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
values | bibtex large_stringlengths 305 619 | abstract large_stringlengths 449 1.99k |
|---|---|---|---|---|---|---|---|---|---|
Yang_L2M-GAN_Learning_To_Manipulate_Latent_Space_Semantics_for_Facial_Attribute_CVPR_2021_paper | L2M-GAN: Learning To Manipulate Latent Space Semantics for Facial Attribute Editing | [
"Guoxing Yang",
"Nanyi Fei",
"Mingyu Ding",
"Guangzhen Liu",
"Zhiwu Lu",
"Tao Xiang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_L2M-GAN_Learning_To_Manipulate_Latent_Space_Semantics_for_Facial_Attribute_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_L2M-GAN_Learning_To_Manipulate_Latent_Space_Semantics_for_Facial_Attribute_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_L2M-GAN_Learning_To_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yang_2021_CVPR,
author = {Yang, Guoxing and Fei, Nanyi and Ding, Mingyu and Liu, Guangzhen and Lu, Zhiwu and Xiang, Tao},
title = {L2M-GAN: Learning To Manipulate Latent Space Semantics for Facial Attribute Editing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | A deep facial attribute editing model strives to meet two requirements: (1) attribute correctness -- the target attribute should correctly appear on the edited face image; (2) irrelevance preservation -- any irrelevant information (e.g., identity) should not be changed after editing. Meeting both requirements challenge... |
Abdelsalam_IIRC_Incremental_Implicitly-Refined_Classification_CVPR_2021_paper | IIRC: Incremental Implicitly-Refined Classification | [
"Mohamed Abdelsalam",
"Mojtaba Faramarzi",
"Shagun Sodhani",
"Sarath Chandar"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Abdelsalam_IIRC_Incremental_Implicitly-Refined_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Abdelsalam_IIRC_Incremental_Implicitly-Refined_Classification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Abdelsalam_IIRC_Incremental_Implicitly-Refined_CVPR_2021_supplemental.pdf | 2012.12477 | cvf | @InProceedings{Abdelsalam_2021_CVPR,
author = {Abdelsalam, Mohamed and Faramarzi, Mojtaba and Sodhani, Shagun and Chandar, Sarath},
title = {IIRC: Incremental Implicitly-Refined Classification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We introduce the 'Incremental Implicitly-Refined Classification (IIRC)' setup, an extension to the class incremental learning setup where the incoming batches of classes have two granularity levels. i.e., each sample could have a high-level (coarse) label like 'bear' and a low-level (fine) label like 'polar bear'. Only... |
Han_Learning_To_Fuse_Asymmetric_Feature_Maps_in_Siamese_Trackers_CVPR_2021_paper | Learning To Fuse Asymmetric Feature Maps in Siamese Trackers | [
"Wencheng Han",
"Xingping Dong",
"Fahad Shahbaz Khan",
"Ling Shao",
"Jianbing Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Han_Learning_To_Fuse_Asymmetric_Feature_Maps_in_Siamese_Trackers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Han_Learning_To_Fuse_Asymmetric_Feature_Maps_in_Siamese_Trackers_CVPR_2021_paper.pdf | null | 2012.02776 | cvf | @InProceedings{Han_2021_CVPR,
author = {Han, Wencheng and Dong, Xingping and Khan, Fahad Shahbaz and Shao, Ling and Shen, Jianbing},
title = {Learning To Fuse Asymmetric Feature Maps in Siamese Trackers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCorr) to obtain multi-channel correlation information from the two feature maps (target and search region). However, DW-XCorr has several limit... |
Li_Generalizing_to_the_Open_World_Deep_Visual_Odometry_With_Online_CVPR_2021_paper | Generalizing to the Open World: Deep Visual Odometry With Online Adaptation | [
"Shunkai Li",
"Xin Wu",
"Yingdian Cao",
"Hongbin Zha"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Generalizing_to_the_Open_World_Deep_Visual_Odometry_With_Online_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Generalizing_to_the_Open_World_Deep_Visual_Odometry_With_Online_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Generalizing_to_the_CVPR_2021_supplemental.pdf | 2103.15279 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Shunkai and Wu, Xin and Cao, Yingdian and Zha, Hongbin},
title = {Generalizing to the Open World: Deep Visual Odometry With Online Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Despite learning-based visual odometry (VO) has shown impressive results in recent years, the pretrained networks may easily collapse in unseen environments. The large domain gap between training and testing data makes them difficult to generalize to new scenes. In this paper, we propose an online adaptation framework ... |
Qi_PQA_Perceptual_Question_Answering_CVPR_2021_paper | PQA: Perceptual Question Answering | [
"Yonggang Qi",
"Kai Zhang",
"Aneeshan Sain",
"Yi-Zhe Song"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qi_PQA_Perceptual_Question_Answering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qi_PQA_Perceptual_Question_Answering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qi_PQA_Perceptual_Question_CVPR_2021_supplemental.pdf | 2104.03589 | cvf | @InProceedings{Qi_2021_CVPR,
author = {Qi, Yonggang and Zhang, Kai and Sain, Aneeshan and Song, Yi-Zhe},
title = {PQA: Perceptual Question Answering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}... | Perceptual organization remains one of the very few established theories on the human visual system. It underpinned many pre-deep seminal works on segmentation and detection, yet research has seen a rapid decline since the preferential shift to learning deep models. Of the limited attempts, most aimed at interpreting c... |
Duan_Adversarial_Laser_Beam_Effective_Physical-World_Attack_to_DNNs_in_a_CVPR_2021_paper | Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink | [
"Ranjie Duan",
"Xiaofeng Mao",
"A. K. Qin",
"Yuefeng Chen",
"Shaokai Ye",
"Yuan He",
"Yun Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Duan_Adversarial_Laser_Beam_Effective_Physical-World_Attack_to_DNNs_in_a_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Duan_Adversarial_Laser_Beam_Effective_Physical-World_Attack_to_DNNs_in_a_CVPR_2021_paper.pdf | null | 2103.06504 | cvf | @InProceedings{Duan_2021_CVPR,
author = {Duan, Ranjie and Mao, Xiaofeng and Qin, A. K. and Chen, Yuefeng and Ye, Shaokai and He, Yuan and Yang, Yun},
title = {Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario. In this work, we show by simply using a laser beam that DNN... |
Fu_Robust_Point_Cloud_Registration_Framework_Based_on_Deep_Graph_Matching_CVPR_2021_paper | Robust Point Cloud Registration Framework Based on Deep Graph Matching | [
"Kexue Fu",
"Shaolei Liu",
"Xiaoyuan Luo",
"Manning Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Robust_Point_Cloud_Registration_Framework_Based_on_Deep_Graph_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Robust_Point_Cloud_Registration_Framework_Based_on_Deep_Graph_Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_Robust_Point_Cloud_CVPR_2021_supplemental.pdf | 2103.04256 | cvf | @InProceedings{Fu_2021_CVPR,
author = {Fu, Kexue and Liu, Shaolei and Luo, Xiaoyuan and Wang, Manning},
title = {Robust Point Cloud Registration Framework Based on Deep Graph Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | 3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-... |
Wang_Dense_Contrastive_Learning_for_Self-Supervised_Visual_Pre-Training_CVPR_2021_paper | Dense Contrastive Learning for Self-Supervised Visual Pre-Training | [
"Xinlong Wang",
"Rufeng Zhang",
"Chunhua Shen",
"Tao Kong",
"Lei Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Dense_Contrastive_Learning_for_Self-Supervised_Visual_Pre-Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Dense_Contrastive_Learning_for_Self-Supervised_Visual_Pre-Training_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Dense_Contrastive_Learning_CVPR_2021_supplemental.pdf | 2011.09157 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Xinlong and Zhang, Rufeng and Shen, Chunhua and Kong, Tao and Li, Lei},
title = {Dense Contrastive Learning for Self-Supervised Visual Pre-Training},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction. To fill this gap, we aim to design an effective, dense sel... |
Wang_Birds_of_a_Feather_Capturing_Avian_Shape_Models_From_Images_CVPR_2021_paper | Birds of a Feather: Capturing Avian Shape Models From Images | [
"Yufu Wang",
"Nikos Kolotouros",
"Kostas Daniilidis",
"Marc Badger"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Birds_of_a_Feather_Capturing_Avian_Shape_Models_From_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Birds_of_a_Feather_Capturing_Avian_Shape_Models_From_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Birds_of_a_CVPR_2021_supplemental.pdf | 2105.09396 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Yufu and Kolotouros, Nikos and Daniilidis, Kostas and Badger, Marc},
title = {Birds of a Feather: Capturing Avian Shape Models From Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | Animals are diverse in shape, but building a deformable shape model for a new species is not always possible due to the lack of 3D data. We present a method to capture new species using an articulated template and images of that species. In this work, we focus mainly on birds. Although birds represent almost twice the ... |
Zhang_Learning_Temporal_Consistency_for_Low_Light_Video_Enhancement_From_Single_CVPR_2021_paper | Learning Temporal Consistency for Low Light Video Enhancement From Single Images | [
"Fan Zhang",
"Yu Li",
"Shaodi You",
"Ying Fu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_Temporal_Consistency_for_Low_Light_Video_Enhancement_From_Single_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_Temporal_Consistency_for_Low_Light_Video_Enhancement_From_Single_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Learning_Temporal_Consistency_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Fan and Li, Yu and You, Shaodi and Fu, Ying},
title = {Learning Temporal Consistency for Low Light Video Enhancement From Single Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Single image low light enhancement is an important task and it has many practical applications. Most existing methods adopt a single image approach. Although their performance is satisfying on a static single image, we found, however, they suffer serious temporal instability when handling low light videos. We notice th... |
Huang_Brain_Image_Synthesis_With_Unsupervised_Multivariate_Canonical_CSCl4Net_CVPR_2021_paper | Brain Image Synthesis With Unsupervised Multivariate Canonical CSCl4Net | [
"Yawen Huang",
"Feng Zheng",
"Danyang Wang",
"Weilin Huang",
"Matthew R. Scott",
"Ling Shao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Brain_Image_Synthesis_With_Unsupervised_Multivariate_Canonical_CSCl4Net_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Brain_Image_Synthesis_With_Unsupervised_Multivariate_Canonical_CSCl4Net_CVPR_2021_paper.pdf | null | 2103.11587 | title_judge | @InProceedings{Huang_2021_CVPR,
author = {Huang, Yawen and Zheng, Feng and Wang, Danyang and Huang, Weilin and Scott, Matthew R. and Shao, Ling},
title = {Brain Image Synthesis With Unsupervised Multivariate Canonical CSCl4Net},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | Recent advances in neuroscience have highlighted the effectiveness of multi-modal medical data for investigating certain pathologies and understanding human cognition. However, obtaining full sets of different modalities is limited by various factors, such as long acquisition times, high examination costs and artifact ... |
Guo_Inverse_Simulation_Reconstructing_Dynamic_Geometry_of_Clothed_Humans_via_Optimal_CVPR_2021_paper | Inverse Simulation: Reconstructing Dynamic Geometry of Clothed Humans via Optimal Control | [
"Jingfan Guo",
"Jie Li",
"Rahul Narain",
"Hyun Soo Park"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Inverse_Simulation_Reconstructing_Dynamic_Geometry_of_Clothed_Humans_via_Optimal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Inverse_Simulation_Reconstructing_Dynamic_Geometry_of_Clothed_Humans_via_Optimal_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Guo_2021_CVPR,
author = {Guo, Jingfan and Li, Jie and Narain, Rahul and Park, Hyun Soo},
title = {Inverse Simulation: Reconstructing Dynamic Geometry of Clothed Humans via Optimal Control},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | This paper studies the problem of inverse cloth simulation---to estimate shape and time-varying poses of the underlying body that generates physically plausible cloth motion, which matches to the point cloud measurements on the clothed humans. A key innovation is to represent the dynamics of the cloth geometry using a ... |
Gupta_Rotation_Equivariant_Siamese_Networks_for_Tracking_CVPR_2021_paper | Rotation Equivariant Siamese Networks for Tracking | [
"Deepak K. Gupta",
"Devanshu Arya",
"Efstratios Gavves"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gupta_Rotation_Equivariant_Siamese_Networks_for_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gupta_Rotation_Equivariant_Siamese_Networks_for_Tracking_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gupta_Rotation_Equivariant_Siamese_CVPR_2021_supplemental.pdf | 2012.13078 | cvf | @InProceedings{Gupta_2021_CVPR,
