paper_id stringlengths 33 138 | title stringlengths 13 147 | authors listlengths 1 17 | cvf_url stringlengths 90 195 | pdf_url stringlengths 91 196 | supp_url stringlengths 101 137 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
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Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper | 3D Shape Generation With Grid-Based Implicit Functions | [
"Moritz Ibing",
"Isaak Lim",
"Leif Kobbelt"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ibing_3D_Shape_Generation_CVPR_2021_supplemental.pdf | 2107.10607 | title_snapshot | @InProceedings{Ibing_2021_CVPR,
author = {Ibing, Moritz and Lim, Isaak and Kobbelt, Leif},
title = {3D Shape Generation With Grid-Based Implicit Functions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = ... | Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE). Even though this produces convincing results, it has two major shortcomings. As the GAN is limited to reproduce the dataset the AE was trained on, we cannot reuse a trained AE for novel data. Furthermore, it i... |
Teed_Tangent_Space_Backpropagation_for_3D_Transformation_Groups_CVPR_2021_paper | Tangent Space Backpropagation for 3D Transformation Groups | [
"Zachary Teed",
"Jia Deng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Teed_Tangent_Space_Backpropagation_for_3D_Transformation_Groups_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Teed_Tangent_Space_Backpropagation_for_3D_Transformation_Groups_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Teed_Tangent_Space_Backpropagation_CVPR_2021_supplemental.pdf | 2103.12032 | cvf | @InProceedings{Teed_2021_CVPR,
author = {Teed, Zachary and Deng, Jia},
title = {Tangent Space Backpropagation for 3D Transformation Groups},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
page... | We address the problem of performing backpropagation for computation graphs involving 3D transformation groups SO(3), SE(3), and Sim(3). 3D transformation groups are widely used in 3D vision and robotics, but they do not form vector spaces and instead lie on smooth manifolds. The standard backpropagation approach, whic... |
Wang_FAIEr_Fidelity_and_Adequacy_Ensured_Image_Caption_Evaluation_CVPR_2021_paper | FAIEr: Fidelity and Adequacy Ensured Image Caption Evaluation | [
"Sijin Wang",
"Ziwei Yao",
"Ruiping Wang",
"Zhongqin Wu",
"Xilin Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_FAIEr_Fidelity_and_Adequacy_Ensured_Image_Caption_Evaluation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_FAIEr_Fidelity_and_Adequacy_Ensured_Image_Caption_Evaluation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_FAIEr_Fidelity_and_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Sijin and Yao, Ziwei and Wang, Ruiping and Wu, Zhongqin and Chen, Xilin},
title = {FAIEr: Fidelity and Adequacy Ensured Image Caption Evaluation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Image caption evaluation is a crucial task, which involves the semantic perception and matching of image and text. Good evaluation metrics aim to be fair, comprehensive, and consistent with human judge intentions. When humans evaluate a caption, they usually consider multiple aspects, such as whether it is related to t... |
Wang_HLA-Face_Joint_High-Low_Adaptation_for_Low_Light_Face_Detection_CVPR_2021_paper | HLA-Face: Joint High-Low Adaptation for Low Light Face Detection | [
"Wenjing Wang",
"Wenhan Yang",
"Jiaying Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_HLA-Face_Joint_High-Low_Adaptation_for_Low_Light_Face_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_HLA-Face_Joint_High-Low_Adaptation_for_Low_Light_Face_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_HLA-Face_Joint_High-Low_CVPR_2021_supplemental.pdf | 2104.01984 | title_snapshot | @InProceedings{Wang_2021_CVPR,
author = {Wang, Wenjing and Yang, Wenhan and Liu, Jiaying},
title = {HLA-Face: Joint High-Low Adaptation for Low Light Face Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
ye... | Face detection in low light scenarios is challenging but vital to many practical applications, e.g., surveillance video, autonomous driving at night. Most existing face detectors heavily rely on extensive annotations, while collecting data is time-consuming and laborious. To reduce the burden of building new datasets f... |
Bodla_Hierarchical_Video_Prediction_Using_Relational_Layouts_for_Human-Object_Interactions_CVPR_2021_paper | Hierarchical Video Prediction Using Relational Layouts for Human-Object Interactions | [
"Navaneeth Bodla",
"Gaurav Shrivastava",
"Rama Chellappa",
"Abhinav Shrivastava"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bodla_Hierarchical_Video_Prediction_Using_Relational_Layouts_for_Human-Object_Interactions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bodla_Hierarchical_Video_Prediction_Using_Relational_Layouts_for_Human-Object_Interactions_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bodla_Hierarchical_Video_Prediction_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Bodla_2021_CVPR,
author = {Bodla, Navaneeth and Shrivastava, Gaurav and Chellappa, Rama and Shrivastava, Abhinav},
title = {Hierarchical Video Prediction Using Relational Layouts for Human-Object Interactions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | Learning to model and predict how humans interact with objects while performing an action is challenging, and most of the existing video prediction models are ineffective in modeling complicated human-object interactions. Our work builds on hierarchical video prediction models, which disentangle the video generation pr... |
Wang_From_Rain_Generation_to_Rain_Removal_CVPR_2021_paper | From Rain Generation to Rain Removal | [
"Hong Wang",
"Zongsheng Yue",
"Qi Xie",
"Qian Zhao",
"Yefeng Zheng",
"Deyu Meng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_From_Rain_Generation_to_Rain_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_From_Rain_Generation_to_Rain_Removal_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_From_Rain_Generation_CVPR_2021_supplemental.zip | 2008.03580 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Hong and Yue, Zongsheng and Xie, Qi and Zhao, Qian and Zheng, Yefeng and Meng, Deyu},
title = {From Rain Generation to Rain Removal},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets. Most of current state-of-the-art focus on constructing powerful deep models to obtain better deraining results. In this paper, to further improve ... |
Wertheimer_Few-Shot_Classification_With_Feature_Map_Reconstruction_Networks_CVPR_2021_paper | Few-Shot Classification With Feature Map Reconstruction Networks | [
"Davis Wertheimer",
"Luming Tang",
"Bharath Hariharan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wertheimer_Few-Shot_Classification_With_Feature_Map_Reconstruction_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wertheimer_Few-Shot_Classification_With_Feature_Map_Reconstruction_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wertheimer_Few-Shot_Classification_With_CVPR_2021_supplemental.pdf | 2012.01506 | cvf | @InProceedings{Wertheimer_2021_CVPR,
author = {Wertheimer, Davis and Tang, Luming and Hariharan, Bharath},
title = {Few-Shot Classification With Feature Map Reconstruction Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing ... |
Ahmed_Object_Classification_From_Randomized_EEG_Trials_CVPR_2021_paper | Object Classification From Randomized EEG Trials | [
"Hamad Ahmed",
"Ronnie B. Wilbur",
"Hari M. Bharadwaj",
"Jeffrey Mark Siskind"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ahmed_Object_Classification_From_Randomized_EEG_Trials_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ahmed_Object_Classification_From_Randomized_EEG_Trials_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ahmed_Object_Classification_From_CVPR_2021_supplemental.zip | 2004.06046 | cvf | @InProceedings{Ahmed_2021_CVPR,
author = {Ahmed, Hamad and Wilbur, Ronnie B. and Bharadwaj, Hari M. and Siskind, Jeffrey Mark},
title = {Object Classification From Randomized EEG Trials},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | New results suggest strong limits to the feasibility of object classification from human brain activity evoked by image stimuli, as measured through EEG. Considerable prior work suffers from a confound between the stimulus class and the time since the start of the experiment. A prior attempt to avoid this confound usin... |
Kokkinos_Learning_Monocular_3D_Reconstruction_of_Articulated_Categories_From_Motion_CVPR_2021_paper | Learning Monocular 3D Reconstruction of Articulated Categories From Motion | [
"Filippos Kokkinos",
"Iasonas Kokkinos"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kokkinos_Learning_Monocular_3D_Reconstruction_of_Articulated_Categories_From_Motion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kokkinos_Learning_Monocular_3D_Reconstruction_of_Articulated_Categories_From_Motion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kokkinos_Learning_Monocular_3D_CVPR_2021_supplemental.pdf | 2103.16352 | cvf | @InProceedings{Kokkinos_2021_CVPR,
author = {Kokkinos, Filippos and Kokkinos, Iasonas},
title = {Learning Monocular 3D Reconstruction of Articulated Categories From Motion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | Monocular 3D reconstruction of articulated object categories is challenging due to the lack of training data and the inherent ill-posedness of the problem. In this work we use video self-supervision, forcing the consistency of consecutive 3D reconstructions by a motion-based cycle loss. This largely improves both optim... |
Wu_De-Rendering_the_Worlds_Revolutionary_Artefacts_CVPR_2021_paper | De-Rendering the World's Revolutionary Artefacts | [
"Shangzhe Wu",
"Ameesh Makadia",
"Jiajun Wu",
"Noah Snavely",
"Richard Tucker",
"Angjoo Kanazawa"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_De-Rendering_the_Worlds_Revolutionary_Artefacts_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_De-Rendering_the_Worlds_Revolutionary_Artefacts_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_De-Rendering_the_Worlds_CVPR_2021_supplemental.pdf | 2104.03954 | title_snapshot | @InProceedings{Wu_2021_CVPR,
author = {Wu, Shangzhe and Makadia, Ameesh and Wu, Jiajun and Snavely, Noah and Tucker, Richard and Kanazawa, Angjoo},
title = {De-Rendering the World's Revolutionary Artefacts},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | Recent works have shown exciting results in unsupervised image de-rendering--learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. However, many of these assume simplistic material and lighting models. We propose a method, termed RADAR, that can recover env... |
Yang_Progressively_Complementary_Network_for_Fisheye_Image_Rectification_Using_Appearance_Flow_CVPR_2021_paper | Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow | [
"Shangrong Yang",
"Chunyu Lin",
"Kang Liao",
"Chunjie Zhang",
"Yao Zhao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Progressively_Complementary_Network_for_Fisheye_Image_Rectification_Using_Appearance_Flow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Progressively_Complementary_Network_for_Fisheye_Image_Rectification_Using_Appearance_Flow_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Progressively_Complementary_Network_CVPR_2021_supplemental.pdf | 2103.16026 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Shangrong and Lin, Chunyu and Liao, Kang and Zhang, Chunjie and Zhao, Yao},
title = {Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Distortion rectification is often required for fisheye images. The generation-based method is one mainstream solution due to its label-free property, but its naive skip-connection and overburdened decoder will cause blur and incomplete correction. First, the skip-connection directly transfers the image features, which ... |
Chen_DECOR-GAN_3D_Shape_Detailization_by_Conditional_Refinement_CVPR_2021_paper | DECOR-GAN: 3D Shape Detailization by Conditional Refinement | [
"Zhiqin Chen",
"Vladimir G. Kim",
"Matthew Fisher",
"Noam Aigerman",
"Hao Zhang",
"Siddhartha Chaudhuri"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_DECOR-GAN_3D_Shape_Detailization_by_Conditional_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_DECOR-GAN_3D_Shape_Detailization_by_Conditional_Refinement_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_DECOR-GAN_3D_Shape_CVPR_2021_supplemental.pdf | 2012.09159 | title_snapshot | @InProceedings{Chen_2021_CVPR,
author = {Chen, Zhiqin and Kim, Vladimir G. and Fisher, Matthew and Aigerman, Noam and Zhang, Hao and Chaudhuri, Siddhartha},
title = {DECOR-GAN: 3D Shape Detailization by Conditional Refinement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision an... | We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We address the challenge of creating large varieties of high-resolution and detailed 3D geometry from a small set of exemplars by treating the problem as that of geometric detail transfer. Give... |
