paper_id stringlengths 33 138 | title stringlengths 13 147 | authors listlengths 1 17 | cvf_url stringlengths 90 195 | pdf_url stringlengths 91 196 | supp_url stringlengths 101 137 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
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Fu_Double_Low-Rank_Representation_With_Projection_Distance_Penalty_for_Clustering_CVPR_2021_paper | Double Low-Rank Representation With Projection Distance Penalty for Clustering | [
"Zhiqiang Fu",
"Yao Zhao",
"Dongxia Chang",
"Xingxing Zhang",
"Yiming Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Double_Low-Rank_Representation_With_Projection_Distance_Penalty_for_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Double_Low-Rank_Representation_With_Projection_Distance_Penalty_for_Clustering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_Double_Low-Rank_Representation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Fu_2021_CVPR,
author = {Fu, Zhiqiang and Zhao, Yao and Chang, Dongxia and Zhang, Xingxing and Wang, Yiming},
title = {Double Low-Rank Representation With Projection Distance Penalty for Clustering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | This paper presents a novel, simple yet robust self-representation method, i.e., Double Low-Rank Representation with Projection Distance penalty (DLRRPD) for clustering. With the learned optimal projected representations, DLRRPD is capable of obtaining an effective similarity graph to capture the multi-subspace structu... |
Hou_Towards_High_Fidelity_Face_Relighting_With_Realistic_Shadows_CVPR_2021_paper | Towards High Fidelity Face Relighting With Realistic Shadows | [
"Andrew Hou",
"Ze Zhang",
"Michel Sarkis",
"Ning Bi",
"Yiying Tong",
"Xiaoming Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Towards_High_Fidelity_Face_Relighting_With_Realistic_Shadows_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Towards_High_Fidelity_Face_Relighting_With_Realistic_Shadows_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hou_Towards_High_Fidelity_CVPR_2021_supplemental.zip | 2104.00825 | cvf | @InProceedings{Hou_2021_CVPR,
author = {Hou, Andrew and Zhang, Ze and Sarkis, Michel and Bi, Ning and Tong, Yiying and Liu, Xiaoming},
title = {Towards High Fidelity Face Relighting With Realistic Shadows},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Existing face relighting methods often struggle with two problems: maintaining the local facial details of the subject and accurately removing and synthesizing shadows in the relit image, especially hard shadows. We propose a novel deep face relighting method that addresses both problems. Our method learns to predict t... |
Lin_Multi-View_Multi-Person_3D_Pose_Estimation_With_Plane_Sweep_Stereo_CVPR_2021_paper | Multi-View Multi-Person 3D Pose Estimation With Plane Sweep Stereo | [
"Jiahao Lin",
"Gim Hee Lee"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Multi-View_Multi-Person_3D_Pose_Estimation_With_Plane_Sweep_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Multi-View_Multi-Person_3D_Pose_Estimation_With_Plane_Sweep_Stereo_CVPR_2021_paper.pdf | null | 2104.02273 | cvf | @InProceedings{Lin_2021_CVPR,
author = {Lin, Jiahao and Lee, Gim Hee},
title = {Multi-View Multi-Person 3D Pose Estimation With Plane Sweep Stereo},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | Existing approaches for multi-view multi-person 3D pose estimation explicitly establish cross-view correspondences to group 2D pose detections from multiple camera views and solve for the 3D pose estimation for each person. Establishing cross-view correspondences is challenging in multi-person scenes, and incorrect cor... |
Zhuang_Fusing_the_Old_with_the_New_Learning_Relative_Camera_Pose_CVPR_2021_paper | Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty | [
"Bingbing Zhuang",
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhuang_Fusing_the_Old_with_the_New_Learning_Relative_Camera_Pose_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhuang_Fusing_the_Old_with_the_New_Learning_Relative_Camera_Pose_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhuang_Fusing_the_Old_CVPR_2021_supplemental.pdf | 2104.08278 | cvf | @InProceedings{Zhuang_2021_CVPR,
author = {Zhuang, Bingbing and Chandraker, Manmohan},
title = {Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | Learning methods for relative camera pose estimation have been developed largely in isolation from classical geometric approaches. The question of how to integrate predictions from deep neural networks (DNNs) and solutions from geometric solvers, such as the 5-point algorithm, has as yet remained under-explored. In thi... |
Wei_CReST_A_Class-Rebalancing_Self-Training_Framework_for_Imbalanced_Semi-Supervised_Learning_CVPR_2021_paper | CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning | [
"Chen Wei",
"Kihyuk Sohn",
"Clayton Mellina",
"Alan Yuille",
"Fan Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_CReST_A_Class-Rebalancing_Self-Training_Framework_for_Imbalanced_Semi-Supervised_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_CReST_A_Class-Rebalancing_Self-Training_Framework_for_Imbalanced_Semi-Supervised_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_CReST_A_Class-Rebalancing_CVPR_2021_supplemental.pdf | 2102.09559 | cvf | @InProceedings{Wei_2021_CVPR,
author = {Wei, Chen and Sohn, Kihyuk and Mellina, Clayton and Yuille, Alan and Yang, Fan},
title = {CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploiting this property, ... |
Song_Towards_Diverse_Paragraph_Captioning_for_Untrimmed_Videos_CVPR_2021_paper | Towards Diverse Paragraph Captioning for Untrimmed Videos | [
"Yuqing Song",
"Shizhe Chen",
"Qin Jin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Towards_Diverse_Paragraph_Captioning_for_Untrimmed_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Towards_Diverse_Paragraph_Captioning_for_Untrimmed_Videos_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Towards_Diverse_Paragraph_CVPR_2021_supplemental.pdf | 2105.14477 | cvf | @InProceedings{Song_2021_CVPR,
author = {Song, Yuqing and Chen, Shizhe and Jin, Qin},
title = {Towards Diverse Paragraph Captioning for Untrimmed Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2... | Video paragraph captioning aims to describe multiple events in untrimmed videos with descriptive paragraphs. Existing approaches mainly solve the problem in two steps: event detection and then event captioning. Such two-step manner makes the quality of generated paragraphs highly dependent on the accuracy of event prop... |
Kittenplon_FlowStep3D_Model_Unrolling_for_Self-Supervised_Scene_Flow_Estimation_CVPR_2021_paper | FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation | [
"Yair Kittenplon",
"Yonina C. Eldar",
"Dan Raviv"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kittenplon_FlowStep3D_Model_Unrolling_for_Self-Supervised_Scene_Flow_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kittenplon_FlowStep3D_Model_Unrolling_for_Self-Supervised_Scene_Flow_Estimation_CVPR_2021_paper.pdf | null | 2011.10147 | cvf | @InProceedings{Kittenplon_2021_CVPR,
author = {Kittenplon, Yair and Eldar, Yonina C. and Raviv, Dan},
title = {FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision. Traditional learning-based methods designed to learn end-to-end 3D flow often suffer from poor generalization. Here we present a recurrent architecture that learns a single step of an unrolled iterative alignment p... |
Awasthi_Adversarial_Robustness_Across_Representation_Spaces_CVPR_2021_paper | Adversarial Robustness Across Representation Spaces | [
"Pranjal Awasthi",
"George Yu",
"Chun-Sung Ferng",
"Andrew Tomkins",
"Da-Cheng Juan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Awasthi_Adversarial_Robustness_Across_Representation_Spaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Awasthi_Adversarial_Robustness_Across_Representation_Spaces_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Awasthi_Adversarial_Robustness_Across_CVPR_2021_supplemental.pdf | 2012.00802 | cvf | @InProceedings{Awasthi_2021_CVPR,
author = {Awasthi, Pranjal and Yu, George and Ferng, Chun-Sung and Tomkins, Andrew and Juan, Da-Cheng},
title = {Adversarial Robustness Across Representation Spaces},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Adversarial robustness corresponds to the susceptibility of deep neural networks to imperceptible perturbations made at test time. In the context of image tasks, many algorithms have been proposed to make neural networks robust to adversarial perturbations made to the input pixels. These perturbations are typically mea... |
Chen_MagDR_Mask-Guided_Detection_and_Reconstruction_for_Defending_Deepfakes_CVPR_2021_paper | MagDR: Mask-Guided Detection and Reconstruction for Defending Deepfakes | [
"Zhikai Chen",
"Lingxi Xie",
"Shanmin Pang",
"Yong He",
"Bo Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_MagDR_Mask-Guided_Detection_and_Reconstruction_for_Defending_Deepfakes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_MagDR_Mask-Guided_Detection_and_Reconstruction_for_Defending_Deepfakes_CVPR_2021_paper.pdf | null | 2103.14211 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Zhikai and Xie, Lingxi and Pang, Shanmin and He, Yong and Zhang, Bo},
title = {MagDR: Mask-Guided Detection and Reconstruction for Defending Deepfakes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet. This paper presents MagDR, a mask-guided detection and reconstruction pipe... |
Bozic_Neural_Deformation_Graphs_for_Globally-Consistent_Non-Rigid_Reconstruction_CVPR_2021_paper | Neural Deformation Graphs for Globally-Consistent Non-Rigid Reconstruction | [
"Aljaz Bozic",
"Pablo Palafox",
"Michael Zollhofer",
"Justus Thies",
"Angela Dai",
"Matthias Niessner"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bozic_Neural_Deformation_Graphs_for_Globally-Consistent_Non-Rigid_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bozic_Neural_Deformation_Graphs_for_Globally-Consistent_Non-Rigid_Reconstruction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bozic_Neural_Deformation_Graphs_CVPR_2021_supplemental.pdf | 2012.01451 | cvf | @InProceedings{Bozic_2021_CVPR,
author = {Bozic, Aljaz and Palafox, Pablo and Zollhofer, Michael and Thies, Justus and Dai, Angela and Niessner, Matthias},
title = {Neural Deformation Graphs for Globally-Consistent Non-Rigid Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Comp... | We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects. Specifically, we implicitly model a deformation graph via a deep neural network. This neural deformation graph does not rely on any object-specific structure and, thus, can be applied to gener... |
Bechtold_Fostering_Generalization_in_Single-View_3D_Reconstruction_by_Learning_a_Hierarchy_CVPR_2021_paper | Fostering Generalization in Single-View 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors | [
"Jan Bechtold",
"Maxim Tatarchenko",
"Volker Fischer",
"Thomas Brox"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bechtold_Fostering_Generalization_in_Single-View_3D_Reconstruction_by_Learning_a_Hierarchy_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bechtold_Fostering_Generalization_in_Single-View_3D_Reconstruction_by_Learning_a_Hierarchy_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bechtold_Fostering_Generalization_in_CVPR_2021_supplemental.pdf | 2104.00476 | cvf | @InProceedings{Bechtold_2021_CVPR,
author = {Bechtold, Jan and Tatarchenko, Maxim and Fischer, Volker and Brox, Thomas},
title = {Fostering Generalization in Single-View 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training. Common approaches predominantly rely on learned global shape priors and, hence, disregard detailed local observations. In this work, we address this issue by learning a hierarchy ... |
Chen_Progressive_Semantic-Aware_Style_Transformation_for_Blind_Face_Restoration_CVPR_2021_paper | Progressive Semantic-Aware Style Transformation for Blind Face Restoration | [
"Chaofeng Chen",
"Xiaoming Li",
"Lingbo Yang",
"Xianhui Lin",
"Lei Zhang",
"Kwan-Yee K. Wong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Progressive_Semantic-Aware_Style_Transformation_for_Blind_Face_Restoration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Progressive_Semantic-Aware_Style_Transformation_for_Blind_Face_Restoration_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Progressive_Semantic-Aware_Style_CVPR_2021_supplemental.pdf | 2009.08709 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Chaofeng and Li, Xiaoming and Yang, Lingbo and Lin, Xianhui and Zhang, Lei and Wong, Kwan-Yee K.},
