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Kim_High-Quality_Stereo_Image_Restoration_From_Double_Refraction_CVPR_2021_paper | High-Quality Stereo Image Restoration From Double Refraction | [
"Hakyeong Kim",
"Andreas Meuleman",
"Daniel S. Jeon",
"Min H. Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_High-Quality_Stereo_Image_Restoration_From_Double_Refraction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_High-Quality_Stereo_Image_Restoration_From_Double_Refraction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_High-Quality_Stereo_Image_CVPR_2021_supplemental.zip | null | null | @InProceedings{Kim_2021_CVPR,
author = {Kim, Hakyeong and Meuleman, Andreas and Jeon, Daniel S. and Kim, Min H.},
title = {High-Quality Stereo Image Restoration From Double Refraction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Single-shot monocular birefractive stereo methods have been used for estimating sparse depth from double refraction over edges. They also obtain an ordinary-ray (o-ray) image concurrently or subsequently through additional post-processing of depth densification and deconvolution. However, when an extraordinary-ray (e-r... |
Harley_Track_Check_Repeat_An_EM_Approach_to_Unsupervised_Tracking_CVPR_2021_paper | Track, Check, Repeat: An EM Approach to Unsupervised Tracking | [
"Adam W. Harley",
"Yiming Zuo",
"Jing Wen",
"Ayush Mangal",
"Shubhankar Potdar",
"Ritwick Chaudhry",
"Katerina Fragkiadaki"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Harley_Track_Check_Repeat_An_EM_Approach_to_Unsupervised_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Harley_Track_Check_Repeat_An_EM_Approach_to_Unsupervised_Tracking_CVPR_2021_paper.pdf | null | 2104.03424 | cvf | @InProceedings{Harley_2021_CVPR,
author = {Harley, Adam W. and Zuo, Yiming and Wen, Jing and Mangal, Ayush and Potdar, Shubhankar and Chaudhry, Ritwick and Fragkiadaki, Katerina},
title = {Track, Check, Repeat: An EM Approach to Unsupervised Tracking},
booktitle = {Proceedings of the IEEE/CVF Confere... | We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos. The method begins with classic handcrafted techniques for segmenting objects using motion cues: we estimate optical flow and camera motion, and conservatively segment regions that appear to be moving independe... |
Yang_LayoutTransformer_Scene_Layout_Generation_With_Conceptual_and_Spatial_Diversity_CVPR_2021_paper | LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity | [
"Cheng-Fu Yang",
"Wan-Cyuan Fan",
"Fu-En Yang",
"Yu-Chiang Frank Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_LayoutTransformer_Scene_Layout_Generation_With_Conceptual_and_Spatial_Diversity_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_LayoutTransformer_Scene_Layout_Generation_With_Conceptual_and_Spatial_Diversity_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_LayoutTransformer_Scene_Layout_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yang_2021_CVPR,
author = {Yang, Cheng-Fu and Fan, Wan-Cyuan and Yang, Fu-En and Wang, Yu-Chiang Frank},
title = {LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | When translating text inputs into layouts or images, existing works typically require explicit descriptions of each object in a scene, including their spatial information or the associated relationships. To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout g... |
Tan_Practical_Wide-Angle_Portraits_Correction_With_Deep_Structured_Models_CVPR_2021_paper | Practical Wide-Angle Portraits Correction With Deep Structured Models | [
"Jing Tan",
"Shan Zhao",
"Pengfei Xiong",
"Jiangyu Liu",
"Haoqiang Fan",
"Shuaicheng Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Practical_Wide-Angle_Portraits_Correction_With_Deep_Structured_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_Practical_Wide-Angle_Portraits_Correction_With_Deep_Structured_Models_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_Practical_Wide-Angle_Portraits_CVPR_2021_supplemental.pdf | 2104.12464 | cvf | @InProceedings{Tan_2021_CVPR,
author = {Tan, Jing and Zhao, Shan and Xiong, Pengfei and Liu, Jiangyu and Fan, Haoqiang and Liu, Shuaicheng},
title = {Practical Wide-Angle Portraits Correction With Deep Structured Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | Wide-angle portraits often enjoy expanded views. However, they contain perspective distortions, especially noticeable when capturing group portrait photos, where the background is skewed and faces are stretched. This paper introduces the first deep learning based approach to remove such artifacts from freely-shot photo... |
Wandt_CanonPose_Self-Supervised_Monocular_3D_Human_Pose_Estimation_in_the_Wild_CVPR_2021_paper | CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild | [
"Bastian Wandt",
"Marco Rudolph",
"Petrissa Zell",
"Helge Rhodin",
"Bodo Rosenhahn"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wandt_CanonPose_Self-Supervised_Monocular_3D_Human_Pose_Estimation_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wandt_CanonPose_Self-Supervised_Monocular_3D_Human_Pose_Estimation_in_the_Wild_CVPR_2021_paper.pdf | null | 2011.14679 | cvf | @InProceedings{Wandt_2021_CVPR,
author = {Wandt, Bastian and Rudolph, Marco and Zell, Petrissa and Rhodin, Helge and Rosenhahn, Bodo},
title = {CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Human pose estimation from single images is a challenging problem in computer vision that requires large amounts of labeled training data to be solved accurately. Unfortunately, for many human activities (e.g. outdoor sports) such training data does not exist and is hard or even impossible to acquire with traditional m... |
Zeng_Pushing_It_Out_of_the_Way_Interactive_Visual_Navigation_CVPR_2021_paper | Pushing It Out of the Way: Interactive Visual Navigation | [
"Kuo-Hao Zeng",
"Luca Weihs",
"Ali Farhadi",
"Roozbeh Mottaghi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_Pushing_It_Out_of_the_Way_Interactive_Visual_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zeng_Pushing_It_Out_of_the_Way_Interactive_Visual_Navigation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zeng_Pushing_It_Out_CVPR_2021_supplemental.zip | 2104.14040 | cvf | @InProceedings{Zeng_2021_CVPR,
author = {Zeng, Kuo-Hao and Weihs, Luca and Farhadi, Ali and Mottaghi, Roozbeh},
title = {Pushing It Out of the Way: Interactive Visual Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | We have observed significant progress in visual navigation for embodied agents. A common assumption in studying visual navigation is that the environments are static; this is a limiting assumption. Intelligent navigation may involve interacting with the environment beyond just moving forward/backward and turning left/r... |
Wang_Improving_Weakly_Supervised_Visual_Grounding_by_Contrastive_Knowledge_Distillation_CVPR_2021_paper | Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation | [
"Liwei Wang",
"Jing Huang",
"Yin Li",
"Kun Xu",
"Zhengyuan Yang",
"Dong Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Improving_Weakly_Supervised_Visual_Grounding_by_Contrastive_Knowledge_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Improving_Weakly_Supervised_Visual_Grounding_by_Contrastive_Knowledge_Distillation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Improving_Weakly_Supervised_CVPR_2021_supplemental.pdf | 2007.01951 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Liwei and Huang, Jing and Li, Yin and Xu, Kun and Yang, Zhengyuan and Yu, Dong},
title = {Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | Weakly supervised phrase grounding aims at learning region-phrase correspondences using only image-sentence pairs. A major challenge thus lies in the missing links between image regions and sentence phrases during training. To address this challenge, we leverage a generic object detector at training time, and propose a... |
Wang_EvDistill_Asynchronous_Events_To_End-Task_Learning_via_Bidirectional_Reconstruction-Guided_Cross-Modal_CVPR_2021_paper | EvDistill: Asynchronous Events To End-Task Learning via Bidirectional Reconstruction-Guided Cross-Modal Knowledge Distillation | [
"Lin Wang",
"Yujeong Chae",
"Sung-Hoon Yoon",
"Tae-Kyun Kim",