author = {Gupta, Deepak K. and Arya, Devanshu and Gavves, Efstratios},
title = {Rotation Equivariant Siamese Networks for Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year... | Rotation is among the long prevailing, yet still unresolved, hard challenges encountered in visual object tracking. The existing deep learning-based tracking algorithms use regular CNNs that are inherently translation equivariant, but not designed to tackle rotations. In this paper, we first demonstrate that in the pre... |
Alaniz_Learning_Decision_Trees_Recurrently_Through_Communication_CVPR_2021_paper | Learning Decision Trees Recurrently Through Communication | [
"Stephan Alaniz",
"Diego Marcos",
"Bernt Schiele",
"Zeynep Akata"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Alaniz_Learning_Decision_Trees_Recurrently_Through_Communication_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Alaniz_Learning_Decision_Trees_Recurrently_Through_Communication_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Alaniz_Learning_Decision_Trees_CVPR_2021_supplemental.pdf | 1902.01780 | cvf | @InProceedings{Alaniz_2021_CVPR,
author = {Alaniz, Stephan and Marcos, Diego and Schiele, Bernt and Akata, Zeynep},
title = {Learning Decision Trees Recurrently Through Communication},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision ... |
Wang_PatchmatchNet_Learned_Multi-View_Patchmatch_Stereo_CVPR_2021_paper | PatchmatchNet: Learned Multi-View Patchmatch Stereo | [
"Fangjinhua Wang",
"Silvano Galliani",
"Christoph Vogel",
"Pablo Speciale",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PatchmatchNet_Learned_Multi-View_Patchmatch_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_PatchmatchNet_Learned_Multi-View_Patchmatch_Stereo_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_PatchmatchNet_Learned_Multi-View_CVPR_2021_supplemental.pdf | 2012.01411 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Fangjinhua and Galliani, Silvano and Vogel, Christoph and Speciale, Pablo and Pollefeys, Marc},
title = {PatchmatchNet: Learned Multi-View Patchmatch Stereo},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for high-resolution multi-view stereo. With high computation speed and low memory requirement, PatchmatchNet can process higher resolution imagery and is more suited to run on resource limited devices than competitors that employ 3D cost ... |
Sharma_Instance_Level_Affinity-Based_Transfer_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper | Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation | [
"Astuti Sharma",
"Tarun Kalluri",
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sharma_Instance_Level_Affinity-Based_Transfer_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sharma_Instance_Level_Affinity-Based_Transfer_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sharma_Instance_Level_Affinity-Based_CVPR_2021_supplemental.pdf | 2104.01286 | cvf | @InProceedings{Sharma_2021_CVPR,
author = {Sharma, Astuti and Kalluri, Tarun and Chandraker, Manmohan},
title = {Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Domain adaptation deals with training models using large scale labeled data from a specific source domain and then adapting the knowledge to certain target domains that have few or no labels. Many prior works learn domain agnostic feature representations for this purpose using a global distribution alignment objective ... |
Lin_COMPLETER_Incomplete_Multi-View_Clustering_via_Contrastive_Prediction_CVPR_2021_paper | COMPLETER: Incomplete Multi-View Clustering via Contrastive Prediction | [
"Yijie Lin",
"Yuanbiao Gou",
"Zitao Liu",
"Boyun Li",
"Jiancheng Lv",
"Xi Peng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_COMPLETER_Incomplete_Multi-View_Clustering_via_Contrastive_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_COMPLETER_Incomplete_Multi-View_Clustering_via_Contrastive_Prediction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_COMPLETER_Incomplete_Multi-View_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Lin_2021_CVPR,
author = {Lin, Yijie and Gou, Yuanbiao and Liu, Zitao and Li, Boyun and Lv, Jiancheng and Peng, Xi},
title = {COMPLETER: Incomplete Multi-View Clustering via Contrastive Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data. To this end, we propose a novel objective that incorpor... |
Li_Image-to-Image_Translation_via_Hierarchical_Style_Disentanglement_CVPR_2021_paper | Image-to-Image Translation via Hierarchical Style Disentanglement | [
"Xinyang Li",
"Shengchuan Zhang",
"Jie Hu",
"Liujuan Cao",
"Xiaopeng Hong",
"Xudong Mao",
"Feiyue Huang",
"Yongjian Wu",
"Rongrong Ji"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Image-to-Image_Translation_via_Hierarchical_Style_Disentanglement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Image-to-Image_Translation_via_Hierarchical_Style_Disentanglement_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Image-to-Image_Translation_via_CVPR_2021_supplemental.pdf | 2103.01456 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Xinyang and Zhang, Shengchuan and Hu, Jie and Cao, Liujuan and Hong, Xiaopeng and Mao, Xudong and Huang, Feiyue and Wu, Yongjian and Ji, Rongrong},
title = {Image-to-Image Translation via Hierarchical Style Disentanglement},
booktitle = {Proceedings of the I... | Recently, image-to-image translation has made significant progress in achieving both multi-label (i.e., translation conditioned on different labels) and multi-style (i.e., generation with diverse styles) tasks. However, due to the unexplored independence and exclusiveness in the labels, existing endeavors are defeated ... |
Lin_What_Can_Style_Transfer_and_Paintings_Do_for_Model_Robustness_CVPR_2021_paper | What Can Style Transfer and Paintings Do for Model Robustness? | [
"Hubert Lin",
"Mitchell van Zuijlen",
"Sylvia C. Pont",
"Maarten W.A. Wijntjes",
"Kavita Bala"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_What_Can_Style_Transfer_and_Paintings_Do_for_Model_Robustness_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_What_Can_Style_Transfer_and_Paintings_Do_for_Model_Robustness_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_What_Can_Style_CVPR_2021_supplemental.pdf | 2011.14477 | cvf | @InProceedings{Lin_2021_CVPR,
author = {Lin, Hubert and van Zuijlen, Mitchell and Pont, Sylvia C. and Wijntjes, Maarten W.A. and Bala, Kavita},
title = {What Can Style Transfer and Paintings Do for Model Robustness?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | A common strategy for improving model robustness is through data augmentations. Data augmentations encourage models to learn desired invariances, such as invariance to horizontal flipping or small changes in color. Recent work has shown that arbitrary style transfer can be used as a form of data augmentation to encoura... |
Esser_Taming_Transformers_for_High-Resolution_Image_Synthesis_CVPR_2021_paper | Taming Transformers for High-Resolution Image Synthesis | [
"Patrick Esser",
"Robin Rombach",
"Bjorn Ommer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Esser_Taming_Transformers_for_High-Resolution_Image_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Esser_Taming_Transformers_for_High-Resolution_Image_Synthesis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Esser_Taming_Transformers_for_CVPR_2021_supplemental.pdf | 2012.09841 | cvf | @InProceedings{Esser_2021_CVPR,
author = {Esser, Patrick and Rombach, Robin and Ommer, Bjorn},
title = {Taming Transformers for High-Resolution Image Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, su... |
Suris_Learning_the_Predictability_of_the_Future_CVPR_2021_paper | Learning the Predictability of the Future | [
"Didac Suris",
"Ruoshi Liu",
"Carl Vondrick"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Suris_Learning_the_Predictability_of_the_Future_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Suris_Learning_the_Predictability_of_the_Future_CVPR_2021_paper.pdf | null | 2101.01600 | cvf | @InProceedings{Suris_2021_CVPR,
author = {Suris, Didac and Liu, Ruoshi and Vondrick, Carl},
title = {Learning the Predictability of the Future},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | We introduce a framework for learning from unlabeled video what is predictable in the future. Instead of committing up front to features to predict, our approach learns from data which features are predictable. Based on the observation that hyperbolic geometry naturally and compactly encodes hierarchical structure, we ... |
Gamper_Multiple_Instance_Captioning_Learning_Representations_From_Histopathology_Textbooks_and_Articles_CVPR_2021_paper | Multiple Instance Captioning: Learning Representations From Histopathology Textbooks and Articles | [
"Jevgenij Gamper",
"Nasir Rajpoot"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gamper_Multiple_Instance_Captioning_Learning_Representations_From_Histopathology_Textbooks_and_Articles_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gamper_Multiple_Instance_Captioning_Learning_Representations_From_Histopathology_Textbooks_and_Articles_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gamper_Multiple_Instance_Captioning_CVPR_2021_supplemental.pdf | 2103.05121 | cvf | @InProceedings{Gamper_2021_CVPR,
author = {Gamper, Jevgenij and Rajpoot, Nasir},
title = {Multiple Instance Captioning: Learning Representations From Histopathology Textbooks and Articles},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH on the other hand contains dense diagnostic and morphological descriptions for a range of stains, tissue types and pathologies. Using intrins... |
Li_Beyond_Max-Margin_Class_Margin_Equilibrium_for_Few-Shot_Object_Detection_CVPR_2021_paper | Beyond Max-Margin: Class Margin Equilibrium for Few-Shot Object Detection | [
"Bohao Li",
"Boyu Yang",
"Chang Liu",
"Feng Liu",
"Rongrong Ji",
"Qixiang Ye"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Beyond_Max-Margin_Class_Margin_Equilibrium_for_Few-Shot_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Beyond_Max-Margin_Class_Margin_Equilibrium_for_Few-Shot_Object_Detection_CVPR_2021_paper.pdf | null | 2103.04612 | title_snapshot | @InProceedings{Li_2021_CVPR,
author = {Li, Bohao and Yang, Boyu and Liu, Chang and Liu, Feng and Ji, Rongrong and Ye, Qixiang},
title = {Beyond Max-Margin: Class Margin Equilibrium for Few-Shot Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | Few-shot object detection has made encouraging progress by reconstructing novel class objects using the feature representation learned upon a set of base classes. However, an implicit contradiction about reconstruction and classification is unfortunately ignored. On the one hand, to precisely reconstruct novel classes,... |
Xu_Consistent_Instance_False_Positive_Improves_Fairness_in_Face_Recognition_CVPR_2021_paper | Consistent Instance False Positive Improves Fairness in Face Recognition | [
"Xingkun Xu",
"Yuge Huang",
"Pengcheng Shen",
"Shaoxin Li",
"Jilin Li",
"Feiyue Huang",
"Yong Li",
"Zhen Cui"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Consistent_Instance_False_Positive_Improves_Fairness_in_Face_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Consistent_Instance_False_Positive_Improves_Fairness_in_Face_Recognition_CVPR_2021_paper.pdf | null | 2106.05519 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Xingkun and Huang, Yuge and Shen, Pengcheng and Li, Shaoxin and Li, Jilin and Huang, Feiyue and Li, Yong and Cui, Zhen},
title = {Consistent Instance False Positive Improves Fairness in Face Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference o... | Demographic bias is a significant challenge in practical face recognition systems. Several methods have been proposed to reduce the bias, which rely on accurate demographic annotations. However, such annotations are usually not available in real scenarios. Moreover, these methods are explicitly designed for a specific ... |
Park_Learning_Dynamic_Network_Using_a_Reuse_Gate_Function_in_Semi-Supervised_CVPR_2021_paper | Learning Dynamic Network Using a Reuse Gate Function in Semi-Supervised Video Object Segmentation | [
"Hyojin Park",
"Jayeon Yoo",
"Seohyeong Jeong",
"Ganesh Venkatesh",
"Nojun Kwak"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Park_Learning_Dynamic_Network_Using_a_Reuse_Gate_Function_in_Semi-Supervised_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Park_Learning_Dynamic_Network_Using_a_Reuse_Gate_Function_in_Semi-Supervised_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Park_Learning_Dynamic_Network_CVPR_2021_supplemental.pdf | 2012.11655 | cvf | @InProceedings{Park_2021_CVPR,
author = {Park, Hyojin and Yoo, Jayeon and Jeong, Seohyeong and Venkatesh, Ganesh and Kwak, Nojun},
title = {Learning Dynamic Network Using a Reuse Gate Function in Semi-Supervised Video Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame. This results in high-quality segmentation across challenging scenarios such as changes in appearance and occlusion. But it also le... |
Yoo_RaScaNet_Learning_Tiny_Models_by_Raster-Scanning_Images_CVPR_2021_paper | RaScaNet: Learning Tiny Models by Raster-Scanning Images | [
"Jaehyoung Yoo",
"Dongwook Lee",
"Changyong Son",
"Sangil Jung",
"ByungIn Yoo",
"Changkyu Choi",
"Jae-Joon Han",