title = {Progressive Semantic-Aware Style Transformation for Blind Face Restoration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Face restoration is important in face image processing, and has been widely studied in recent years. However, previous works often fail to generate plausible high quality (HQ) results for real-world low quality (LQ) face images. In this paper, we propose a new progressive semantic-aware style transformation framework, ... |
Wen_Seeking_the_Shape_of_Sound_An_Adaptive_Framework_for_Learning_CVPR_2021_paper | Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association | [
"Peisong Wen",
"Qianqian Xu",
"Yangbangyan Jiang",
"Zhiyong Yang",
"Yuan He",
"Qingming Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Seeking_the_Shape_of_Sound_An_Adaptive_Framework_for_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Seeking_the_Shape_of_Sound_An_Adaptive_Framework_for_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wen_Seeking_the_Shape_CVPR_2021_supplemental.zip | 2103.07293 | cvf | @InProceedings{Wen_2021_CVPR,
author = {Wen, Peisong and Xu, Qianqian and Jiang, Yangbangyan and Yang, Zhiyong and He, Yuan and Huang, Qingming},
title = {Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association},
booktitle = {Proceedings of the IEEE/CVF Conference on Com... | Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community. However, most of the prior arts along this line (a) merely adopt local information to perform modality alignment and (b) ignore the divers... |
Xing_Invertible_Image_Signal_Processing_CVPR_2021_paper | Invertible Image Signal Processing | [
"Yazhou Xing",
"Zian Qian",
"Qifeng Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xing_Invertible_Image_Signal_Processing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xing_Invertible_Image_Signal_Processing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xing_Invertible_Image_Signal_CVPR_2021_supplemental.pdf | 2103.15061 | cvf | @InProceedings{Xing_2021_CVPR,
author = {Xing, Yazhou and Qian, Zian and Chen, Qifeng},
title = {Invertible Image Signal Processing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages =... | Unprocessed RAW data is a highly valuable image format for image editing and computer vision. However, since the file size of RAW data is huge, most users can only get access to processed and compressed sRGB images. To bridge this gap, we design an Invertible Image Signal Processing (InvISP) pipeline, which not only en... |
Li_Lighting_Reflectance_and_Geometry_Estimation_From_360deg_Panoramic_Stereo_CVPR_2021_paper | Lighting, Reflectance and Geometry Estimation From 360deg Panoramic Stereo | [
"Junxuan Li",
"Hongdong Li",
"Yasuyuki Matsushita"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Lighting_Reflectance_and_Geometry_Estimation_From_360deg_Panoramic_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Lighting_Reflectance_and_Geometry_Estimation_From_360deg_Panoramic_Stereo_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Lighting_Reflectance_and_CVPR_2021_supplemental.zip | 2104.09886 | title_judge | @InProceedings{Li_2021_CVPR,
author = {Li, Junxuan and Li, Hongdong and Matsushita, Yasuyuki},
title = {Lighting, Reflectance and Geometry Estimation From 360deg Panoramic Stereo},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | We propose a method for estimating high-definition spatially-varying lighting, reflectance, and geometry of a scene from 360deg stereo images. Our model takes advantage of the 360deg input to observe the entire scene with geometric detail, then jointly estimates the scene's properties with physical constraints. We firs... |
Lim_Building_Reliable_Explanations_of_Unreliable_Neural_Networks_Locally_Smoothing_Perspective_CVPR_2021_paper | Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation | [
"Dohun Lim",
"Hyeonseok Lee",
"Sungchan Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lim_Building_Reliable_Explanations_of_Unreliable_Neural_Networks_Locally_Smoothing_Perspective_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lim_Building_Reliable_Explanations_of_Unreliable_Neural_Networks_Locally_Smoothing_Perspective_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lim_Building_Reliable_Explanations_CVPR_2021_supplemental.pdf | 2103.14332 | cvf | @InProceedings{Lim_2021_CVPR,
author = {Lim, Dohun and Lee, Hyeonseok and Kim, Sungchan},
title = {Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | We present a novel method for reliably explaining the predictions of neural networks. We consider an explanation reliable if it identifies input features relevant to the model output by considering the input and the neighboring data points. Our method is built on top of the assumption of smooth landscape in a loss func... |
Wizadwongsa_NeX_Real-Time_View_Synthesis_With_Neural_Basis_Expansion_CVPR_2021_paper | NeX: Real-Time View Synthesis With Neural Basis Expansion | [
"Suttisak Wizadwongsa",
"Pakkapon Phongthawee",
"Jiraphon Yenphraphai",
"Supasorn Suwajanakorn"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wizadwongsa_NeX_Real-Time_View_Synthesis_With_Neural_Basis_Expansion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wizadwongsa_NeX_Real-Time_View_Synthesis_With_Neural_Basis_Expansion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wizadwongsa_NeX_Real-Time_View_CVPR_2021_supplemental.pdf | 2103.05606 | cvf | @InProceedings{Wizadwongsa_2021_CVPR,
author = {Wizadwongsa, Suttisak and Phongthawee, Pakkapon and Yenphraphai, Jiraphon and Suwajanakorn, Supasorn},
title = {NeX: Real-Time View Synthesis With Neural Basis Expansion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce next-level view-dependent effects--in real time. Unlike traditional MPI that uses a set of simple RGBa planes, our technique models view-dependent effects by instead parameterizing each pixel as a l... |
Qu_DAT_Training_Deep_Networks_Robust_To_Label-Noise_by_Matching_the_CVPR_2021_paper | DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature Distributions | [
"Yuntao Qu",
"Shasha Mo",
"Jianwei Niu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qu_DAT_Training_Deep_Networks_Robust_To_Label-Noise_by_Matching_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qu_DAT_Training_Deep_Networks_Robust_To_Label-Noise_by_Matching_the_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qu_DAT_Training_Deep_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Qu_2021_CVPR,
author = {Qu, Yuntao and Mo, Shasha and Niu, Jianwei},
title = {DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature Distributions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | In real application scenarios, the performance of deep networks may be degraded when the dataset contains noisy labels. Existing methods for learning with noisy labels are limited by two aspects. Firstly, methods based on the noise probability modeling can only be applied to class-level noisy labels. Secondly, others b... |
Zhang_Repetitive_Activity_Counting_by_Sight_and_Sound_CVPR_2021_paper | Repetitive Activity Counting by Sight and Sound | [
"Yunhua Zhang",
"Ling Shao",
"Cees G. M. Snoek"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Repetitive_Activity_Counting_by_Sight_and_Sound_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Repetitive_Activity_Counting_by_Sight_and_Sound_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Repetitive_Activity_Counting_CVPR_2021_supplemental.zip | 2103.13096 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Yunhua and Shao, Ling and Snoek, Cees G. M.},
title = {Repetitive Activity Counting by Sight and Sound},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {20... | This paper strives for repetitive activity counting in videos. Different from existing works, which all analyze the visual video content only, we incorporate for the first time the corresponding sound into the repetition counting process. This benefits accuracy in challenging vision conditions such as occlusion, dramat... |
Liu_PointGuard_Provably_Robust_3D_Point_Cloud_Classification_CVPR_2021_paper | PointGuard: Provably Robust 3D Point Cloud Classification | [
"Hongbin Liu",
"Jinyuan Jia",
"Neil Zhenqiang Gong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_PointGuard_Provably_Robust_3D_Point_Cloud_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_PointGuard_Provably_Robust_3D_Point_Cloud_Classification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_PointGuard_Provably_Robust_CVPR_2021_supplemental.pdf | 2103.03046 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Hongbin and Jia, Jinyuan and Gong, Neil Zhenqiang},
title = {PointGuard: Provably Robust 3D Point Cloud Classification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | 3D point cloud classification has many safety-critical applications such as autonomous driving and robotic grasping. However, several studies showed that it is vulnerable to adversarial attacks. In particular, an attacker can make a classifier predict an incorrect label for a 3D point cloud via carefully modifying, add... |
Bai_Unsupervised_Multi-Source_Domain_Adaptation_for_Person_Re-Identification_CVPR_2021_paper | Unsupervised Multi-Source Domain Adaptation for Person Re-Identification | [
"Zechen Bai",
"Zhigang Wang",
"Jian Wang",
"Di Hu",
"Errui Ding"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Unsupervised_Multi-Source_Domain_Adaptation_for_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_Unsupervised_Multi-Source_Domain_Adaptation_for_Person_Re-Identification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bai_Unsupervised_Multi-Source_Domain_CVPR_2021_supplemental.pdf | 2104.12961 | cvf | @InProceedings{Bai_2021_CVPR,
author = {Bai, Zechen and Wang, Zhigang and Wang, Jian and Hu, Di and Ding, Errui},
title = {Unsupervised Multi-Source Domain Adaptation for Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | Unsupervised domain adaptation (UDA) methods for person re-identification (re-ID) aim at transferring re-ID knowledge from labeled source data to unlabeled target data. Among these methods, the pseudo-label-based branch has achieved great success, whereas most of them only use limited data from a single-source domain f... |
Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper | BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation | [
"Jungbeom Lee",
"Jihun Yi",
"Chaehun Shin",
"Sungroh Yoon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_BBAM_Bounding_Box_CVPR_2021_supplemental.pdf | 2103.08907 | cvf | @InProceedings{Lee_2021_CVPR,
author = {Lee, Jungbeom and Yi, Jihun and Shin, Chaehun and Yoon, Sungroh},
title = {BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on a class-agnostic mask generator, which operates on the low-level information intrinsic to an image. In this work, we utilize higher-level i... |
Qiu_Boosting_Video_Representation_Learning_With_Multi-Faceted_Integration_CVPR_2021_paper | Boosting Video Representation Learning With Multi-Faceted Integration | [
"Zhaofan Qiu",
"Ting Yao",
"Chong-Wah Ngo",
"Xiao-Ping Zhang",
"Dong Wu",
"Tao Mei"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Boosting_Video_Representation_Learning_With_Multi-Faceted_Integration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_Boosting_Video_Representation_Learning_With_Multi-Faceted_Integration_CVPR_2021_paper.pdf | null | 2201.04023 | title_snapshot | @InProceedings{Qiu_2021_CVPR,
author = {Qiu, Zhaofan and Yao, Ting and Ngo, Chong-Wah and Zhang, Xiao-Ping and Wu, Dong and Mei, Tao},
title = {Boosting Video Representation Learning With Multi-Faceted Integration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video repres... |
Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper | Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection | [
"Zonghao Guo",
"Chang Liu",
"Xiaosong Zhang",
"Jianbin Jiao",
"Xiangyang Ji",
"Qixiang Ye"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Guo_2021_CVPR,
author = {Guo, Zonghao and Liu, Chang and Zhang, Xiaosong and Jiao, Jianbin and Ji, Xiangyang and Ye, Qixiang},
title = {Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection},
booktitle = {Proceedings of the IEEE/CVF Confer... | Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects. In this paper, we propose a convex-hull feature adaptation (CFA) approach for configuring convolutional features in accordance with oriented and densely packed o... |
Zhao_3D_Graph_Anatomy_Geometry-Integrated_Network_for_Pancreatic_Mass_Segmentation_Diagnosis_CVPR_2021_paper | 3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management | [
"Tianyi Zhao",
"Kai Cao",
"Jiawen Yao",
"Isabella Nogues",
"Le Lu",
"Lingyun Huang",
"Jing Xiao",
"Zhaozheng Yin",