"Kuk-Jin Yoon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_EvDistill_Asynchronous_Events_To_End-Task_Learning_via_Bidirectional_Reconstruction-Guided_Cross-Modal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_EvDistill_Asynchronous_Events_To_End-Task_Learning_via_Bidirectional_Reconstruction-Guided_Cross-Modal_CVPR_2021_paper.pdf | null | 2111.12341 | title_snapshot | @InProceedings{Wang_2021_CVPR,
author = {Wang, Lin and Chae, Yujeong and Yoon, Sung-Hoon and Kim, Tae-Kyun and Yoon, Kuk-Jin},
title = {EvDistill: Asynchronous Events To End-Task Learning via Bidirectional Reconstruction-Guided Cross-Modal Knowledge Distillation},
booktitle = {Proceedings of the IEEE... | Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur, showing advantages over the conventional cameras. A hurdle of training event-based models is the lack of large qualitative labeled data. Prior works learning end-tasks mostly rely on labe... |
Sun_LoFTR_Detector-Free_Local_Feature_Matching_With_Transformers_CVPR_2021_paper | LoFTR: Detector-Free Local Feature Matching With Transformers | [
"Jiaming Sun",
"Zehong Shen",
"Yuang Wang",
"Hujun Bao",
"Xiaowei Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_LoFTR_Detector-Free_Local_Feature_Matching_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_LoFTR_Detector-Free_Local_Feature_Matching_With_Transformers_CVPR_2021_paper.pdf | null | 2104.00680 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Jiaming and Shen, Zehong and Wang, Yuang and Bao, Hujun and Zhou, Xiaowei},
title = {LoFTR: Detector-Free Local Feature Matching With Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We present a novel method for local image feature matching. Instead of performing image feature detection, description, and matching sequentially, we propose to first establish pixel-wise dense matches at a coarse level and later refine the good matches at a fine level. In contrast to dense methods that use a cost volu... |
Wang_Combinatorial_Learning_of_Graph_Edit_Distance_via_Dynamic_Embedding_CVPR_2021_paper | Combinatorial Learning of Graph Edit Distance via Dynamic Embedding | [
"Runzhong Wang",
"Tianqi Zhang",
"Tianshu Yu",
"Junchi Yan",
"Xiaokang Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Combinatorial_Learning_of_Graph_Edit_Distance_via_Dynamic_Embedding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Combinatorial_Learning_of_Graph_Edit_Distance_via_Dynamic_Embedding_CVPR_2021_paper.pdf | null | 2011.15039 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Runzhong and Zhang, Tianqi and Yu, Tianshu and Yan, Junchi and Yang, Xiaokang},
title = {Combinatorial Learning of Graph Edit Distance via Dynamic Embedding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Traditional A* algorithm suffers scalability issues due to its exhaustive nature, whose search heuristics heavily rely on human prior knowledge... |
Long_Radar-Camera_Pixel_Depth_Association_for_Depth_Completion_CVPR_2021_paper | Radar-Camera Pixel Depth Association for Depth Completion | [
"Yunfei Long",
"Daniel Morris",
"Xiaoming Liu",
"Marcos Castro",
"Punarjay Chakravarty",
"Praveen Narayanan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Long_Radar-Camera_Pixel_Depth_Association_for_Depth_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Long_Radar-Camera_Pixel_Depth_Association_for_Depth_Completion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Long_Radar-Camera_Pixel_Depth_CVPR_2021_supplemental.zip | 2106.02778 | cvf | @InProceedings{Long_2021_CVPR,
author = {Long, Yunfei and Morris, Daniel and Liu, Xiaoming and Castro, Marcos and Chakravarty, Punarjay and Narayanan, Praveen},
title = {Radar-Camera Pixel Depth Association for Depth Completion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | While radar and video data can be readily fused at the detection level, fusing them at the pixel level is potentially more beneficial. This is also more challenging in part due to the sparsity of radar, but also because automotive radar beams are much wider than a typical pixel combined with a large baseline between ca... |
Wei_Improved_Image_Matting_via_Real-Time_User_Clicks_and_Uncertainty_Estimation_CVPR_2021_paper | Improved Image Matting via Real-Time User Clicks and Uncertainty Estimation | [
"Tianyi Wei",
"Dongdong Chen",
"Wenbo Zhou",
"Jing Liao",
"Hanqing Zhao",
"Weiming Zhang",
"Nenghai Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_Improved_Image_Matting_via_Real-Time_User_Clicks_and_Uncertainty_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_Improved_Image_Matting_via_Real-Time_User_Clicks_and_Uncertainty_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_Improved_Image_Matting_CVPR_2021_supplemental.pdf | 2012.08323 | cvf | @InProceedings{Wei_2021_CVPR,
author = {Wei, Tianyi and Chen, Dongdong and Zhou, Wenbo and Liao, Jing and Zhao, Hanqing and Zhang, Weiming and Yu, Nenghai},
title = {Improved Image Matting via Real-Time User Clicks and Uncertainty Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous, thus restricting the application of these methods. Recently, ... |
Cai_Revisiting_Superpixels_for_Active_Learning_in_Semantic_Segmentation_With_Realistic_CVPR_2021_paper | Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs | [
"Lile Cai",
"Xun Xu",
"Jun Hao Liew",
"Chuan Sheng Foo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Revisiting_Superpixels_for_Active_Learning_in_Semantic_Segmentation_With_Realistic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Revisiting_Superpixels_for_Active_Learning_in_Semantic_Segmentation_With_Realistic_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cai_Revisiting_Superpixels_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Cai_2021_CVPR,
author = {Cai, Lile and Xu, Xun and Liew, Jun Hao and Foo, Chuan Sheng},
title = {Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | State-of-the-art methods for semantic segmentation are based on deep neural networks that are known to be data-hungry. Region-based active learning has shown to be a promising method for reducing data annotation costs. A key design choice for region-based AL is whether to use regularly-shaped regions (e.g., rectangles)... |
Lacroix_IMODAL_Creating_Learnable_User-Defined_Deformation_Models_CVPR_2021_paper | IMODAL: Creating Learnable User-Defined Deformation Models | [
"Leander Lacroix",
"Benjamin Charlier",
"Alain Trouve",
"Barbara Gris"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lacroix_IMODAL_Creating_Learnable_User-Defined_Deformation_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lacroix_IMODAL_Creating_Learnable_User-Defined_Deformation_Models_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lacroix_IMODAL_Creating_Learnable_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Lacroix_2021_CVPR,
author = {Lacroix, Leander and Charlier, Benjamin and Trouve, Alain and Gris, Barbara},
title = {IMODAL: Creating Learnable User-Defined Deformation Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | A natural way to model the evolution of an object (growth of a leaf for instance) is to estimate a plausible deforming path between two observations. This interpolation process can generate deceiving results when the set of considered deformations is not relevant to the observed data. To overcome this issue, the framew... |
Sverrisson_Fast_End-to-End_Learning_on_Protein_Surfaces_CVPR_2021_paper | Fast End-to-End Learning on Protein Surfaces | [
"Freyr Sverrisson",
"Jean Feydy",
"Bruno E. Correia",
"Michael M. Bronstein"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sverrisson_Fast_End-to-End_Learning_on_Protein_Surfaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sverrisson_Fast_End-to-End_Learning_on_Protein_Surfaces_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sverrisson_Fast_End-to-End_Learning_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Sverrisson_2021_CVPR,
author = {Sverrisson, Freyr and Feydy, Jean and Correia, Bruno E. and Bronstein, Michael M.},
title = {Fast End-to-End Learning on Protein Surfaces},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Proteins' biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional sites, such as binding targets for potential drugs. Unfortunately... |