Hu_Model-Aware_Gesture-to-Gesture_Translation_CVPR_2021_paper | Model-Aware Gesture-to-Gesture Translation | [
"Hezhen Hu",
"Weilun Wang",
"Wengang Zhou",
"Weichao Zhao",
"Houqiang Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Model-Aware_Gesture-to-Gesture_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Model-Aware_Gesture-to-Gesture_Translation_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Hu_2021_CVPR,
author = {Hu, Hezhen and Wang, Weilun and Zhou, Wengang and Zhao, Weichao and Li, Houqiang},
title = {Model-Aware Gesture-to-Gesture Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | Hand gesture-to-gesture translation is a significant and interesting problem, which serves as a key role in many applications, such as sign language production. This task involves fine-grained structure understanding of the mapping between the source and target gestures. Current works follow a data-driven paradigm base... |
Song_Spatio-temporal_Contrastive_Domain_Adaptation_for_Action_Recognition_CVPR_2021_paper | Spatio-temporal Contrastive Domain Adaptation for Action Recognition | [
"Xiaolin Song",
"Sicheng Zhao",
"Jingyu Yang",
"Huanjing Yue",
"Pengfei Xu",
"Runbo Hu",
"Hua Chai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Spatio-temporal_Contrastive_Domain_Adaptation_for_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Spatio-temporal_Contrastive_Domain_Adaptation_for_Action_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Spatio-temporal_Contrastive_Domain_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Song_2021_CVPR,
author = {Song, Xiaolin and Zhao, Sicheng and Yang, Jingyu and Yue, Huanjing and Xu, Pengfei and Hu, Runbo and Chai, Hua},
title = {Spatio-temporal Contrastive Domain Adaptation for Action Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | Unsupervised domain adaptation (UDA) for human action recognition is a practical and challenging problem. Compared with image-based UDA, video-based UDA is comprehensive to bridge the domain shift on both spatial representation and temporal dynamics. Most previous works focus on short-term modeling and alignment with f... |
Yang_Exploiting_Semantic_Embedding_and_Visual_Feature_for_Facial_Action_Unit_CVPR_2021_paper | Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection | [
"Huiyuan Yang",
"Lijun Yin",
"Yi Zhou",
"Jiuxiang Gu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Exploiting_Semantic_Embedding_and_Visual_Feature_for_Facial_Action_Unit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Exploiting_Semantic_Embedding_and_Visual_Feature_for_Facial_Action_Unit_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Exploiting_Semantic_Embedding_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yang_2021_CVPR,
author = {Yang, Huiyuan and Yin, Lijun and Zhou, Yi and Gu, Jiuxiang},
title = {Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Recent study on detecting facial action units (AU) has utilized auxiliary information (i.e., facial landmarks, relationship among AUs and expressions, web facial images, etc.), in order to improve the AU detection performance. As of now, no semantic information of AUs has yet been explored for such a task. As a matter ... |
Reading_Categorical_Depth_Distribution_Network_for_Monocular_3D_Object_Detection_CVPR_2021_paper | Categorical Depth Distribution Network for Monocular 3D Object Detection | [
"Cody Reading",
"Ali Harakeh",
"Julia Chae",
"Steven L. Waslander"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Reading_Categorical_Depth_Distribution_Network_for_Monocular_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Reading_Categorical_Depth_Distribution_Network_for_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reading_Categorical_Depth_Distribution_CVPR_2021_supplemental.pdf | 2103.01100 | cvf | @InProceedings{Reading_2021_CVPR,
author = {Reading, Cody and Harakeh, Ali and Chae, Julia and Waslander, Steven L.},
title = {Categorical Depth Distribution Network for Monocular 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a solution with simple configuration compared to typical multi-sensor systems. The main challenge in monocular 3D detection lies in accurately predicting object depth, which must be inferred from object and scene cues due to the lack... |
Ren_Learning_From_the_Master_Distilling_Cross-Modal_Advanced_Knowledge_for_Lip_CVPR_2021_paper | Learning From the Master: Distilling Cross-Modal Advanced Knowledge for Lip Reading | [
"Sucheng Ren",
"Yong Du",
"Jianming Lv",
"Guoqiang Han",
"Shengfeng He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Learning_From_the_Master_Distilling_Cross-Modal_Advanced_Knowledge_for_Lip_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ren_Learning_From_the_Master_Distilling_Cross-Modal_Advanced_Knowledge_for_Lip_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Ren_2021_CVPR,
author = {Ren, Sucheng and Du, Yong and Lv, Jianming and Han, Guoqiang and He, Shengfeng},
title = {Learning From the Master: Distilling Cross-Modal Advanced Knowledge for Lip Reading},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | Lip reading aims to predict the spoken sentences from silent lip videos. Due to the fact that such a vision task usually performs worse than its counterpart speech recognition, one potential scheme is to distill knowledge from a teacher pretrained by audio signals. However, the latent domain gap between the cross-modal... |
Zhu_Spatially-Varying_Outdoor_Lighting_Estimation_From_Intrinsics_CVPR_2021_paper | Spatially-Varying Outdoor Lighting Estimation From Intrinsics | [
"Yongjie Zhu",
"Yinda Zhang",
"Si Li",
"Boxin Shi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Spatially-Varying_Outdoor_Lighting_Estimation_From_Intrinsics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Spatially-Varying_Outdoor_Lighting_Estimation_From_Intrinsics_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_Spatially-Varying_Outdoor_Lighting_CVPR_2021_supplemental.pdf | 2104.04160 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Yongjie and Zhang, Yinda and Li, Si and Shi, Boxin},
title = {Spatially-Varying Outdoor Lighting Estimation From Intrinsics},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We present SOLID-Net, a neural network for spatially-varying outdoor lighting estimation from a single outdoor image for any 2D pixel location. Previous work has used a unified sky environment map to represent outdoor lighting. Instead, we generate spatially-varying local lighting environment maps by combining global s... |
Choi_VITON-HD_High-Resolution_Virtual_Try-On_via_Misalignment-Aware_Normalization_CVPR_2021_paper | VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization | [
"Seunghwan Choi",
"Sunghyun Park",
"Minsoo Lee",
"Jaegul Choo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_VITON-HD_High-Resolution_Virtual_Try-On_via_Misalignment-Aware_Normalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_VITON-HD_High-Resolution_Virtual_Try-On_via_Misalignment-Aware_Normalization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_VITON-HD_High-Resolution_Virtual_CVPR_2021_supplemental.zip | 2103.16874 | title_snapshot | @InProceedings{Choi_2021_CVPR,
author = {Choi, Seunghwan and Park, Sunghyun and Lee, Minsoo and Choo, Jaegul},
title = {VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | The task of image-based virtual try-on aims to transfer a target clothing item onto the corresponding region of a person, which is commonly tackled by fitting the item to the desired body part and fusing the warped item with the person. While an increasing number of studies have been conducted, the resolution of synthe... |
Zheng_Ultra-High-Definition_Image_Dehazing_via_Multi-Guided_Bilateral_Learning_CVPR_2021_paper | Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning | [
"Zhuoran Zheng",
"Wenqi Ren",
"Xiaochun Cao",
"Xiaobin Hu",
"Tao Wang",
"Fenglong Song",
"Xiuyi Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Ultra-High-Definition_Image_Dehazing_via_Multi-Guided_Bilateral_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Ultra-High-Definition_Image_Dehazing_via_Multi-Guided_Bilateral_Learning_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Zhuoran and Ren, Wenqi and Cao, Xiaochun and Hu, Xiaobin and Wang, Tao and Song, Fenglong and Jia, Xiuyi},
title = {Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | During the last couple of years, convolutional neural networks (CNNs) have achieved significant success in the single image dehazing task. Unfortunately, most existing deep dehazing models have high computational complexity, which hinders their application to high-resolution images, especially for UHD (ultra-high-defin... |
Liu_RankDetNet_Delving_Into_Ranking_Constraints_for_Object_Detection_CVPR_2021_paper | RankDetNet: Delving Into Ranking Constraints for Object Detection | [
"Ji Liu",
"Dong Li",
"Rongzhang Zheng",
"Lu Tian",
"Yi Shan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_RankDetNet_Delving_Into_Ranking_Constraints_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_RankDetNet_Delving_Into_Ranking_Constraints_for_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_RankDetNet_Delving_Into_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Liu_2021_CVPR,
author = {Liu, Ji and Li, Dong and Zheng, Rongzhang and Tian, Lu and Shan, Yi},
title = {RankDetNet: Delving Into Ranking Constraints for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Modern object detection approaches cast detecting objects as optimizing two subtasks of classification and localization simultaneously. Existing methods often learn the classification task by optimizing each proposal separately and neglect the relationship among different proposals. Such detection paradigm also encount... |
Sarlin_Back_to_the_Feature_Learning_Robust_Camera_Localization_From_Pixels_CVPR_2021_paper | Back to the Feature: Learning Robust Camera Localization From Pixels To Pose | [
"Paul-Edouard Sarlin",
"Ajaykumar Unagar",
"Mans Larsson",
"Hugo Germain",
"Carl Toft",
"Viktor Larsson",
"Marc Pollefeys",
"Vincent Lepetit",
"Lars Hammarstrand",
"Fredrik Kahl",
"Torsten Sattler"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sarlin_Back_to_the_Feature_Learning_Robust_Camera_Localization_From_Pixels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sarlin_Back_to_the_Feature_Learning_Robust_Camera_Localization_From_Pixels_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sarlin_Back_to_the_CVPR_2021_supplemental.pdf | 2103.09213 | cvf | @InProceedings{Sarlin_2021_CVPR,
author = {Sarlin, Paul-Edouard and Unagar, Ajaykumar and Larsson, Mans and Germain, Hugo and Toft, Carl and Larsson, Viktor and Pollefeys, Marc and Lepetit, Vincent and Hammarstrand, Lars and Kahl, Fredrik and Sattler, Torsten},
title = {Back to the Feature: Learning Robu... | Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, from an input image. This either fails to generalize to new viewpoints or ties the model parameters to a specific scene. In this paper, we go... |
Tang_Learning_Parallel_Dense_Correspondence_From_Spatio-Temporal_Descriptors_for_Efficient_and_CVPR_2021_paper | Learning Parallel Dense Correspondence From Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction | [
"Jiapeng Tang",
"Dan Xu",
"Kui Jia",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Learning_Parallel_Dense_Correspondence_From_Spatio-Temporal_Descriptors_for_Efficient_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Learning_Parallel_Dense_Correspondence_From_Spatio-Temporal_Descriptors_for_Efficient_and_CVPR_2021_paper.pdf | null | 2103.16341 | cvf | @InProceedings{Tang_2021_CVPR,
author = {Tang, Jiapeng and Xu, Dan and Jia, Kui and Zhang, Lei},
title = {Learning Parallel Dense Correspondence From Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | This paper focuses on the task of 4D shape reconstruction from a sequence of point clouds. Despite the recent success achieved by extending deep implicit representations into 4D space, it is still a great challenge in two respects, i.e. how to design a flexible framework for learning robust spatio-temporal shape repres... |
Prakash_Multi-Modal_Fusion_Transformer_for_End-to-End_Autonomous_Driving_CVPR_2021_paper | Multi-Modal Fusion Transformer for End-to-End Autonomous Driving | [
"Aditya Prakash",
"Kashyap Chitta",
"Andreas Geiger"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Prakash_Multi-Modal_Fusion_Transformer_for_End-to-End_Autonomous_Driving_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Prakash_Multi-Modal_Fusion_Transformer_for_End-to-End_Autonomous_Driving_CVPR_2021_paper.pdf | null | 2104.09224 | cvf | @InProceedings{Prakash_2021_CVPR,