"Ling Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_3D_Graph_Anatomy_Geometry-Integrated_Network_for_Pancreatic_Mass_Segmentation_Diagnosis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_3D_Graph_Anatomy_Geometry-Integrated_Network_for_Pancreatic_Mass_Segmentation_Diagnosis_CVPR_2021_paper.pdf | null | 2012.04701 | cvf | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Tianyi and Cao, Kai and Yao, Jiawen and Nogues, Isabella and Lu, Le and Huang, Lingyun and Xiao, Jing and Yin, Zhaozheng and Zhang, Ling},
title = {3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Pat... | The pancreatic disease taxonomy includes ten types of masses (tumors or cysts) [20, 8]. Previous work focuses on developing segmentation or classification methods only for certain mass types. Differential diagnosis of all mass types is clinically highly desirable [20] but has not been investigated using an automated im... |
Ong_Protecting_Intellectual_Property_of_Generative_Adversarial_Networks_From_Ambiguity_Attacks_CVPR_2021_paper | Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks | [
"Ding Sheng Ong",
"Chee Seng Chan",
"Kam Woh Ng",
"Lixin Fan",
"Qiang Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ong_Protecting_Intellectual_Property_of_Generative_Adversarial_Networks_From_Ambiguity_Attacks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ong_Protecting_Intellectual_Property_of_Generative_Adversarial_Networks_From_Ambiguity_Attacks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ong_Protecting_Intellectual_Property_CVPR_2021_supplemental.pdf | 2102.04362 | cvf | @InProceedings{Ong_2021_CVPR,
author = {Ong, Ding Sheng and Chan, Chee Seng and Ng, Kam Woh and Fan, Lixin and Yang, Qiang},
title = {Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | Ever since Machine Learning as a Service emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. ... |
Robidoux_End-to-End_High_Dynamic_Range_Camera_Pipeline_Optimization_CVPR_2021_paper | End-to-End High Dynamic Range Camera Pipeline Optimization | [
"Nicolas Robidoux",
"Luis E. Garcia Capel",
"Dong-eun Seo",
"Avinash Sharma",
"Federico Ariza",
"Felix Heide"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Robidoux_End-to-End_High_Dynamic_Range_Camera_Pipeline_Optimization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Robidoux_End-to-End_High_Dynamic_Range_Camera_Pipeline_Optimization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Robidoux_End-to-End_High_Dynamic_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Robidoux_2021_CVPR,
author = {Robidoux, Nicolas and Capel, Luis E. Garcia and Seo, Dong-eun and Sharma, Avinash and Ariza, Federico and Heide, Felix},
title = {End-to-End High Dynamic Range Camera Pipeline Optimization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | With a 280 dB dynamic range, the real world is a High Dynamic Range (HDR) world. Today's sensors cannot record this dynamic range in a single shot. Instead, HDR cameras acquire multiple measurements with different exposures, gains and photodiodes, from which an Image Signal Processor (ISP) reconstructs an HDR image. HD... |
Ge_Parser-Free_Virtual_Try-On_via_Distilling_Appearance_Flows_CVPR_2021_paper | Parser-Free Virtual Try-On via Distilling Appearance Flows | [
"Yuying Ge",
"Yibing Song",
"Ruimao Zhang",
"Chongjian Ge",
"Wei Liu",
"Ping Luo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Parser-Free_Virtual_Try-On_via_Distilling_Appearance_Flows_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_Parser-Free_Virtual_Try-On_via_Distilling_Appearance_Flows_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ge_Parser-Free_Virtual_Try-On_CVPR_2021_supplemental.pdf | 2103.04559 | cvf | @InProceedings{Ge_2021_CVPR,
author = {Ge, Yuying and Song, Yibing and Zhang, Ruimao and Ge, Chongjian and Liu, Wei and Luo, Ping},
title = {Parser-Free Virtual Try-On via Distilling Appearance Flows},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Image virtual try-on aims to fit a garment image (target clothes) to a person image. Prior methods are heavily based on human parsing. However, slightly-wrong segmentation results would lead to unrealistic try-on images with large artifacts. Inaccurate parsing misleads parser-based methods to produce visually unrealist... |
Niemeyer_GIRAFFE_Representing_Scenes_As_Compositional_Generative_Neural_Feature_Fields_CVPR_2021_paper | GIRAFFE: Representing Scenes As Compositional Generative Neural Feature Fields | [
"Michael Niemeyer",
"Andreas Geiger"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Niemeyer_GIRAFFE_Representing_Scenes_As_Compositional_Generative_Neural_Feature_Fields_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Niemeyer_GIRAFFE_Representing_Scenes_As_Compositional_Generative_Neural_Feature_Fields_CVPR_2021_paper.pdf | null | 2011.12100 | cvf | @InProceedings{Niemeyer_2021_CVPR,
author = {Niemeyer, Michael and Geiger, Andreas},
title = {GIRAFFE: Representing Scenes As Compositional Generative Neural Feature Fields},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable. While several recent works investigate how to disentangle underlying factors of variation in the data, most of them operate in 2D and hence ... |
Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper | Single-Stage Instance Shadow Detection With Bidirectional Relation Learning | [
"Tianyu Wang",
"Xiaowei Hu",
"Chi-Wing Fu",
"Pheng-Ann Heng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Single-Stage_Instance_Shadow_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Tianyu and Hu, Xiaowei and Fu, Chi-Wing and Heng, Pheng-Ann},
title = {Single-Stage Instance Shadow Detection With Bidirectional Relation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows. The previous work adopts a two-stage framework to first predict shadow instances, object instances, and shadow-object associations from the region proposals, then leverage a post-processing to match the predictions to... |
Zheng_High-Speed_Image_Reconstruction_Through_Short-Term_Plasticity_for_Spiking_Cameras_CVPR_2021_paper | High-Speed Image Reconstruction Through Short-Term Plasticity for Spiking Cameras | [
"Yajing Zheng",
"Lingxiao Zheng",
"Zhaofei Yu",
"Boxin Shi",
"Yonghong Tian",
"Tiejun Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_High-Speed_Image_Reconstruction_Through_Short-Term_Plasticity_for_Spiking_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_High-Speed_Image_Reconstruction_Through_Short-Term_Plasticity_for_Spiking_Cameras_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_High-Speed_Image_Reconstruction_CVPR_2021_supplemental.zip | null | null | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Yajing and Zheng, Lingxiao and Yu, Zhaofei and Shi, Boxin and Tian, Yonghong and Huang, Tiejun},
title = {High-Speed Image Reconstruction Through Short-Term Plasticity for Spiking Cameras},
booktitle = {Proceedings of the IEEE/CVF Conference on Compute... | Fovea, located in the centre of the retina, is specialized for high-acuity vision. Mimicking the sampling mechanism of the fovea, a retina-inspired camera, named spiking camera, is developed to record the external information with a sampling rate of 40,000 Hz, and outputs asynchronous binary spike streams. Although the... |
Hu_Self-Supervised_3D_Mesh_Reconstruction_From_Single_Images_CVPR_2021_paper | Self-Supervised 3D Mesh Reconstruction From Single Images | [
"Tao Hu",
"Liwei Wang",
"Xiaogang Xu",
"Shu Liu",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Self-Supervised_3D_Mesh_Reconstruction_From_Single_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Self-Supervised_3D_Mesh_Reconstruction_From_Single_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Self-Supervised_3D_Mesh_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Hu_2021_CVPR,
author = {Hu, Tao and Wang, Liwei and Xu, Xiaogang and Liu, Shu and Jia, Jiaya},
title = {Self-Supervised 3D Mesh Reconstruction From Single Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | Recent single-view 3D reconstruction methods reconstruct object's shape and texture from a single image with only 2D image-level annotation. However, without explicit 3D attribute-level supervision, it is still difficult to achieve satisfying reconstruction accuracy. In this paper, we propose a Self-supervised Mesh Rec... |
Lu_Dual-GAN_Joint_BVP_and_Noise_Modeling_for_Remote_Physiological_Measurement_CVPR_2021_paper | Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement | [
"Hao Lu",
"Hu Han",
"S. Kevin Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Dual-GAN_Joint_BVP_and_Noise_Modeling_for_Remote_Physiological_Measurement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Dual-GAN_Joint_BVP_and_Noise_Modeling_for_Remote_Physiological_Measurement_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Lu_2021_CVPR,
author = {Lu, Hao and Han, Hu and Zhou, S. Kevin},
title = {Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
ye... | Remote photoplethysmography (rPPG) based physiological measurement has great application values in health monitoring, emotion analysis, etc. Existing methods mainly focus on how to enhance or extract the very weak blood volume pulse (BVP) signals from face videos, but seldom explicitly model the noises that dominate fa... |
Morgado_Audio-Visual_Instance_Discrimination_with_Cross-Modal_Agreement_CVPR_2021_paper | Audio-Visual Instance Discrimination with Cross-Modal Agreement | [
"Pedro Morgado",
"Nuno Vasconcelos",
"Ishan Misra"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Morgado_Audio-Visual_Instance_Discrimination_with_Cross-Modal_Agreement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Morgado_Audio-Visual_Instance_Discrimination_with_Cross-Modal_Agreement_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Morgado_Audio-Visual_Instance_Discrimination_CVPR_2021_supplemental.pdf | 2004.12943 | cvf | @InProceedings{Morgado_2021_CVPR,
author = {Morgado, Pedro and Vasconcelos, Nuno and Misra, Ishan},
title = {Audio-Visual Instance Discrimination with Cross-Modal Agreement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | We present a self-supervised learning approach to learn audio-visual representations from video and audio. Our method uses contrastive learning for cross-modal discrimination of video from audio and vice-versa. We show that optimizing for cross-modal discrimination, rather than within-modal discrimination, is important... |
Li_Combined_Depth_Space_Based_Architecture_Search_for_Person_Re-Identification_CVPR_2021_paper | Combined Depth Space Based Architecture Search for Person Re-Identification | [
"Hanjun Li",
"Gaojie Wu",
"Wei-Shi Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Combined_Depth_Space_Based_Architecture_Search_for_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Combined_Depth_Space_Based_Architecture_Search_for_Person_Re-Identification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Combined_Depth_Space_CVPR_2021_supplemental.pdf | 2104.04163 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Hanjun and Wu, Gaojie and Zheng, Wei-Shi},
title = {Combined Depth Space Based Architecture Search for Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Most works on person re-identification (ReID) take advantage of large backbone networks such as ResNet, which are designed for image classification instead of ReID, for feature extraction. However, these backbones may not be computationally efficient or the most suitable architectures for ReID. In this work, we aim to ... |
Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper | Rethinking BiSeNet for Real-Time Semantic Segmentation | [
"Mingyuan Fan",
"Shenqi Lai",
"Junshi Huang",
"Xiaoming Wei",
"Zhenhua Chai",
"Junfeng Luo",
"Xiaolin Wei"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.pdf | null | 2104.13188 | cvf | @InProceedings{Fan_2021_CVPR,
author = {Fan, Mingyuan and Lai, Shenqi and Huang, Junshi and Wei, Xiaoming and Chai, Zhenhua and Luo, Junfeng and Wei, Xiaolin},
title = {Rethinking BiSeNet for Real-Time Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | BiSeNet has been proved to be a popular two-stream network for real-time segmentation. However, its principle of adding an extra path to encode spatial information is time-consuming, and the backbones borrowed from pretrained tasks, e.g., image classification, may be inefficient for image segmentation due to the defici... |
Zheng_The_Spatially-Correlative_Loss_for_Various_Image_Translation_Tasks_CVPR_2021_paper | The Spatially-Correlative Loss for Various Image Translation Tasks | [
"Chuanxia Zheng",
"Tat-Jen Cham",
"Jianfei Cai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_The_Spatially-Correlative_Loss_for_Various_Image_Translation_Tasks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_The_Spatially-Correlative_Loss_for_Various_Image_Translation_Tasks_CVPR_2021_paper.pdf | null | 2104.00854 | cvf | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei},