Urooj_Found_a_Reason_for_me_Weakly-supervised_Grounded_Visual_Question_Answering_CVPR_2021_paper | Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules | [
"Aisha Urooj",
"Hilde Kuehne",
"Kevin Duarte",
"Chuang Gan",
"Niels Lobo",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Urooj_Found_a_Reason_for_me_Weakly-supervised_Grounded_Visual_Question_Answering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Urooj_Found_a_Reason_for_me_Weakly-supervised_Grounded_Visual_Question_Answering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Urooj_Found_a_Reason_CVPR_2021_supplemental.pdf | 2105.04836 | cvf | @InProceedings{Urooj_2021_CVPR,
author = {Urooj, Aisha and Kuehne, Hilde and Duarte, Kevin and Gan, Chuang and Lobo, Niels and Shah, Mubarak},
title = {Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules},
booktitle = {Proceedings of the IEEE/CVF Conference on C... | The problem of grounding VQA tasks has seen an increased attention in the research community recently, with most attempts usually focusing on solving this task by using pretrained object detectors. However, pre-trained object detectors require bounding box annotations for detecting relevant objects in the vocabulary, w... |
Zhang_Person_Re-Identification_Using_Heterogeneous_Local_Graph_Attention_Networks_CVPR_2021_paper | Person Re-Identification Using Heterogeneous Local Graph Attention Networks | [
"Zhong Zhang",
"Haijia Zhang",
"Shuang Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Person_Re-Identification_Using_Heterogeneous_Local_Graph_Attention_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Person_Re-Identification_Using_Heterogeneous_Local_Graph_Attention_Networks_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Zhong and Zhang, Haijia and Liu, Shuang},
title = {Person Re-Identification Using Heterogeneous Local Graph Attention Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | Recently, some methods have focused on learning local relation among parts of pedestrian images for person re-identification (Re-ID), as it offers powerful representation capabilities. However, they only provide the intra-local relation among parts within single pedestrian image and ignore the inter-local relation amon... |
Feng_Recurrent_Multi-View_Alignment_Network_for_Unsupervised_Surface_Registration_CVPR_2021_paper | Recurrent Multi-View Alignment Network for Unsupervised Surface Registration | [
"Wanquan Feng",
"Juyong Zhang",
"Hongrui Cai",
"Haofei Xu",
"Junhui Hou",
"Hujun Bao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Recurrent_Multi-View_Alignment_Network_for_Unsupervised_Surface_Registration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Recurrent_Multi-View_Alignment_Network_for_Unsupervised_Surface_Registration_CVPR_2021_paper.pdf | null | 2011.12104 | cvf | @InProceedings{Feng_2021_CVPR,
author = {Feng, Wanquan and Zhang, Juyong and Cai, Hongrui and Xu, Haofei and Hou, Junhui and Bao, Hujun},
title = {Recurrent Multi-View Alignment Network for Unsupervised Surface Registration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data. In this paper, we resolve these two challenges simultaneously. First, we propose to represent the non-rigid transformation with a point-wise combination of several ri... |
Narayanan_Divide-and-Conquer_for_Lane-Aware_Diverse_Trajectory_Prediction_CVPR_2021_paper | Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction | [
"Sriram Narayanan",
"Ramin Moslemi",
"Francesco Pittaluga",
"Buyu Liu",
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Narayanan_Divide-and-Conquer_for_Lane-Aware_Diverse_Trajectory_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Narayanan_Divide-and-Conquer_for_Lane-Aware_Diverse_Trajectory_Prediction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Narayanan_Divide-and-Conquer_for_Lane-Aware_CVPR_2021_supplemental.pdf | 2104.08277 | cvf | @InProceedings{Narayanan_2021_CVPR,
author = {Narayanan, Sriram and Moslemi, Ramin and Pittaluga, Francesco and Liu, Buyu and Chandraker, Manmohan},
title = {Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using... |
Sengupta_Probabilistic_3D_Human_Shape_and_Pose_Estimation_From_Multiple_Unconstrained_CVPR_2021_paper | Probabilistic 3D Human Shape and Pose Estimation From Multiple Unconstrained Images in the Wild | [
"Akash Sengupta",
"Ignas Budvytis",
"Roberto Cipolla"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sengupta_Probabilistic_3D_Human_Shape_and_Pose_Estimation_From_Multiple_Unconstrained_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sengupta_Probabilistic_3D_Human_Shape_and_Pose_Estimation_From_Multiple_Unconstrained_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sengupta_Probabilistic_3D_Human_CVPR_2021_supplemental.pdf | 2103.10978 | cvf | @InProceedings{Sengupta_2021_CVPR,
author = {Sengupta, Akash and Budvytis, Ignas and Cipolla, Roberto},
title = {Probabilistic 3D Human Shape and Pose Estimation From Multiple Unconstrained Images in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | This paper addresses the problem of 3D human body shape and pose estimation from RGB images. Recent progress in this field has focused on single images, video or multi-view images as inputs. In contrast, we propose a new task: shape and pose estimation from a group of multiple images of a human subject, without constra... |
Liu_Weakly_Supervised_Instance_Segmentation_for_Videos_With_Temporal_Mask_Consistency_CVPR_2021_paper | Weakly Supervised Instance Segmentation for Videos With Temporal Mask Consistency | [
"Qing Liu",
"Vignesh Ramanathan",
"Dhruv Mahajan",
"Alan Yuille",
"Zhenheng Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Weakly_Supervised_Instance_Segmentation_for_Videos_With_Temporal_Mask_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Weakly_Supervised_Instance_Segmentation_for_Videos_With_Temporal_Mask_Consistency_CVPR_2021_paper.pdf | null | 2103.12886 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Qing and Ramanathan, Vignesh and Mahajan, Dhruv and Yuille, Alan and Yang, Zhenheng},
title = {Weakly Supervised Instance Segmentation for Videos With Temporal Mask Consistency},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions. We show that these issues can be bett... |
Hou_Exploring_Data-Efficient_3D_Scene_Understanding_With_Contrastive_Scene_Contexts_CVPR_2021_paper | Exploring Data-Efficient 3D Scene Understanding With Contrastive Scene Contexts | [
"Ji Hou",
"Benjamin Graham",
"Matthias Niessner",
"Saining Xie"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Exploring_Data-Efficient_3D_Scene_Understanding_With_Contrastive_Scene_Contexts_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Exploring_Data-Efficient_3D_Scene_Understanding_With_Contrastive_Scene_Contexts_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hou_Exploring_Data-Efficient_3D_CVPR_2021_supplemental.pdf | 2012.09165 | cvf | @InProceedings{Hou_2021_CVPR,
author = {Hou, Ji and Graham, Benjamin and Niessner, Matthias and Xie, Saining},
title = {Exploring Data-Efficient 3D Scene Understanding With Contrastive Scene Contexts},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited; even given sufficient data, acquiring 3D lab... |
Bhunia_MetaHTR_Towards_Writer-Adaptive_Handwritten_Text_Recognition_CVPR_2021_paper | MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition | [
"Ayan Kumar Bhunia",
"Shuvozit Ghose",
"Amandeep Kumar",
"Pinaki Nath Chowdhury",
"Aneeshan Sain",
"Yi-Zhe Song"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bhunia_MetaHTR_Towards_Writer-Adaptive_Handwritten_Text_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhunia_MetaHTR_Towards_Writer-Adaptive_Handwritten_Text_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhunia_MetaHTR_Towards_Writer-Adaptive_CVPR_2021_supplemental.pdf | 2104.01876 | cvf | @InProceedings{Bhunia_2021_CVPR,
author = {Bhunia, Ayan Kumar and Ghose, Shuvozit and Kumar, Amandeep and Chowdhury, Pinaki Nath and Sain, Aneeshan and Song, Yi-Zhe},
title = {MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | Handwritten Text Recognition (HTR) remains a challenging problem to date, largely due to the varying writing styles that exist amongst us. Prior works however generally operate with the assumption that there is a limited number of styles, most of which have already been captured by existing datasets. In this paper, we ... |