author = {Prakash, Aditya and Chitta, Kashyap and Geiger, Andreas},
title = {Multi-Modal Fusion Transformer for End-to-End Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | How should representations from complementary sensors be integrated for autonomous driving? Geometry-based sensor fusion has shown great promise for perception tasks such as object detection and motion forecasting. However, for the actual driving task, the global context of the 3D scene is key, e.g. a change in traffic... |
Yan_LightTrack_Finding_Lightweight_Neural_Networks_for_Object_Tracking_via_One-Shot_CVPR_2021_paper | LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search | [
"Bin Yan",
"Houwen Peng",
"Kan Wu",
"Dong Wang",
"Jianlong Fu",
"Huchuan Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_LightTrack_Finding_Lightweight_Neural_Networks_for_Object_Tracking_via_One-Shot_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_LightTrack_Finding_Lightweight_Neural_Networks_for_Object_Tracking_via_One-Shot_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_LightTrack_Finding_Lightweight_CVPR_2021_supplemental.zip | 2104.14545 | cvf | @InProceedings{Yan_2021_CVPR,
author = {Yan, Bin and Peng, Houwen and Wu, Kan and Wang, Dong and Fu, Jianlong and Lu, Huchuan},
title = {LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | Object tracking has achieved significant progress over the past few years. However, state-of-the-art trackers become increasingly heavy and expensive, which limits their deployments in resource-constrained applications. In this work, we present LightTrack, which uses neural architecture search (NAS) to design more ligh... |
Zheng_Unsupervised_Disentanglement_of_Linear-Encoded_Facial_Semantics_CVPR_2021_paper | Unsupervised Disentanglement of Linear-Encoded Facial Semantics | [
"Yutong Zheng",
"Yu-Kai Huang",
"Ran Tao",
"Zhiqiang Shen",
"Marios Savvides"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Unsupervised_Disentanglement_of_Linear-Encoded_Facial_Semantics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Unsupervised_Disentanglement_of_Linear-Encoded_Facial_Semantics_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Unsupervised_Disentanglement_of_CVPR_2021_supplemental.pdf | 2103.16605 | cvf | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Yutong and Huang, Yu-Kai and Tao, Ran and Shen, Zhiqiang and Savvides, Marios},
title = {Unsupervised Disentanglement of Linear-Encoded Facial Semantics},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted as well. We start by coupling StyleGAN with a stabilized... |
Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper | Learning Position and Target Consistency for Memory-Based Video Object Segmentation | [
"Li Hu",
"Peng Zhang",
"Bang Zhang",
"Pan Pan",
"Yinghui Xu",
"Rong Jin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper.pdf | null | 2104.04329 | cvf | @InProceedings{Hu_2021_CVPR,
author = {Hu, Li and Zhang, Peng and Zhang, Bang and Pan, Pan and Xu, Yinghui and Jin, Rong},
title = {Learning Position and Target Consistency for Memory-Based Video Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that t... |
Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper | Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation | [
"Pan Zhang",
"Bo Zhang",
"Ting Zhang",
"Dong Chen",
"Yong Wang",
"Fang Wen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Prototypical_Pseudo_Label_CVPR_2021_supplemental.pdf | 2101.10979 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Pan and Zhang, Bo and Zhang, Ting and Chen, Dong and Wang, Yong and Wen, Fang},
title = {Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference o... | Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on represe... |
Xia_Deep_Denoising_of_Flash_and_No-Flash_Pairs_for_Photography_in_CVPR_2021_paper | Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments | [
"Zhihao Xia",
"Michael Gharbi",
"Federico Perazzi",
"Kalyan Sunkavalli",
"Ayan Chakrabarti"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xia_Deep_Denoising_of_Flash_and_No-Flash_Pairs_for_Photography_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xia_Deep_Denoising_of_Flash_and_No-Flash_Pairs_for_Photography_in_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xia_Deep_Denoising_of_CVPR_2021_supplemental.pdf | 2012.05116 | cvf | @InProceedings{Xia_2021_CVPR,
author = {Xia, Zhihao and Gharbi, Michael and Perazzi, Federico and Sunkavalli, Kalyan and Chakrabarti, Ayan},
title = {Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer ... | We introduce a neural network-based method to denoise pairs of images taken in quick succession in low-light environments, with and without a flash. Our goal is to produce a high-quality rendering of the scene that preserves the color and mood from the ambient illumination of the noisy no-flash image, while recovering ... |
Chefer_Transformer_Interpretability_Beyond_Attention_Visualization_CVPR_2021_paper | Transformer Interpretability Beyond Attention Visualization | [
"Hila Chefer",
"Shir Gur",
"Lior Wolf"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chefer_Transformer_Interpretability_Beyond_Attention_Visualization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chefer_Transformer_Interpretability_Beyond_Attention_Visualization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chefer_Transformer_Interpretability_Beyond_CVPR_2021_supplemental.pdf | 2012.09838 | cvf | @InProceedings{Chefer_2021_CVPR,
author = {Chefer, Hila and Gur, Shir and Wolf, Lior},
title = {Transformer Interpretability Beyond Attention Visualization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year =... | Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In order to visualize the parts of the image that led to a certain classification, existing methods either rely on the obtained attention m... |
Truong_Unsupervised_Learning_for_Robust_Fitting_A_Reinforcement_Learning_Approach_CVPR_2021_paper | Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach | [
"Giang Truong",
"Huu Le",
"David Suter",
"Erchuan Zhang",
"Syed Zulqarnain Gilani"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Truong_Unsupervised_Learning_for_Robust_Fitting_A_Reinforcement_Learning_Approach_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Truong_Unsupervised_Learning_for_Robust_Fitting_A_Reinforcement_Learning_Approach_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Truong_Unsupervised_Learning_for_CVPR_2021_supplemental.pdf | 2103.03501 | cvf | @InProceedings{Truong_2021_CVPR,
author = {Truong, Giang and Le, Huu and Suter, David and Zhang, Erchuan and Gilani, Syed Zulqarnain},
title = {Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for highly contaminated datasets is, however, still challenging due to its underlying computational complexity. Recent attention has been focused on learning-based algorithms. However, most appro... |
Wei_Unsupervised_Real-World_Image_Super_Resolution_via_Domain-Distance_Aware_Training_CVPR_2021_paper | Unsupervised Real-World Image Super Resolution via Domain-Distance Aware Training | [
"Yunxuan Wei",
"Shuhang Gu",
"Yawei Li",
"Radu Timofte",
"Longcun Jin",
"Hengjie Song"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_Unsupervised_Real-World_Image_Super_Resolution_via_Domain-Distance_Aware_Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_Unsupervised_Real-World_Image_Super_Resolution_via_Domain-Distance_Aware_Training_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_Unsupervised_Real-World_Image_CVPR_2021_supplemental.pdf | 2004.01178 | cvf | @InProceedings{Wei_2021_CVPR,
author = {Wei, Yunxuan and Gu, Shuhang and Li, Yawei and Timofte, Radu and Jin, Longcun and Song, Hengjie},
title = {Unsupervised Real-World Image Super Resolution via Domain-Distance Aware Training},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | These days, unsupervised super-resolution (SR) is soaring due to its practical and promising potential in real scenarios. The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i.e. first generating synthetic low-resolution (LR) images Y^g corresponding to real-world high-resolution (HR) ... |
Fu_Learning_to_Track_Instances_without_Video_Annotations_CVPR_2021_paper | Learning to Track Instances without Video Annotations | [
"Yang Fu",
"Sifei Liu",
"Umar Iqbal",
"Shalini De Mello",
"Humphrey Shi",
"Jan Kautz"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Learning_to_Track_Instances_without_Video_Annotations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Learning_to_Track_Instances_without_Video_Annotations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_Learning_to_Track_CVPR_2021_supplemental.pdf | 2104.00287 | cvf | @InProceedings{Fu_2021_CVPR,
author = {Fu, Yang and Liu, Sifei and Iqbal, Umar and De Mello, Shalini and Shi, Humphrey and Kautz, Jan},
title = {Learning to Track Instances without Video Annotations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches. To resolve these challenges, we introduce a novel semi-supervised framework by learning ins... |
Wang_Unsupervised_Feature_Learning_by_Cross-Level_Instance-Group_Discrimination_CVPR_2021_paper | Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination | [
"Xudong Wang",
"Ziwei Liu",
"Stella X. Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Feature_Learning_by_Cross-Level_Instance-Group_Discrimination_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Unsupervised_Feature_Learning_by_Cross-Level_Instance-Group_Discrimination_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Unsupervised_Feature_Learning_CVPR_2021_supplemental.pdf | 2008.03813 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Xudong and Liu, Ziwei and Yu, Stella X.},
title = {Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced datasets. However, natural data could be highly correlated and long-tail distributed. Natural between-instance similarity conflicts with the pre... |
Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper | Representation Learning via Global Temporal Alignment and Cycle-Consistency | [
"Isma Hadji",
"Konstantinos G. Derpanis",
"Allan D. Jepson"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hadji_Representation_Learning_via_CVPR_2021_supplemental.zip | 2105.05217 | cvf | @InProceedings{Hadji_2021_CVPR,
author = {Hadji, Isma and Derpanis, Konstantinos G. and Jepson, Allan D.},
title = {Representation Learning via Global Temporal Alignment and Cycle-Consistency},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal. In particular, we propose a loss... |
Lu_Personalized_Outfit_Recommendation_With_Learnable_Anchors_CVPR_2021_paper | Personalized Outfit Recommendation With Learnable Anchors | [
"Zhi Lu",
"Yang Hu",
"Yan Chen",
"Bing Zeng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Personalized_Outfit_Recommendation_With_Learnable_Anchors_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Personalized_Outfit_Recommendation_With_Learnable_Anchors_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_Personalized_Outfit_Recommendation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Lu_2021_CVPR,
author = {Lu, Zhi and Hu, Yang and Chen, Yan and Zeng, Bing},
title = {Personalized Outfit Recommendation With Learnable Anchors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | The multimedia community has recently seen a tremendous surge of interest in the fashion recommendation problem. A lot of efforts have been made to model the compatibility between fashion items. Some have also studied users' personal preferences for the outfits. There is, however, another difficulty in the task that ha... |
Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper | When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework | [
"Zhizhong Huang",
"Junping Zhang",
"Hongming Shan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_When_Age-Invariant_Face_CVPR_2021_supplemental.pdf | 2103.01520 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Zhizhong and Zhang, Junping and Shan, Hongming},
title = {When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | To minimize the effects of age variation in face recognition, previous work either extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features, called age-invariant face recognition (AIFR), or removes age variation by transforming the faces of different age... |
Yang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper | Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking | [
"Yiding Yang",
"Zhou Ren",
"Haoxiang Li",
"Chunluan Zhou",
"Xinchao Wang",
"Gang Hua"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.pdf | null | 2106.03772 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Yiding and Ren, Zhou and Li, Haoxiang and Zhou, Chunluan and Wang, Xinchao and Hua, Gang},