"Bohyung Han"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yoo_RaScaNet_Learning_Tiny_Models_by_Raster-Scanning_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yoo_RaScaNet_Learning_Tiny_Models_by_Raster-Scanning_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yoo_RaScaNet_Learning_Tiny_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yoo_2021_CVPR,
author = {Yoo, Jaehyoung and Lee, Dongwook and Son, Changyong and Jung, Sangil and Yoo, ByungIn and Choi, Changkyu and Han, Jae-Joon and Han, Bohyung},
title = {RaScaNet: Learning Tiny Models by Raster-Scanning Images},
booktitle = {Proceedings of the IEEE/CVF Conference... | Deploying deep convolutional neural networks on ultra-low power systems is challenging due to the extremely limited resources. Especially, the memory becomes a bottleneck as the systems put a hard limit on the size of on-chip memory. Because peak memory explosion in the lower layers is critical even in tiny models, the... |
Grunde-McLaughlin_AGQA_A_Benchmark_for_Compositional_Spatio-Temporal_Reasoning_CVPR_2021_paper | AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning | [
"Madeleine Grunde-McLaughlin",
"Ranjay Krishna",
"Maneesh Agrawala"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Grunde-McLaughlin_AGQA_A_Benchmark_for_Compositional_Spatio-Temporal_Reasoning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Grunde-McLaughlin_AGQA_A_Benchmark_for_Compositional_Spatio-Temporal_Reasoning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Grunde-McLaughlin_AGQA_A_Benchmark_CVPR_2021_supplemental.pdf | 2103.16002 | title_snapshot | @InProceedings{Grunde-McLaughlin_2021_CVPR,
author = {Grunde-McLaughlin, Madeleine and Krishna, Ranjay and Agrawala, Maneesh},
title = {AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Visual events are a composition of temporal actions involving actors spatially interacting with objects. When developing computer vision models that can reason about compositional spatio-temporal events, we need benchmarks that can analyze progress and uncover shortcomings. Existing video question answering benchmarks ... |
Li_Exploring_intermediate_representation_for_monocular_vehicle_pose_estimation_CVPR_2021_paper | Exploring intermediate representation for monocular vehicle pose estimation | [
"Shichao Li",
"Zengqiang Yan",
"Hongyang Li",
"Kwang-Ting Cheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Exploring_intermediate_representation_for_monocular_vehicle_pose_estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Exploring_intermediate_representation_for_monocular_vehicle_pose_estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Exploring_intermediate_representation_CVPR_2021_supplemental.zip | 2011.08464 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Shichao and Yan, Zengqiang and Li, Hongyang and Cheng, Kwang-Ting},
title = {Exploring intermediate representation for monocular vehicle pose estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | We present a new learning-based framework to recover vehicle pose in SO(3) from a single RGB image. In contrast to previous works that map local appearance to observation angles, we explore a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs) to estimate egocentric vehicle ori... |
Wei_Shallow_Feature_Matters_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper | Shallow Feature Matters for Weakly Supervised Object Localization | [
"Jun Wei",
"Qin Wang",
"Zhen Li",
"Sheng Wang",
"S. Kevin Zhou",
"Shuguang Cui"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_Shallow_Feature_Matters_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_Shallow_Feature_Matters_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper.pdf | null | 2108.00873 | title_snapshot | @InProceedings{Wei_2021_CVPR,
author = {Wei, Jun and Wang, Qin and Li, Zhen and Wang, Sheng and Zhou, S. Kevin and Cui, Shuguang},
title = {Shallow Feature Matters for Weakly Supervised Object Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | Weakly supervised object localization (WSOL) aims to localize objects by only utilizing image-level labels. Class activation maps (CAMs) are the commonly used features to achieve WSOL. However, previous CAM-based methods did not take full advantage of the shallow features, despite their importance for WSOL. Because sha... |
Yang_Capturing_Omni-Range_Context_for_Omnidirectional_Segmentation_CVPR_2021_paper | Capturing Omni-Range Context for Omnidirectional Segmentation | [
"Kailun Yang",
"Jiaming Zhang",
"Simon Reiss",
"Xinxin Hu",
"Rainer Stiefelhagen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Capturing_Omni-Range_Context_for_Omnidirectional_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Capturing_Omni-Range_Context_for_Omnidirectional_Segmentation_CVPR_2021_paper.pdf | null | 2103.05687 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Kailun and Zhang, Jiaming and Reiss, Simon and Hu, Xinxin and Stiefelhagen, Rainer},
title = {Capturing Omni-Range Context for Omnidirectional Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving. Enabling an all-encompassing view of street-scenes, omnidirectional cameras present themselves as a perfect fit in such systems. Most segmentation models for parsing urban environment... |
Gonzalez_PLADE-Net_Towards_Pixel-Level_Accuracy_for_Self-Supervised_Single-View_Depth_Estimation_With_CVPR_2021_paper | PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation With Neural Positional Encoding and Distilled Matting Loss | [
"Juan Luis Gonzalez",
"Munchurl Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gonzalez_PLADE-Net_Towards_Pixel-Level_Accuracy_for_Self-Supervised_Single-View_Depth_Estimation_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gonzalez_PLADE-Net_Towards_Pixel-Level_Accuracy_for_Self-Supervised_Single-View_Depth_Estimation_With_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gonzalez_PLADE-Net_Towards_Pixel-Level_CVPR_2021_supplemental.pdf | 2103.07362 | title_snapshot | @InProceedings{Gonzalez_2021_CVPR,
author = {Gonzalez, Juan Luis and Kim, Munchurl},
title = {PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation With Neural Positional Encoding and Distilled Matting Loss},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | In this paper, we propose a self-supervised single-view pixel-level accurate depth estimation network, called PLADE-Net. The PLADE-Net is the first work that shows unprecedented accuracy levels, exceeding 95% in terms of the \delta^1 metric on the challenging KITTI dataset. Our PLADE-Net is based on a new network archi... |
Lin_Reciprocal_Landmark_Detection_and_Tracking_With_Extremely_Few_Annotations_CVPR_2021_paper | Reciprocal Landmark Detection and Tracking With Extremely Few Annotations | [
"Jianzhe Lin",
"Ghazal Sahebzamani",
"Christina Luong",
"Fatemeh Taheri Dezaki",
"Mohammad Jafari",
"Purang Abolmaesumi",
"Teresa Tsang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Reciprocal_Landmark_Detection_and_Tracking_With_Extremely_Few_Annotations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Reciprocal_Landmark_Detection_and_Tracking_With_Extremely_Few_Annotations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Reciprocal_Landmark_Detection_CVPR_2021_supplemental.pdf | 2101.11224 | cvf | @InProceedings{Lin_2021_CVPR,
author = {Lin, Jianzhe and Sahebzamani, Ghazal and Luong, Christina and Dezaki, Fatemeh Taheri and Jafari, Mohammad and Abolmaesumi, Purang and Tsang, Teresa},
title = {Reciprocal Landmark Detection and Tracking With Extremely Few Annotations},
booktitle = {Proceedings o... | Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly desirable to improve workflow and reduce interobserver variability. Training a machine learning framewor... |
Jo_Practical_Single-Image_Super-Resolution_Using_Look-Up_Table_CVPR_2021_paper | Practical Single-Image Super-Resolution Using Look-Up Table | [
"Younghyun Jo",
"Seon Joo Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jo_Practical_Single-Image_Super-Resolution_Using_Look-Up_Table_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jo_Practical_Single-Image_Super-Resolution_Using_Look-Up_Table_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jo_Practical_Single-Image_Super-Resolution_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Jo_2021_CVPR,
author = {Jo, Younghyun and Kim, Seon Joo},
title = {Practical Single-Image Super-Resolution Using Look-Up Table},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
p... | A number of super-resolution (SR) algorithms from interpolation to deep neural networks (DNN) have emerged to restore or create missing details of the input low-resolution image. As mobile devices and display hardware develops, the demand for practical SR technology has increased. Current state-of-the-art SR methods ... |
Wang_Removing_the_Background_by_Adding_the_Background_Towards_Background_Robust_CVPR_2021_paper | Removing the Background by Adding the Background: Towards Background Robust Self-Supervised Video Representation Learning | [
"Jinpeng Wang",
"Yuting Gao",
"Ke Li",
"Yiqi Lin",
"Andy J. Ma",
"Hao Cheng",
"Pai Peng",
"Feiyue Huang",
"Rongrong Ji",
"Xing Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Removing_the_Background_by_Adding_the_Background_Towards_Background_Robust_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Removing_the_Background_by_Adding_the_Background_Towards_Background_Robust_CVPR_2021_paper.pdf | null | 2009.05769 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Jinpeng and Gao, Yuting and Li, Ke and Lin, Yiqi and Ma, Andy J. and Cheng, Hao and Peng, Pai and Huang, Feiyue and Ji, Rongrong and Sun, Xing},
title = {Removing the Background by Adding the Background: Towards Background Robust Self-Supervised Video Repres... | Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself. However, some of the current methods tend to cheat from the background, i.e., the prediction is highly dependent on the video background instead of the m... |
Wang_GDR-Net_Geometry-Guided_Direct_Regression_Network_for_Monocular_6D_Object_Pose_CVPR_2021_paper | GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation | [
"Gu Wang",
"Fabian Manhardt",
"Federico Tombari",
"Xiangyang Ji"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_GDR-Net_Geometry-Guided_Direct_Regression_Network_for_Monocular_6D_Object_Pose_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_GDR-Net_Geometry-Guided_Direct_Regression_Network_for_Monocular_6D_Object_Pose_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_GDR-Net_Geometry-Guided_Direct_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Gu and Manhardt, Fabian and Tombari, Federico and Ji, Xiangyang},
title = {GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | 6D pose estimation from a single RGB image is a fundamental task in computer vision. The current top-performing deep learning-based methods rely on an indirect strategy, i.e., first establishing 2D-3D correspondences between the coordinates in the image plane and object coordinate system, and then applying a variant of... |
Li_Point_Cloud_Upsampling_via_Disentangled_Refinement_CVPR_2021_paper | Point Cloud Upsampling via Disentangled Refinement | [
"Ruihui Li",
"Xianzhi Li",
"Pheng-Ann Heng",
"Chi-Wing Fu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Point_Cloud_Upsampling_via_Disentangled_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Point_Cloud_Upsampling_via_Disentangled_Refinement_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Point_Cloud_Upsampling_CVPR_2021_supplemental.pdf | 2106.04779 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Ruihui and Li, Xianzhi and Heng, Pheng-Ann and Fu, Chi-Wing},
title = {Point Cloud Upsampling via Disentangled Refinement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
ye... | Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface, and possibly amending small holes, all in a single network. After revisiting the task, we propose to disen... |
Chi_Feature-Level_Collaboration_Joint_Unsupervised_Learning_of_Optical_Flow_Stereo_Depth_CVPR_2021_paper | Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion | [
"Cheng Chi",
"Qingjie Wang",
"Tianyu Hao",
"Peng Guo",
"Xin Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chi_Feature-Level_Collaboration_Joint_Unsupervised_Learning_of_Optical_Flow_Stereo_Depth_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chi_Feature-Level_Collaboration_Joint_Unsupervised_Learning_of_Optical_Flow_Stereo_Depth_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Chi_2021_CVPR,
author = {Chi, Cheng and Wang, Qingjie and Hao, Tianyu and Guo, Peng and Yang, Xin},
title = {Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | Precise estimation of optical flow, stereo depth and camera motion are important for the real-world 3D scene understanding and visual perception. Since the three tasks are tightly coupled with the inherent 3D geometric constraints, current studies have demonstrated that the three tasks can be improved through jointly o... |
Wan_A_Generalized_Loss_Function_for_Crowd_Counting_and_Localization_CVPR_2021_paper | A Generalized Loss Function for Crowd Counting and Localization | [
"Jia Wan",
"Ziquan Liu",