Zou_Learning_To_Reconstruct_High_Speed_and_High_Dynamic_Range_Videos_CVPR_2021_paper | Learning To Reconstruct High Speed and High Dynamic Range Videos From Events | [
"Yunhao Zou",
"Yinqiang Zheng",
"Tsuyoshi Takatani",
"Ying Fu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_Learning_To_Reconstruct_High_Speed_and_High_Dynamic_Range_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zou_Learning_To_Reconstruct_High_Speed_and_High_Dynamic_Range_Videos_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zou_2021_CVPR,
author = {Zou, Yunhao and Zheng, Yinqiang and Takatani, Tsuyoshi and Fu, Ying},
title = {Learning To Reconstruct High Speed and High Dynamic Range Videos From Events},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | Event cameras are novel sensors that capture the dynamics of a scene asynchronously. Such cameras record event streams with much shorter response latency than images captured by conventional cameras, and are also highly sensitive to intensity change, which is brought by the triggering mechanism of events. On the basis ... |
Zhang_PSRR-MaxpoolNMS_Pyramid_Shifted_MaxpoolNMS_With_Relationship_Recovery_CVPR_2021_paper | PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS With Relationship Recovery | [
"Tianyi Zhang",
"Jie Lin",
"Peng Hu",
"Bin Zhao",
"Mohamed M. Sabry Aly"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_PSRR-MaxpoolNMS_Pyramid_Shifted_MaxpoolNMS_With_Relationship_Recovery_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_PSRR-MaxpoolNMS_Pyramid_Shifted_MaxpoolNMS_With_Relationship_Recovery_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_PSRR-MaxpoolNMS_Pyramid_Shifted_CVPR_2021_supplemental.pdf | 2105.12990 | title_snapshot | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Tianyi and Lin, Jie and Hu, Peng and Zhao, Bin and Aly, Mohamed M. Sabry},
title = {PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS With Relationship Recovery},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Non-maximum Suppression (NMS) is an essential post-processing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional... |
Zhang_Flow-Guided_One-Shot_Talking_Face_Generation_With_a_High-Resolution_Audio-Visual_Dataset_CVPR_2021_paper | Flow-Guided One-Shot Talking Face Generation With a High-Resolution Audio-Visual Dataset | [
"Zhimeng Zhang",
"Lincheng Li",
"Yu Ding",
"Changjie Fan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Flow-Guided_One-Shot_Talking_Face_Generation_With_a_High-Resolution_Audio-Visual_Dataset_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Flow-Guided_One-Shot_Talking_Face_Generation_With_a_High-Resolution_Audio-Visual_Dataset_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Flow-Guided_One-Shot_Talking_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Zhimeng and Li, Lincheng and Ding, Yu and Fan, Changjie},
title = {Flow-Guided One-Shot Talking Face Generation With a High-Resolution Audio-Visual Dataset},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | One-shot talking face generation should synthesize high visual quality facial videos with reasonable animations of expression and head pose, and just utilize arbitrary driving audio and arbitrary single face image as the source. Current works fail to generate over 256 x 256 resolution realistic-looking videos due to th... |
Zhu_VIGOR_Cross-View_Image_Geo-Localization_Beyond_One-to-One_Retrieval_CVPR_2021_paper | VIGOR: Cross-View Image Geo-Localization Beyond One-to-One Retrieval | [
"Sijie Zhu",
"Taojiannan Yang",
"Chen Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_VIGOR_Cross-View_Image_Geo-Localization_Beyond_One-to-One_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_VIGOR_Cross-View_Image_Geo-Localization_Beyond_One-to-One_Retrieval_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_VIGOR_Cross-View_Image_CVPR_2021_supplemental.pdf | 2011.12172 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Sijie and Yang, Taojiannan and Chen, Chen},
title = {VIGOR: Cross-View Image Geo-Localization Beyond One-to-One Retrieval},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Cross-view image geo-localization aims to determine the locations of street-view query images by matching with GPS-tagged reference images from aerial view. Recent works have achieved surprisingly high retrieval accuracy on city-scale datasets. However, these results rely on the assumption that there exists a reference... |
Pumarola_D-NeRF_Neural_Radiance_Fields_for_Dynamic_Scenes_CVPR_2021_paper | D-NeRF: Neural Radiance Fields for Dynamic Scenes | [
"Albert Pumarola",
"Enric Corona",
"Gerard Pons-Moll",
"Francesc Moreno-Noguer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pumarola_D-NeRF_Neural_Radiance_Fields_for_Dynamic_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pumarola_D-NeRF_Neural_Radiance_Fields_for_Dynamic_Scenes_CVPR_2021_paper.pdf | null | 2011.13961 | title_snapshot | @InProceedings{Pumarola_2021_CVPR,
author = {Pumarola, Albert and Corona, Enric and Pons-Moll, Gerard and Moreno-Noguer, Francesc},
title = {D-NeRF: Neural Radiance Fields for Dynamic Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF), which trains a deep network to map 5D input coordinates (rep... |
Liu_Towards_Unified_Surgical_Skill_Assessment_CVPR_2021_paper | Towards Unified Surgical Skill Assessment | [
"Daochang Liu",
"Qiyue Li",
"Tingting Jiang",
"Yizhou Wang",
"Rulin Miao",
"Fei Shan",
"Ziyu Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Towards_Unified_Surgical_Skill_Assessment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Towards_Unified_Surgical_Skill_Assessment_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Towards_Unified_Surgical_CVPR_2021_supplemental.zip | 2106.01035 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Daochang and Li, Qiyue and Jiang, Tingting and Wang, Yizhou and Miao, Rulin and Shan, Fei and Li, Ziyu},
title = {Towards Unified Surgical Skill Assessment},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Surgical skills have a great influence on surgical safety and patients' well-being. Traditional assessment of surgical skills involves strenuous manual efforts, which lacks efficiency and repeatability. Therefore, we attempt to automatically predict how well the surgery is performed using the surgical video. In this pa... |
Varol_Read_and_Attend_Temporal_Localisation_in_Sign_Language_Videos_CVPR_2021_paper | Read and Attend: Temporal Localisation in Sign Language Videos | [
"Gul Varol",
"Liliane Momeni",
"Samuel Albanie",
"Triantafyllos Afouras",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Varol_Read_and_Attend_Temporal_Localisation_in_Sign_Language_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Varol_Read_and_Attend_Temporal_Localisation_in_Sign_Language_Videos_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Varol_Read_and_Attend_CVPR_2021_supplemental.pdf | 2103.16481 | cvf | @InProceedings{Varol_2021_CVPR,
author = {Varol, Gul and Momeni, Liliane and Albanie, Samuel and Afouras, Triantafyllos and Zisserman, Andrew},
title = {Read and Attend: Temporal Localisation in Sign Language Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | The objective of this work is to annotate sign instances across a broad vocabulary in continuous sign language. We train a Transformer model to ingest a continuous signing stream and output a sequence of written tokens on a large-scale collection of signing footage with weakly-aligned subtitles. We show that through th... |
Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_Modality_Difference_Reduction_Network_for_RGB-T_Semantic_CVPR_2021_paper | ABMDRNet: Adaptive-Weighted Bi-Directional Modality Difference Reduction Network for RGB-T Semantic Segmentation | [
"Qiang Zhang",
"Shenlu Zhao",
"Yongjiang Luo",
"Dingwen Zhang",
"Nianchang Huang",
"Jungong Han"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_Modality_Difference_Reduction_Network_for_RGB-T_Semantic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_Modality_Difference_Reduction_Network_for_RGB-T_Semantic_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Qiang and Zhao, Shenlu and Luo, Yongjiang and Zhang, Dingwen and Huang, Nianchang and Han, Jungong},
