work, we attempt at building a holistic model that can jointly solve many different design tasks. Our model, which we denote by FlexDM, treats vector graphic documents as a set of multi-modal elements, and learns to predict masked fields such as element type, position, styling attributes, image, or text, using a unified architecture. Through the use of explicit multi-task learning and in-domain pre-training, our model can better capture the multi-modal relationships among the different document fields. Experimental results corroborate that our single FlexDM is able to successfully solve a multitude of different design tasks, while achieving performance that is competitive with task-specific and costly baselines. | https://openaccess.thecvf.com/content/CVPR2023/papers/Inoue_Towards_Flexible_Multi-Modal_Document_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Inoue_Towards_Flexible_Multi-Modal_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.18248 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Inoue_Towards_Flexible_Multi-Modal_Document_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Inoue_Towards_Flexible_Multi-Modal_Document_Models_CVPR_2023_paper.html | CVPR 2023 | null |
DegAE: A New Pretraining Paradigm for Low-Level Vision | Yihao Liu, Jingwen He, Jinjin Gu, Xiangtao Kong, Yu Qiao, Chao Dong | Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acquisition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (DegAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tailored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_DegAE_A_New_Pretraining_Paradigm_for_Low-Level_Vision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_DegAE_A_New_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_DegAE_A_New_Pretraining_Paradigm_for_Low-Level_Vision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_DegAE_A_New_Pretraining_Paradigm_for_Low-Level_Vision_CVPR_2023_paper.html | CVPR 2023 | null |
The Differentiable Lens: Compound Lens Search Over Glass Surfaces and Materials for Object Detection | Geoffroi Côté, Fahim Mannan, Simon Thibault, Jean-François Lalonde, Felix Heide | Most camera lens systems are designed in isolation, separately from downstream computer vision methods. Recently, joint optimization approaches that design lenses alongside other components of the image acquisition and processing pipeline--notably, downstream neural networks--have achieved improved imaging quality or better performance on vision tasks. However, these existing methods optimize only a subset of lens parameters and cannot optimize glass materials given their categorical nature. In this work, we develop a differentiable spherical lens simulation model that accurately captures geometrical aberrations. We propose an optimization strategy to address the challenges of lens design--notorious for non-convex loss function landscapes and many manufacturing constraints--that are exacerbated in joint optimization tasks. Specifically, we introduce quantized continuous glass variables to facilitate the optimization and selection of glass materials in an end-to-end design context, and couple this with carefully designed constraints to support manufacturability. In automotive object detection, we report improved detection performance over existing designs even when simplifying designs to two- or three-element lenses, despite significantly degrading the image quality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cote_The_Differentiable_Lens_Compound_Lens_Search_Over_Glass_Surfaces_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cote_The_Differentiable_Lens_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cote_The_Differentiable_Lens_Compound_Lens_Search_Over_Glass_Surfaces_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cote_The_Differentiable_Lens_Compound_Lens_Search_Over_Glass_Surfaces_and_CVPR_2023_paper.html | CVPR 2023 | null |
Adversarially Masking Synthetic To Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation | Guangrui Li, Guoliang Kang, Xiaohan Wang, Yunchao Wei, Yi Yang | This paper considers the synthetic-to-real adaptation of point cloud semantic segmentation, which aims to segment the real-world point clouds with only synthetic labels available. Contrary to synthetic data which is integral and clean, point clouds collected by real-world sensors typically contain unexpected and irregular noise because the sensors may be impacted by various environmental conditions. Consequently, the model trained on ideal synthetic data may fail to achieve satisfactory segmentation results on real data. Influenced by such noise, previous adversarial training methods, which are conventional for 2D adaptation tasks, become less effective. In this paper, we aim to mitigate the domain gap caused by target noise via learning to mask the source points during the adaptation procedure. To this end, we design a novel learnable masking module, which takes source features and 3D coordinates as inputs. We incorporate Gumbel-Softmax operation into the masking module so that it can generate binary masks and be trained end-to-end via gradient back-propagation. With the help of adversarial training, the masking module can learn to generate source masks to mimic the pattern of irregular target noise, thereby narrowing the domain gap. We name our method "Adversarial Masking" as adversarial training and learnable masking module depend on each other and cooperate with each other to mitigate the domain gap. Experiments on two synthetic-to-real adaptation benchmarks verify the effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Adversarially_Masking_Synthetic_To_Mimic_Real_Adaptive_Noise_Injection_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Adversarially_Masking_Synthetic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Adversarially_Masking_Synthetic_To_Mimic_Real_Adaptive_Noise_Injection_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Adversarially_Masking_Synthetic_To_Mimic_Real_Adaptive_Noise_Injection_for_CVPR_2023_paper.html | CVPR 2023 | null |
KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation | Xiangyang Li, Zihan Wang, Jiahao Yang, Yaowei Wang, Shuqiang Jiang | Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent to perform actions to arrive the target location. As knowledge provides crucial information which is complementary to visible content, in this paper, we propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability. Specifically, we first retrieve facts (i.e., knowledge described by language descriptions) for the navigation views based on local regions from the constructed knowledge base. The retrieved facts range from properties of a single object (e.g., color, shape) to relationships between objects (e.g., action, spatial position), providing crucial information for VLN. We further present the KERM which contains the purification, fact-aware interaction, and instruction-guided aggregation modules to integrate visual, history, instruction, and fact features. The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction. Experimental results on the REVERIE, R2R, and SOON datasets demonstrate the effectiveness of the proposed method. The source code is available at https://github.com/XiangyangLi20/KERM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_KERM_Knowledge_Enhanced_Reasoning_for_Vision-and-Language_Navigation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_KERM_Knowledge_Enhanced_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15796 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_KERM_Knowledge_Enhanced_Reasoning_for_Vision-and-Language_Navigation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_KERM_Knowledge_Enhanced_Reasoning_for_Vision-and-Language_Navigation_CVPR_2023_paper.html | CVPR 2023 | null |
LiDAR-in-the-Loop Hyperparameter Optimization | Félix Goudreault, Dominik Scheuble, Mario Bijelic, Nicolas Robidoux, Felix Heide | LiDAR has become a cornerstone sensing modality for 3D vision. LiDAR systems emit pulses of light into the scene, take measurements of the returned signal, and rely on hardware digital signal processing (DSP) pipelines to construct 3D point clouds from these measurements. The resulting point clouds output by these DSPs are input to downstream 3D vision models -- both, in the form of training datasets or as input at inference time. Existing LiDAR DSPs are composed of cascades of parameterized operations; modifying configuration parameters results in significant changes in the point clouds and consequently the output of downstream methods. Existing methods treat LiDAR systems as fixed black boxes and construct downstream task networks more robust with respect to measurement fluctuations. Departing from this approach, the proposed method directly optimizes LiDAR sensing and DSP parameters for downstream tasks. To investigate the optimization of LiDAR system parameters, we devise a realistic LiDAR simulation method that generates raw waveforms as input to a LiDAR DSP pipeline. We optimize LiDAR parameters for both 3D object detection IoU losses and depth error metrics by solving a nonlinear multi-objective optimization problem with a 0th-order stochastic algorithm. For automotive 3D object detection models, the proposed method outperforms manual expert tuning by 39.5% mean Average Precision (mAP). | https://openaccess.thecvf.com/content/CVPR2023/papers/Goudreault_LiDAR-in-the-Loop_Hyperparameter_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Goudreault_LiDAR-in-the-Loop_Hyperparameter_Optimization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Goudreault_LiDAR-in-the-Loop_Hyperparameter_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Goudreault_LiDAR-in-the-Loop_Hyperparameter_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Local 3D Editing via 3D Distillation of CLIP Knowledge | Junha Hyung, Sungwon Hwang, Daejin Kim, Hyunji Lee, Jaegul Choo | 3D content manipulation is an important computer vision task with many real-world applications (e.g., product design, cartoon generation, and 3D Avatar editing). Recently proposed 3D GANs can generate diverse photo-realistic 3D-aware contents using Neural Radiance fields (NeRF). However, manipulation of NeRF still remains a challenging problem since the visual quality tends to degrade after manipulation and suboptimal control handles such as semantic maps are used for manipulations. While text-guided manipulations have shown potential in 3D editing, such approaches often lack locality. To overcome the problems, we propose Local Editing NeRF (LENeRF), which only requires text inputs for fine-grained and localized manipulation. Specifically, we present three add-on modules of LENeRF, the Latent Residual Mapper, the Attention Field Network, and the Deformation Network, which are jointly used for local manipulations of 3D features by estimating a 3D attention field. The 3D attention field is learned in an unsupervised way, by distilling the CLIP's zero-shot mask generation capability to 3D with multi-view guidance. We conduct diverse experiments and thorough evaluations both quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hyung_Local_3D_Editing_via_3D_Distillation_of_CLIP_Knowledge_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hyung_Local_3D_Editing_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hyung_Local_3D_Editing_via_3D_Distillation_of_CLIP_Knowledge_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hyung_Local_3D_Editing_via_3D_Distillation_of_CLIP_Knowledge_CVPR_2023_paper.html | CVPR 2023 | null |
Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices | Jingyi Xu, Tushar Vaidya, Yufei Wu, Saket Chandra, Zhangsheng Lai, Kai Fong Ernest Chong | We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult process of novel problem-solving to routine algebraic computation. The fundamental algebraic objects of interest are the ideals of some suitably initialized polynomial ring. We shall explain how solving Raven's Progressive Matrices (RPMs) can be realized as computational problems in algebra, which combine various well-known algebraic subroutines that include: Computing the Grobner basis of an ideal, checking for ideal containment, etc. Crucially, the additional algebraic structure satisfied by ideals allows for more operations on ideals beyond set-theoretic operations. Our algebraic machine reasoning framework is not only able to select the correct answer from a given answer set, but also able to generate the correct answer with only the question matrix given. Experiments on the I-RAVEN dataset yield an overall 93.2% accuracy, which significantly outperforms the current state-of-the-art accuracy of 77.0% and exceeds human performance at 84.4% accuracy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Abstract_Visual_Reasoning_An_Algebraic_Approach_for_Solving_Ravens_Progressive_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Abstract_Visual_Reasoning_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Abstract_Visual_Reasoning_An_Algebraic_Approach_for_Solving_Ravens_Progressive_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Abstract_Visual_Reasoning_An_Algebraic_Approach_for_Solving_Ravens_Progressive_CVPR_2023_paper.html | CVPR 2023 | null |