title = {The Spatially-Correlative Loss for Various Image Translation Tasks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We propose a novel spatially-correlative loss that is simple, efficient, and yet effective for preserving scene structure consistency while supporting large appearance changes during unpaired image-to-image (I2I) translation. Previous methods attempt this by using pixel-level cycle-consistency or feature-level matching... |
Zhang_Learning_To_Restore_Hazy_Video_A_New_Real-World_Dataset_and_CVPR_2021_paper | Learning To Restore Hazy Video: A New Real-World Dataset and a New Method | [
"Xinyi Zhang",
"Hang Dong",
"Jinshan Pan",
"Chao Zhu",
"Ying Tai",
"Chengjie Wang",
"Jilin Li",
"Feiyue Huang",
"Fei Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_To_Restore_Hazy_Video_A_New_Real-World_Dataset_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_To_Restore_Hazy_Video_A_New_Real-World_Dataset_and_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Learning_To_Restore_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Xinyi and Dong, Hang and Pan, Jinshan and Zhu, Chao and Tai, Ying and Wang, Chengjie and Li, Jilin and Huang, Feiyue and Wang, Fei},
title = {Learning To Restore Hazy Video: A New Real-World Dataset and a New Method},
booktitle = {Proceedings of the IE... | Most of the existing deep learning-based dehazing methods are trained and evaluated on the image dehazing datasets, where the dehazed images are generated by only exploiting the information from the corresponding hazy ones. On the other hand, the video dehazing algorithms, which can acquire more satisfying dehazing res... |
Quach_DyGLIP_A_Dynamic_Graph_Model_With_Link_Prediction_for_Accurate_CVPR_2021_paper | DyGLIP: A Dynamic Graph Model With Link Prediction for Accurate Multi-Camera Multiple Object Tracking | [
"Kha Gia Quach",
"Pha Nguyen",
"Huu Le",
"Thanh-Dat Truong",
"Chi Nhan Duong",
"Minh-Triet Tran",
"Khoa Luu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Quach_DyGLIP_A_Dynamic_Graph_Model_With_Link_Prediction_for_Accurate_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Quach_DyGLIP_A_Dynamic_Graph_Model_With_Link_Prediction_for_Accurate_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Quach_DyGLIP_A_Dynamic_CVPR_2021_supplemental.zip | 2106.06856 | cvf | @InProceedings{Quach_2021_CVPR,
author = {Quach, Kha Gia and Nguyen, Pha and Le, Huu and Truong, Thanh-Dat and Duong, Chi Nhan and Tran, Minh-Triet and Luu, Khoa},
title = {DyGLIP: A Dynamic Graph Model With Link Prediction for Accurate Multi-Camera Multiple Object Tracking},
booktitle = {Proceedings... | Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a ... |
Yin_Towards_Efficient_Tensor_Decomposition-Based_DNN_Model_Compression_With_Optimization_Framework_CVPR_2021_paper | Towards Efficient Tensor Decomposition-Based DNN Model Compression With Optimization Framework | [
"Miao Yin",
"Yang Sui",
"Siyu Liao",
"Bo Yuan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Towards_Efficient_Tensor_Decomposition-Based_DNN_Model_Compression_With_Optimization_Framework_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Towards_Efficient_Tensor_Decomposition-Based_DNN_Model_Compression_With_Optimization_Framework_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_Towards_Efficient_Tensor_CVPR_2021_supplemental.pdf | 2107.12422 | title_snapshot | @InProceedings{Yin_2021_CVPR,
author = {Yin, Miao and Sui, Yang and Liao, Siyu and Yuan, Bo},
title = {Towards Efficient Tensor Decomposition-Based DNN Model Compression With Optimization Framework},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Advanced tensor decomposition, such as Tensor train (TT) and Tensor ring (TR), has been widely studied for deep neural network (DNN) model compression, especially for recurrent neural networks (RNNs). However, compressing convolutional neural networks (CNNs) using TT/TR always suffers significant accuracy loss. In this... |
Zhang_User-Guided_Line_Art_Flat_Filling_With_Split_Filling_Mechanism_CVPR_2021_paper | User-Guided Line Art Flat Filling With Split Filling Mechanism | [
"Lvmin Zhang",
"Chengze Li",
"Edgar Simo-Serra",
"Yi Ji",
"Tien-Tsin Wong",
"Chunping Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_User-Guided_Line_Art_Flat_Filling_With_Split_Filling_Mechanism_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_User-Guided_Line_Art_Flat_Filling_With_Split_Filling_Mechanism_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Lvmin and Li, Chengze and Simo-Serra, Edgar and Ji, Yi and Wong, Tien-Tsin and Liu, Chunping},
title = {User-Guided Line Art Flat Filling With Split Filling Mechanism},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | Flat filling is a critical step in digital artistic content creation with the objective of filling line arts with flat colors. We present a deep learning framework for user-guided line art flat filling that can compute the "influence areas" of the user color scribbles, i.e., the areas where the user scribbles should pr... |
Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper | Restore From Restored: Video Restoration With Pseudo Clean Video | [
"Seunghwan Lee",
"Donghyeon Cho",
"Jiwon Kim",
"Tae Hyun Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper.pdf | null | 2003.04279 | cvf | @InProceedings{Lee_2021_CVPR,
author = {Lee, Seunghwan and Cho, Donghyeon and Kim, Jiwon and Kim, Tae Hyun},
title = {Restore From Restored: Video Restoration With Pseudo Clean Video},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | In this study, we propose a self-supervised video denoising method called ""restore-from-restored."" This method fine-tunes a pre-trained network by using a pseudo clean video during the test phase. The pseudo clean video is obtained by applying a noisy video to the baseline network. By adopting a fully convolutional n... |
Qiu_Semantic_Segmentation_for_Real_Point_Cloud_Scenes_via_Bilateral_Augmentation_CVPR_2021_paper | Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion | [
"Shi Qiu",
"Saeed Anwar",
"Nick Barnes"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Semantic_Segmentation_for_Real_Point_Cloud_Scenes_via_Bilateral_Augmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_Semantic_Segmentation_for_Real_Point_Cloud_Scenes_via_Bilateral_Augmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiu_Semantic_Segmentation_for_CVPR_2021_supplemental.pdf | 2103.07074 | cvf | @InProceedings{Qiu_2021_CVPR,
author = {Qiu, Shi and Anwar, Saeed and Barnes, Nick},
title = {Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation. Particularly, real point cloud scenes can intuitively capture complex surroundings in the real world, but due to 3D data's raw nature, it is very challenging for machine perception. In t... |
Yang_Interactive_Self-Training_With_Mean_Teachers_for_Semi-Supervised_Object_Detection_CVPR_2021_paper | Interactive Self-Training With Mean Teachers for Semi-Supervised Object Detection | [
"Qize Yang",
"Xihan Wei",
"Biao Wang",
"Xian-Sheng Hua",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Interactive_Self-Training_With_Mean_Teachers_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Interactive_Self-Training_With_Mean_Teachers_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Yang_2021_CVPR,
author = {Yang, Qize and Wei, Xihan and Wang, Biao and Hua, Xian-Sheng and Zhang, Lei},
title = {Interactive Self-Training With Mean Teachers for Semi-Supervised Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | The goal of semi-supervised object detection is to learn a detection model using only a few labeled data and large amounts of unlabeled data, thereby reducing the cost of data labeling. Although a few studies have proposed various self-training-based methods or consistency regularization-based methods, they ignore the ... |
Liu_DeFLOCNet_Deep_Image_Editing_via_Flexible_Low-Level_Controls_CVPR_2021_paper | DeFLOCNet: Deep Image Editing via Flexible Low-Level Controls | [
"Hongyu Liu",
"Ziyu Wan",
"Wei Huang",
"Yibing Song",
"Xintong Han",
"Jing Liao",
"Bin Jiang",
"Wei Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DeFLOCNet_Deep_Image_Editing_via_Flexible_Low-Level_Controls_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_DeFLOCNet_Deep_Image_Editing_via_Flexible_Low-Level_Controls_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_DeFLOCNet_Deep_Image_CVPR_2021_supplemental.pdf | 2103.12723 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Hongyu and Wan, Ziyu and Huang, Wei and Song, Yibing and Han, Xintong and Liao, Jing and Jiang, Bin and Liu, Wei},
title = {DeFLOCNet: Deep Image Editing via Flexible Low-Level Controls},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse lowlevel inputs, which typically consist of sparse sketch lines and color dots, convey user intentions for content creation (i.e., free-form editing). While existing methods combine an input image and these l... |
Lin_Vx2Text_End-to-End_Learning_of_Video-Based_Text_Generation_From_Multimodal_Inputs_CVPR_2021_paper | Vx2Text: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs | [
"Xudong Lin",
"Gedas Bertasius",
"Jue Wang",
"Shih-Fu Chang",
"Devi Parikh",
"Lorenzo Torresani"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Vx2Text_End-to-End_Learning_of_Video-Based_Text_Generation_From_Multimodal_Inputs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Vx2Text_End-to-End_Learning_of_Video-Based_Text_Generation_From_Multimodal_Inputs_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Vx2Text_End-to-End_Learning_CVPR_2021_supplemental.pdf | 2101.12059 | cvf | @InProceedings{Lin_2021_CVPR,
author = {Lin, Xudong and Bertasius, Gedas and Wang, Jue and Chang, Shih-Fu and Parikh, Devi and Torresani, Lorenzo},
title = {Vx2Text: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs},
booktitle = {Proceedings of the IEEE/CVF Conference on Comp... | We present Vx2Text, a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. In order to leverage transformer networks, which have been shown to be effective at modeling language, each modality is first converted into a set of language embeddings by a learnable tokenizer. ... |
Yang_KSM_Fast_Multiple_Task_Adaption_via_Kernel-Wise_Soft_Mask_Learning_CVPR_2021_paper | KSM: Fast Multiple Task Adaption via Kernel-Wise Soft Mask Learning | [
"Li Yang",
"Zhezhi He",
"Junshan Zhang",
"Deliang Fan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_KSM_Fast_Multiple_Task_Adaption_via_Kernel-Wise_Soft_Mask_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_KSM_Fast_Multiple_Task_Adaption_via_Kernel-Wise_Soft_Mask_Learning_CVPR_2021_paper.pdf | null | 2009.05668 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Li and He, Zhezhi and Zhang, Junshan and Fan, Deliang},
title = {KSM: Fast Multiple Task Adaption via Kernel-Wise Soft Mask Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks, and this is known as catastrophic forgetting. To learn new task without forgetting, recently, the mask-based learning method (e.g. piggyback ) is proposed to address these issues by learning only a binary element-wise mas... |
Lin_Rich_Context_Aggregation_With_Reflection_Prior_for_Glass_Surface_Detection_CVPR_2021_paper | Rich Context Aggregation With Reflection Prior for Glass Surface Detection | [
"Jiaying Lin",
"Zebang He",
"Rynson W.H. Lau"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Rich_Context_Aggregation_With_Reflection_Prior_for_Glass_Surface_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Rich_Context_Aggregation_With_Reflection_Prior_for_Glass_Surface_Detection_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Lin_2021_CVPR,
author = {Lin, Jiaying and He, Zebang and Lau, Rynson W.H.},
title = {Rich Context Aggregation With Reflection Prior for Glass Surface Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | Glass surfaces appear everywhere. Their existence can however pose a serious problem to computer vision tasks. Recently, a method is proposed to detect glass surfaces by learning multi-scale contextual information. However, as it is only based on a general context integration operation and does not consider any specifi... |
Toker_Coming_Down_to_Earth_Satellite-to-Street_View_Synthesis_for_Geo-Localization_CVPR_2021_paper | Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization | [
"Aysim Toker",
"Qunjie Zhou",
"Maxim Maximov",
"Laura Leal-Taixe"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Toker_Coming_Down_to_Earth_Satellite-to-Street_View_Synthesis_for_Geo-Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Toker_Coming_Down_to_Earth_Satellite-to-Street_View_Synthesis_for_Geo-Localization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Toker_Coming_Down_to_CVPR_2021_supplemental.pdf | 2103.06818 | cvf | @InProceedings{Toker_2021_CVPR,