"Antoni B. Chan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wan_A_Generalized_Loss_Function_for_Crowd_Counting_and_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wan_A_Generalized_Loss_Function_for_Crowd_Counting_and_Localization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wan_A_Generalized_Loss_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wan_2021_CVPR,
author = {Wan, Jia and Liu, Ziquan and Chan, Antoni B.},
title = {A Generalized Loss Function for Crowd Counting and Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Previous work shows that a better density map representation can improve the performance of crowd counting. In this paper, we investigate learning the density map representation through an unbalanced optimal transport problem, and propose a generalized loss function to learn density maps for crowd counting and localiza... |
Wang_Learning_Fine-Grained_Segmentation_of_3D_Shapes_Without_Part_Labels_CVPR_2021_paper | Learning Fine-Grained Segmentation of 3D Shapes Without Part Labels | [
"Xiaogang Wang",
"Xun Sun",
"Xinyu Cao",
"Kai Xu",
"Bin Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Learning_Fine-Grained_Segmentation_of_3D_Shapes_Without_Part_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Learning_Fine-Grained_Segmentation_of_3D_Shapes_Without_Part_Labels_CVPR_2021_paper.pdf | null | 2103.13030 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Xiaogang and Sun, Xun and Cao, Xinyu and Xu, Kai and Zhou, Bin},
title = {Learning Fine-Grained Segmentation of 3D Shapes Without Part Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | Existing learning-based approaches to 3D shape segmentation usually formulate it as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of labels. This assumption, however, is unrealistic for training fine-grained segmentation on large datasets since the annotation of ... |
Hong_Fine-Grained_Shape-Appearance_Mutual_Learning_for_Cloth-Changing_Person_Re-Identification_CVPR_2021_paper | Fine-Grained Shape-Appearance Mutual Learning for Cloth-Changing Person Re-Identification | [
"Peixian Hong",
"Tao Wu",
"Ancong Wu",
"Xintong Han",
"Wei-Shi Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Fine-Grained_Shape-Appearance_Mutual_Learning_for_Cloth-Changing_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_Fine-Grained_Shape-Appearance_Mutual_Learning_for_Cloth-Changing_Person_Re-Identification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Fine-Grained_Shape-Appearance_Mutual_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Hong_2021_CVPR,
author = {Hong, Peixian and Wu, Tao and Wu, Ancong and Han, Xintong and Zheng, Wei-Shi},
title = {Fine-Grained Shape-Appearance Mutual Learning for Cloth-Changing Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | Recently, person re-identification (Re-ID) has achieved great progress. However, current methods largely depend on color appearance, which is not reliable when a person changes the clothes. Cloth-changing Re-ID is challenging since pedestrian images with clothes change exhibit large intra-class variation and small inte... |
Mihajlovic_DeepSurfels_Learning_Online_Appearance_Fusion_CVPR_2021_paper | DeepSurfels: Learning Online Appearance Fusion | [
"Marko Mihajlovic",
"Silvan Weder",
"Marc Pollefeys",
"Martin R. Oswald"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mihajlovic_DeepSurfels_Learning_Online_Appearance_Fusion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mihajlovic_DeepSurfels_Learning_Online_Appearance_Fusion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mihajlovic_DeepSurfels_Learning_Online_CVPR_2021_supplemental.pdf | 2012.14240 | cvf | @InProceedings{Mihajlovic_2021_CVPR,
author = {Mihajlovic, Marko and Weder, Silvan and Pollefeys, Marc and Oswald, Martin R.},
title = {DeepSurfels: Learning Online Appearance Fusion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information. DeepSurfels combines explicit and neural building blocks to jointly encode geometry and appearance information. In contrast to established representations, DeepSurfels better represents high-frequency textures, is well-... |
Kim_Joint_Negative_and_Positive_Learning_for_Noisy_Labels_CVPR_2021_paper | Joint Negative and Positive Learning for Noisy Labels | [
"Youngdong Kim",
"Juseung Yun",
"Hyounguk Shon",
"Junmo Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Joint_Negative_and_Positive_Learning_for_Noisy_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Joint_Negative_and_Positive_Learning_for_Noisy_Labels_CVPR_2021_paper.pdf | null | 2104.06574 | cvf | @InProceedings{Kim_2021_CVPR,
author = {Kim, Youngdong and Yun, Juseung and Shon, Hyounguk and Kim, Junmo},
title = {Joint Negative and Positive Learning for Noisy Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Training of Convolutional Neural Networks (CNNs) with data with noisy labels is known to be a challenge. Based on the fact that directly providing the label to the data (Positive Learning; PL) has a risk of allowing CNNs to memorize the contaminated labels for the case of noisy data, the indirect learning approach that... |
Luo_Generalizing_Face_Forgery_Detection_With_High-Frequency_Features_CVPR_2021_paper | Generalizing Face Forgery Detection With High-Frequency Features | [
"Yuchen Luo",
"Yong Zhang",
"Junchi Yan",
"Wei Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Generalizing_Face_Forgery_Detection_With_High-Frequency_Features_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Generalizing_Face_Forgery_Detection_With_High-Frequency_Features_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Generalizing_Face_Forgery_CVPR_2021_supplemental.pdf | 2103.12376 | cvf | @InProceedings{Luo_2021_CVPR,
author = {Luo, Yuchen and Zhang, Yong and Yan, Junchi and Liu, Wei},
title = {Generalizing Face Forgery Detection With High-Frequency Features},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different... |
Li_The_Heterogeneity_Hypothesis_Finding_Layer-Wise_Differentiated_Network_Architectures_CVPR_2021_paper | The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures | [
"Yawei Li",
"Wen Li",
"Martin Danelljan",
"Kai Zhang",
"Shuhang Gu",
"Luc Van Gool",
"Radu Timofte"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_The_Heterogeneity_Hypothesis_Finding_Layer-Wise_Differentiated_Network_Architectures_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_The_Heterogeneity_Hypothesis_Finding_Layer-Wise_Differentiated_Network_Architectures_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_The_Heterogeneity_Hypothesis_CVPR_2021_supplemental.pdf | 2006.16242 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Yawei and Li, Wen and Danelljan, Martin and Zhang, Kai and Gu, Shuhang and Van Gool, Luc and Timofte, Radu},
title = {The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures},
booktitle = {Proceedings of the IEEE/CVF Conference ... | In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the design of the overall architecture, we investigate a design space that is usually overlooked, i.e. adjusting the channel configurations of predefined networks. We find that this adjustment can be achieved by shrinkin... |
Dong_Robust_Neural_Routing_Through_Space_Partitions_for_Camera_Relocalization_in_CVPR_2021_paper | Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments | [
"Siyan Dong",
"Qingnan Fan",
"He Wang",
"Ji Shi",
"Li Yi",
"Thomas Funkhouser",
"Baoquan Chen",
"Leonidas J. Guibas"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dong_Robust_Neural_Routing_Through_Space_Partitions_for_Camera_Relocalization_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dong_Robust_Neural_Routing_Through_Space_Partitions_for_Camera_Relocalization_in_CVPR_2021_paper.pdf | null | 2012.04746 | cvf | @InProceedings{Dong_2021_CVPR,
author = {Dong, Siyan and Fan, Qingnan and Wang, He and Shi, Ji and Yi, Li and Funkhouser, Thomas and Chen, Baoquan and Guibas, Leonidas J.},
title = {Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments},
booktitle = {... | Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc. Recent advances estimate the camera pose via optimization over the 2D/3D-3D correspondences established between the coordinates in 2D/3D camera space and 3D world space. Such a mapping is estimated ... |
Jacob_Facial_Action_Unit_Detection_With_Transformers_CVPR_2021_paper | Facial Action Unit Detection With Transformers | [
"Geethu Miriam Jacob",
"Bjorn Stenger"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jacob_Facial_Action_Unit_Detection_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jacob_Facial_Action_Unit_Detection_With_Transformers_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Jacob_2021_CVPR,
author = {Jacob, Geethu Miriam and Stenger, Bjorn},
title = {Facial Action Unit Detection With Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pag... | The Facial Action Coding System is a taxonomy for fine-grained facial expression analysis. This paper proposes a method for detecting Facial Action Units (FAU), which define particular face muscle activity, from an input image. FAU detection is formulated as a multi-task learning problem, where image features and atten... |
Xie_Exploiting_Aliasing_for_Manga_Restoration_CVPR_2021_paper | Exploiting Aliasing for Manga Restoration | [
"Minshan Xie",
"Menghan Xia",
"Tien-Tsin Wong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Exploiting_Aliasing_for_Manga_Restoration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Exploiting_Aliasing_for_Manga_Restoration_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xie_Exploiting_Aliasing_for_CVPR_2021_supplemental.pdf | 2105.06830 | cvf | @InProceedings{Xie_2021_CVPR,
author = {Xie, Minshan and Xia, Menghan and Wong, Tien-Tsin},
title = {Exploiting Aliasing for Manga Restoration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screentone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative two-stage method to restore quality bitonal... |
Liu_Discovering_Hidden_Physics_Behind_Transport_Dynamics_CVPR_2021_paper | Discovering Hidden Physics Behind Transport Dynamics | [
"Peirong Liu",
"Lin Tian",
"Yubo Zhang",
"Stephen Aylward",
"Yueh Lee",
"Marc Niethammer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Discovering_Hidden_Physics_Behind_Transport_Dynamics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Discovering_Hidden_Physics_Behind_Transport_Dynamics_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Discovering_Hidden_Physics_CVPR_2021_supplemental.pdf | 2011.12222 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Peirong and Tian, Lin and Zhang, Yubo and Aylward, Stephen and Lee, Yueh and Niethammer, Marc},
title = {Discovering Hidden Physics Behind Transport Dynamics},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Transport processes are ubiquitous. They are, for example, at the heart of optical flow approaches; or of perfusion imaging, where blood transport is assessed, most commonly by injecting a tracer. An advection-diffusion equation is widely used to describe these transport phenomena. Our goal is estimating the underlying... |
Zhang_Cross-View_Gait_Recognition_With_Deep_Universal_Linear_Embeddings_CVPR_2021_paper | Cross-View Gait Recognition With Deep Universal Linear Embeddings | [
"Shaoxiong Zhang",
"Yunhong Wang",
"Annan Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Cross-View_Gait_Recognition_With_Deep_Universal_Linear_Embeddings_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Cross-View_Gait_Recognition_With_Deep_Universal_Linear_Embeddings_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Shaoxiong and Wang, Yunhong and Li, Annan},
title = {Cross-View Gait Recognition With Deep Universal Linear Embeddings},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Gait is considered an attractive biometric identifier for its non-invasive and non-cooperative features compared with other biometric identifiers such as fingerprint and iris. At present, cross-view gait recognition methods always establish representations from various deep convolutional networks for recognition and ig... |
Sun_Tuning_IR-Cut_Filter_for_Illumination-Aware_Spectral_Reconstruction_From_RGB_CVPR_2021_paper | Tuning IR-Cut Filter for Illumination-Aware Spectral Reconstruction From RGB | [
"Bo Sun",
"Junchi Yan",
"Xiao Zhou",
"Yinqiang Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Tuning_IR-Cut_Filter_for_Illumination-Aware_Spectral_Reconstruction_From_RGB_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Tuning_IR-Cut_Filter_for_Illumination-Aware_Spectral_Reconstruction_From_RGB_CVPR_2021_paper.pdf | null | 2103.14708 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Bo and Yan, Junchi and Zhou, Xiao and Zheng, Yinqiang},
title = {Tuning IR-Cut Filter for Illumination-Aware Spectral Reconstruction From RGB},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | To reconstruct spectral signals from multi-channel observations, in particular trichromatic RGBs, has recently emerged as a promising alternative to traditional scanning-based spectral imager. It has been proven that the reconstruction accuracy relies heavily on the spectral response of the RGB camera in use. To improv... |
Kan_Relative_Order_Analysis_and_Optimization_for_Unsupervised_Deep_Metric_Learning_CVPR_2021_paper | Relative Order Analysis and Optimization for Unsupervised Deep Metric Learning | [
"Shichao Kan",
"Yigang Cen",
"Yang Li",