title = {Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Multi-person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only then implementing data association and refinement. Despite the promising results achieved, such a strategy is inevitably prone to missed detect... |
Liu_Smoothing_the_Disentangled_Latent_Style_Space_for_Unsupervised_Image-to-Image_Translation_CVPR_2021_paper | Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation | [
"Yahui Liu",
"Enver Sangineto",
"Yajing Chen",
"Linchao Bao",
"Haoxian Zhang",
"Nicu Sebe",
"Bruno Lepri",
"Wei Wang",
"Marco De Nadai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Smoothing_the_Disentangled_Latent_Style_Space_for_Unsupervised_Image-to-Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Smoothing_the_Disentangled_Latent_Style_Space_for_Unsupervised_Image-to-Image_Translation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Smoothing_the_Disentangled_CVPR_2021_supplemental.pdf | 2106.09016 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yahui and Sangineto, Enver and Chen, Yajing and Bao, Linchao and Zhang, Haoxian and Sebe, Nicu and Lepri, Bruno and Wang, Wei and De Nadai, Marco},
title = {Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation},
booktit... | Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequently show abrupt changes in the image appearance during interpolation, and usually perform poorly in interpolations across domains. In this pa... |
Yuan_Robust_Instance_Segmentation_Through_Reasoning_About_Multi-Object_Occlusion_CVPR_2021_paper | Robust Instance Segmentation Through Reasoning About Multi-Object Occlusion | [
"Xiaoding Yuan",
"Adam Kortylewski",
"Yihong Sun",
"Alan Yuille"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Robust_Instance_Segmentation_Through_Reasoning_About_Multi-Object_Occlusion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Robust_Instance_Segmentation_Through_Reasoning_About_Multi-Object_Occlusion_CVPR_2021_paper.pdf | null | 2012.02107 | cvf | @InProceedings{Yuan_2021_CVPR,
author = {Yuan, Xiaoding and Kortylewski, Adam and Sun, Yihong and Yuille, Alan},
title = {Robust Instance Segmentation Through Reasoning About Multi-Object Occlusion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches to image analysis mostly process objects independently and do not take into account the relative occlusion of nearby objects. In this paper, ... |
Cazenavette_Architectural_Adversarial_Robustness_The_Case_for_Deep_Pursuit_CVPR_2021_paper | Architectural Adversarial Robustness: The Case for Deep Pursuit | [
"George Cazenavette",
"Calvin Murdock",
"Simon Lucey"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cazenavette_Architectural_Adversarial_Robustness_The_Case_for_Deep_Pursuit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cazenavette_Architectural_Adversarial_Robustness_The_Case_for_Deep_Pursuit_CVPR_2021_paper.pdf | null | 2011.14427 | cvf | @InProceedings{Cazenavette_2021_CVPR,
author = {Cazenavette, George and Murdock, Calvin and Lucey, Simon},
title = {Architectural Adversarial Robustness: The Case for Deep Pursuit},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by reframing each layer of a feed-forward network as an appr... |
Qi_Multi-Scale_Aligned_Distillation_for_Low-Resolution_Detection_CVPR_2021_paper | Multi-Scale Aligned Distillation for Low-Resolution Detection | [
"Lu Qi",
"Jason Kuen",
"Jiuxiang Gu",
"Zhe Lin",
"Yi Wang",
"Yukang Chen",
"Yanwei Li",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qi_Multi-Scale_Aligned_Distillation_for_Low-Resolution_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qi_Multi-Scale_Aligned_Distillation_for_Low-Resolution_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qi_Multi-Scale_Aligned_Distillation_CVPR_2021_supplemental.pdf | 2109.06875 | title_snapshot | @InProceedings{Qi_2021_CVPR,
author = {Qi, Lu and Kuen, Jason and Gu, Jiuxiang and Lin, Zhe and Wang, Yi and Chen, Yukang and Li, Yanwei and Jia, Jiaya},
title = {Multi-Scale Aligned Distillation for Low-Resolution Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | In instance-level detection tasks (e.g., object detection), reducing input resolution is an easy option to improve runtime efficiency. However, this option severely hurts the detection performance. This paper focuses on boosting the performance of a low-resolution model, by distilling knowledge from a high/multi-resolu... |
Wickramasinghe_Deep_Active_Surface_Models_CVPR_2021_paper | Deep Active Surface Models | [
"Udaranga Wickramasinghe",
"Pascal Fua",
"Graham Knott"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wickramasinghe_Deep_Active_Surface_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wickramasinghe_Deep_Active_Surface_Models_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wickramasinghe_Deep_Active_Surface_CVPR_2021_supplemental.zip | 2011.08826 | cvf | @InProceedings{Wickramasinghe_2021_CVPR,
author = {Wickramasinghe, Udaranga and Fua, Pascal and Knott, Graham},
title = {Deep Active Surface Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},... | Active Surface Models have a long history of being useful to model complex 3D surfaces. But only Active Contours have been used in conjunction with deep networks, and then only to produce the data term as well as meta-parameter maps controlling them. In this paper, we advocate a much tighter integration. We introduce l... |
Wallace_Can_We_Characterize_Tasks_Without_Labels_or_Features_CVPR_2021_paper | Can We Characterize Tasks Without Labels or Features? | [
"Bram Wallace",
"Ziyang Wu",
"Bharath Hariharan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wallace_Can_We_Characterize_Tasks_Without_Labels_or_Features_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wallace_Can_We_Characterize_Tasks_Without_Labels_or_Features_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wallace_Can_We_Characterize_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wallace_2021_CVPR,
author = {Wallace, Bram and Wu, Ziyang and Hariharan, Bharath},
title = {Can We Characterize Tasks Without Labels or Features?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | The problem of expert model selection deals with choosing the appropriate pretrained network ("expert") to transfer to a target task. Methods, however, generally depend on two separate assumptions: the presence of labeled images and access to powerful "probe" networks that yield useful features. In this work, we demons... |
Qiu_Scene_Essence_CVPR_2021_paper | Scene Essence | [
"Jiayan Qiu",
"Yiding Yang",
"Xinchao Wang",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Scene_Essence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_Scene_Essence_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Qiu_2021_CVPR,
author = {Qiu, Jiayan and Yang, Yiding and Wang, Xinchao and Tao, Dacheng},
title = {Scene Essence},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {8... | What scene elements, if any, are indispensable for recognizing a scene? We strive to answer this question through the lens of an end-to-end learning scheme. Our goal is to identify a collection of such pivotal elements, which we term as Scene Essence, to be those that would alter scene recognition if taken out from the... |
Weihs_Visual_Room_Rearrangement_CVPR_2021_paper | Visual Room Rearrangement | [
"Luca Weihs",
"Matt Deitke",
"Aniruddha Kembhavi",
"Roozbeh Mottaghi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Weihs_Visual_Room_Rearrangement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Weihs_Visual_Room_Rearrangement_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Weihs_Visual_Room_Rearrangement_CVPR_2021_supplemental.pdf | 2103.16544 | cvf | @InProceedings{Weihs_2021_CVPR,
author = {Weihs, Luca and Deitke, Matt and Kembhavi, Aniruddha and Mottaghi, Roozbeh},
title = {Visual Room Rearrangement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {... | There has been a significant recent progress in the field of Embodied AI with researchers developing models and algorithms enabling embodied agents to navigate and interact within completely unseen environments. In this paper, we propose a new dataset and baseline models for the task of Rearrangement. We particularly f... |
Vowels_VDSM_Unsupervised_Video_Disentanglement_With_State-Space_Modeling_and_Deep_Mixtures_CVPR_2021_paper | VDSM: Unsupervised Video Disentanglement With State-Space Modeling and Deep Mixtures of Experts | [
"Matthew J. Vowels",
"Necati Cihan Camgoz",
"Richard Bowden"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Vowels_VDSM_Unsupervised_Video_Disentanglement_With_State-Space_Modeling_and_Deep_Mixtures_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Vowels_VDSM_Unsupervised_Video_Disentanglement_With_State-Space_Modeling_and_Deep_Mixtures_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Vowels_VDSM_Unsupervised_Video_CVPR_2021_supplemental.pdf | 2103.07292 | cvf | @InProceedings{Vowels_2021_CVPR,
author = {Vowels, Matthew J. and Camgoz, Necati Cihan and Bowden, Richard},
title = {VDSM: Unsupervised Video Disentanglement With State-Space Modeling and Deep Mixtures of Experts},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. Unfortunately, disentanglement has been shown to be impossible without the incorporation of supervision or inductive bias. Given that supervision is often expensive or infeasible ... |
Lee_Rotation-Only_Bundle_Adjustment_CVPR_2021_paper | Rotation-Only Bundle Adjustment | [
"Seong Hun Lee",
"Javier Civera"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Rotation-Only_Bundle_Adjustment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Rotation-Only_Bundle_Adjustment_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Rotation-Only_Bundle_Adjustment_CVPR_2021_supplemental.pdf | 2011.11724 | cvf | @InProceedings{Lee_2021_CVPR,
author = {Lee, Seong Hun and Civera, Javier},
title = {Rotation-Only Bundle Adjustment},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {424-433}
} | We propose a novel method for estimating the global rotations of the cameras independently of their positions and the scene structure. When two calibrated cameras observe five or more of the same points, their relative rotation can be recovered independently of the translation. We extend this idea to multiple views, th... |
Stammer_Right_for_the_Right_Concept_Revising_Neuro-Symbolic_Concepts_by_Interacting_CVPR_2021_paper | Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations | [
"Wolfgang Stammer",
"Patrick Schramowski",
"Kristian Kersting"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Stammer_Right_for_the_Right_Concept_Revising_Neuro-Symbolic_Concepts_by_Interacting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Stammer_Right_for_the_Right_Concept_Revising_Neuro-Symbolic_Concepts_by_Interacting_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Stammer_Right_for_the_CVPR_2021_supplemental.pdf | 2011.12854 | cvf | @InProceedings{Stammer_2021_CVPR,
author = {Stammer, Wolfgang and Schramowski, Patrick and Kersting, Kristian},
title = {Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Most explanation methods in deep learning map importance estimates for a model's prediction back to the original input space. These "visual" explanations are often insufficient, as the model's actual concept remains elusive. Moreover, without insights into the model's semantic concept, it is difficult --if not impossib... |
Nam_Polygonal_Point_Set_Tracking_CVPR_2021_paper | Polygonal Point Set Tracking | [
"Gunhee Nam",
"Miran Heo",
"Seoung Wug Oh",
"Joon-Young Lee",
"Seon Joo Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nam_Polygonal_Point_Set_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nam_Polygonal_Point_Set_Tracking_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nam_Polygonal_Point_Set_CVPR_2021_supplemental.zip | 2105.14584 | cvf | @InProceedings{Nam_2021_CVPR,
author = {Nam, Gunhee and Heo, Miran and Oh, Seoung Wug and Lee, Joon-Young and Kim, Seon Joo},
title = {Polygonal Point Set Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | In this paper, we propose a novel learning-based polygonal point set tracking method. Compared to existing video object segmentation (VOS) methods that propagate pixel-wise object mask information, we propagate a polygonal point set over frames. Specifically, the set is defined as a subset of points in the target conto... |
Deng_Deformed_Implicit_Field_Modeling_3D_Shapes_With_Learned_Dense_Correspondence_CVPR_2021_paper | Deformed Implicit Field: Modeling 3D Shapes With Learned Dense Correspondence | [
"Yu Deng",
"Jiaolong Yang",