author = {Toker, Aysim and Zhou, Qunjie and Maximov, Maxim and Leal-Taixe, Laura},
title = {Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images. This task is notoriously challenging due to the drastic viewpoint and appearance differences between the two domains. We show that we can addr... |
Lindell_AutoInt_Automatic_Integration_for_Fast_Neural_Volume_Rendering_CVPR_2021_paper | AutoInt: Automatic Integration for Fast Neural Volume Rendering | [
"David B. Lindell",
"Julien N. P. Martel",
"Gordon Wetzstein"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lindell_AutoInt_Automatic_Integration_for_Fast_Neural_Volume_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lindell_AutoInt_Automatic_Integration_for_Fast_Neural_Volume_Rendering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lindell_AutoInt_Automatic_Integration_CVPR_2021_supplemental.zip | 2012.01714 | cvf | @InProceedings{Lindell_2021_CVPR,
author = {Lindell, David B. and Martel, Julien N. P. and Wetzstein, Gordon},
title = {AutoInt: Automatic Integration for Fast Neural Volume Rendering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to... |
Yoon_Pose-Guided_Human_Animation_From_a_Single_Image_in_the_Wild_CVPR_2021_paper | Pose-Guided Human Animation From a Single Image in the Wild | [
"Jae Shin Yoon",
"Lingjie Liu",
"Vladislav Golyanik",
"Kripasindhu Sarkar",
"Hyun Soo Park",
"Christian Theobalt"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yoon_Pose-Guided_Human_Animation_From_a_Single_Image_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yoon_Pose-Guided_Human_Animation_From_a_Single_Image_in_the_Wild_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yoon_Pose-Guided_Human_Animation_CVPR_2021_supplemental.pdf | 2012.03796 | cvf | @InProceedings{Yoon_2021_CVPR,
author = {Yoon, Jae Shin and Liu, Lingjie and Golyanik, Vladislav and Sarkar, Kripasindhu and Park, Hyun Soo and Theobalt, Christian},
title = {Pose-Guided Human Animation From a Single Image in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer ... | We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses. Existing pose transfer methods exhibit significant visual artifacts when applying to a novel scene, resulting in temporal inconsistency and failures in preserving the identity... |
Gao_Room-and-Object_Aware_Knowledge_Reasoning_for_Remote_Embodied_Referring_Expression_CVPR_2021_paper | Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression | [
"Chen Gao",
"Jinyu Chen",
"Si Liu",
"Luting Wang",
"Qiong Zhang",
"Qi Wu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Room-and-Object_Aware_Knowledge_Reasoning_for_Remote_Embodied_Referring_Expression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Room-and-Object_Aware_Knowledge_Reasoning_for_Remote_Embodied_Referring_Expression_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gao_Room-and-Object_Aware_Knowledge_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Gao_2021_CVPR,
author = {Gao, Chen and Chen, Jinyu and Liu, Si and Wang, Luting and Zhang, Qiong and Wu, Qi},
title = {Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | The Remote Embodied Referring Expression (REVERIE) is a recently raised task that requires an agent to navigate to and localise a referred remote object according to a high-level language instruction. Different from related VLN tasks, the key to REVERIE is to conduct goal-oriented exploration instead of strict instruct... |
Chen_Equivariant_Point_Network_for_3D_Point_Cloud_Analysis_CVPR_2021_paper | Equivariant Point Network for 3D Point Cloud Analysis | [
"Haiwei Chen",
"Shichen Liu",
"Weikai Chen",
"Hao Li",
"Randall Hill"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Equivariant_Point_Network_for_3D_Point_Cloud_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Equivariant_Point_Network_for_3D_Point_Cloud_Analysis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Equivariant_Point_Network_CVPR_2021_supplemental.pdf | 2103.14147 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Haiwei and Liu, Shichen and Chen, Weikai and Li, Hao and Hill, Randall},
title = {Equivariant Point Network for 3D Point Cloud Analysis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Features that are equivariant to a larger group of symmetries have been shown to be more discriminative and powerful in recent studies. However, higher-order equivariant features often come with an exponentially-growing computational cost. Furthermore, it remains relatively less explored how rotation-equivariant featur... |
Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper | Learning Graph Embeddings for Compositional Zero-Shot Learning | [
"Muhammad Ferjad Naeem",
"Yongqin Xian",
"Federico Tombari",
"Zeynep Akata"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Naeem_Learning_Graph_Embeddings_CVPR_2021_supplemental.pdf | 2102.01987 | cvf | @InProceedings{Naeem_2021_CVPR,
author = {Naeem, Muhammad Ferjad and Xian, Yongqin and Tombari, Federico and Akata, Zeynep},
title = {Learning Graph Embeddings for Compositional Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog)in the training set. This is challenging because the same state can for example alter the visual appearance of a dog drastically differently f... |
Zhou_NeRD_Neural_3D_Reflection_Symmetry_Detector_CVPR_2021_paper | NeRD: Neural 3D Reflection Symmetry Detector | [
"Yichao Zhou",
"Shichen Liu",
"Yi Ma"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_NeRD_Neural_3D_Reflection_Symmetry_Detector_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_NeRD_Neural_3D_Reflection_Symmetry_Detector_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_NeRD_Neural_3D_CVPR_2021_supplemental.pdf | 2105.03211 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Yichao and Liu, Shichen and Ma, Yi},
title = {NeRD: Neural 3D Reflection Symmetry Detector},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages... | Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks. However, detecting 3D symmetry from an image remains a challenging task. Previous works either assume the symmetry is given or detect the symmetry with a heuristic-based m... |
He_Checkerboard_Context_Model_for_Efficient_Learned_Image_Compression_CVPR_2021_paper | Checkerboard Context Model for Efficient Learned Image Compression | [
"Dailan He",
"Yaoyan Zheng",
"Baocheng Sun",
"Yan Wang",
"Hongwei Qin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/He_Checkerboard_Context_Model_for_Efficient_Learned_Image_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Checkerboard_Context_Model_for_Efficient_Learned_Image_Compression_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_Checkerboard_Context_Model_CVPR_2021_supplemental.pdf | 2103.15306 | cvf | @InProceedings{He_2021_CVPR,
author = {He, Dailan and Zheng, Yaoyan and Sun, Baocheng and Wang, Yan and Qin, Hongwei},
title = {Checkerboard Context Model for Efficient Learned Image Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | For learned image compression, the autoregressive context model is proved effective in improving the rate-distortion (RD) performance. Because it helps remove spatial redundancies among latent representations. However, the decoding process must be done in a strict scan order, which breaks the parallelization. We propos... |
Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper | Zero-Shot Adversarial Quantization | [
"Yuang Liu",
"Wei Zhang",
"Jun Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper.pdf | null | 2103.15263 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yuang and Zhang, Wei and Wang, Jun},
title = {Zero-Shot Adversarial Quantization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {1512... | Model quantization is a promising approach to compress deep neural networks and accelerate inference, making it possible to be deployed on mobile and edge devices. To retain the high performance of full-precision models, most existing quantization methods focus on fine-tuning quantized model by assuming training datase... |
Huang_Group_Whitening_Balancing_Learning_Efficiency_and_Representational_Capacity_CVPR_2021_paper | Group Whitening: Balancing Learning Efficiency and Representational Capacity | [
"Lei Huang",
"Yi Zhou",
"Li Liu",
"Fan Zhu",
"Ling Shao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Group_Whitening_Balancing_Learning_Efficiency_and_Representational_Capacity_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Group_Whitening_Balancing_Learning_Efficiency_and_Representational_Capacity_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Group_Whitening_Balancing_CVPR_2021_supplemental.pdf | 2009.13333 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Lei and Zhou, Yi and Liu, Li and Zhu, Fan and Shao, Ling},
title = {Group Whitening: Balancing Learning Efficiency and Representational Capacity},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Batch normalization (BN) is an important technique commonly incorporated into deep learning models to perform standardization within mini-batches. The merits of BN in improving a model's learning efficiency can be further amplified by applying whitening, while its drawbacks in estimating population statistics for infer... |
Wu_Adversarial_Robustness_Under_Long-Tailed_Distribution_CVPR_2021_paper | Adversarial Robustness Under Long-Tailed Distribution | [
"Tong Wu",
"Ziwei Liu",
"Qingqiu Huang",
"Yu Wang",
"Dahua Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Adversarial_Robustness_Under_Long-Tailed_Distribution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Adversarial_Robustness_Under_Long-Tailed_Distribution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Adversarial_Robustness_Under_CVPR_2021_supplemental.pdf | 2104.02703 | cvf | @InProceedings{Wu_2021_CVPR,
author = {Wu, Tong and Liu, Ziwei and Huang, Qingqiu and Wang, Yu and Lin, Dahua},
title = {Adversarial Robustness Under Long-Tailed Distribution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However, existing works on adversarial robustness mainly focus on balanced datasets, while real-world data usually exhibits a long-tailed distribution. To push adversarial robus... |
Nirkin_HyperSeg_Patch-Wise_Hypernetwork_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper | HyperSeg: Patch-Wise Hypernetwork for Real-Time Semantic Segmentation | [
"Yuval Nirkin",
"Lior Wolf",
"Tal Hassner"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nirkin_HyperSeg_Patch-Wise_Hypernetwork_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nirkin_HyperSeg_Patch-Wise_Hypernetwork_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nirkin_HyperSeg_Patch-Wise_Hypernetwork_CVPR_2021_supplemental.pdf | 2012.11582 | cvf | @InProceedings{Nirkin_2021_CVPR,
author = {Nirkin, Yuval and Wolf, Lior and Hassner, Tal},
title = {HyperSeg: Patch-Wise Hypernetwork for Real-Time Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the weights at each decoder block vary spatially. For this purpose, we design a new type of hypernetwork, composed of a nested U... |
Nishi_Augmentation_Strategies_for_Learning_With_Noisy_Labels_CVPR_2021_paper | Augmentation Strategies for Learning With Noisy Labels | [
"Kento Nishi",
"Yi Ding",
"Alex Rich",
"Tobias Hollerer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nishi_Augmentation_Strategies_for_Learning_With_Noisy_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nishi_Augmentation_Strategies_for_Learning_With_Noisy_Labels_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nishi_Augmentation_Strategies_for_CVPR_2021_supplemental.pdf | 2103.02130 | cvf | @InProceedings{Nishi_2021_CVPR,
author = {Nishi, Kento and Ding, Yi and Rich, Alex and Hollerer, Tobias},
title = {Augmentation Strategies for Learning With Noisy Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Imperfect labels are ubiquitous in real-world datasets. Several recent successful methods for training deep neural networks (DNNs) robust to label noise have used two primary techniques: filtering samples based on loss during a warm-up phase to curate an initial set of cleanly labeled samples, and using the output of a... |
Song_AdaStereo_A_Simple_and_Efficient_Approach_for_Adaptive_Stereo_Matching_CVPR_2021_paper | AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching | [
"Xiao Song",
"Guorun Yang",
"Xinge Zhu",
"Hui Zhou",
"Zhe Wang",
"Jianping Shi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Song_AdaStereo_A_Simple_and_Efficient_Approach_for_Adaptive_Stereo_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_AdaStereo_A_Simple_and_Efficient_Approach_for_Adaptive_Stereo_Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_AdaStereo_A_Simple_CVPR_2021_supplemental.pdf | 2004.04627 | cvf | @InProceedings{Song_2021_CVPR,