"Vladimir Mladenovic",
"Zhihai He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kan_Relative_Order_Analysis_and_Optimization_for_Unsupervised_Deep_Metric_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kan_Relative_Order_Analysis_and_Optimization_for_Unsupervised_Deep_Metric_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kan_Relative_Order_Analysis_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Kan_2021_CVPR,
author = {Kan, Shichao and Cen, Yigang and Li, Yang and Mladenovic, Vladimir and He, Zhihai},
title = {Relative Order Analysis and Optimization for Unsupervised Deep Metric Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | In unsupervised learning of image features without labels, especially on datasets with fine-grained object classes, it is often very difficult to tell if a given image belongs to one specific object class or another, even for human eyes. However, we can reliably tell if image C is more similar to image A than image B. ... |
Yan_Anchor-Free_Person_Search_CVPR_2021_paper | Anchor-Free Person Search | [
"Yichao Yan",
"Jinpeng Li",
"Jie Qin",
"Song Bai",
"Shengcai Liao",
"Li Liu",
"Fan Zhu",
"Ling Shao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Anchor-Free_Person_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Anchor-Free_Person_Search_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_Anchor-Free_Person_Search_CVPR_2021_supplemental.pdf | 2103.11617 | cvf | @InProceedings{Yan_2021_CVPR,
author = {Yan, Yichao and Li, Jinpeng and Qin, Jie and Bai, Song and Liao, Shengcai and Liu, Li and Zhu, Fan and Shao, Ling},
title = {Anchor-Free Person Search},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id). Most existing works employ two-stage detectors like Faster-RCNN, yielding encouraging accuracy but with high... |
Deng_Are_Labels_Always_Necessary_for_Classifier_Accuracy_Evaluation_CVPR_2021_paper | Are Labels Always Necessary for Classifier Accuracy Evaluation? | [
"Weijian Deng",
"Liang Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Are_Labels_Always_Necessary_for_Classifier_Accuracy_Evaluation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Are_Labels_Always_Necessary_for_Classifier_Accuracy_Evaluation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Deng_Are_Labels_Always_CVPR_2021_supplemental.pdf | 2007.02915 | cvf | @InProceedings{Deng_2021_CVPR,
author = {Deng, Weijian and Zheng, Liang},
title = {Are Labels Always Necessary for Classifier Accuracy Evaluation?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | To calculate the model accuracy on a computer vision task, e.g., object recognition, we usually require a test set composing of test samples and their ground truth labels. Whilst standard usage cases satisfy this requirement, many real-world scenarios involve unlabeled test data, rendering common model evaluation metho... |
Huang_Self-Supervised_Motion_Learning_From_Static_Images_CVPR_2021_paper | Self-Supervised Motion Learning From Static Images | [
"Ziyuan Huang",
"Shiwei Zhang",
"Jianwen Jiang",
"Mingqian Tang",
"Rong Jin",
"Marcelo H. Ang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Self-Supervised_Motion_Learning_From_Static_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Self-Supervised_Motion_Learning_From_Static_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Self-Supervised_Motion_Learning_CVPR_2021_supplemental.pdf | 2104.00240 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Ziyuan and Zhang, Shiwei and Jiang, Jianwen and Tang, Mingqian and Jin, Rong and Ang, Marcelo H.},
title = {Self-Supervised Motion Learning From Static Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit... | Motions are reflected in videos as the movement of pixels, and actions are essentially patterns of inconsistent motions between the foreground and the background. To well distinguish the actions, especially those with complicated spatio-temporal interactions, correctly locating the prominent motion areas is of crucial ... |
Wang_AttentiveNAS_Improving_Neural_Architecture_Search_via_Attentive_Sampling_CVPR_2021_paper | AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling | [
"Dilin Wang",
"Meng Li",
"Chengyue Gong",
"Vikas Chandra"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_AttentiveNAS_Improving_Neural_Architecture_Search_via_Attentive_Sampling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_AttentiveNAS_Improving_Neural_Architecture_Search_via_Attentive_Sampling_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_AttentiveNAS_Improving_Neural_CVPR_2021_supplemental.pdf | 2011.09011 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Dilin and Li, Meng and Gong, Chengyue and Chandra, Vikas},
title = {AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | Neural architecture search (NAS) has shown great promise in designing state-of-the-art (SOTA) models that are both accurate and efficient. Recently, two-stage NAS, e.g. BigNAS, decouples the model training and searching process and achieves remarkable search efficiency and accuracy. Two-stage NAS requires sampling from... |
Shi_StablePose_Learning_6D_Object_Poses_From_Geometrically_Stable_Patches_CVPR_2021_paper | StablePose: Learning 6D Object Poses From Geometrically Stable Patches | [
"Yifei Shi",
"Junwen Huang",
"Xin Xu",
"Yifan Zhang",
"Kai Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_StablePose_Learning_6D_Object_Poses_From_Geometrically_Stable_Patches_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_StablePose_Learning_6D_Object_Poses_From_Geometrically_Stable_Patches_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_StablePose_Learning_6D_CVPR_2021_supplemental.pdf | 2102.09334 | cvf | @InProceedings{Shi_2021_CVPR,
author = {Shi, Yifei and Huang, Junwen and Xu, Xin and Zhang, Yifan and Xu, Kai},
title = {StablePose: Learning 6D Object Poses From Geometrically Stable Patches},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from observed 3D point clouds. According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are ge... |
Lyu_Towards_Evaluating_and_Training_Verifiably_Robust_Neural_Networks_CVPR_2021_paper | Towards Evaluating and Training Verifiably Robust Neural Networks | [
"Zhaoyang Lyu",
"Minghao Guo",
"Tong Wu",
"Guodong Xu",
"Kehuan Zhang",
"Dahua Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lyu_Towards_Evaluating_and_Training_Verifiably_Robust_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lyu_Towards_Evaluating_and_Training_Verifiably_Robust_Neural_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lyu_Towards_Evaluating_and_CVPR_2021_supplemental.pdf | 2104.00447 | cvf | @InProceedings{Lyu_2021_CVPR,
author = {Lyu, Zhaoyang and Guo, Minghao and Wu, Tong and Xu, Guodong and Zhang, Kehuan and Lin, Dahua},
title = {Towards Evaluating and Training Verifiably Robust Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP trained networks: CROWN, a bounding method based on tight linear relaxation, often gives very loose bounds on these networks. We also observe tha... |
Jeong_Interpolation-Based_Semi-Supervised_Learning_for_Object_Detection_CVPR_2021_paper | Interpolation-Based Semi-Supervised Learning for Object Detection | [
"Jisoo Jeong",
"Vikas Verma",
"Minsung Hyun",
"Juho Kannala",
"Nojun Kwak"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jeong_Interpolation-Based_Semi-Supervised_Learning_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jeong_Interpolation-Based_Semi-Supervised_Learning_for_Object_Detection_CVPR_2021_paper.pdf | null | 2006.02158 | cvf | @InProceedings{Jeong_2021_CVPR,
author = {Jeong, Jisoo and Verma, Vikas and Hyun, Minsung and Kannala, Juho and Kwak, Nojun},
title = {Interpolation-Based Semi-Supervised Learning for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much. In this paper, we propose an Interpolation-based Semi-supervised learning method for object Detection (ISD), whi... |
Jin_Teachers_Do_More_Than_Teach_Compressing_Image-to-Image_Models_CVPR_2021_paper | Teachers Do More Than Teach: Compressing Image-to-Image Models | [
"Qing Jin",
"Jian Ren",
"Oliver J. Woodford",
"Jiazhuo Wang",
"Geng Yuan",
"Yanzhi Wang",
"Sergey Tulyakov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jin_Teachers_Do_More_Than_Teach_Compressing_Image-to-Image_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jin_Teachers_Do_More_Than_Teach_Compressing_Image-to-Image_Models_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jin_Teachers_Do_More_CVPR_2021_supplemental.pdf | 2103.03467 | cvf | @InProceedings{Jin_2021_CVPR,
author = {Jin, Qing and Ren, Jian and Woodford, Oliver J. and Wang, Jiazhuo and Yuan, Geng and Wang, Yanzhi and Tulyakov, Sergey},
title = {Teachers Do More Than Teach: Compressing Image-to-Image Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vi... | Generative Adversarial Networks (GANs) have achieved huge success in generating high-fidelity images, however, they suffer from low efficiency due to tremendous computational cost and bulky memory usage. Recent efforts on compression GANs show noticeable progress in obtaining smaller generators by sacrificing image qua... |
Xiong_Seeing_in_Extra_Darkness_Using_a_Deep-Red_Flash_CVPR_2021_paper | Seeing in Extra Darkness Using a Deep-Red Flash | [
"Jinhui Xiong",
"Jian Wang",
"Wolfgang Heidrich",
"Shree Nayar"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xiong_Seeing_in_Extra_Darkness_Using_a_Deep-Red_Flash_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiong_Seeing_in_Extra_Darkness_Using_a_Deep-Red_Flash_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiong_Seeing_in_Extra_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Xiong_2021_CVPR,
author = {Xiong, Jinhui and Wang, Jian and Heidrich, Wolfgang and Nayar, Shree},
title = {Seeing in Extra Darkness Using a Deep-Red Flash},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source. Our main observation is that in a dim environment, the human eye mainly uses rods for the perception of light, which are not sensitive to wavelengths longer than 620nm, yet the camera sensor still has a spectral resp... |
Chen_PSD_Principled_Synthetic-to-Real_Dehazing_Guided_by_Physical_Priors_CVPR_2021_paper | PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors | [
"Zeyuan Chen",
"Yangchao Wang",
"Yang Yang",
"Dong Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_PSD_Principled_Synthetic-to-Real_Dehazing_Guided_by_Physical_Priors_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_PSD_Principled_Synthetic-to-Real_Dehazing_Guided_by_Physical_Priors_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_PSD_Principled_Synthetic-to-Real_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Zeyuan and Wang, Yangchao and Yang, Yang and Liu, Dong},
title = {PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Deep learning-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on training models with synthetic hazy images, which incurs performance drop when the models are used for real-world hazy images. We propose a Principled Synthetic-to-real Dehazing (PSD) fra... |
Ren_3D_Spatial_Recognition_Without_Spatially_Labeled_3D_CVPR_2021_paper | 3D Spatial Recognition Without Spatially Labeled 3D | [
"Zhongzheng Ren",
"Ishan Misra",
"Alexander G. Schwing",
"Rohit Girdhar"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_3D_Spatial_Recognition_Without_Spatially_Labeled_3D_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ren_3D_Spatial_Recognition_Without_Spatially_Labeled_3D_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ren_3D_Spatial_Recognition_CVPR_2021_supplemental.pdf | 2105.06461 | cvf | @InProceedings{Ren_2021_CVPR,
author = {Ren, Zhongzheng and Misra, Ishan and Schwing, Alexander G. and Girdhar, Rohit},
title = {3D Spatial Recognition Without Spatially Labeled 3D},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision. WyPR jointly addresses three core 3D recognition tasks: point-level semantic segmentation, 3D proposal generation, and 3D object detection, coupling their predictions through self and cros... |
Jiang_Robust_Reference-Based_Super-Resolution_via_C2-Matching_CVPR_2021_paper | Robust Reference-Based Super-Resolution via C2-Matching | [
"Yuming Jiang",
"Kelvin C.K. Chan",
"Xintao Wang",
"Chen Change Loy",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Robust_Reference-Based_Super-Resolution_via_C2-Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiang_Robust_Reference-Based_Super-Resolution_via_C2-Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jiang_Robust_Reference-Based_Super-Resolution_CVPR_2021_supplemental.pdf | 2106.01863 | cvf | @InProceedings{Jiang_2021_CVPR,
author = {Jiang, Yuming and Chan, Kelvin C.K. and Wang, Xintao and Loy, Chen Change and Liu, Ziwei},
title = {Robust Reference-Based Super-Resolution via C2-Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to co... |
Perrett_Temporal-Relational_CrossTransformers_for_Few-Shot_Action_Recognition_CVPR_2021_paper | Temporal-Relational CrossTransformers for Few-Shot Action Recognition | [
"Toby Perrett",
"Alessandro Masullo",
"Tilo Burghardt",
"Majid Mirmehdi",
"Dima Damen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Perrett_Temporal-Relational_CrossTransformers_for_Few-Shot_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Perrett_Temporal-Relational_CrossTransformers_for_Few-Shot_Action_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Perrett_Temporal-Relational_CrossTransformers_for_CVPR_2021_supplemental.pdf | 2101.06184 | cvf | @InProceedings{Perrett_2021_CVPR,