title = {ABMDRNet: Adaptive-Weighted Bi-Directional Modality Difference Reduction Network for RGB-T Semantic Segmentation},
booktitle = {Proceedings of... | Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images. Despite its importance, most existing RGB-T semantic segmentation models perform primitive fusion strategies, such as concatenation, element-wise summation and weig... |
Hamaguchi_Heterogeneous_Grid_Convolution_for_Adaptive_Efficient_and_Controllable_Computation_CVPR_2021_paper | Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation | [
"Ryuhei Hamaguchi",
"Yasutaka Furukawa",
"Masaki Onishi",
"Ken Sakurada"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hamaguchi_Heterogeneous_Grid_Convolution_for_Adaptive_Efficient_and_Controllable_Computation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hamaguchi_Heterogeneous_Grid_Convolution_for_Adaptive_Efficient_and_Controllable_Computation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hamaguchi_Heterogeneous_Grid_Convolution_CVPR_2021_supplemental.pdf | 2104.11176 | cvf | @InProceedings{Hamaguchi_2021_CVPR,
author = {Hamaguchi, Ryuhei and Furukawa, Yasutaka and Onishi, Masaki and Sakurada, Ken},
title = {Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | This paper proposes a novel heterogeneous grid convolution that builds a graph-based image representation by exploiting heterogeneity in the image content, enabling adaptive, efficient, and controllable computations in a convolutional architecture. More concretely, the approach builds a data-adaptive graph structure fr... |
Zhang_Learning_a_Facial_Expression_Embedding_Disentangled_From_Identity_CVPR_2021_paper | Learning a Facial Expression Embedding Disentangled From Identity | [
"Wei Zhang",
"Xianpeng Ji",
"Keyu Chen",
"Yu Ding",
"Changjie Fan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_a_Facial_Expression_Embedding_Disentangled_From_Identity_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_a_Facial_Expression_Embedding_Disentangled_From_Identity_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Learning_a_Facial_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Wei and Ji, Xianpeng and Chen, Keyu and Ding, Yu and Fan, Changjie},
title = {Learning a Facial Expression Embedding Disentangled From Identity},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | The facial expression analysis requires a compact and identity-ignored expression representation. In this paper, we model the expression as the deviation from the identity by a subtraction operation, extracting a continuous and identity-invariant expression embedding. We propose a Deviation Learning Network (DLN) with ... |
Zhang_Robust_Bayesian_Neural_Networks_by_Spectral_Expectation_Bound_Regularization_CVPR_2021_paper | Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization | [
"Jiaru Zhang",
"Yang Hua",
"Zhengui Xue",
"Tao Song",
"Chengyu Zheng",
"Ruhui Ma",
"Haibing Guan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Robust_Bayesian_Neural_Networks_by_Spectral_Expectation_Bound_Regularization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Robust_Bayesian_Neural_Networks_by_Spectral_Expectation_Bound_Regularization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Robust_Bayesian_Neural_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Jiaru and Hua, Yang and Xue, Zhengui and Song, Tao and Zheng, Chengyu and Ma, Ruhui and Guan, Haibing},
title = {Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization},
booktitle = {Proceedings of the IEEE/CVF Conference on Compu... | Bayesian neural networks have been widely used in many applications because of the distinctive probabilistic representation framework. Even though Bayesian neural networks have been found more robust to adversarial attacks compared with vanilla neural networks, their ability to deal with adversarial noises in practice ... |
Li_Learning_Probabilistic_Ordinal_Embeddings_for_Uncertainty-Aware_Regression_CVPR_2021_paper | Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression | [
"Wanhua Li",
"Xiaoke Huang",
"Jiwen Lu",
"Jianjiang Feng",
"Jie Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Learning_Probabilistic_Ordinal_Embeddings_for_Uncertainty-Aware_Regression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Learning_Probabilistic_Ordinal_Embeddings_for_Uncertainty-Aware_Regression_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Learning_Probabilistic_Ordinal_CVPR_2021_supplemental.pdf | 2103.13629 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Wanhua and Huang, Xiaoke and Lu, Jiwen and Feng, Jianjiang and Zhou, Jie},
title = {Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | Uncertainty is the only certainty there is. Modeling data uncertainty is essential for regression, especially in unconstrained settings. Traditionally the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. On the o... |
Hong_StyleMix_Separating_Content_and_Style_for_Enhanced_Data_Augmentation_CVPR_2021_paper | StyleMix: Separating Content and Style for Enhanced Data Augmentation | [
"Minui Hong",
"Jinwoo Choi",
"Gunhee Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_StyleMix_Separating_Content_and_Style_for_Enhanced_Data_Augmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_StyleMix_Separating_Content_and_Style_for_Enhanced_Data_Augmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_StyleMix_Separating_Content_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Hong_2021_CVPR,
author = {Hong, Minui and Choi, Jinwoo and Kim, Gunhee},
title = {StyleMix: Separating Content and Style for Enhanced Data Augmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | In spite of the great success of deep neural networks for many challenging classification tasks, the learned networks are vulnerable to overfitting and adversarial attacks. Recently, mixup based augmentation methods have been actively studied as one practical remedy for these drawbacks. However, these approaches do not... |
Zhuge_Kaleido-BERT_Vision-Language_Pre-Training_on_Fashion_Domain_CVPR_2021_paper | Kaleido-BERT: Vision-Language Pre-Training on Fashion Domain | [
"Mingchen Zhuge",
"Dehong Gao",
"Deng-Ping Fan",
"Linbo Jin",
"Ben Chen",
"Haoming Zhou",
"Minghui Qiu",
"Ling Shao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhuge_Kaleido-BERT_Vision-Language_Pre-Training_on_Fashion_Domain_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhuge_Kaleido-BERT_Vision-Language_Pre-Training_on_Fashion_Domain_CVPR_2021_paper.pdf | null | 2103.16110 | title_snapshot | @InProceedings{Zhuge_2021_CVPR,
author = {Zhuge, Mingchen and Gao, Dehong and Fan, Deng-Ping and Jin, Linbo and Chen, Ben and Zhou, Haoming and Qiu, Minghui and Shao, Ling},
title = {Kaleido-BERT: Vision-Language Pre-Training on Fashion Domain},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers. In contrast to random masking strategy of recent VL models, we design alignment guided masking to jointly focus more on image-text semanti... |
Song_Co-Grounding_Networks_With_Semantic_Attention_for_Referring_Expression_Comprehension_in_CVPR_2021_paper | Co-Grounding Networks With Semantic Attention for Referring Expression Comprehension in Videos | [
"Sijie Song",
"Xudong Lin",
"Jiaying Liu",
"Zongming Guo",
"Shih-Fu Chang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Co-Grounding_Networks_With_Semantic_Attention_for_Referring_Expression_Comprehension_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Co-Grounding_Networks_With_Semantic_Attention_for_Referring_Expression_Comprehension_in_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Co-Grounding_Networks_With_CVPR_2021_supplemental.pdf | 2103.12346 | cvf | @InProceedings{Song_2021_CVPR,
author = {Song, Sijie and Lin, Xudong and Liu, Jiaying and Guo, Zongming and Chang, Shih-Fu},
title = {Co-Grounding Networks With Semantic Attention for Referring Expression Comprehension in Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics. Unlike previous methods which solve the problem in multiple stages (i.e., tracking, proposal-based matching), we tackle the problem from a novel perspective, co-groun... |