author = {Song, Xiao and Yang, Guorun and Zhu, Xinge and Zhou, Hui and Wang, Zhe and Shi, Jianping},
title = {AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite poor. Addressing such problem, we present a novel domain-adaptive pipeline called AdaStereo that aims to align multi-level representations for deep... |
Kong_ClassSR_A_General_Framework_to_Accelerate_Super-Resolution_Networks_by_Data_CVPR_2021_paper | ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic | [
"Xiangtao Kong",
"Hengyuan Zhao",
"Yu Qiao",
"Chao Dong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kong_ClassSR_A_General_Framework_to_Accelerate_Super-Resolution_Networks_by_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kong_ClassSR_A_General_Framework_to_Accelerate_Super-Resolution_Networks_by_Data_CVPR_2021_paper.pdf | null | 2103.04039 | cvf | @InProceedings{Kong_2021_CVPR,
author = {Kong, Xiangtao and Zhao, Hengyuan and Qiao, Yu and Dong, Chao},
title = {ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large images are usually decomposed into small sub-images in practical usages. Based on this processing, we found that different image regions have different restoration difficulties and can be processed by networks with different capaci... |
Armandpour_Partition-Guided_GANs_CVPR_2021_paper | Partition-Guided GANs | [
"Mohammadreza Armandpour",
"Ali Sadeghian",
"Chunyuan Li",
"Mingyuan Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Armandpour_Partition-Guided_GANs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Armandpour_Partition-Guided_GANs_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Armandpour_Partition-Guided_GANs_CVPR_2021_supplemental.pdf | 2104.00816 | cvf | @InProceedings{Armandpour_2021_CVPR,
author = {Armandpour, Mohammadreza and Sadeghian, Ali and Li, Chunyuan and Zhou, Mingyuan},
title = {Partition-Guided GANs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Despite the success of Generative Adversarial Networks (GANs), their training suffers from several well-known problems, including mode collapse and difficulties learning a disconnected set of manifolds. In this paper, we break down the challenging task of learning complex high dimensional distributions, supporting dive... |
Min_GATSBI_Generative_Agent-Centric_Spatio-Temporal_Object_Interaction_CVPR_2021_paper | GATSBI: Generative Agent-Centric Spatio-Temporal Object Interaction | [
"Cheol-Hui Min",
"Jinseok Bae",
"Junho Lee",
"Young Min Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Min_GATSBI_Generative_Agent-Centric_Spatio-Temporal_Object_Interaction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Min_GATSBI_Generative_Agent-Centric_Spatio-Temporal_Object_Interaction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Min_GATSBI_Generative_Agent-Centric_CVPR_2021_supplemental.zip | 2104.04275 | cvf | @InProceedings{Min_2021_CVPR,
author = {Min, Cheol-Hui and Bae, Jinseok and Lee, Junho and Kim, Young Min},
title = {GATSBI: Generative Agent-Centric Spatio-Temporal Object Interaction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We present GATSBI, a generative model that can transform a sequence of raw observations into a structured latent representation that fully captures the spatio-temporal context of the agent's actions. In vision-based decision-making scenarios, an agent faces complex high-dimensional observations where multiple entities ... |
Gao_Privacy-Preserving_Collaborative_Learning_With_Automatic_Transformation_Search_CVPR_2021_paper | Privacy-Preserving Collaborative Learning With Automatic Transformation Search | [
"Wei Gao",
"Shangwei Guo",
"Tianwei Zhang",
"Han Qiu",
"Yonggang Wen",
"Yang Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Privacy-Preserving_Collaborative_Learning_With_Automatic_Transformation_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Privacy-Preserving_Collaborative_Learning_With_Automatic_Transformation_Search_CVPR_2021_paper.pdf | null | 2011.12505 | cvf | @InProceedings{Gao_2021_CVPR,
author = {Gao, Wei and Guo, Shangwei and Zhang, Tianwei and Qiu, Han and Wen, Yonggang and Liu, Yang},
title = {Privacy-Preserving Collaborative Learning With Automatic Transformation Search},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such ... |
Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper | Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval | [
"Yawen Zeng",
"Da Cao",
"Xiaochi Wei",
"Meng Liu",
"Zhou Zhao",
"Zheng Qin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zeng_2021_CVPR,
author = {Zeng, Yawen and Cao, Da and Wei, Xiaochi and Liu, Meng and Zhao, Zhou and Qin, Zheng},
title = {Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically learns the representations of the textual and visual content separately and then obtains the interactio... |
Zhang_Point_Cloud_Instance_Segmentation_Using_Probabilistic_Embeddings_CVPR_2021_paper | Point Cloud Instance Segmentation Using Probabilistic Embeddings | [
"Biao Zhang",
"Peter Wonka"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Point_Cloud_Instance_Segmentation_Using_Probabilistic_Embeddings_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Point_Cloud_Instance_Segmentation_Using_Probabilistic_Embeddings_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Point_Cloud_Instance_CVPR_2021_supplemental.zip | 1912.00145 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Biao and Wonka, Peter},
title = {Point Cloud Instance Segmentation Using Probabilistic Embeddings},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | In this paper, we propose a new framework for point cloud instance segmentation. Our framework has two steps: an embedding step and a clustering step. In the embedding step, our main contribution is to propose a probabilistic embedding space for point cloud embedding. Specifically, each point is represented as a tri-va... |
Yu_pixelNeRF_Neural_Radiance_Fields_From_One_or_Few_Images_CVPR_2021_paper | pixelNeRF: Neural Radiance Fields From One or Few Images | [
"Alex Yu",
"Vickie Ye",
"Matthew Tancik",
"Angjoo Kanazawa"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_pixelNeRF_Neural_Radiance_Fields_From_One_or_Few_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_pixelNeRF_Neural_Radiance_Fields_From_One_or_Few_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_pixelNeRF_Neural_Radiance_CVPR_2021_supplemental.pdf | 2012.02190 | cvf | @InProceedings{Yu_2021_CVPR,
author = {Yu, Alex and Ye, Vickie and Tancik, Matthew and Kanazawa, Angjoo},
title = {pixelNeRF: Neural Radiance Fields From One or Few Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. The existing approach for constructing neural radiance fields (NeRFs) involves optimizing the representation to every scene independently, requiring many calibrated views and signifi... |
Cherepkov_Navigating_the_GAN_Parameter_Space_for_Semantic_Image_Editing_CVPR_2021_paper | Navigating the GAN Parameter Space for Semantic Image Editing | [
"Anton Cherepkov",
"Andrey Voynov",
"Artem Babenko"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cherepkov_Navigating_the_GAN_Parameter_Space_for_Semantic_Image_Editing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cherepkov_Navigating_the_GAN_Parameter_Space_for_Semantic_Image_Editing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cherepkov_Navigating_the_GAN_CVPR_2021_supplemental.pdf | 2011.13786 | cvf | @InProceedings{Cherepkov_2021_CVPR,
author = {Cherepkov, Anton and Voynov, Andrey and Babenko, Artem},
title = {Navigating the GAN Parameter Space for Semantic Image Editing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | Generative Adversarial Networks (GANs) are currently an indispensable tool for visual editing, being a standard component of image-to-image translation and image restoration pipelines. Furthermore, GANs are especially useful for controllable generation since their latent spaces contain a wide range of interpretable dir... |
Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper | Large-Capacity Image Steganography Based on Invertible Neural Networks | [
"Shao-Ping Lu",
"Rong Wang",
"Tao Zhong",
"Paul L. Rosin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_Large-Capacity_Image_Steganography_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Lu_2021_CVPR,
author = {Lu, Shao-Ping and Wang, Rong and Zhong, Tao and Rosin, Paul L.},
title = {Large-Capacity Image Steganography Based on Invertible Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Many attempts have been made to hide information in images, where the main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganograph... |
Zhang_Exploiting_Edge-Oriented_Reasoning_for_3D_Point-Based_Scene_Graph_Analysis_CVPR_2021_paper | Exploiting Edge-Oriented Reasoning for 3D Point-Based Scene Graph Analysis | [
"Chaoyi Zhang",
"Jianhui Yu",
"Yang Song",
"Weidong Cai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Exploiting_Edge-Oriented_Reasoning_for_3D_Point-Based_Scene_Graph_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Exploiting_Edge-Oriented_Reasoning_for_3D_Point-Based_Scene_Graph_Analysis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Exploiting_Edge-Oriented_Reasoning_CVPR_2021_supplemental.pdf | 2103.05558 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Chaoyi and Yu, Jianhui and Song, Yang and Cai, Weidong},
title = {Exploiting Edge-Oriented Reasoning for 3D Point-Based Scene Graph Analysis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | Scene understanding is a critical problem in computer vision. In this paper, we propose a 3D point-based scene graph generation (SGGpoint) framework to effectively bridge perception and reasoning to achieve scene understanding via three sequential stages, namely scene graph construction, reasoning, and inference. Withi... |
Zhang_CoLA_Weakly-Supervised_Temporal_Action_Localization_With_Snippet_Contrastive_Learning_CVPR_2021_paper | CoLA: Weakly-Supervised Temporal Action Localization With Snippet Contrastive Learning | [
"Can Zhang",
"Meng Cao",
"Dongming Yang",
"Jie Chen",
"Yuexian Zou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_CoLA_Weakly-Supervised_Temporal_Action_Localization_With_Snippet_Contrastive_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_CoLA_Weakly-Supervised_Temporal_Action_Localization_With_Snippet_Contrastive_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_CoLA_Weakly-Supervised_Temporal_CVPR_2021_supplemental.zip | 2103.16392 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Can and Cao, Meng and Yang, Dongming and Chen, Jie and Zou, Yuexian},
title = {CoLA: Weakly-Supervised Temporal Action Localization With Snippet Contrastive Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | Weakly-supervised temporal action localization (WS-TAL) aims to localize actions in untrimmed videos with only video-level labels. Most existing models follow the "localization by classification" procedure: locate temporal regions contributing most to the video-level classification. Generally, they process each snippet... |
Li_MetaSAug_Meta_Semantic_Augmentation_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper | MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition | [
"Shuang Li",
"Kaixiong Gong",
"Chi Harold Liu",
"Yulin Wang",
"Feng Qiao",
"Xinjing Cheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_MetaSAug_Meta_Semantic_Augmentation_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_MetaSAug_Meta_Semantic_Augmentation_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.pdf | null | 2103.12579 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Shuang and Gong, Kaixiong and Liu, Chi Harold and Wang, Yulin and Qiao, Feng and Cheng, Xinjing},
title = {MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes. This imbalance degrades the performance of typical supervised learning algorithms designed for balanced training sets. In this paper, we a... |
Burns_Limitations_of_Post-Hoc_Feature_Alignment_for_Robustness_CVPR_2021_paper | Limitations of Post-Hoc Feature Alignment for Robustness | [
"Collin Burns",
"Jacob Steinhardt"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Burns_Limitations_of_Post-Hoc_Feature_Alignment_for_Robustness_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Burns_Limitations_of_Post-Hoc_Feature_Alignment_for_Robustness_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Burns_Limitations_of_Post-Hoc_CVPR_2021_supplemental.pdf | 2103.05898 | cvf | @InProceedings{Burns_2021_CVPR,
author = {Burns, Collin and Steinhardt, Jacob},