author = {Perrett, Toby and Masullo, Alessandro and Burghardt, Tilo and Mirmehdi, Majid and Damen, Dima},
title = {Temporal-Relational CrossTransformers for Few-Shot Action Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set. Distinct from previous few-shot works, we construct class prototypes using the CrossTransformer attention mechanism to observe relevant sub-sequences of all support ... |
Singla_Understanding_Failures_of_Deep_Networks_via_Robust_Feature_Extraction_CVPR_2021_paper | Understanding Failures of Deep Networks via Robust Feature Extraction | [
"Sahil Singla",
"Besmira Nushi",
"Shital Shah",
"Ece Kamar",
"Eric Horvitz"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Singla_Understanding_Failures_of_Deep_Networks_via_Robust_Feature_Extraction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Singla_Understanding_Failures_of_Deep_Networks_via_Robust_Feature_Extraction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Singla_Understanding_Failures_of_CVPR_2021_supplemental.pdf | 2012.01750 | cvf | @InProceedings{Singla_2021_CVPR,
author = {Singla, Sahil and Nushi, Besmira and Shah, Shital and Kamar, Ece and Horvitz, Eric},
title = {Understanding Failures of Deep Networks via Robust Feature Extraction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances. We introduce and study a method aimed at characterizing and explaining failures by identifying visual attributes whose ... |
Liu_Relation-aware_Instance_Refinement_for_Weakly_Supervised_Visual_Grounding_CVPR_2021_paper | Relation-aware Instance Refinement for Weakly Supervised Visual Grounding | [
"Yongfei Liu",
"Bo Wan",
"Lin Ma",
"Xuming He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Relation-aware_Instance_Refinement_for_Weakly_Supervised_Visual_Grounding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Relation-aware_Instance_Refinement_for_Weakly_Supervised_Visual_Grounding_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Relation-aware_Instance_Refinement_CVPR_2021_supplemental.pdf | 2103.12989 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yongfei and Wan, Bo and Ma, Lin and He, Xuming},
title = {Relation-aware Instance Refinement for Weakly Supervised Visual Grounding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | Visual grounding, which aims to build a correspondence between visual objects and their language entities, plays a key role in cross-modal scene understanding. One promising and scalable strategy for learning visual grounding is to utilize weak supervision from only image-caption pairs. Previous methods typically rely ... |
Deng_Spatially-Invariant_Style-Codes_Controlled_Makeup_Transfer_CVPR_2021_paper | Spatially-Invariant Style-Codes Controlled Makeup Transfer | [
"Han Deng",
"Chu Han",
"Hongmin Cai",
"Guoqiang Han",
"Shengfeng He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Spatially-Invariant_Style-Codes_Controlled_Makeup_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Spatially-Invariant_Style-Codes_Controlled_Makeup_Transfer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Deng_Spatially-Invariant_Style-Codes_Controlled_CVPR_2021_supplemental.zip | null | null | @InProceedings{Deng_2021_CVPR,
author = {Deng, Han and Han, Chu and Cai, Hongmin and Han, Guoqiang and He, Shengfeng},
title = {Spatially-Invariant Style-Codes Controlled Makeup Transfer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Transferring makeup from the misaligned reference image is challenging. Previous methods overcome this barrier by computing pixel-wise correspondences between two images, which is inaccurate and computational-expensive. In this paper, we take a different perspective to break down the makeup transfer problem into a two-... |
Chen_Adaptive_Image_Transformer_for_One-Shot_Object_Detection_CVPR_2021_paper | Adaptive Image Transformer for One-Shot Object Detection | [
"Ding-Jie Chen",
"He-Yen Hsieh",
"Tyng-Luh Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Adaptive_Image_Transformer_for_One-Shot_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Adaptive_Image_Transformer_for_One-Shot_Object_Detection_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Ding-Jie and Hsieh, He-Yen and Liu, Tyng-Luh},
title = {Adaptive Image Transformer for One-Shot Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | One-shot object detection tackles a challenging task that aims at identifying within a target image all object instances of the same class, implied by a query image patch. The main difficulty lies in the situation that the class label of the query patch and its respective examples are not available in the training data... |
Xu_Bilateral_Grid_Learning_for_Stereo_Matching_Networks_CVPR_2021_paper | Bilateral Grid Learning for Stereo Matching Networks | [
"Bin Xu",
"Yuhua Xu",
"Xiaoli Yang",
"Wei Jia",
"Yulan Guo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Bilateral_Grid_Learning_for_Stereo_Matching_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Bilateral_Grid_Learning_for_Stereo_Matching_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Bilateral_Grid_Learning_CVPR_2021_supplemental.pdf | 2101.01601 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Bin and Xu, Yuhua and Yang, Xiaoli and Jia, Wei and Guo, Yulan},
title = {Bilateral Grid Learning for Stereo Matching Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Real-time performance of stereo matching networks is important for many applications, such as automatic driving, robot navigation and augmented reality (AR). Although significant progress has been made in stereo matching networks in recent years, it is still challenging to balance real-time performance and accuracy. In... |
Fu_A_Multi-Task_Network_for_Joint_Specular_Highlight_Detection_and_Removal_CVPR_2021_paper | A Multi-Task Network for Joint Specular Highlight Detection and Removal | [
"Gang Fu",
"Qing Zhang",
"Lei Zhu",
"Ping Li",
"Chunxia Xiao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_A_Multi-Task_Network_for_Joint_Specular_Highlight_Detection_and_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_A_Multi-Task_Network_for_Joint_Specular_Highlight_Detection_and_Removal_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Fu_2021_CVPR,
author = {Fu, Gang and Zhang, Qing and Zhu, Lei and Li, Ping and Xiao, Chunxia},
title = {A Multi-Task Network for Joint Specular Highlight Detection and Removal},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Specular highlight detection and removal are fundamental and challenging tasks. Although recent methods achieve promising results on the two tasks by supervised training on synthetic training data, they are typically solely designed for highlight detection or removal, and their performance usually deteriorates signific... |
Zheng_A_Deep_Emulator_for_Secondary_Motion_of_3D_Characters_CVPR_2021_paper | A Deep Emulator for Secondary Motion of 3D Characters | [
"Mianlun Zheng",
"Yi Zhou",
"Duygu Ceylan",
"Jernej Barbic"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_A_Deep_Emulator_for_Secondary_Motion_of_3D_Characters_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_A_Deep_Emulator_for_Secondary_Motion_of_3D_Characters_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_A_Deep_Emulator_CVPR_2021_supplemental.pdf | 2103.01261 | cvf | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Mianlun and Zhou, Yi and Ceylan, Duygu and Barbic, Jernej},
title = {A Deep Emulator for Secondary Motion of 3D Characters},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | Fast and light-weight methods for animating 3D characters are desirable in various applications such as computer games. We present a learning-based approach to enhance skinning-based animations of 3D characters with vivid secondary motion effects. We represent each local patch of a character simulation mesh as a graph ... |
Gong_Omni-Supervised_Point_Cloud_Segmentation_via_Gradual_Receptive_Field_Component_Reasoning_CVPR_2021_paper | Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning | [
"Jingyu Gong",
"Jiachen Xu",
"Xin Tan",
"Haichuan Song",
"Yanyun Qu",
"Yuan Xie",
"Lizhuang Ma"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_Omni-Supervised_Point_Cloud_Segmentation_via_Gradual_Receptive_Field_Component_Reasoning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_Omni-Supervised_Point_Cloud_Segmentation_via_Gradual_Receptive_Field_Component_Reasoning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gong_Omni-Supervised_Point_Cloud_CVPR_2021_supplemental.pdf | 2105.10203 | cvf | @InProceedings{Gong_2021_CVPR,
author = {Gong, Jingyu and Xu, Jiachen and Tan, Xin and Song, Haichuan and Qu, Yanyun and Xie, Yuan and Ma, Lizhuang},
title = {Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning},
booktitle = {Proceedings of the IEEE/CVF Conference... | Hidden features in neural network usually fail to learn informative representation for 3D segmentation as supervisions are only given on output prediction, while this can be solved by omni-scale supervision on intermediate layers. In this paper, we bring the first omni-scale supervision method to point cloud segmentati... |
Nassar_All_Labels_Are_Not_Created_Equal_Enhancing_Semi-Supervision_via_Label_CVPR_2021_paper | All Labels Are Not Created Equal: Enhancing Semi-Supervision via Label Grouping and Co-Training | [
"Islam Nassar",
"Samitha Herath",
"Ehsan Abbasnejad",
"Wray Buntine",
"Gholamreza Haffari"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nassar_All_Labels_Are_Not_Created_Equal_Enhancing_Semi-Supervision_via_Label_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nassar_All_Labels_Are_Not_Created_Equal_Enhancing_Semi-Supervision_via_Label_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nassar_All_Labels_Are_CVPR_2021_supplemental.pdf | 2104.05248 | cvf | @InProceedings{Nassar_2021_CVPR,
author = {Nassar, Islam and Herath, Samitha and Abbasnejad, Ehsan and Buntine, Wray and Haffari, Gholamreza},
title = {All Labels Are Not Created Equal: Enhancing Semi-Supervision via Label Grouping and Co-Training},
booktitle = {Proceedings of the IEEE/CVF Conference... | Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against. A common property among its various methods is that they only rely on the model's prediction to make labeling decisions without considerin... |
Wen_PMP-Net_Point_Cloud_Completion_by_Learning_Multi-Step_Point_Moving_Paths_CVPR_2021_paper | PMP-Net: Point Cloud Completion by Learning Multi-Step Point Moving Paths | [
"Xin Wen",
"Peng Xiang",
"Zhizhong Han",
"Yan-Pei Cao",
"Pengfei Wan",
"Wen Zheng",
"Yu-Shen Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_PMP-Net_Point_Cloud_Completion_by_Learning_Multi-Step_Point_Moving_Paths_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_PMP-Net_Point_Cloud_Completion_by_Learning_Multi-Step_Point_Moving_Paths_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wen_PMP-Net_Point_Cloud_CVPR_2021_supplemental.pdf | 2012.03408 | title_snapshot | @InProceedings{Wen_2021_CVPR,
author = {Wen, Xin and Xiang, Peng and Han, Zhizhong and Cao, Yan-Pei and Wan, Pengfei and Zheng, Wen and Liu, Yu-Shen},
title = {PMP-Net: Point Cloud Completion by Learning Multi-Step Point Moving Paths},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | The task of point cloud completion aims to predict the missing part for an incomplete 3D shape. A widely used strategy is to generate a complete point cloud from the incomplete one. However, the unordered nature of point clouds will degrade the generation of high-quality 3D shapes, as the detailed topology and structur... |
Wang_Gradient-Based_Algorithms_for_Machine_Teaching_CVPR_2021_paper | Gradient-Based Algorithms for Machine Teaching | [
"Pei Wang",
"Kabir Nagrecha",
"Nuno Vasconcelos"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Gradient-Based_Algorithms_for_Machine_Teaching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Gradient-Based_Algorithms_for_Machine_Teaching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Gradient-Based_Algorithms_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Pei and Nagrecha, Kabir and Vasconcelos, Nuno},
title = {Gradient-Based Algorithms for Machine Teaching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {202... | The problem of machine teaching is considered. A new formulation is proposed under the assumption of an optimal student, where optimality is defined in the usual machine learning sense of empirical risk minimization. This is a sensible assumption for machine learning students and for human students in crowdsourcing pla... |
Wang_MetaSCI_Scalable_and_Adaptive_Reconstruction_for_Video_Compressive_Sensing_CVPR_2021_paper | MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing | [
"Zhengjue Wang",
"Hao Zhang",
"Ziheng Cheng",
"Bo Chen",
"Xin Yuan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_MetaSCI_Scalable_and_Adaptive_Reconstruction_for_Video_Compressive_Sensing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_MetaSCI_Scalable_and_Adaptive_Reconstruction_for_Video_Compressive_Sensing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_MetaSCI_Scalable_and_CVPR_2021_supplemental.pdf | 2103.01786 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Zhengjue and Zhang, Hao and Cheng, Ziheng and Chen, Bo and Yuan, Xin},