Bahri_Binary_Graph_Neural_Networks_CVPR_2021_paper | Binary Graph Neural Networks | [
"Mehdi Bahri",
"Gaetan Bahl",
"Stefanos Zafeiriou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bahri_Binary_Graph_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bahri_Binary_Graph_Neural_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bahri_Binary_Graph_Neural_CVPR_2021_supplemental.pdf | 2012.15823 | cvf | @InProceedings{Bahri_2021_CVPR,
author = {Bahri, Mehdi and Bahl, Gaetan and Zafeiriou, Stefanos},
title = {Binary Graph Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. As they generalize the operations of classical CNNs on grids to arbitrary topologies, GNNs also bring much of the implementation challenges of their Euclidean counterparts. Model size, memory fo... |
Fayyaz_3D_CNNs_With_Adaptive_Temporal_Feature_Resolutions_CVPR_2021_paper | 3D CNNs With Adaptive Temporal Feature Resolutions | [
"Mohsen Fayyaz",
"Emad Bahrami",
"Ali Diba",
"Mehdi Noroozi",
"Ehsan Adeli",
"Luc Van Gool",
"Jurgen Gall"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fayyaz_3D_CNNs_With_Adaptive_Temporal_Feature_Resolutions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fayyaz_3D_CNNs_With_Adaptive_Temporal_Feature_Resolutions_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fayyaz_3D_CNNs_With_CVPR_2021_supplemental.pdf | 2011.08652 | cvf | @InProceedings{Fayyaz_2021_CVPR,
author = {Fayyaz, Mohsen and Bahrami, Emad and Diba, Ali and Noroozi, Mehdi and Adeli, Ehsan and Van Gool, Luc and Gall, Jurgen},
title = {3D CNNs With Adaptive Temporal Feature Resolutions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that ... |
Xian_Space-Time_Neural_Irradiance_Fields_for_Free-Viewpoint_Video_CVPR_2021_paper | Space-Time Neural Irradiance Fields for Free-Viewpoint Video | [
"Wenqi Xian",
"Jia-Bin Huang",
"Johannes Kopf",
"Changil Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xian_Space-Time_Neural_Irradiance_Fields_for_Free-Viewpoint_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xian_Space-Time_Neural_Irradiance_Fields_for_Free-Viewpoint_Video_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xian_Space-Time_Neural_Irradiance_CVPR_2021_supplemental.pdf | 2011.12950 | cvf | @InProceedings{Xian_2021_CVPR,
author = {Xian, Wenqi and Huang, Jia-Bin and Kopf, Johannes and Kim, Changil},
title = {Space-Time Neural Irradiance Fields for Free-Viewpoint Video},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video. Our learned representation enables free-viewpoint rendering of the input video. Our method builds upon recent advances in implicit representations. Learning a spatiotemporal irradiance field from a single vi... |
Gudovskiy_AutoDO_Robust_AutoAugment_for_Biased_Data_With_Label_Noise_via_CVPR_2021_paper | AutoDO: Robust AutoAugment for Biased Data With Label Noise via Scalable Probabilistic Implicit Differentiation | [
"Denis Gudovskiy",
"Luca Rigazio",
"Shun Ishizaka",
"Kazuki Kozuka",
"Sotaro Tsukizawa"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gudovskiy_AutoDO_Robust_AutoAugment_for_Biased_Data_With_Label_Noise_via_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gudovskiy_AutoDO_Robust_AutoAugment_for_Biased_Data_With_Label_Noise_via_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gudovskiy_AutoDO_Robust_AutoAugment_CVPR_2021_supplemental.pdf | 2103.05863 | cvf | @InProceedings{Gudovskiy_2021_CVPR,
author = {Gudovskiy, Denis and Rigazio, Luca and Ishizaka, Shun and Kozuka, Kazuki and Tsukizawa, Sotaro},
title = {AutoDO: Robust AutoAugment for Biased Data With Label Noise via Scalable Probabilistic Implicit Differentiation},
booktitle = {Proceedings of the IEE... | AutoAugment has sparked an interest in automated augmentation methods for deep learning models. These methods estimate image transformation policies for train data that improve generalization to test data. While recent papers evolved in the direction of decreasing policy search complexity, we show that those methods ar... |
Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper | Multiple Instance Active Learning for Object Detection | [
"Tianning Yuan",
"Fang Wan",
"Mengying Fu",
"Jianzhuang Liu",
"Songcen Xu",
"Xiangyang Ji",
"Qixiang Ye"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.pdf | null | 2104.02324 | cvf | @InProceedings{Yuan_2021_CVPR,
author = {Yuan, Tianning and Wan, Fang and Fu, Mengying and Liu, Jianzhuang and Xu, Songcen and Ji, Xiangyang and Ye, Qixiang},
title = {Multiple Instance Active Learning for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection. In this paper, we propose Multiple Instance Active Object Detection (MI-AOD), to select the most informative images for detector training by observing ins... |
Wu_Forecasting_Irreversible_Disease_via_Progression_Learning_CVPR_2021_paper | Forecasting Irreversible Disease via Progression Learning | [
"Botong Wu",
"Sijie Ren",
"Jing Li",
"Xinwei Sun",
"Shi-Ming Li",
"Yizhou Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Forecasting_Irreversible_Disease_via_Progression_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Forecasting_Irreversible_Disease_via_Progression_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Forecasting_Irreversible_Disease_CVPR_2021_supplemental.zip | 2012.11107 | cvf | @InProceedings{Wu_2021_CVPR,
author = {Wu, Botong and Ren, Sijie and Li, Jing and Sun, Xinwei and Li, Shi-Ming and Wang, Yizhou},
title = {Forecasting Irreversible Disease via Progression Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Forecasting Parapapillary atrophy (PPA), i.e., a symptom related to most irreversible eye diseases, provides an alarm for implementing an intervention to slow down the disease progression at early stage. A key question for this forecast is: how to fully utilize the historical data (e.g., retinal image) up to the curren... |
Wang_Understanding_the_Robustness_of_Skeleton-Based_Action_Recognition_Under_Adversarial_Attack_CVPR_2021_paper | Understanding the Robustness of Skeleton-Based Action Recognition Under Adversarial Attack | [
"He Wang",
"Feixiang He",
"Zhexi Peng",
"Tianjia Shao",
"Yong-Liang Yang",
"Kun Zhou",
"David Hogg"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Understanding_the_Robustness_of_Skeleton-Based_Action_Recognition_Under_Adversarial_Attack_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Understanding_the_Robustness_of_Skeleton-Based_Action_Recognition_Under_Adversarial_Attack_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Understanding_the_Robustness_CVPR_2021_supplemental.pdf | 2103.05347 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, He and He, Feixiang and Peng, Zhexi and Shao, Tianjia and Yang, Yong-Liang and Zhou, Kun and Hogg, David},
title = {Understanding the Robustness of Skeleton-Based Action Recognition Under Adversarial Attack},
booktitle = {Proceedings of the IEEE/CVF Conf... | Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we examine the robustness of state-of-the-art action recognizers against adversarial attack, which has been rarely investigated so far. To this end,... |
Li_Learning_Invariant_Representations_and_Risks_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper | Learning Invariant Representations and Risks for Semi-Supervised Domain Adaptation | [
"Bo Li",
"Yezhen Wang",
"Shanghang Zhang",
"Dongsheng Li",
"Kurt Keutzer",
"Trevor Darrell",
"Han Zhao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Learning_Invariant_Representations_and_Risks_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Learning_Invariant_Representations_and_Risks_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Learning_Invariant_Representations_CVPR_2021_supplemental.pdf | 2010.04647 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Bo and Wang, Yezhen and Zhang, Shanghang and Li, Dongsheng and Keutzer, Kurt and Darrell, Trevor and Zhao, Han},
title = {Learning Invariant Representations and Risks for Semi-Supervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference... | The success of supervised learning crucially hinges on the assumption that training data matches test data, which rarely holds in practice due to potential distribution shift. In light of this, most existing methods for unsupervised domain adaptation focus on achieving domain-invariant representations and small source ... |