"Xin Tong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Deformed_Implicit_Field_Modeling_3D_Shapes_With_Learned_Dense_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Deformed_Implicit_Field_Modeling_3D_Shapes_With_Learned_Dense_Correspondence_CVPR_2021_paper.pdf | null | 2011.13650 | cvf | @InProceedings{Deng_2021_CVPR,
author = {Deng, Yu and Yang, Jiaolong and Tong, Xin},
title = {Deformed Implicit Field: Modeling 3D Shapes With Learned Dense Correspondence},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes. With DIF, a 3D shape is represented by a template implicit field shared across the category, together with a 3D deformation field and a correction field dedicated for e... |
Shen_Verifiability_and_Predictability_Interpreting_Utilities_of_Network_Architectures_for_Point_CVPR_2021_paper | Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing | [
"Wen Shen",
"Zhihua Wei",
"Shikun Huang",
"Binbin Zhang",
"Panyue Chen",
"Ping Zhao",
"Quanshi Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Verifiability_and_Predictability_Interpreting_Utilities_of_Network_Architectures_for_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Verifiability_and_Predictability_Interpreting_Utilities_of_Network_Architectures_for_Point_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_Verifiability_and_Predictability_CVPR_2021_supplemental.pdf | 1911.09053 | cvf | @InProceedings{Shen_2021_CVPR,
author = {Shen, Wen and Wei, Zhihua and Huang, Shikun and Zhang, Binbin and Chen, Panyue and Zhao, Ping and Zhang, Quanshi},
title = {Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing},
booktitle = {Proceedings ... | In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different network architectures. We propose a number of hypotheses on the effects of specific network architectures on the representation capacity of DNNs. In order to prove the hypotheses, we design five metrics to di... |
Sundararaman_Tracking_Pedestrian_Heads_in_Dense_Crowd_CVPR_2021_paper | Tracking Pedestrian Heads in Dense Crowd | [
"Ramana Sundararaman",
"Cedric De Almeida Braga",
"Eric Marchand",
"Julien Pettre"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sundararaman_Tracking_Pedestrian_Heads_in_Dense_Crowd_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sundararaman_Tracking_Pedestrian_Heads_in_Dense_Crowd_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sundararaman_Tracking_Pedestrian_Heads_CVPR_2021_supplemental.pdf | 2103.13516 | cvf | @InProceedings{Sundararaman_2021_CVPR,
author = {Sundararaman, Ramana and De Almeida Braga, Cedric and Marchand, Eric and Pettre, Julien},
title = {Tracking Pedestrian Heads in Dense Crowd},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | Tracking humans in crowded video sequences is an important constituent of visual scene understanding. Increasing crowd density challenges visibility of humans, limiting the scalability of existing pedestrian trackers to higher crowd densities. For that reason, we propose to revitalize head tracking with Crowd of Heads ... |
Williams_Neural_Splines_Fitting_3D_Surfaces_With_Infinitely-Wide_Neural_Networks_CVPR_2021_paper | Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks | [
"Francis Williams",
"Matthew Trager",
"Joan Bruna",
"Denis Zorin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Williams_Neural_Splines_Fitting_3D_Surfaces_With_Infinitely-Wide_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Williams_Neural_Splines_Fitting_3D_Surfaces_With_Infinitely-Wide_Neural_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Williams_Neural_Splines_Fitting_CVPR_2021_supplemental.pdf | 2006.13782 | cvf | @InProceedings{Williams_2021_CVPR,
author = {Williams, Francis and Trager, Matthew and Bruna, Joan and Zorin, Denis},
title = {Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves state-of-the-art results, outperforming recent neural network-based techniques and widely used Poisson Surface Reconstruction (which, as we... |
Yan_Alpha-Refine_Boosting_Tracking_Performance_by_Precise_Bounding_Box_Estimation_CVPR_2021_paper | Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation | [
"Bin Yan",
"Xinyu Zhang",
"Dong Wang",
"Huchuan Lu",
"Xiaoyun Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Alpha-Refine_Boosting_Tracking_Performance_by_Precise_Bounding_Box_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Alpha-Refine_Boosting_Tracking_Performance_by_Precise_Bounding_Box_Estimation_CVPR_2021_paper.pdf | null | 2012.06815 | title_snapshot | @InProceedings{Yan_2021_CVPR,
author = {Yan, Bin and Zhang, Xinyu and Wang, Dong and Lu, Huchuan and Yang, Xiaoyun},
title = {Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Visual object tracking aims to precisely estimate the bounding box for the given target, which is a challenging problem due to factors such as deformation and occlusion. Many recent trackers adopt the multiple-stage tracking strategy to improve the quality of bounding box estimation. These methods first coarsely locate... |
Liu_Adaptive_Cross-Modal_Prototypes_for_Cross-Domain_Visual-Language_Retrieval_CVPR_2021_paper | Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval | [
"Yang Liu",
"Qingchao Chen",
"Samuel Albanie"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Adaptive_Cross-Modal_Prototypes_for_Cross-Domain_Visual-Language_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Adaptive_Cross-Modal_Prototypes_for_Cross-Domain_Visual-Language_Retrieval_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yang and Chen, Qingchao and Albanie, Samuel},
title = {Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | In this paper, we study the task of visual-text retrieval in the highly practical setting in which labelled visual data with paired text descriptions are available in one domain (the "source"), but only unlabelled visual data (without text descriptions) are available in the domain of interest (the "target"). We propose... |
Changpinyo_Conceptual_12M_Pushing_Web-Scale_Image-Text_Pre-Training_To_Recognize_Long-Tail_Visual_CVPR_2021_paper | Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts | [
"Soravit Changpinyo",
"Piyush Sharma",
"Nan Ding",
"Radu Soricut"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Changpinyo_Conceptual_12M_Pushing_Web-Scale_Image-Text_Pre-Training_To_Recognize_Long-Tail_Visual_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Changpinyo_Conceptual_12M_Pushing_Web-Scale_Image-Text_Pre-Training_To_Recognize_Long-Tail_Visual_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Changpinyo_Conceptual_12M_Pushing_CVPR_2021_supplemental.pdf | 2102.08981 | cvf | @InProceedings{Changpinyo_2021_CVPR,
author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
title = {Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision an... | The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption gen... |
Kim_SetVAE_Learning_Hierarchical_Composition_for_Generative_Modeling_of_Set-Structured_Data_CVPR_2021_paper | SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data | [
"Jinwoo Kim",
"Jaehoon Yoo",
"Juho Lee",
"Seunghoon Hong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_SetVAE_Learning_Hierarchical_Composition_for_Generative_Modeling_of_Set-Structured_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_SetVAE_Learning_Hierarchical_Composition_for_Generative_Modeling_of_Set-Structured_Data_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_SetVAE_Learning_Hierarchical_CVPR_2021_supplemental.pdf | 2103.15619 | cvf | @InProceedings{Kim_2021_CVPR,
author = {Kim, Jinwoo and Yoo, Jaehoon and Lee, Juho and Hong, Seunghoon},
title = {SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks for ordinary sequential data to a set-structured data is nontrivial as it should be invariant to the permutation of its elements. In this pape... |
Zhao_Few-Shot_3D_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper | Few-Shot 3D Point Cloud Semantic Segmentation | [
"Na Zhao",
"Tat-Seng Chua",
"Gim Hee Lee"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Few-Shot_3D_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Few-Shot_3D_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Few-Shot_3D_Point_CVPR_2021_supplemental.pdf | 2006.12052 | cvf | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Na and Chua, Tat-Seng and Lee, Gim Hee},
title = {Few-Shot 3D Point Cloud Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | Many existing approaches for 3D point cloud semantic segmentation are fully supervised. These fully supervised approaches heavily rely on large amounts of labeled training data that are difficult to obtain and cannot segment new classes after training. To mitigate these limitations, we propose a novel attention-aware m... |
Shen_CFNet_Cascade_and_Fused_Cost_Volume_for_Robust_Stereo_Matching_CVPR_2021_paper | CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching | [
"Zhelun Shen",
"Yuchao Dai",
"Zhibo Rao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_CFNet_Cascade_and_Fused_Cost_Volume_for_Robust_Stereo_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_CFNet_Cascade_and_Fused_Cost_Volume_for_Robust_Stereo_Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_CFNet_Cascade_and_CVPR_2021_supplemental.zip | 2104.04314 | cvf | @InProceedings{Shen_2021_CVPR,
author = {Shen, Zhelun and Dai, Yuchao and Rao, Zhibo},
title = {CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound progress in stereo matching. However, most of these successes are limited to a specific dataset and cannot generalize well to other datasets. The main difficulties lie in the large domain differences and unbalanced disparity di... |
Ren_Adaptive_Consistency_Prior_Based_Deep_Network_for_Image_Denoising_CVPR_2021_paper | Adaptive Consistency Prior Based Deep Network for Image Denoising | [
"Chao Ren",
"Xiaohai He",
"Chuncheng Wang",
"Zhibo Zhao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Adaptive_Consistency_Prior_Based_Deep_Network_for_Image_Denoising_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ren_Adaptive_Consistency_Prior_Based_Deep_Network_for_Image_Denoising_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ren_Adaptive_Consistency_Prior_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Ren_2021_CVPR,
author = {Ren, Chao and He, Xiaohai and Wang, Chuncheng and Zhao, Zhibo},
title = {Adaptive Consistency Prior Based Deep Network for Image Denoising},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | Recent studies have shown that deep networks can achieve promising results for image denoising. However, how to simultaneously incorporate the valuable achievements of traditional methods into the network design and improve network interpretability is still an open problem. To solve this problem, we propose a novel mod... |
Chen_Topological_Planning_With_Transformers_for_Vision-and-Language_Navigation_CVPR_2021_paper | Topological Planning With Transformers for Vision-and-Language Navigation | [
"Kevin Chen",
"Junshen K. Chen",
"Jo Chuang",
"Marynel Vazquez",
"Silvio Savarese"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Topological_Planning_With_Transformers_for_Vision-and-Language_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Topological_Planning_With_Transformers_for_Vision-and-Language_Navigation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Topological_Planning_With_CVPR_2021_supplemental.zip | 2012.05292 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Kevin and Chen, Junshen K. and Chuang, Jo and Vazquez, Marynel and Savarese, Silvio},
title = {Topological Planning With Transformers for Vision-and-Language Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics community, we propose a modular approach to VLN using topological maps. Given a natural language instruction and topological map, our approach ... |
Na_FixBi_Bridging_Domain_Spaces_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper | FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation | [
"Jaemin Na",
"Heechul Jung",
"Hyung Jin Chang",
"Wonjun Hwang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Na_FixBi_Bridging_Domain_Spaces_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Na_FixBi_Bridging_Domain_Spaces_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | null | 2011.09230 | cvf | @InProceedings{Na_2021_CVPR,
author = {Na, Jaemin and Jung, Heechul and Chang, Hyung Jin and Hwang, Wonjun},
title = {FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies. In this paper, we propose a UDA method... |
Fan_Generalized_Few-Shot_Object_Detection_Without_Forgetting_CVPR_2021_paper | Generalized Few-Shot Object Detection Without Forgetting | [
"Zhibo Fan",
"Yuchen Ma",
"Zeming Li",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Generalized_Few-Shot_Object_Detection_Without_Forgetting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Generalized_Few-Shot_Object_Detection_Without_Forgetting_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fan_Generalized_Few-Shot_Object_CVPR_2021_supplemental.pdf | 2105.09491 | cvf | @InProceedings{Fan_2021_CVPR,