title = {Limitations of Post-Hoc Feature Alignment for Robustness},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | Feature alignment is an approach to improving robustness to distribution shift that matches the distribution of feature activations between the training distribution and test distribution. A particularly simple but effective approach to feature alignment involves aligning the batch normalization statistics between the ... |
Reiss_Every_Annotation_Counts_Multi-Label_Deep_Supervision_for_Medical_Image_Segmentation_CVPR_2021_paper | Every Annotation Counts: Multi-Label Deep Supervision for Medical Image Segmentation | [
"Simon Reiss",
"Constantin Seibold",
"Alexander Freytag",
"Erik Rodner",
"Rainer Stiefelhagen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Reiss_Every_Annotation_Counts_Multi-Label_Deep_Supervision_for_Medical_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Reiss_Every_Annotation_Counts_Multi-Label_Deep_Supervision_for_Medical_Image_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reiss_Every_Annotation_Counts_CVPR_2021_supplemental.pdf | 2104.13243 | cvf | @InProceedings{Reiss_2021_CVPR,
author = {Reiss, Simon and Seibold, Constantin and Freytag, Alexander and Rodner, Erik and Stiefelhagen, Rainer},
title = {Every Annotation Counts: Multi-Label Deep Supervision for Medical Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Comp... | Pixel-wise segmentation is one of the most data and annotation hungry tasks in our field. Providing representative and accurate annotations is often mission-critical especially for challenging medical applications. In this paper, we propose a semi-weakly supervised segmentation algorithm to overcome this barrier. Our a... |
Kervadec_Roses_Are_Red_Violets_Are_Blue..._but_Should_VQA_Expect_CVPR_2021_paper | Roses Are Red, Violets Are Blue... but Should VQA Expect Them To? | [
"Corentin Kervadec",
"Grigory Antipov",
"Moez Baccouche",
"Christian Wolf"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kervadec_Roses_Are_Red_Violets_Are_Blue..._but_Should_VQA_Expect_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kervadec_Roses_Are_Red_Violets_Are_Blue..._but_Should_VQA_Expect_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kervadec_Roses_Are_Red_CVPR_2021_supplemental.pdf | 2006.05121 | cvf | @InProceedings{Kervadec_2021_CVPR,
author = {Kervadec, Corentin and Antipov, Grigory and Baccouche, Moez and Wolf, Christian},
title = {Roses Are Red, Violets Are Blue... but Should VQA Expect Them To?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | Models for Visual Question Answering (VQA) are notorious for their tendency to rely on dataset biases, as the large and unbalanced diversity of questions and concepts involved and tends to prevent models from learning to ""reason"", leading them to perform ""educated guesses"" instead. In this paper, we claim that the ... |
Nguyen_FAPIS_A_Few-Shot_Anchor-Free_Part-Based_Instance_Segmenter_CVPR_2021_paper | FAPIS: A Few-Shot Anchor-Free Part-Based Instance Segmenter | [
"Khoi Nguyen",
"Sinisa Todorovic"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_FAPIS_A_Few-Shot_Anchor-Free_Part-Based_Instance_Segmenter_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nguyen_FAPIS_A_Few-Shot_Anchor-Free_Part-Based_Instance_Segmenter_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nguyen_FAPIS_A_Few-Shot_CVPR_2021_supplemental.pdf | 2104.00073 | cvf | @InProceedings{Nguyen_2021_CVPR,
author = {Nguyen, Khoi and Todorovic, Sinisa},
title = {FAPIS: A Few-Shot Anchor-Free Part-Based Instance Segmenter},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}... | This paper is about few-shot instance segmentation, where training and test image sets do not share the same object classes. We specify and evaluate a new few-shot anchor-free part-based instance segmenter (FAPIS). Our key novelty is in explicit modeling of latent object parts shared across training object classes, whi... |
Hong_Disentangling_Label_Distribution_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper | Disentangling Label Distribution for Long-Tailed Visual Recognition | [
"Youngkyu Hong",
"Seungju Han",
"Kwanghee Choi",
"Seokjun Seo",
"Beomsu Kim",
"Buru Chang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Disentangling_Label_Distribution_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_Disentangling_Label_Distribution_for_Long-Tailed_Visual_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Disentangling_Label_Distribution_CVPR_2021_supplemental.pdf | 2012.00321 | cvf | @InProceedings{Hong_2021_CVPR,
author = {Hong, Youngkyu and Han, Seungju and Choi, Kwanghee and Seo, Seokjun and Kim, Beomsu and Chang, Buru},
title = {Disentangling Label Distribution for Long-Tailed Visual Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the uniform target label distribution. Such protocol has questionable practicality since the target may also be long-tailed. Therefore, we formu... |
Malinowski_Gradient_Forward-Propagation_for_Large-Scale_Temporal_Video_Modelling_CVPR_2021_paper | Gradient Forward-Propagation for Large-Scale Temporal Video Modelling | [
"Mateusz Malinowski",
"Dimitrios Vytiniotis",
"Grzegorz Swirszcz",
"Viorica Patraucean",
"Joao Carreira"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Malinowski_Gradient_Forward-Propagation_for_Large-Scale_Temporal_Video_Modelling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Malinowski_Gradient_Forward-Propagation_for_Large-Scale_Temporal_Video_Modelling_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Malinowski_Gradient_Forward-Propagation_for_CVPR_2021_supplemental.pdf | 2106.08318 | cvf | @InProceedings{Malinowski_2021_CVPR,
author = {Malinowski, Mateusz and Vytiniotis, Dimitrios and Swirszcz, Grzegorz and Patraucean, Viorica and Carreira, Joao},
title = {Gradient Forward-Propagation for Large-Scale Temporal Video Modelling},
booktitle = {Proceedings of the IEEE/CVF Conference on Comp... | How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the forward and backward passes are completed. For temporal signals, this introduces high latency and hinders real-time learning. It also create... |
Chen_Learning_a_Non-Blind_Deblurring_Network_for_Night_Blurry_Images_CVPR_2021_paper | Learning a Non-Blind Deblurring Network for Night Blurry Images | [
"Liang Chen",
"Jiawei Zhang",
"Jinshan Pan",
"Songnan Lin",
"Faming Fang",
"Jimmy S. Ren"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_a_Non-Blind_Deblurring_Network_for_Night_Blurry_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_a_Non-Blind_Deblurring_Network_for_Night_Blurry_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Learning_a_Non-Blind_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Liang and Zhang, Jiawei and Pan, Jinshan and Lin, Songnan and Fang, Faming and Ren, Jimmy S.},
title = {Learning a Non-Blind Deblurring Network for Night Blurry Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we propose a non-blind deblurring network (NBDN) to restore night blurry images. To mitigate the side effects... |
Khan_Differentiable_Diffusion_for_Dense_Depth_Estimation_From_Multi-View_Images_CVPR_2021_paper | Differentiable Diffusion for Dense Depth Estimation From Multi-View Images | [
"Numair Khan",
"Min H. Kim",
"James Tompkin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Khan_Differentiable_Diffusion_for_Dense_Depth_Estimation_From_Multi-View_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Khan_Differentiable_Diffusion_for_Dense_Depth_Estimation_From_Multi-View_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Khan_Differentiable_Diffusion_for_CVPR_2021_supplemental.pdf | 2106.08917 | cvf | @InProceedings{Khan_2021_CVPR,
author = {Khan, Numair and Kim, Min H. and Tompkin, James},
title = {Differentiable Diffusion for Dense Depth Estimation From Multi-View Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view reprojection error from RGB supervision. We optimize point positions, depths, and weights with respect to the loss by differential splatting that models points as Gaussians ... |
Zheng_Deep_Compositional_Metric_Learning_CVPR_2021_paper | Deep Compositional Metric Learning | [
"Wenzhao Zheng",
"Chengkun Wang",
"Jiwen Lu",
"Jie Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Compositional_Metric_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Deep_Compositional_Metric_Learning_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Wenzhao and Wang, Chengkun and Lu, Jiwen and Zhou, Jie},
title = {Deep Compositional Metric Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images. Conventional deep metric learning methods minimize a discriminative loss to enlarge interclass distances while suppressing intraclass variations, which might lead to inf... |
Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper | Representing Videos As Discriminative Sub-Graphs for Action Recognition | [
"Dong Li",
"Zhaofan Qiu",
"Yingwei Pan",
"Ting Yao",
"Houqiang Li",
"Tao Mei"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper.pdf | null | 2201.04027 | title_snapshot | @InProceedings{Li_2021_CVPR,
author = {Li, Dong and Qiu, Zhaofan and Pan, Yingwei and Yao, Ting and Li, Houqiang and Mei, Tao},
title = {Representing Videos As Discriminative Sub-Graphs for Action Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-gra... |
Fieraru_AIFit_Automatic_3D_Human-Interpretable_Feedback_Models_for_Fitness_Training_CVPR_2021_paper | AIFit: Automatic 3D Human-Interpretable Feedback Models for Fitness Training | [
"Mihai Fieraru",
"Mihai Zanfir",
"Silviu Cristian Pirlea",
"Vlad Olaru",
"Cristian Sminchisescu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fieraru_AIFit_Automatic_3D_Human-Interpretable_Feedback_Models_for_Fitness_Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fieraru_AIFit_Automatic_3D_Human-Interpretable_Feedback_Models_for_Fitness_Training_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fieraru_AIFit_Automatic_3D_CVPR_2021_supplemental.zip | null | null | @InProceedings{Fieraru_2021_CVPR,
author = {Fieraru, Mihai and Zanfir, Mihai and Pirlea, Silviu Cristian and Olaru, Vlad and Sminchisescu, Cristian},
title = {AIFit: Automatic 3D Human-Interpretable Feedback Models for Fitness Training},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer... | I went to the gym today, but how well did I do? And where should I improve? Ah, my back hurts slightly... User engagement can be sustained and injuries avoided by being able to reconstruct 3d human pose and motion, relate it to good training practices, identify errors, and provide early, real-time feedback. In this pap... |
Wang_Synthesizing_Long-Term_3D_Human_Motion_and_Interaction_in_3D_Scenes_CVPR_2021_paper | Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes | [
"Jiashun Wang",
"Huazhe Xu",
"Jingwei Xu",
"Sifei Liu",
"Xiaolong Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Synthesizing_Long-Term_3D_Human_Motion_and_Interaction_in_3D_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Synthesizing_Long-Term_3D_Human_Motion_and_Interaction_in_3D_Scenes_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Synthesizing_Long-Term_3D_CVPR_2021_supplemental.pdf | 2012.05522 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Jiashun and Xu, Huazhe and Xu, Jingwei and Liu, Sifei and Wang, Xiaolong},
title = {Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realistic and natural human motion, most approaches neglect the importance of modeling human-scene interactions and affordances. On the other hand, ... |
Ericsson_How_Well_Do_Self-Supervised_Models_Transfer_CVPR_2021_paper | How Well Do Self-Supervised Models Transfer? | [
"Linus Ericsson",
"Henry Gouk",
"Timothy M. Hospedales"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ericsson_How_Well_Do_Self-Supervised_Models_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ericsson_How_Well_Do_Self-Supervised_Models_Transfer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ericsson_How_Well_Do_CVPR_2021_supplemental.pdf | 2011.13377 | cvf | @InProceedings{Ericsson_2021_CVPR,
author = {Ericsson, Linus and Gouk, Henry and Hospedales, Timothy M.},
title = {How Well Do Self-Supervised Models Transfer?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Self-supervised visual representation learning has seen huge progress recently, but no large scale evaluation has compared the many models now available. We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dens... |
Blattmann_Understanding_Object_Dynamics_for_Interactive_Image-to-Video_Synthesis_CVPR_2021_paper | Understanding Object Dynamics for Interactive Image-to-Video Synthesis | [
"Andreas Blattmann",
"Timo Milbich",
"Michael Dorkenwald",