Zhou_Cross-MPI_Cross-Scale_Stereo_for_Image_Super-Resolution_Using_Multiplane_Images_CVPR_2021_paper | Cross-MPI: Cross-Scale Stereo for Image Super-Resolution Using Multiplane Images | [
"Yuemei Zhou",
"Gaochang Wu",
"Ying Fu",
"Kun Li",
"Yebin Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Cross-MPI_Cross-Scale_Stereo_for_Image_Super-Resolution_Using_Multiplane_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Cross-MPI_Cross-Scale_Stereo_for_Image_Super-Resolution_Using_Multiplane_Images_CVPR_2021_paper.pdf | null | 2011.14631 | title_snapshot | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Yuemei and Wu, Gaochang and Fu, Ying and Li, Kun and Liu, Yebin},
title = {Cross-MPI: Cross-Scale Stereo for Image Super-Resolution Using Multiplane Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Various combinations of cameras enrich computational photography, among which reference-based superresolution (RefSR) plays a critical role in multiscale imaging systems. However, existing RefSR approaches fail to accomplish high-fidelity super-resolution under a large resolution gap, e.g., 8x upscaling, due to the low... |
Hernandez_Neural_Cellular_Automata_Manifold_CVPR_2021_paper | Neural Cellular Automata Manifold | [
"Alejandro Hernandez",
"Armand Vilalta",
"Francesc Moreno-Noguer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hernandez_Neural_Cellular_Automata_Manifold_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hernandez_Neural_Cellular_Automata_Manifold_CVPR_2021_paper.pdf | null | 2006.12155 | title_snapshot | @InProceedings{Hernandez_2021_CVPR,
author = {Hernandez, Alejandro and Vilalta, Armand and Moreno-Noguer, Francesc},
title = {Neural Cellular Automata Manifold},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Very recently, the Neural Cellular Automata (NCA) has been proposed to simulate the morphogenesis process with deep networks. NCA learns to grow an image starting from a fixed single pixel. In this work, we show that the neural network (NN) architecture of the NCA can be encapsulated in a larger NN. This allows us to p... |
Yang_Few-Shot_Transformation_of_Common_Actions_Into_Time_and_Space_CVPR_2021_paper | Few-Shot Transformation of Common Actions Into Time and Space | [
"Pengwan Yang",
"Pascal Mettes",
"Cees G. M. Snoek"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Few-Shot_Transformation_of_Common_Actions_Into_Time_and_Space_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Few-Shot_Transformation_of_Common_Actions_Into_Time_and_Space_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Few-Shot_Transformation_of_CVPR_2021_supplemental.pdf | 2104.02439 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Pengwan and Mettes, Pascal and Snoek, Cees G. M.},
title = {Few-Shot Transformation of Common Actions Into Time and Space},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | This paper introduces the task of few-shot common action localization in time and space. Given a few trimmed support videos containing the same but unknown action, we strive for spatio-temporal localization of that action in a long untrimmed query video. We do not require any class labels, interval bounds, or bounding ... |
Magri_MultiLink_Multi-Class_Structure_Recovery_via_Agglomerative_Clustering_and_Model_Selection_CVPR_2021_paper | MultiLink: Multi-Class Structure Recovery via Agglomerative Clustering and Model Selection | [
"Luca Magri",
"Filippo Leveni",
"Giacomo Boracchi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Magri_MultiLink_Multi-Class_Structure_Recovery_via_Agglomerative_Clustering_and_Model_Selection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Magri_MultiLink_Multi-Class_Structure_Recovery_via_Agglomerative_Clustering_and_Model_Selection_CVPR_2021_paper.pdf | null | 2505.10874 | title_snapshot | @InProceedings{Magri_2021_CVPR,
author = {Magri, Luca and Leveni, Filippo and Boracchi, Giacomo},
title = {MultiLink: Multi-Class Structure Recovery via Agglomerative Clustering and Model Selection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defined by a mixture of underlying parametric models (e.g. planes and cylinders, homographies and fundamental matrices), and we tackle the robust... |
Pham_Meta_Pseudo_Labels_CVPR_2021_paper | Meta Pseudo Labels | [
"Hieu Pham",
"Zihang Dai",
"Qizhe Xie",
"Quoc V. Le"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pham_Meta_Pseudo_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pham_Meta_Pseudo_Labels_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pham_Meta_Pseudo_Labels_CVPR_2021_supplemental.pdf | 2003.10580 | cvf | @InProceedings{Pham_2021_CVPR,
author = {Pham, Hieu and Dai, Zihang and Xie, Qizhe and Le, Quoc V.},
title = {Meta Pseudo Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {1... | We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the existing state-of-the-art. Like Pseudo Labels, Meta Pseudo Labels has a teacher network to generate pseudo labels on unlabeled data to teach a student ... |
Shi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper | SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction | [
"Liushuai Shi",
"Le Wang",
"Chengjiang Long",
"Sanping Zhou",
"Mo Zhou",
"Zhenxing Niu",
"Gang Hua"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_SGCN_Sparse_Graph_CVPR_2021_supplemental.pdf | 2104.01528 | cvf | @InProceedings{Shi_2021_CVPR,
author = {Shi, Liushuai and Wang, Le and Long, Chengjiang and Zhou, Sanping and Zhou, Mo and Niu, Zhenxing and Hua, Gang},
title = {SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very challenging due to complex interactions between pedestrians. However, previous works based on dense undirected interaction suffer from modeling superfluous interactions and neglect of trajectory motion tendency, and thus inevita... |
Lee_Depth_Completion_Using_Plane-Residual_Representation_CVPR_2021_paper | Depth Completion Using Plane-Residual Representation | [
"Byeong-Uk Lee",
"Kyunghyun Lee",
"In So Kweon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Depth_Completion_Using_Plane-Residual_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Depth_Completion_Using_Plane-Residual_Representation_CVPR_2021_paper.pdf | null | 2104.07350 | cvf | @InProceedings{Lee_2021_CVPR,
author = {Lee, Byeong-Uk and Lee, Kyunghyun and Kweon, In So},
title = {Depth Completion Using Plane-Residual Representation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = ... | The basic framework of depth completion is to predict a pixel-wise dense depth map using very sparse input data. In this paper, we try to solve this problem in a more effective way, by reformulating the regression-based depth estimation problem into a combination of depth plane classification and residual regression. O... |
Rui_Learning_an_Explicit_Weighting_Scheme_for_Adapting_Complex_HSI_Noise_CVPR_2021_paper | Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise | [
"Xiangyu Rui",
"Xiangyong Cao",
"Qi Xie",
"Zongsheng Yue",
"Qian Zhao",
"Deyu Meng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Rui_Learning_an_Explicit_Weighting_Scheme_for_Adapting_Complex_HSI_Noise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Rui_Learning_an_Explicit_Weighting_Scheme_for_Adapting_Complex_HSI_Noise_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rui_Learning_an_Explicit_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Rui_2021_CVPR,
author = {Rui, Xiangyu and Cao, Xiangyong and Xie, Qi and Yue, Zongsheng and Zhao, Qian and Meng, Deyu},
title = {Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | A general approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements. Such weighting scheme, however, largely depends on the prior understanding or subjective distribution assumption on HSI noises, making them eas... |
Paschalidou_Neural_Parts_Learning_Expressive_3D_Shape_Abstractions_With_Invertible_Neural_CVPR_2021_paper | Neural Parts: Learning Expressive 3D Shape Abstractions With Invertible Neural Networks | [
"Despoina Paschalidou",
"Angelos Katharopoulos",
"Andreas Geiger",