author = {Fan, Zhibo and Ma, Yuchen and Li, Zeming and Sun, Jian},
title = {Generalized Few-Shot Object Detection Without Forgetting},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year... | Learning object detection from few examples recently emerged to deal with data-limited situations. While most previous works merely focus on the performance on few-shot categories, we claim that the ability to detect all classes is crucial as test samples may contain any instances in realistic applications, which requi... |
Chaman_Truly_Shift-Invariant_Convolutional_Neural_Networks_CVPR_2021_paper | Truly Shift-Invariant Convolutional Neural Networks | [
"Anadi Chaman",
"Ivan Dokmanic"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chaman_Truly_Shift-Invariant_Convolutional_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chaman_Truly_Shift-Invariant_Convolutional_Neural_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chaman_Truly_Shift-Invariant_Convolutional_CVPR_2021_supplemental.pdf | 2011.14214 | cvf | @InProceedings{Chaman_2021_CVPR,
author = {Chaman, Anadi and Dokmanic, Ivan},
title = {Truly Shift-Invariant Convolutional Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
page... | Thanks to the use of convolution and pooling layers, convolutional neural networks were for a long time thought to be shift-invariant. However, recent works have shown that the output of a CNN can change significantly with small shifts in input--a problem caused by the presence of downsampling (stride) layers. The exis... |
Abdelhamed_Leveraging_the_Availability_of_Two_Cameras_for_Illuminant_Estimation_CVPR_2021_paper | Leveraging the Availability of Two Cameras for Illuminant Estimation | [
"Abdelrahman Abdelhamed",
"Abhijith Punnappurath",
"Michael S. Brown"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Abdelhamed_Leveraging_the_Availability_of_Two_Cameras_for_Illuminant_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Abdelhamed_Leveraging_the_Availability_of_Two_Cameras_for_Illuminant_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Abdelhamed_Leveraging_the_Availability_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Abdelhamed_2021_CVPR,
author = {Abdelhamed, Abdelrahman and Punnappurath, Abhijith and Brown, Michael S.},
title = {Leveraging the Availability of Two Cameras for Illuminant Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Most modern smartphones are now equipped with two rear-facing cameras -- a main camera for standard imaging and an additional camera to provide wide-angle or telephoto zoom capabilities. In this paper, we leverage the availability of these two cameras for the task of illumination estimation using a small neural network... |
Hong_LiDAR-Based_Panoptic_Segmentation_via_Dynamic_Shifting_Network_CVPR_2021_paper | LiDAR-Based Panoptic Segmentation via Dynamic Shifting Network | [
"Fangzhou Hong",
"Hui Zhou",
"Xinge Zhu",
"Hongsheng Li",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_LiDAR-Based_Panoptic_Segmentation_via_Dynamic_Shifting_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_LiDAR-Based_Panoptic_Segmentation_via_Dynamic_Shifting_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_LiDAR-Based_Panoptic_Segmentation_CVPR_2021_supplemental.pdf | 2011.11964 | cvf | @InProceedings{Hong_2021_CVPR,
author = {Hong, Fangzhou and Zhou, Hui and Zhu, Xinge and Li, Hongsheng and Liu, Ziwei},
title = {LiDAR-Based Panoptic Segmentation via Dynamic Shifting Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | With the rapid advances of autonomous driving, it becomes critical to equip its sensing system with more holistic 3D perception. However, existing works focus on parsing either the objects (e.g. cars and pedestrians) or scenes (e.g. trees and buildings) from the LiDAR sensor. In this work, we address the task of LiDAR-... |
Cui_Towards_Accurate_3D_Human_Motion_Prediction_From_Incomplete_Observations_CVPR_2021_paper | Towards Accurate 3D Human Motion Prediction From Incomplete Observations | [
"Qiongjie Cui",
"Huaijiang Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cui_Towards_Accurate_3D_Human_Motion_Prediction_From_Incomplete_Observations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cui_Towards_Accurate_3D_Human_Motion_Prediction_From_Incomplete_Observations_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Cui_2021_CVPR,
author = {Cui, Qiongjie and Sun, Huaijiang},
title = {Towards Accurate 3D Human Motion Prediction From Incomplete Observations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Predicting accurate and realistic future human poses from historically observed sequences is a fundamental task in the intersection of computer vision, graphics, and artificial intelligence. Recently, continuous efforts have been devoted to addressing this issue, which has achieved remarkable progress. However, the exi... |
Shuai_SiamMOT_Siamese_Multi-Object_Tracking_CVPR_2021_paper | SiamMOT: Siamese Multi-Object Tracking | [
"Bing Shuai",
"Andrew Berneshawi",
"Xinyu Li",
"Davide Modolo",
"Joseph Tighe"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shuai_SiamMOT_Siamese_Multi-Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shuai_SiamMOT_Siamese_Multi-Object_Tracking_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shuai_SiamMOT_Siamese_Multi-Object_CVPR_2021_supplemental.pdf | 2105.11595 | cvf | @InProceedings{Shuai_2021_CVPR,
author = {Shuai, Bing and Berneshawi, Andrew and Li, Xinyu and Modolo, Davide and Tighe, Joseph},
title = {SiamMOT: Siamese Multi-Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | In this work, we focus on improving online multi-object tracking (MOT). In particular, we propose a novel region-based Siamese Multi-Object Tracking network, which we name SiamMOT. SiamMOT is based upon Faster-RCNN and adds a forward tracker that models the instance's motion across two frames such that detected instanc... |
Zhang_Open-Book_Video_Captioning_With_Retrieve-Copy-Generate_Network_CVPR_2021_paper | Open-Book Video Captioning With Retrieve-Copy-Generate Network | [
"Ziqi Zhang",
"Zhongang Qi",
"Chunfeng Yuan",
"Ying Shan",
"Bing Li",
"Ying Deng",
"Weiming Hu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Open-Book_Video_Captioning_With_Retrieve-Copy-Generate_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Open-Book_Video_Captioning_With_Retrieve-Copy-Generate_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Open-Book_Video_Captioning_CVPR_2021_supplemental.pdf | 2103.05284 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Ziqi and Qi, Zhongang and Yuan, Chunfeng and Shan, Ying and Li, Bing and Deng, Ying and Hu, Weiming},
title = {Open-Book Video Captioning With Retrieve-Copy-Generate Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | In this paper, we convert traditional video captioning task into a new paradigm, i.e., Open-book Video Captioning, which generates natural language under the prompts of video-content-relevant sentences, not limited to the video itself. To address the open-book video captioning problem, we propose a novel Retrieve-Copy-... |
Ma_MUST-GAN_Multi-Level_Statistics_Transfer_for_Self-Driven_Person_Image_Generation_CVPR_2021_paper | MUST-GAN: Multi-Level Statistics Transfer for Self-Driven Person Image Generation | [
"Tianxiang Ma",
"Bo Peng",
"Wei Wang",
"Jing Dong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_MUST-GAN_Multi-Level_Statistics_Transfer_for_Self-Driven_Person_Image_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_MUST-GAN_Multi-Level_Statistics_Transfer_for_Self-Driven_Person_Image_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_MUST-GAN_Multi-Level_Statistics_CVPR_2021_supplemental.pdf | 2011.09084 | title_snapshot | @InProceedings{Ma_2021_CVPR,
author = {Ma, Tianxiang and Peng, Bo and Wang, Wei and Dong, Jing},
title = {MUST-GAN: Multi-Level Statistics Transfer for Self-Driven Person Image Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | Pose-guided person image generation usually involves using paired source-target images to supervise the training, which significantly increases the data preparation effort and limits the application of the models. To deal with this problem, we propose a novel multi-level statistics transfer model, which disentangles an... |
Tang_Learning_Camera_Localization_via_Dense_Scene_Matching_CVPR_2021_paper | Learning Camera Localization via Dense Scene Matching | [
"Shitao Tang",
"Chengzhou Tang",
"Rui Huang",
"Siyu Zhu",
"Ping Tan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Learning_Camera_Localization_via_Dense_Scene_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Learning_Camera_Localization_via_Dense_Scene_Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Learning_Camera_Localization_CVPR_2021_supplemental.pdf | 2103.16792 | cvf | @InProceedings{Tang_2021_CVPR,
author = {Tang, Shitao and Tang, Chengzhou and Huang, Rui and Zhu, Siyu and Tan, Ping},
title = {Learning Camera Localization via Dense Scene Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Camera localization aims to estimate 6 DoF camera poses from RGB images. Traditional methods detect and match interest points between a query image and a pre-built 3D model. Recent learning-based approaches encode scene structures into a specific convolutional neural network(CNN) and thus are able to predict dense coor... |
Ou_SDD-FIQA_Unsupervised_Face_Image_Quality_Assessment_With_Similarity_Distribution_Distance_CVPR_2021_paper | SDD-FIQA: Unsupervised Face Image Quality Assessment With Similarity Distribution Distance | [
"Fu-Zhao Ou",
"Xingyu Chen",
"Ruixin Zhang",
"Yuge Huang",
"Shaoxin Li",
"Jilin Li",
"Yong Li",
"Liujuan Cao",
"Yuan-Gen Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ou_SDD-FIQA_Unsupervised_Face_Image_Quality_Assessment_With_Similarity_Distribution_Distance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ou_SDD-FIQA_Unsupervised_Face_Image_Quality_Assessment_With_Similarity_Distribution_Distance_CVPR_2021_paper.pdf | null | 2103.05977 | title_snapshot | @InProceedings{Ou_2021_CVPR,
author = {Ou, Fu-Zhao and Chen, Xingyu and Zhang, Ruixin and Huang, Yuge and Li, Shaoxin and Li, Jilin and Li, Yong and Cao, Liujuan and Wang, Yuan-Gen},
title = {SDD-FIQA: Unsupervised Face Image Quality Assessment With Similarity Distribution Distance},
booktitle = {Pro... | In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition performance in an unconstrained scenario. For this purpose, the FIQA method should consider both the intrinsic property and the recognizability ... |
Yan_Self-Aligned_Video_Deraining_With_Transmission-Depth_Consistency_CVPR_2021_paper | Self-Aligned Video Deraining With Transmission-Depth Consistency | [
"Wending Yan",
"Robby T. Tan",
"Wenhan Yang",
"Dengxin Dai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Self-Aligned_Video_Deraining_With_Transmission-Depth_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Self-Aligned_Video_Deraining_With_Transmission-Depth_Consistency_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_Self-Aligned_Video_Deraining_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yan_2021_CVPR,
author = {Yan, Wending and Tan, Robby T. and Yang, Wenhan and Dai, Dengxin},
title = {Self-Aligned Video Deraining With Transmission-Depth Consistency},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | In this paper, we address the problems of rain streaks and rain accumulation removal in video, by developing a self-aligned network with transmission-depth consistency. Existing video based deraining method focus only on rain streak removal, and commonly use optical flow to align the rain video frames. However, besides... |
Zhu_Self-Promoted_Prototype_Refinement_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper | Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning | [
"Kai Zhu",
"Yang Cao",
"Wei Zhai",
"Jie Cheng",
"Zheng-Jun Zha"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Self-Promoted_Prototype_Refinement_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Self-Promoted_Prototype_Refinement_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.pdf | null | 2107.08918 | title_snapshot | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Kai and Cao, Yang and Zhai, Wei and Cheng, Jie and Zha, Zheng-Jun},
title = {Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and prototype reorganization can only be achieved under little supervision. To address this problem, we propose a novel incremental prototype l... |
Reiss_PANDA_Adapting_Pretrained_Features_for_Anomaly_Detection_and_Segmentation_CVPR_2021_paper | PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation | [
"Tal Reiss",
"Niv Cohen",
"Liron Bergman",