title = {MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement. Following this, efficient algorithms are desired to reconstruct the high-speed frames, where t... |
Quan_Removing_Raindrops_and_Rain_Streaks_in_One_Go_CVPR_2021_paper | Removing Raindrops and Rain Streaks in One Go | [
"Ruijie Quan",
"Xin Yu",
"Yuanzhi Liang",
"Yi Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Quan_Removing_Raindrops_and_Rain_Streaks_in_One_Go_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Quan_Removing_Raindrops_and_Rain_Streaks_in_One_Go_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Quan_Removing_Raindrops_and_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Quan_2021_CVPR,
author = {Quan, Ruijie and Yu, Xin and Liang, Yuanzhi and Yang, Yi},
title = {Removing Raindrops and Rain Streaks in One Go},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = ... | Existing rain-removal algorithms often tackle either rain streak removal or raindrop removal, and thus may fail to handle real-world rainy scenes. Besides, the lack of real-world deraining datasets comprising different types of rain and their corresponding rain-free ground-truth also impedes deraining algorithm develop... |
Luo_Action_Unit_Memory_Network_for_Weakly_Supervised_Temporal_Action_Localization_CVPR_2021_paper | Action Unit Memory Network for Weakly Supervised Temporal Action Localization | [
"Wang Luo",
"Tianzhu Zhang",
"Wenfei Yang",
"Jingen Liu",
"Tao Mei",
"Feng Wu",
"Yongdong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Action_Unit_Memory_Network_for_Weakly_Supervised_Temporal_Action_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Action_Unit_Memory_Network_for_Weakly_Supervised_Temporal_Action_Localization_CVPR_2021_paper.pdf | null | 2104.14135 | cvf | @InProceedings{Luo_2021_CVPR,
author = {Luo, Wang and Zhang, Tianzhu and Yang, Wenfei and Liu, Jingen and Mei, Tao and Wu, Feng and Zhang, Yongdong},
title = {Action Unit Memory Network for Weakly Supervised Temporal Action Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Compute... | Weakly supervised temporal action localization aims to detect and localize actions in untrimmed videos with only video-level labels during training. However, without frame-level annotations, it is challenging to achieve localization completeness and relieve background interference. In this paper, we present an Action U... |
Wang_IMAGINE_Image_Synthesis_by_Image-Guided_Model_Inversion_CVPR_2021_paper | IMAGINE: Image Synthesis by Image-Guided Model Inversion | [
"Pei Wang",
"Yijun Li",
"Krishna Kumar Singh",
"Jingwan Lu",
"Nuno Vasconcelos"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_IMAGINE_Image_Synthesis_by_Image-Guided_Model_Inversion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_IMAGINE_Image_Synthesis_by_Image-Guided_Model_Inversion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_IMAGINE_Image_Synthesis_CVPR_2021_supplemental.pdf | 2104.05895 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Pei and Li, Yijun and Singh, Krishna Kumar and Lu, Jingwan and Vasconcelos, Nuno},
title = {IMAGINE: Image Synthesis by Image-Guided Model Inversion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | Synthesizing variations of a specific reference image with semantically valid content is an important task in terms of personalized generation as well as for data augmentation. In this work, we propose an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse ima... |
Ost_Neural_Scene_Graphs_for_Dynamic_Scenes_CVPR_2021_paper | Neural Scene Graphs for Dynamic Scenes | [
"Julian Ost",
"Fahim Mannan",
"Nils Thuerey",
"Julian Knodt",
"Felix Heide"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ost_Neural_Scene_Graphs_for_Dynamic_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ost_Neural_Scene_Graphs_for_Dynamic_Scenes_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ost_Neural_Scene_Graphs_CVPR_2021_supplemental.zip | 2011.10379 | cvf | @InProceedings{Ost_2021_CVPR,
author = {Ost, Julian and Mannan, Fahim and Thuerey, Nils and Knodt, Julian and Heide, Felix},
title = {Neural Scene Graphs for Dynamic Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Recent implicit neural rendering methods have demonstrated that it is possible to learn accurate view synthesis for complex scenes by predicting their volumetric density and color supervised solely by a set of RGB images. However, existing methods are restricted to learning efficient representations of static scenes th... |
Zhang_RSTNet_Captioning_With_Adaptive_Attention_on_Visual_and_Non-Visual_Words_CVPR_2021_paper | RSTNet: Captioning With Adaptive Attention on Visual and Non-Visual Words | [
"Xuying Zhang",
"Xiaoshuai Sun",
"Yunpeng Luo",
"Jiayi Ji",
"Yiyi Zhou",
"Yongjian Wu",
"Feiyue Huang",
"Rongrong Ji"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_RSTNet_Captioning_With_Adaptive_Attention_on_Visual_and_Non-Visual_Words_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_RSTNet_Captioning_With_Adaptive_Attention_on_Visual_and_Non-Visual_Words_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Xuying and Sun, Xiaoshuai and Luo, Yunpeng and Ji, Jiayi and Zhou, Yiyi and Wu, Yongjian and Huang, Feiyue and Ji, Rongrong},
title = {RSTNet: Captioning With Adaptive Attention on Visual and Non-Visual Words},
booktitle = {Proceedings of the IEEE/CVF ... | Recent progress on visual question answering has explored the merits of grid features for vision language tasks. Meanwhile, transformer-based models have shown remarkable performance in various sequence prediction problems. However, the spatial information loss of grid features caused by flattening operation, as well a... |
Tulyakov_Time_Lens_Event-Based_Video_Frame_Interpolation_CVPR_2021_paper | Time Lens: Event-Based Video Frame Interpolation | [
"Stepan Tulyakov",
"Daniel Gehrig",
"Stamatios Georgoulis",
"Julius Erbach",
"Mathias Gehrig",
"Yuanyou Li",
"Davide Scaramuzza"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tulyakov_Time_Lens_Event-Based_Video_Frame_Interpolation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tulyakov_Time_Lens_Event-Based_Video_Frame_Interpolation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tulyakov_Time_Lens_Event-Based_CVPR_2021_supplemental.zip | 2106.07286 | title_judge | @InProceedings{Tulyakov_2021_CVPR,
author = {Tulyakov, Stepan and Gehrig, Daniel and Georgoulis, Stamatios and Erbach, Julius and Gehrig, Mathias and Li, Yuanyou and Scaramuzza, Davide},
title = {Time Lens: Event-Based Video Frame Interpolation},
booktitle = {Proceedings of the IEEE/CVF Conference on... | State-of-the-art frame interpolation methods generate intermediate frames by inferring object motions in the image from consecutive key-frames. In the absence of additional information, first-order approximations, i.e. optical flow, must be used, but this choice restricts the types of motions that can be modeled, leadi... |
Liu_FedDG_Federated_Domain_Generalization_on_Medical_Image_Segmentation_via_Episodic_CVPR_2021_paper | FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space | [
"Quande Liu",
"Cheng Chen",
"Jing Qin",
"Qi Dou",
"Pheng-Ann Heng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_FedDG_Federated_Domain_Generalization_on_Medical_Image_Segmentation_via_Episodic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_FedDG_Federated_Domain_Generalization_on_Medical_Image_Segmentation_via_Episodic_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_FedDG_Federated_Domain_CVPR_2021_supplemental.pdf | 2103.06030 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Quande and Chen, Cheng and Qin, Jing and Dou, Qi and Heng, Pheng-Ann},
title = {FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space},
booktitle = {Proceedings of the IEEE/CVF Conference o... | Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when applied to completely unseen hospitals outside the federation. In this... |
Georgescu_Anomaly_Detection_in_Video_via_Self-Supervised_and_Multi-Task_Learning_CVPR_2021_paper | Anomaly Detection in Video via Self-Supervised and Multi-Task Learning | [
"Mariana-Iuliana Georgescu",
"Antonio Barbalau",
"Radu Tudor Ionescu",
"Fahad Shahbaz Khan",
"Marius Popescu",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Georgescu_Anomaly_Detection_in_Video_via_Self-Supervised_and_Multi-Task_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Georgescu_Anomaly_Detection_in_Video_via_Self-Supervised_and_Multi-Task_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Georgescu_Anomaly_Detection_in_CVPR_2021_supplemental.pdf | 2011.07491 | cvf | @InProceedings{Georgescu_2021_CVPR,
author = {Georgescu, Mariana-Iuliana and Barbalau, Antonio and Ionescu, Radu Tudor and Khan, Fahad Shahbaz and Popescu, Marius and Shah, Mubarak},
title = {Anomaly Detection in Video via Self-Supervised and Multi-Task Learning},
booktitle = {Proceedings of the IEEE... | Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at ... |
Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper | Multiresolution Knowledge Distillation for Anomaly Detection | [
"Mohammadreza Salehi",
"Niousha Sadjadi",
"Soroosh Baselizadeh",
"Mohammad H. Rohban",
"Hamid R. Rabiee"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Salehi_Multiresolution_Knowledge_Distillation_CVPR_2021_supplemental.pdf | 2011.11108 | cvf | @InProceedings{Salehi_2021_CVPR,
author = {Salehi, Mohammadreza and Sadjadi, Niousha and Baselizadeh, Soroosh and Rohban, Mohammad H. and Rabiee, Hamid R.},
title = {Multiresolution Knowledge Distillation for Anomaly Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not often large enough to learn a rich generalizable representation through conventional techniques. Secondly, w... |
Uy_Joint_Learning_of_3D_Shape_Retrieval_and_Deformation_CVPR_2021_paper | Joint Learning of 3D Shape Retrieval and Deformation | [
"Mikaela Angelina Uy",
"Vladimir G. Kim",
"Minhyuk Sung",
"Noam Aigerman",
"Siddhartha Chaudhuri",
"Leonidas J. Guibas"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Uy_Joint_Learning_of_3D_Shape_Retrieval_and_Deformation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Uy_Joint_Learning_of_3D_Shape_Retrieval_and_Deformation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Uy_Joint_Learning_of_CVPR_2021_supplemental.pdf | 2101.07889 | cvf | @InProceedings{Uy_2021_CVPR,
author = {Uy, Mikaela Angelina and Kim, Vladimir G. and Sung, Minhyuk and Aigerman, Noam and Chaudhuri, Siddhartha and Guibas, Leonidas J.},
title = {Joint Learning of 3D Shape Retrieval and Deformation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | We propose a novel technique for producing high-quality 3D models that match a given target object image or scan. Our method is based on retrieving an existing shape from a database of 3D models and then deforming its parts to match the target shape. Unlike previous approaches that independently focus on either shape r... |
Dong_Learning_Spatially-Variant_MAP_Models_for_Non-Blind_Image_Deblurring_CVPR_2021_paper | Learning Spatially-Variant MAP Models for Non-Blind Image Deblurring | [
"Jiangxin Dong",
"Stefan Roth",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dong_Learning_Spatially-Variant_MAP_Models_for_Non-Blind_Image_Deblurring_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dong_Learning_Spatially-Variant_MAP_Models_for_Non-Blind_Image_Deblurring_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dong_Learning_Spatially-Variant_MAP_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Dong_2021_CVPR,
author = {Dong, Jiangxin and Roth, Stefan and Schiele, Bernt},
title = {Learning Spatially-Variant MAP Models for Non-Blind Image Deblurring},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | The classical maximum a-posteriori (MAP) framework for non-blind image deblurring requires defining suitable data and regularization terms, whose interplay yields the desired clear image through optimization. The vast majority of prior work focuses on advancing one of these two crucial ingredients, while keeping the ot... |
Mao_FCPose_Fully_Convolutional_Multi-Person_Pose_Estimation_With_Dynamic_Instance-Aware_Convolutions_CVPR_2021_paper | FCPose: Fully Convolutional Multi-Person Pose Estimation With Dynamic Instance-Aware Convolutions | [
"Weian Mao",
"Zhi Tian",
"Xinlong Wang",
"Chunhua Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mao_FCPose_Fully_Convolutional_Multi-Person_Pose_Estimation_With_Dynamic_Instance-Aware_Convolutions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mao_FCPose_Fully_Convolutional_Multi-Person_Pose_Estimation_With_Dynamic_Instance-Aware_Convolutions_CVPR_2021_paper.pdf | null | 2105.14185 | cvf | @InProceedings{Mao_2021_CVPR,
author = {Mao, Weian and Tian, Zhi and Wang, Xinlong and Shen, Chunhua},
title = {FCPose: Fully Convolutional Multi-Person Pose Estimation With Dynamic Instance-Aware Convolutions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eliminates the ROIs and grouping post-processing with dynamic... |