3D-Aware Conditional Image Synthesis | Kangle Deng, Gengshan Yang, Deva Ramanan, Jun-Yan Zhu | We propose pix2pix3D, a 3D-aware conditional generative model for controllable photorealistic image synthesis. Given a 2D label map, such as a segmentation or edge map, our model learns to synthesize a corresponding image from different viewpoints. To enable explicit 3D user control, we extend conditional generative models with neural radiance fields. Given widely-available posed monocular image and label map pairs, our model learns to assign a label to every 3D point in addition to color and density, which enables it to render the image and pixel-aligned label map simultaneously. Finally, we build an interactive system that allows users to edit the label map from different viewpoints and generate outputs accordingly. | https://openaccess.thecvf.com/content/CVPR2023/papers/Deng_3D-Aware_Conditional_Image_Synthesis_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2302.08509 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_3D-Aware_Conditional_Image_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_3D-Aware_Conditional_Image_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding Deep Generative Models With Generalized Empirical Likelihoods | Suman Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth | Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge. It is especially difficult for certain model classes, such as Generative Adversarial Networks and Diffusion Models, whose models do not admit exact likelihoods. In this work, we demonstrate that generalized empirical likelihood (GEL) methods offer a family of diagnostic tools that can identify many deficiencies of deep generative models (DGMs). We show, with appropriate specification of moment conditions, that the proposed method can identify which modes have been dropped, the degree to which DGMs are mode imbalanced, and whether DGMs sufficiently capture intra-class diversity. We show how to combine techniques from Maximum Mean Discrepancy and Generalized Empirical Likelihood to create not only distribution tests that retain per-sample interpretability, but also metrics that include label information. We find that such tests predict the degree of mode dropping and mode imbalance up to 60% better than metrics such as improved precision/recall. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ravuri_Understanding_Deep_Generative_Models_With_Generalized_Empirical_Likelihoods_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ravuri_Understanding_Deep_Generative_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ravuri_Understanding_Deep_Generative_Models_With_Generalized_Empirical_Likelihoods_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ravuri_Understanding_Deep_Generative_Models_With_Generalized_Empirical_Likelihoods_CVPR_2023_paper.html | CVPR 2023 | null |
ABCD: Arbitrary Bitwise Coefficient for De-Quantization | Woo Kyoung Han, Byeonghun Lee, Sang Hyun Park, Kyong Hwan Jin | Modern displays and contents support more than 8bits image and video. However, bit-starving situations such as compression codecs make low bit-depth (LBD) images (<8bits), occurring banding and blurry artifacts. Previous bit depth expansion (BDE) methods still produce unsatisfactory high bit-depth (HBD) images. To this end, we propose an implicit neural function with a bit query to recover de-quantized images from arbitrarily quantized inputs. We develop a phasor estimator to exploit the information of the nearest pixels. Our method shows superior performance against prior BDE methods on natural and animation images. We also demonstrate our model on YouTube UGC datasets for de-banding. Our source code is available at https://github.com/WooKyoungHan/ABCD | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_ABCD_Arbitrary_Bitwise_Coefficient_for_De-Quantization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_ABCD_Arbitrary_Bitwise_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_ABCD_Arbitrary_Bitwise_Coefficient_for_De-Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_ABCD_Arbitrary_Bitwise_Coefficient_for_De-Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
Event-Based Blurry Frame Interpolation Under Blind Exposure | Wenming Weng, Yueyi Zhang, Zhiwei Xiong | Restoring sharp high frame-rate videos from low frame-rate blurry videos is a challenging problem. Existing blurry frame interpolation methods assume a predefined and known exposure time, which suffer from severe performance drop when applied to videos captured in the wild. In this paper, we study the problem of blurry frame interpolation under blind exposure with the assistance of an event camera. The high temporal resolution of the event camera is beneficial to obtain the exposure prior that is lost during the imaging process. Besides, sharp frames can be restored using event streams and blurry frames relying on the mutual constraint among them. Therefore, we first propose an exposure estimation strategy guided by event streams to estimate the lost exposure prior, transforming the blind exposure problem well-posed. Second, we propose to model the mutual constraint with a temporal-exposure control strategy through iterative residual learning. Our blurry frame interpolation method achieves a distinct performance boost over existing methods on both synthetic and self-collected real-world datasets under blind exposure. | https://openaccess.thecvf.com/content/CVPR2023/papers/Weng_Event-Based_Blurry_Frame_Interpolation_Under_Blind_Exposure_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Weng_Event-Based_Blurry_Frame_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Weng_Event-Based_Blurry_Frame_Interpolation_Under_Blind_Exposure_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Weng_Event-Based_Blurry_Frame_Interpolation_Under_Blind_Exposure_CVPR_2023_paper.html | CVPR 2023 | null |
Subsets and Splits