"Bjorn Ommer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Blattmann_Understanding_Object_Dynamics_for_Interactive_Image-to-Video_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Blattmann_Understanding_Object_Dynamics_for_Interactive_Image-to-Video_Synthesis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Blattmann_Understanding_Object_Dynamics_CVPR_2021_supplemental.zip | 2106.11303 | cvf | @InProceedings{Blattmann_2021_CVPR,
author = {Blattmann, Andreas and Milbich, Timo and Dorkenwald, Michael and Ommer, Bjorn},
title = {Understanding Object Dynamics for Interactive Image-to-Video Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative mo... |
Chan_Pi-GAN_Periodic_Implicit_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2021_paper | Pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis | [
"Eric R. Chan",
"Marco Monteiro",
"Petr Kellnhofer",
"Jiajun Wu",
"Gordon Wetzstein"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chan_Pi-GAN_Periodic_Implicit_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chan_Pi-GAN_Periodic_Implicit_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chan_Pi-GAN_Periodic_Implicit_CVPR_2021_supplemental.pdf | 2012.00926 | title_snapshot | @InProceedings{Chan_2021_CVPR,
author = {Chan, Eric R. and Monteiro, Marco and Kellnhofer, Petr and Wu, Jiajun and Wetzstein, Gordon},
title = {Pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are n... |
Ding_Diverse_Branch_Block_Building_a_Convolution_as_an_Inception-Like_Unit_CVPR_2021_paper | Diverse Branch Block: Building a Convolution as an Inception-Like Unit | [
"Xiaohan Ding",
"Xiangyu Zhang",
"Jungong Han",
"Guiguang Ding"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_Diverse_Branch_Block_Building_a_Convolution_as_an_Inception-Like_Unit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_Diverse_Branch_Block_Building_a_Convolution_as_an_Inception-Like_Unit_CVPR_2021_paper.pdf | null | 2103.13425 | cvf | @InProceedings{Ding_2021_CVPR,
author = {Ding, Xiaohan and Zhang, Xiangyu and Han, Jungong and Ding, Guiguang},
title = {Diverse Branch Block: Building a Convolution as an Inception-Like Unit},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexit... |
Tomani_Post-Hoc_Uncertainty_Calibration_for_Domain_Drift_Scenarios_CVPR_2021_paper | Post-Hoc Uncertainty Calibration for Domain Drift Scenarios | [
"Christian Tomani",
"Sebastian Gruber",
"Muhammed Ebrar Erdem",
"Daniel Cremers",
"Florian Buettner"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tomani_Post-Hoc_Uncertainty_Calibration_for_Domain_Drift_Scenarios_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tomani_Post-Hoc_Uncertainty_Calibration_for_Domain_Drift_Scenarios_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tomani_Post-Hoc_Uncertainty_Calibration_CVPR_2021_supplemental.pdf | 2012.10988 | cvf | @InProceedings{Tomani_2021_CVPR,
author = {Tomani, Christian and Gruber, Sebastian and Erdem, Muhammed Ebrar and Cremers, Daniel and Buettner, Florian},
title = {Post-Hoc Uncertainty Calibration for Domain Drift Scenarios},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved using post-hoc calibration methods. However, to date, the focus of these approaches... |
Yang_Slimmable_Compressive_Autoencoders_for_Practical_Neural_Image_Compression_CVPR_2021_paper | Slimmable Compressive Autoencoders for Practical Neural Image Compression | [
"Fei Yang",
"Luis Herranz",
"Yongmei Cheng",
"Mikhail G. Mozerov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Slimmable_Compressive_Autoencoders_for_Practical_Neural_Image_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Slimmable_Compressive_Autoencoders_for_Practical_Neural_Image_Compression_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Slimmable_Compressive_Autoencoders_CVPR_2021_supplemental.pdf | 2103.15726 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Fei and Herranz, Luis and Cheng, Yongmei and Mozerov, Mikhail G.},
title = {Slimmable Compressive Autoencoders for Practical Neural Image Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computationally demanding and generally optimized for a single rate, limiting their practical use. Focusing on practical image compression, we prop... |
Yu_Function4D_Real-Time_Human_Volumetric_Capture_From_Very_Sparse_Consumer_RGBD_CVPR_2021_paper | Function4D: Real-Time Human Volumetric Capture From Very Sparse Consumer RGBD Sensors | [
"Tao Yu",
"Zerong Zheng",
"Kaiwen Guo",
"Pengpeng Liu",
"Qionghai Dai",
"Yebin Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Function4D_Real-Time_Human_Volumetric_Capture_From_Very_Sparse_Consumer_RGBD_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Function4D_Real-Time_Human_Volumetric_Capture_From_Very_Sparse_Consumer_RGBD_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Function4D_Real-Time_Human_CVPR_2021_supplemental.pdf | 2105.01859 | cvf | @InProceedings{Yu_2021_CVPR,
author = {Yu, Tao and Zheng, Zerong and Guo, Kaiwen and Liu, Pengpeng and Dai, Qionghai and Liu, Yebin},
title = {Function4D: Real-Time Human Volumetric Capture From Very Sparse Consumer RGBD Sensors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | Human volumetric capture is a long-standing topic in computer vision and computer graphics. Although high-quality results can be achieved using sophisticated off-line systems, real-time human volumetric capture of complex scenarios, especially using light-weight setups, remains challenging. In this paper, we propose a ... |
Deng_LAU-Net_Latitude_Adaptive_Upscaling_Network_for_Omnidirectional_Image_Super-Resolution_CVPR_2021_paper | LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution | [
"Xin Deng",
"Hao Wang",
"Mai Xu",
"Yichen Guo",
"Yuhang Song",
"Li Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_LAU-Net_Latitude_Adaptive_Upscaling_Network_for_Omnidirectional_Image_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_LAU-Net_Latitude_Adaptive_Upscaling_Network_for_Omnidirectional_Image_Super-Resolution_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Deng_2021_CVPR,
author = {Deng, Xin and Wang, Hao and Xu, Mai and Guo, Yichen and Song, Yuhang and Yang, Li},
title = {LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | The omnidirectional images (ODIs) are usually at low-resolution, due to the constraints of collection, storage and transmission. The traditional two-dimensional (2D) image super-resolution methods are not effective for spherical ODIs, because ODIs tend to have non-uniformly distributed pixel density and varying texture... |
Dai_UP-DETR_Unsupervised_Pre-Training_for_Object_Detection_With_Transformers_CVPR_2021_paper | UP-DETR: Unsupervised Pre-Training for Object Detection With Transformers | [
"Zhigang Dai",
"Bolun Cai",
"Yugeng Lin",
"Junying Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_UP-DETR_Unsupervised_Pre-Training_for_Object_Detection_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_UP-DETR_Unsupervised_Pre-Training_for_Object_Detection_With_Transformers_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_UP-DETR_Unsupervised_Pre-Training_CVPR_2021_supplemental.pdf | 2011.09094 | title_snapshot | @InProceedings{Dai_2021_CVPR,
author = {Dai, Zhigang and Cai, Bolun and Lin, Yugeng and Chen, Junying},
title = {UP-DETR: Unsupervised Pre-Training for Object Detection With Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Object detection with transformers (DETR) reaches competitive performance with Faster R-CNN via a transformer encoder-decoder architecture. Inspired by the great success of pre-training transformers in natural language processing, we propose a pretext task named random query patch detection to Unsupervisedly Pre-train ... |
Wan_Self-Attention_Based_Text_Knowledge_Mining_for_Text_Detection_CVPR_2021_paper | Self-Attention Based Text Knowledge Mining for Text Detection | [
"Qi Wan",
"Haoqin Ji",
"Linlin Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wan_Self-Attention_Based_Text_Knowledge_Mining_for_Text_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wan_Self-Attention_Based_Text_Knowledge_Mining_for_Text_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wan_Self-Attention_Based_Text_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wan_2021_CVPR,
author = {Wan, Qi and Ji, Haoqin and Shen, Linlin},
title = {Self-Attention Based Text Knowledge Mining for Text Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2... | Pre-trained models play an important role in deep learning based text detectors. However, most methods ignore the gap between natural images and scene text images and directly apply ImageNet for pre-training. To address such a problem, some of them firstly pre-train the model using a large amount of synthetic data and ... |
Zhou_Image_De-Raining_via_Continual_Learning_CVPR_2021_paper | Image De-Raining via Continual Learning | [
"Man Zhou",
"Jie Xiao",
"Yifan Chang",
"Xueyang Fu",
"Aiping Liu",
"Jinshan Pan",
"Zheng-Jun Zha"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Image_De-Raining_via_Continual_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Image_De-Raining_via_Continual_Learning_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Man and Xiao, Jie and Chang, Yifan and Fu, Xueyang and Liu, Aiping and Pan, Jinshan and Zha, Zheng-Jun},
title = {Image De-Raining via Continual Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | While deep convolutional neural networks (CNNs) have achieved great success on image de-raining task, most existing methods can only learn fixed mapping rules between paired rainy/clean images on a single dataset. This limits their applications in practical situations with multiple and incremental datasets where the ma... |
Xu_Layer-Wise_Searching_for_1-Bit_Detectors_CVPR_2021_paper | Layer-Wise Searching for 1-Bit Detectors | [
"Sheng Xu",
"Junhe Zhao",
"Jinhu Lu",
"Baochang Zhang",
"Shumin Han",
"David Doermann"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Layer-Wise_Searching_for_1-Bit_Detectors_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Layer-Wise_Searching_for_1-Bit_Detectors_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Xu_2021_CVPR,
author = {Xu, Sheng and Zhao, Junhe and Lu, Jinhu and Zhang, Baochang and Han, Shumin and Doermann, David},
title = {Layer-Wise Searching for 1-Bit Detectors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | 1-bit detectors show great promise for resource-constrained embedded devices but often suffer from a significant performance gap compared with their real-valued counterparts. The primary reason lies in the layer-wise error during binarization. This paper presents a layer-wise search (LWS) strategy to generate 1-bit det... |
Chen_Distilling_Audio-Visual_Knowledge_by_Compositional_Contrastive_Learning_CVPR_2021_paper | Distilling Audio-Visual Knowledge by Compositional Contrastive Learning | [
"Yanbei Chen",
"Yongqin Xian",
"A. Sophia Koepke",
"Ying Shan",
"Zeynep Akata"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Distilling_Audio-Visual_Knowledge_by_Compositional_Contrastive_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Distilling_Audio-Visual_Knowledge_by_Compositional_Contrastive_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Distilling_Audio-Visual_Knowledge_CVPR_2021_supplemental.pdf | 2104.10955 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Yanbei and Xian, Yongqin and Koepke, A. Sophia and Shan, Ying and Akata, Zeynep},
title = {Distilling Audio-Visual Knowledge by Compositional Contrastive Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Having access to multi-modal cues (e.g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality. In this work, we propose to transfer knowledge across heterogeneous modalities, even though these data modalities may not be semantically correlated. Rather than directl... |
Wang_Unsupervised_Visual_Attention_and_Invariance_for_Reinforcement_Learning_CVPR_2021_paper | Unsupervised Visual Attention and Invariance for Reinforcement Learning | [
"Xudong Wang",
"Long Lian",
"Stella X. Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Visual_Attention_and_Invariance_for_Reinforcement_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Unsupervised_Visual_Attention_and_Invariance_for_Reinforcement_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Unsupervised_Visual_Attention_CVPR_2021_supplemental.pdf | 2104.02921 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Xudong and Lian, Long and Yu, Stella X.},
title = {Unsupervised Visual Attention and Invariance for Reinforcement Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | The vision-based reinforcement learning (RL) has achieved tremendous success. However, generalizing vision-based RL policy to unknown test environments still remains as a challenging problem. Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training ... |
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