Tian_BoxInst_High-Performance_Instance_Segmentation_With_Box_Annotations_CVPR_2021_paper | BoxInst: High-Performance Instance Segmentation With Box Annotations | [
"Zhi Tian",
"Chunhua Shen",
"Xinlong Wang",
"Hao Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_BoxInst_High-Performance_Instance_Segmentation_With_Box_Annotations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tian_BoxInst_High-Performance_Instance_Segmentation_With_Box_Annotations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tian_BoxInst_High-Performance_Instance_CVPR_2021_supplemental.pdf | 2012.02310 | cvf | @InProceedings{Tian_2021_CVPR,
author = {Tian, Zhi and Shen, Chunhua and Wang, Xinlong and Chen, Hao},
title = {BoxInst: High-Performance Instance Segmentation With Box Annotations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training. While this setting has been studied in the literature, here we show significantly stronger performance with a simple design (e.g., dramatically improving previous best reported mask AP... |
Tirupattur_Modeling_Multi-Label_Action_Dependencies_for_Temporal_Action_Localization_CVPR_2021_paper | Modeling Multi-Label Action Dependencies for Temporal Action Localization | [
"Praveen Tirupattur",
"Kevin Duarte",
"Yogesh S Rawat",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tirupattur_Modeling_Multi-Label_Action_Dependencies_for_Temporal_Action_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tirupattur_Modeling_Multi-Label_Action_Dependencies_for_Temporal_Action_Localization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tirupattur_Modeling_Multi-Label_Action_CVPR_2021_supplemental.zip | 2103.03027 | cvf | @InProceedings{Tirupattur_2021_CVPR,
author = {Tirupattur, Praveen and Duarte, Kevin and Rawat, Yogesh S and Shah, Mubarak},
title = {Modeling Multi-Label Action Dependencies for Temporal Action Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit... | Real world videos contain many complex actions with inherent relationships between action classes. In this work, we propose an attention-based architecture that model these action relationships for the task of temporal action localization in untrimmed videos. As opposed to previous works which leverage video-level co-o... |
Li_HCRF-Flow_Scene_Flow_From_Point_Clouds_With_Continuous_High-Order_CRFs_CVPR_2021_paper | HCRF-Flow: Scene Flow From Point Clouds With Continuous High-Order CRFs and Position-Aware Flow Embedding | [
"Ruibo Li",
"Guosheng Lin",
"Tong He",
"Fayao Liu",
"Chunhua Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_HCRF-Flow_Scene_Flow_From_Point_Clouds_With_Continuous_High-Order_CRFs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_HCRF-Flow_Scene_Flow_From_Point_Clouds_With_Continuous_High-Order_CRFs_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_HCRF-Flow_Scene_Flow_CVPR_2021_supplemental.pdf | 2105.07751 | title_snapshot | @InProceedings{Li_2021_CVPR,
author = {Li, Ruibo and Lin, Guosheng and He, Tong and Liu, Fayao and Shen, Chunhua},
title = {HCRF-Flow: Scene Flow From Point Clouds With Continuous High-Order CRFs and Position-Aware Flow Embedding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Scene flow in 3D point clouds plays an important role in understanding dynamic environments. Although significant advances have been made by deep neural networks, the performance is far from satisfactory as only per-point translational motion is considered, neglecting the constraints of the rigid motion in local region... |
Yu_Lite-HRNet_A_Lightweight_High-Resolution_Network_CVPR_2021_paper | Lite-HRNet: A Lightweight High-Resolution Network | [
"Changqian Yu",
"Bin Xiao",
"Changxin Gao",
"Lu Yuan",
"Lei Zhang",
"Nong Sang",
"Jingdong Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Lite-HRNet_A_Lightweight_High-Resolution_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Lite-HRNet_A_Lightweight_High-Resolution_Network_CVPR_2021_paper.pdf | null | 2104.06403 | title_snapshot | @InProceedings{Yu_2021_CVPR,
author = {Yu, Changqian and Xiao, Bin and Gao, Changxin and Yuan, Lu and Zhang, Lei and Sang, Nong and Wang, Jingdong},
title = {Lite-HRNet: A Lightweight High-Resolution Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit... | We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger performance over popular lightweight networks, such as MobileNet, ShuffleNet, and Small HRNet. We find that ... |
Huang_Self-Supervised_Video_Representation_Learning_by_Context_and_Motion_Decoupling_CVPR_2021_paper | Self-Supervised Video Representation Learning by Context and Motion Decoupling | [
"Lianghua Huang",
"Yu Liu",
"Bin Wang",
"Pan Pan",
"Yinghui Xu",
"Rong Jin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Self-Supervised_Video_Representation_Learning_by_Context_and_Motion_Decoupling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Self-Supervised_Video_Representation_Learning_by_Context_and_Motion_Decoupling_CVPR_2021_paper.pdf | null | 2104.00862 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Lianghua and Liu, Yu and Wang, Bin and Pan, Pan and Xu, Yinghui and Jin, Rong},
title = {Self-Supervised Video Representation Learning by Context and Motion Decoupling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | A key challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias. While most existing works implicitly achieve this with video-specific pretext tasks (e.g., predicting clip orders, time arrows, and paces), we develop a method that explicitly decouple... |
Bauer_ReAgent_Point_Cloud_Registration_Using_Imitation_and_Reinforcement_Learning_CVPR_2021_paper | ReAgent: Point Cloud Registration Using Imitation and Reinforcement Learning | [
"Dominik Bauer",
"Timothy Patten",
"Markus Vincze"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bauer_ReAgent_Point_Cloud_Registration_Using_Imitation_and_Reinforcement_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bauer_ReAgent_Point_Cloud_Registration_Using_Imitation_and_Reinforcement_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bauer_ReAgent_Point_Cloud_CVPR_2021_supplemental.pdf | 2103.15231 | cvf | @InProceedings{Bauer_2021_CVPR,
author = {Bauer, Dominik and Patten, Timothy and Vincze, Markus},
title = {ReAgent: Point Cloud Registration Using Imitation and Reinforcement Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Point cloud registration is a common step in many 3D computer vision tasks such as object pose estimation, where a 3D model is aligned to an observation. Classical registration methods generalize well to novel domains but fail when given a noisy observation or a bad initialization. Learning-based methods, in contrast, ... |
Yang_Uncertainty_Guided_Collaborative_Training_for_Weakly_Supervised_Temporal_Action_Detection_CVPR_2021_paper | Uncertainty Guided Collaborative Training for Weakly Supervised Temporal Action Detection | [
"Wenfei Yang",
"Tianzhu Zhang",
"Xiaoyuan Yu",
"Tian Qi",
"Yongdong Zhang",
"Feng Wu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Uncertainty_Guided_Collaborative_Training_for_Weakly_Supervised_Temporal_Action_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Uncertainty_Guided_Collaborative_Training_for_Weakly_Supervised_Temporal_Action_Detection_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Yang_2021_CVPR,
author = {Yang, Wenfei and Zhang, Tianzhu and Yu, Xiaoyuan and Qi, Tian and Zhang, Yongdong and Wu, Feng},
title = {Uncertainty Guided Collaborative Training for Weakly Supervised Temporal Action Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Compute... | Weakly supervised temporal action detection aims to localize temporal boundaries of actions and identify their categories simultaneously with only video-level category labels during training. Among existing methods, attention-based methods have achieved superior performance by separating action and non-action segments.... |
Song_Dynamic_Probabilistic_Graph_Convolution_for_Facial_Action_Unit_Intensity_Estimation_CVPR_2021_paper | Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation | [
"Tengfei Song",
"Zijun Cui",
"Yuru Wang",
"Wenming Zheng",
"Qiang Ji"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Dynamic_Probabilistic_Graph_Convolution_for_Facial_Action_Unit_Intensity_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Dynamic_Probabilistic_Graph_Convolution_for_Facial_Action_Unit_Intensity_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Dynamic_Probabilistic_Graph_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Song_2021_CVPR,
author = {Song, Tengfei and Cui, Zijun and Wang, Yuru and Zheng, Wenming and Ji, Qiang},
title = {Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Deep learning methods have been widely applied to automatic facial action unit (AU) intensity estimation and achieved state-of-the-art performance. These methods, however, are mostly appearance-based and fail to exploit the underlying structural information among the AUs. In this paper, we propose a novel dynamic proba... |
Boudiaf_Few-Shot_Segmentation_Without_Meta-Learning_A_Good_Transductive_Inference_Is_All_CVPR_2021_paper | Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need? | [
"Malik Boudiaf",
"Hoel Kervadec",
"Ziko Imtiaz Masud",
"Pablo Piantanida",
"Ismail Ben Ayed",
"Jose Dolz"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Boudiaf_Few-Shot_Segmentation_Without_Meta-Learning_A_Good_Transductive_Inference_Is_All_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Boudiaf_Few-Shot_Segmentation_Without_Meta-Learning_A_Good_Transductive_Inference_Is_All_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Boudiaf_Few-Shot_Segmentation_Without_CVPR_2021_supplemental.pdf | 2012.06166 | title_snapshot | @InProceedings{Boudiaf_2021_CVPR,
author = {Boudiaf, Malik and Kervadec, Hoel and Masud, Ziko Imtiaz and Piantanida, Pablo and Ben Ayed, Ismail and Dolz, Jose},
title = {Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?},
booktitle = {Proceedings of the IEEE/... | We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances--an aspect often overlooked in the literature in favor of the meta-learning paradigm. We introduce a transductive inference for a given query image, leveraging the statistics of its unlabeled pixels, by o... |
Liu_Spatial-Temporal_Correlation_and_Topology_Learning_for_Person_Re-Identification_in_Videos_CVPR_2021_paper | Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos | [
"Jiawei Liu",
"Zheng-Jun Zha",
"Wei Wu",
"Kecheng Zheng",
"Qibin Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Spatial-Temporal_Correlation_and_Topology_Learning_for_Person_Re-Identification_in_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Spatial-Temporal_Correlation_and_Topology_Learning_for_Person_Re-Identification_in_Videos_CVPR_2021_paper.pdf | null | 2104.08241 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Jiawei and Zha, Zheng-Jun and Wu, Wei and Zheng, Kecheng and Sun, Qibin},
title = {Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping camera views. The key factor for video person re-identification is to effectively exploit both spatial and temporal clues from video sequences. In this work, we propose a novel Spatial-Temporal Correlation and Top... |
Dai_SPSG_Self-Supervised_Photometric_Scene_Generation_From_RGB-D_Scans_CVPR_2021_paper | SPSG: Self-Supervised Photometric Scene Generation From RGB-D Scans | [
"Angela Dai",
"Yawar Siddiqui",
"Justus Thies",
"Julien Valentin",
"Matthias Niessner"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_SPSG_Self-Supervised_Photometric_Scene_Generation_From_RGB-D_Scans_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_SPSG_Self-Supervised_Photometric_Scene_Generation_From_RGB-D_Scans_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_SPSG_Self-Supervised_Photometric_CVPR_2021_supplemental.zip | 2006.14660 | title_snapshot | @InProceedings{Dai_2021_CVPR,
author = {Dai, Angela and Siddiqui, Yawar and Thies, Justus and Valentin, Julien and Niessner, Matthias},
title = {SPSG: Self-Supervised Photometric Scene Generation From RGB-D Scans},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a self-supervised fashion. Our self-supervised approach learns to jointly inpaint geometry and color by correlating an incomplete RGB-D scan wi... |
Onzon_Neural_Auto-Exposure_for_High-Dynamic_Range_Object_Detection_CVPR_2021_paper | Neural Auto-Exposure for High-Dynamic Range Object Detection | [
"Emmanuel Onzon",
"Fahim Mannan",
"Felix Heide"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Onzon_Neural_Auto-Exposure_for_High-Dynamic_Range_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Onzon_Neural_Auto-Exposure_for_High-Dynamic_Range_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Onzon_Neural_Auto-Exposure_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Onzon_2021_CVPR,
author = {Onzon, Emmanuel and Mannan, Fahim and Heide, Felix},
title = {Neural Auto-Exposure for High-Dynamic Range Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
ye... | Real-world scenes have a dynamic range of up to 280 dB that today's imaging sensors cannot directly capture. Existing live vision pipelines tackle this fundamental challenge by relying on high dynamic range (HDR) sensors that try to recover HDR images from multiple captures with different exposures. While HDR sensors s... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.