"Yedid Hoshen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Reiss_PANDA_Adapting_Pretrained_Features_for_Anomaly_Detection_and_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Reiss_PANDA_Adapting_Pretrained_Features_for_Anomaly_Detection_and_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reiss_PANDA_Adapting_Pretrained_CVPR_2021_supplemental.pdf | 2010.05903 | cvf | @InProceedings{Reiss_2021_CVPR,
author = {Reiss, Tal and Cohen, Niv and Bergman, Liron and Hoshen, Yedid},
title = {PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pre-trained deep features, has been mostly overlooked. In this paper,... |
Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper | Towards Compact CNNs via Collaborative Compression | [
"Yuchao Li",
"Shaohui Lin",
"Jianzhuang Liu",
"Qixiang Ye",
"Mengdi Wang",
"Fei Chao",
"Fan Yang",
"Jincheng Ma",
"Qi Tian",
"Rongrong Ji"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Towards_Compact_CNNs_CVPR_2021_supplemental.pdf | 2105.11228 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Yuchao and Lin, Shaohui and Liu, Jianzhuang and Ye, Qixiang and Wang, Mengdi and Chao, Fei and Yang, Fan and Ma, Jincheng and Tian, Qi and Ji, Rongrong},
title = {Towards Compact CNNs via Collaborative Compression},
booktitle = {Proceedings of the IEEE/CVF C... | Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression. However, these two techniques are traditionally deployed in an isolated manner, leading to significant accuracy drop when pursuing high compression rates. In this paper, we propose a Collaborative Com... |
Zhou_Embracing_Uncertainty_Decoupling_and_De-Bias_for_Robust_Temporal_Grounding_CVPR_2021_paper | Embracing Uncertainty: Decoupling and De-Bias for Robust Temporal Grounding | [
"Hao Zhou",
"Chongyang Zhang",
"Yan Luo",
"Yanjun Chen",
"Chuanping Hu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Embracing_Uncertainty_Decoupling_and_De-Bias_for_Robust_Temporal_Grounding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Embracing_Uncertainty_Decoupling_and_De-Bias_for_Robust_Temporal_Grounding_CVPR_2021_paper.pdf | null | 2103.16848 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Hao and Zhang, Chongyang and Luo, Yan and Chen, Yanjun and Hu, Chuanping},
title = {Embracing Uncertainty: Decoupling and De-Bias for Robust Temporal Grounding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Temporal grounding aims to localize temporal boundaries within untrimmed videos by language queries, but it faces the challenge of two types of inevitable human uncertainties: query uncertainty and label uncertainty. The two uncertainties stem from human subjectivity, leading to limited generalization ability of tempor... |
Whitehead_Separating_Skills_and_Concepts_for_Novel_Visual_Question_Answering_CVPR_2021_paper | Separating Skills and Concepts for Novel Visual Question Answering | [
"Spencer Whitehead",
"Hui Wu",
"Heng Ji",
"Rogerio Feris",
"Kate Saenko"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Whitehead_Separating_Skills_and_Concepts_for_Novel_Visual_Question_Answering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Whitehead_Separating_Skills_and_Concepts_for_Novel_Visual_Question_Answering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Whitehead_Separating_Skills_and_CVPR_2021_supplemental.pdf | 2107.09106 | title_snapshot | @InProceedings{Whitehead_2021_CVPR,
author = {Whitehead, Spencer and Wu, Hui and Ji, Heng and Feris, Rogerio and Saenko, Kate},
title = {Separating Skills and Concepts for Novel Visual Question Answering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them into "skills" and "concepts". "Skills" are visual tasks, such as counting or attribute recognition, and are applied to "concepts" mentioned ... |
Yan_Discrete-Continuous_Action_Space_Policy_Gradient-Based_Attention_for_Image-Text_Matching_CVPR_2021_paper | Discrete-Continuous Action Space Policy Gradient-Based Attention for Image-Text Matching | [
"Shiyang Yan",
"Li Yu",
"Yuan Xie"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Discrete-Continuous_Action_Space_Policy_Gradient-Based_Attention_for_Image-Text_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Discrete-Continuous_Action_Space_Policy_Gradient-Based_Attention_for_Image-Text_Matching_CVPR_2021_paper.pdf | null | 2104.10406 | cvf | @InProceedings{Yan_2021_CVPR,
author = {Yan, Shiyang and Yu, Li and Xie, Yuan},
title = {Discrete-Continuous Action Space Policy Gradient-Based Attention for Image-Text Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Image-text matching is an important multi-modal task with massive applications. It tries to match the image and the text with similar semantic information. Existing approaches do not explicitly transform the different modalities into a common space. Meanwhile, the attention mechanism which is widely used in image-text ... |
Luo_Scalable_Differential_Privacy_With_Sparse_Network_Finetuning_CVPR_2021_paper | Scalable Differential Privacy With Sparse Network Finetuning | [
"Zelun Luo",
"Daniel J. Wu",
"Ehsan Adeli",
"Li Fei-Fei"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Scalable_Differential_Privacy_With_Sparse_Network_Finetuning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Scalable_Differential_Privacy_With_Sparse_Network_Finetuning_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Luo_2021_CVPR,
author = {Luo, Zelun and Wu, Daniel J. and Adeli, Ehsan and Fei-Fei, Li},
title = {Scalable Differential Privacy With Sparse Network Finetuning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | We propose a novel method for privacy-preserving training of deep neural networks leveraging public, out-domain data. While differential privacy (DP) has emerged as a mechanism to protect sensitive data in training datasets, its application to complex visual recognition tasks remains challenging. Traditional DP methods... |
Ge_Video_Object_Segmentation_Using_Global_and_Instance_Embedding_Learning_CVPR_2021_paper | Video Object Segmentation Using Global and Instance Embedding Learning | [
"Wenbin Ge",
"Xiankai Lu",
"Jianbing Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_Video_Object_Segmentation_Using_Global_and_Instance_Embedding_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_Video_Object_Segmentation_Using_Global_and_Instance_Embedding_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ge_Video_Object_Segmentation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Ge_2021_CVPR,
author = {Ge, Wenbin and Lu, Xiankai and Shen, Jianbing},
title = {Video Object Segmentation Using Global and Instance Embedding Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | In this paper, we propose a feature embedding based video object segmentation (VOS) method which is simple, fast and effective. The current VOS task involves two main challenges: object instance differentiation and cross-frame instance alignment. Most state-of-the-art matching based VOS methods simplify this task into ... |
Wang_Scene_Text_Retrieval_via_Joint_Text_Detection_and_Similarity_Learning_CVPR_2021_paper | Scene Text Retrieval via Joint Text Detection and Similarity Learning | [
"Hao Wang",
"Xiang Bai",
"Mingkun Yang",
"Shenggao Zhu",
"Jing Wang",
"Wenyu Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scene_Text_Retrieval_via_Joint_Text_Detection_and_Similarity_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Scene_Text_Retrieval_via_Joint_Text_Detection_and_Similarity_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Scene_Text_Retrieval_CVPR_2021_supplemental.pdf | 2104.01552 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Hao and Bai, Xiang and Yang, Mingkun and Zhu, Shenggao and Wang, Jing and Liu, Wenyu},
title = {Scene Text Retrieval via Joint Text Detection and Similarity Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Scene text retrieval aims to localize and search all text instances from an image gallery, which are the same or similar with a given query text. Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter. In this paper, we address this problem by dir... |
Chen_Learning_Continuous_Image_Representation_With_Local_Implicit_Image_Function_CVPR_2021_paper | Learning Continuous Image Representation With Local Implicit Image Function | [
"Yinbo Chen",
"Sifei Liu",
"Xiaolong Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_Continuous_Image_Representation_With_Local_Implicit_Image_Function_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_Continuous_Image_Representation_With_Local_Implicit_Image_Function_CVPR_2021_paper.pdf | null | 2012.09161 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Yinbo and Liu, Sifei and Wang, Xiaolong},
title = {Learning Continuous Image Representation With Local Implicit Image Function},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper, we seek to learn a continuous representation for images. Inspired by the recent progress in 3D reconstruction with implicit neural represent... |
Wang_Locally_Aware_Piecewise_Transformation_Fields_for_3D_Human_Mesh_Registration_CVPR_2021_paper | Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration | [
"Shaofei Wang",
"Andreas Geiger",
"Siyu Tang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Locally_Aware_Piecewise_Transformation_Fields_for_3D_Human_Mesh_Registration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Locally_Aware_Piecewise_Transformation_Fields_for_3D_Human_Mesh_Registration_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Locally_Aware_Piecewise_CVPR_2021_supplemental.pdf | 2104.08160 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Shaofei and Geiger, Andreas and Tang, Siyu},
title = {Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Registering point clouds of dressed humans to parametric human models is a challenging task in computer vision. Traditional approaches often rely on heavily engineered pipelines that require accurate manual initialization of human poses and tedious post-processing. More recently, learning-based methods are proposed in ... |
Guo_Graph_Attention_Tracking_CVPR_2021_paper | Graph Attention Tracking | [
"Dongyan Guo",
"Yanyan Shao",
"Ying Cui",
"Zhenhua Wang",
"Liyan Zhang",
"Chunhua Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Graph_Attention_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Graph_Attention_Tracking_CVPR_2021_paper.pdf | null | 2011.11204 | cvf | @InProceedings{Guo_2021_CVPR,
author = {Guo, Dongyan and Shao, Yanyan and Cui, Ying and Wang, Zhenhua and Zhang, Liyan and Shen, Chunhua},
title = {Graph Attention Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Siamese network based trackers formulate the visual tracking task as a similarity matching problem. Almost all popular Siamese trackers realize the similarity learning via convolutional feature cross-correlation between a target branch and a search branch. However, since the size of target feature region needs to be pr... |
Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper | ReDet: A Rotation-Equivariant Detector for Aerial Object Detection | [
"Jiaming Han",
"Jian Ding",
"Nan Xue",
"Gui-Song Xia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.pdf | null | 2103.07733 | cvf | @InProceedings{Han_2021_CVPR,
author = {Han, Jiaming and Ding, Jian and Xue, Nan and Xia, Gui-Song},
title = {ReDet: A Rotation-Equivariant Detector for Aerial Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | Recently, object detection in aerial images has gained much attention in computer vision. Different from objects in natural images, aerial objects are often distributed with arbitrary orientation. Therefore, the detector requires more parameters to encode the orientation information, which are often highly redundant an... |
Li_Action_Shuffle_Alternating_Learning_for_Unsupervised_Action_Segmentation_CVPR_2021_paper | Action Shuffle Alternating Learning for Unsupervised Action Segmentation | [
"Jun Li",
"Sinisa Todorovic"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Action_Shuffle_Alternating_Learning_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Action_Shuffle_Alternating_Learning_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.pdf | null | 2104.02116 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Jun and Todorovic, Sinisa},
title = {Action Shuffle Alternating Learning for Unsupervised Action Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2... | This paper addresses unsupervised action segmentation. Prior work captures the frame-level temporal structure of videos by a feature embedding that encodes time locations of frames in the video. We advance prior work with a new self-supervised learning (SSL) of a feature embedding that accounts for both frame- and acti... |
Lv_Progressive_Modality_Reinforcement_for_Human_Multimodal_Emotion_Recognition_From_Unaligned_CVPR_2021_paper | Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences | [
"Fengmao Lv",
"Xiang Chen",
"Yanyong Huang",
"Lixin Duan",
"Guosheng Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Progressive_Modality_Reinforcement_for_Human_Multimodal_Emotion_Recognition_From_Unaligned_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Progressive_Modality_Reinforcement_for_Human_Multimodal_Emotion_Recognition_From_Unaligned_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Lv_2021_CVPR,
author = {Lv, Fengmao and Chen, Xiang and Huang, Yanyong and Duan, Lixin and Lin, Guosheng},
title = {Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences},
booktitle = {Proceedings of the IEEE/CVF Conference on C... | Human multimodal emotion recognition involves time-series data of different modalities, such as natural language, visual motions, and acoustic behaviors. Due to the variable sampling rates for sequences from different modalities, the collected multimodal streams are usually unaligned. The asynchrony across modalities i... |
Zhong_OpenMix_Reviving_Known_Knowledge_for_Discovering_Novel_Visual_Categories_in_CVPR_2021_paper | OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in an Open World | [
"Zhun Zhong",
"Linchao Zhu",
"Zhiming Luo",
"Shaozi Li",
"Yi Yang",
"Nicu Sebe"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_OpenMix_Reviving_Known_Knowledge_for_Discovering_Novel_Visual_Categories_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_OpenMix_Reviving_Known_Knowledge_for_Discovering_Novel_Visual_Categories_in_CVPR_2021_paper.pdf | null | 2004.05551 | cvf | @InProceedings{Zhong_2021_CVPR,
author = {Zhong, Zhun and Zhu, Linchao and Luo, Zhiming and Li, Shaozi and Yang, Yi and Sebe, Nicu},
title = {OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in an Open World},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vi... | In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes. Existing methods typically first pre-train a model with labeled data, and then identify new classes in unlabeled data via unsupervised clustering. However, the labeled data that provide ess... |
Singh_Combining_Semantic_Guidance_and_Deep_Reinforcement_Learning_for_Generating_Human_CVPR_2021_paper | Combining Semantic Guidance and Deep Reinforcement Learning for Generating Human Level Paintings | [
"Jaskirat Singh",
"Liang Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Singh_Combining_Semantic_Guidance_and_Deep_Reinforcement_Learning_for_Generating_Human_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Singh_Combining_Semantic_Guidance_and_Deep_Reinforcement_Learning_for_Generating_Human_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Singh_Combining_Semantic_Guidance_CVPR_2021_supplemental.pdf | 2011.12589 | cvf | @InProceedings{Singh_2021_CVPR,
author = {Singh, Jaskirat and Zheng, Liang},
title = {Combining Semantic Guidance and Deep Reinforcement Learning for Generating Human Level Paintings},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint", in a manner similar to a human painter. However, the applicability of previous met... |
Takatani_Event-Based_Bispectral_Photometry_Using_Temporally_Modulated_Illumination_CVPR_2021_paper | Event-Based Bispectral Photometry Using Temporally Modulated Illumination | [
"Tsuyoshi Takatani",
"Yuzuha Ito",
"Ayaka Ebisu",
"Yinqiang Zheng",
"Takahito Aoto"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Takatani_Event-Based_Bispectral_Photometry_Using_Temporally_Modulated_Illumination_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Takatani_Event-Based_Bispectral_Photometry_Using_Temporally_Modulated_Illumination_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Takatani_Event-Based_Bispectral_Photometry_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Takatani_2021_CVPR,
author = {Takatani, Tsuyoshi and Ito, Yuzuha and Ebisu, Ayaka and Zheng, Yinqiang and Aoto, Takahito},
title = {Event-Based Bispectral Photometry Using Temporally Modulated Illumination},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | Analysis of bispectral difference plays a critical role in various applications that involve rays propagating in a light absorbing medium. In general, the bispectral difference is obtained by subtracting signals at two individual wavelengths captured by ordinary digital cameras, which tends to inherit the drawbacks of ... |
Fang_LiDAR-Aug_A_General_Rendering-Based_Augmentation_Framework_for_3D_Object_Detection_CVPR_2021_paper | LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection | [
"Jin Fang",
"Xinxin Zuo",
"Dingfu Zhou",
"Shengze Jin",
"Sen Wang",
"Liangjun Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_LiDAR-Aug_A_General_Rendering-Based_Augmentation_Framework_for_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_LiDAR-Aug_A_General_Rendering-Based_Augmentation_Framework_for_3D_Object_Detection_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Fang_2021_CVPR,
author = {Fang, Jin and Zuo, Xinxin and Zhou, Dingfu and Jin, Shengze and Wang, Sen and Zhang, Liangjun},
title = {LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Annotating the LiDAR point cloud is crucial for deep learning-based 3D object detection tasks. Due to expensive labeling costs, data augmentation has been taken as a necessary module and plays an important role in training the neural network. "Copy" and "paste" (i.e., GT-Aug) is the most commonly used data augmentation... |
Cheraghian_Semantic-Aware_Knowledge_Distillation_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper | Semantic-Aware Knowledge Distillation for Few-Shot Class-Incremental Learning | [
"Ali Cheraghian",
"Shafin Rahman",
"Pengfei Fang",
"Soumava Kumar Roy",
"Lars Petersson",
"Mehrtash Harandi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cheraghian_Semantic-Aware_Knowledge_Distillation_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheraghian_Semantic-Aware_Knowledge_Distillation_for_Few-Shot_Class-Incremental_Learning_CVPR_2021_paper.pdf | null | 2103.04059 | cvf | @InProceedings{Cheraghian_2021_CVPR,
author = {Cheraghian, Ali and Rahman, Shafin and Fang, Pengfei and Roy, Soumava Kumar and Petersson, Lars and Harandi, Mehrtash},
title = {Semantic-Aware Knowledge Distillation for Few-Shot Class-Incremental Learning},
booktitle = {Proceedings of the IEEE/CVF Conf... | Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner. Due to the limited number of examples for training, the techniques developed for standard incremental learning cannot be applied verbatim to FSCIL. In ... |
Dai_General_Instance_Distillation_for_Object_Detection_CVPR_2021_paper | General Instance Distillation for Object Detection | [
"Xing Dai",
"Zeren Jiang",
"Zhao Wu",
"Yiping Bao",
"Zhicheng Wang",
"Si Liu",
"Erjin Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_General_Instance_Distillation_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_General_Instance_Distillation_for_Object_Detection_CVPR_2021_paper.pdf | null | 2103.02340 | cvf | @InProceedings{Dai_2021_CVPR,
author = {Dai, Xing and Jiang, Zeren and Wu, Zhao and Bao, Yiping and Wang, Zhicheng and Liu, Si and Zhou, Erjin},
title = {General Instance Distillation for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | In recent years, knowledge distillation has been proved to be an effective solution for model compression. This approach can make lightweight student models acquire the knowledge extracted from cumbersome teacher models. However, previous distillation methods of detection have weak generalization for different detectio... |
Yang_Joint_Noise-Tolerant_Learning_and_Meta_Camera_Shift_Adaptation_for_Unsupervised_CVPR_2021_paper | Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification | [
"Fengxiang Yang",
"Zhun Zhong",
"Zhiming Luo",
"Yuanzheng Cai",
"Yaojin Lin",
"Shaozi Li",
"Nicu Sebe"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Joint_Noise-Tolerant_Learning_and_Meta_Camera_Shift_Adaptation_for_Unsupervised_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Joint_Noise-Tolerant_Learning_and_Meta_Camera_Shift_Adaptation_for_Unsupervised_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Joint_Noise-Tolerant_Learning_CVPR_2021_supplemental.pdf | 2103.04618 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Fengxiang and Zhong, Zhun and Luo, Zhiming and Cai, Yuanzheng and Lin, Yaojin and Li, Shaozi and Sebe, Nicu},
title = {Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification},
booktitle = {Proceedings of ... | This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data. One popular method is to obtain pseudo-label by clustering and use them to optimize the model. Although this kind of approach has shown promising accuracy, it is hampered by ... |
Zhai_Mutual_Graph_Learning_for_Camouflaged_Object_Detection_CVPR_2021_paper | Mutual Graph Learning for Camouflaged Object Detection | [
"Qiang Zhai",
"Xin Li",
"Fan Yang",
"Chenglizhao Chen",
"Hong Cheng",
"Deng-Ping Fan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhai_Mutual_Graph_Learning_for_Camouflaged_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhai_Mutual_Graph_Learning_for_Camouflaged_Object_Detection_CVPR_2021_paper.pdf | null | 2104.02613 | cvf | @InProceedings{Zhai_2021_CVPR,
author = {Zhai, Qiang and Li, Xin and Yang, Fan and Chen, Chenglizhao and Cheng, Hong and Fan, Deng-Ping},
title = {Mutual Graph Learning for Camouflaged Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between such foreground objects and background surroundings make the features extracted by deep model indistinguishable. To overcome this challenge, an... |
Shacht_Single_Pair_Cross-Modality_Super_Resolution_CVPR_2021_paper | Single Pair Cross-Modality Super Resolution | [
"Guy Shacht",
"Dov Danon",
"Sharon Fogel",
"Daniel Cohen-Or"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shacht_Single_Pair_Cross-Modality_Super_Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shacht_Single_Pair_Cross-Modality_Super_Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shacht_Single_Pair_Cross-Modality_CVPR_2021_supplemental.pdf | 2004.09965 | cvf | @InProceedings{Shacht_2021_CVPR,
author = {Shacht, Guy and Danon, Dov and Fogel, Sharon and Cohen-Or, Daniel},
title = {Single Pair Cross-Modality Super Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Non-visual imaging sensors are widely used in the industry for different purposes. Those sensors are more expensive than visual (RGB) sensors, and usually produce images with lower resolution. To this end, Cross-Modality Super-Resolution methods were introduced, where an RGB image of a high-resolution assists in increa... |
Zhou_Target-Aware_Object_Discovery_and_Association_for_Unsupervised_Video_Multi-Object_Segmentation_CVPR_2021_paper | Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation | [
"Tianfei Zhou",
"Jianwu Li",
"Xueyi Li",
"Ling Shao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Target-Aware_Object_Discovery_and_Association_for_Unsupervised_Video_Multi-Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Target-Aware_Object_Discovery_and_Association_for_Unsupervised_Video_Multi-Object_Segmentation_CVPR_2021_paper.pdf | null | 2104.04782 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Tianfei and Li, Jianwu and Li, Xueyi and Shao, Ling},
title = {Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | This paper addresses the task of unsupervised video multi-object segmentation. Current approaches follow a two-stage paradigm: 1) detect object proposals using pre-trained Mask R-CNN, and 2) conduct generic feature matching for temporal association using re-identification techniques. However, the generic features, wide... |
Huang_Cross-View_Regularization_for_Domain_Adaptive_Panoptic_Segmentation_CVPR_2021_paper | Cross-View Regularization for Domain Adaptive Panoptic Segmentation | [
"Jiaxing Huang",
"Dayan Guan",
"Aoran Xiao",
"Shijian Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Cross-View_Regularization_for_Domain_Adaptive_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Cross-View_Regularization_for_Domain_Adaptive_Panoptic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Cross-View_Regularization_for_CVPR_2021_supplemental.pdf | 2103.02584 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Jiaxing and Guan, Dayan and Xiao, Aoran and Lu, Shijian},
title = {Cross-View Regularization for Domain Adaptive Panoptic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Panoptic segmentation unifies semantic segmentation and instance segmentation which has been attracting increasing attention in recent years. On the other hand, most existing research was conducted under a supervised learning setup whereas domain adaptive panoptic segmentation which is critical in different tasks and a... |
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