"Sanja Fidler"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Paschalidou_Neural_Parts_Learning_Expressive_3D_Shape_Abstractions_With_Invertible_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Paschalidou_Neural_Parts_Learning_Expressive_3D_Shape_Abstractions_With_Invertible_Neural_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Paschalidou_Neural_Parts_Learning_CVPR_2021_supplemental.pdf | 2103.10429 | cvf | @InProceedings{Paschalidou_2021_CVPR,
author = {Paschalidou, Despoina and Katharopoulos, Angelos and Geiger, Andreas and Fidler, Sanja},
title = {Neural Parts: Learning Expressive 3D Shape Abstractions With Invertible Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these methods fail t... |
Wei_PV-RAFT_Point-Voxel_Correlation_Fields_for_Scene_Flow_Estimation_of_Point_CVPR_2021_paper | PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds | [
"Yi Wei",
"Ziyi Wang",
"Yongming Rao",
"Jiwen Lu",
"Jie Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_PV-RAFT_Point-Voxel_Correlation_Fields_for_Scene_Flow_Estimation_of_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_PV-RAFT_Point-Voxel_Correlation_Fields_for_Scene_Flow_Estimation_of_Point_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_PV-RAFT_Point-Voxel_Correlation_CVPR_2021_supplemental.pdf | 2012.00987 | title_snapshot | @InProceedings{Wei_2021_CVPR,
author = {Wei, Yi and Wang, Ziyi and Rao, Yongming and Lu, Jiwen and Zhou, Jie},
title = {PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in the 3D space, where all-pairs correlations play important rol... |
Sun_Improving_the_Efficiency_and_Robustness_of_Deepfakes_Detection_Through_Precise_CVPR_2021_paper | Improving the Efficiency and Robustness of Deepfakes Detection Through Precise Geometric Features | [
"Zekun Sun",
"Yujie Han",
"Zeyu Hua",
"Na Ruan",
"Weijia Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Improving_the_Efficiency_and_Robustness_of_Deepfakes_Detection_Through_Precise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Improving_the_Efficiency_and_Robustness_of_Deepfakes_Detection_Through_Precise_CVPR_2021_paper.pdf | null | 2104.04480 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Zekun and Han, Yujie and Hua, Zeyu and Ruan, Na and Jia, Weijia},
title = {Improving the Efficiency and Robustness of Deepfakes Detection Through Precise Geometric Features},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Deepfakes is a branch of malicious techniques that transplant a target face to the original one in videos, resulting in serious problems such as infringement of copyright, confusion of information, or even public panic. Previous efforts for Deepfakes videos detection mainly focused on appearance features, which have a ... |
Zhang_Sketch2Model_View-Aware_3D_Modeling_From_Single_Free-Hand_Sketches_CVPR_2021_paper | Sketch2Model: View-Aware 3D Modeling From Single Free-Hand Sketches | [
"Song-Hai Zhang",
"Yuan-Chen Guo",
"Qing-Wen Gu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Sketch2Model_View-Aware_3D_Modeling_From_Single_Free-Hand_Sketches_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Sketch2Model_View-Aware_3D_Modeling_From_Single_Free-Hand_Sketches_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Sketch2Model_View-Aware_3D_CVPR_2021_supplemental.pdf | 2105.06663 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Song-Hai and Guo, Yuan-Chen and Gu, Qing-Wen},
title = {Sketch2Model: View-Aware 3D Modeling From Single Free-Hand Sketches},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | We investigate the problem of generating 3D meshes from single free-hand sketches, aiming at fast 3D modeling for novice users. It can be regarded as a single-view reconstruction problem, but with unique challenges, brought by the variation and conciseness of sketches. Ambiguities in poorly-drawn sketches could make it... |
Selvaraju_CASTing_Your_Model_Learning_To_Localize_Improves_Self-Supervised_Representations_CVPR_2021_paper | CASTing Your Model: Learning To Localize Improves Self-Supervised Representations | [
"Ramprasaath R. Selvaraju",
"Karan Desai",
"Justin Johnson",
"Nikhil Naik"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Selvaraju_CASTing_Your_Model_Learning_To_Localize_Improves_Self-Supervised_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Selvaraju_CASTing_Your_Model_Learning_To_Localize_Improves_Self-Supervised_Representations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Selvaraju_CASTing_Your_Model_CVPR_2021_supplemental.pdf | 2012.04630 | cvf | @InProceedings{Selvaraju_2021_CVPR,
author = {Selvaraju, Ramprasaath R. and Desai, Karan and Johnson, Justin and Naik, Nikhil},
title = {CASTing Your Model: Learning To Localize Improves Self-Supervised Representations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. Despite their success these methods have been primarily applied to unlabeled ImageNet images, and show marginal gains when trained on larger sets of uncurated images. We hypothesize that current SSL metho... |
Kopf_Robust_Consistent_Video_Depth_Estimation_CVPR_2021_paper | Robust Consistent Video Depth Estimation | [
"Johannes Kopf",
"Xuejian Rong",
"Jia-Bin Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kopf_Robust_Consistent_Video_Depth_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kopf_Robust_Consistent_Video_Depth_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kopf_Robust_Consistent_Video_CVPR_2021_supplemental.pdf | 2012.05901 | cvf | @InProceedings{Kopf_2021_CVPR,
author = {Kopf, Johannes and Rong, Xuejian and Huang, Jia-Bin},
title = {Robust Consistent Video Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video. We integrate a learning-based depth prior, in the form of a convolutional neural network trained for single-image depth estimation, with geometric optimization, to estimate a smooth camera trajectory as well as d... |
Kim_LaPred_Lane-Aware_Prediction_of_Multi-Modal_Future_Trajectories_of_Dynamic_Agents_CVPR_2021_paper | LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents | [
"ByeoungDo Kim",
"Seong Hyeon Park",
"Seokhwan Lee",
"Elbek Khoshimjonov",
"Dongsuk Kum",
"Junsoo Kim",
"Jeong Soo Kim",
"Jun Won Choi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_LaPred_Lane-Aware_Prediction_of_Multi-Modal_Future_Trajectories_of_Dynamic_Agents_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_LaPred_Lane-Aware_Prediction_of_Multi-Modal_Future_Trajectories_of_Dynamic_Agents_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_LaPred_Lane-Aware_Prediction_CVPR_2021_supplemental.pdf | 2104.00249 | cvf | @InProceedings{Kim_2021_CVPR,
author = {Kim, ByeoungDo and Park, Seong Hyeon and Lee, Seokhwan and Khoshimjonov, Elbek and Kum, Dongsuk and Kim, Junsoo and Kim, Jeong Soo and Choi, Jun Won},
title = {LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents},
booktitle = {Pro... | In this paper, we address the problem of predicting the future motion of a dynamic agent (called a target agent) given its current and past states as well as the information on its environment. It is paramount to develop a prediction model that can exploit the contextual information in both static and dynamic environme... |
Sun_NeuralRecon_Real-Time_Coherent_3D_Reconstruction_From_Monocular_Video_CVPR_2021_paper | NeuralRecon: Real-Time Coherent 3D Reconstruction From Monocular Video | [
"Jiaming Sun",
"Yiming Xie",
"Linghao Chen",
"Xiaowei Zhou",
"Hujun Bao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_NeuralRecon_Real-Time_Coherent_3D_Reconstruction_From_Monocular_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_NeuralRecon_Real-Time_Coherent_3D_Reconstruction_From_Monocular_Video_CVPR_2021_paper.pdf | null | 2104.00681 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Jiaming and Xie, Yiming and Chen, Linghao and Zhou, Xiaowei and Bao, Hujun},
title = {NeuralRecon: Real-Time Coherent 3D Reconstruction From Monocular Video},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we propose to directly reconstruct local surfaces represented as sparse TSDF volumes for each video ... |
Zhou_Pose-Controllable_Talking_Face_Generation_by_Implicitly_Modularized_Audio-Visual_Representation_CVPR_2021_paper | Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation | [
"Hang Zhou",
"Yasheng Sun",
"Wayne Wu",
"Chen Change Loy",
"Xiaogang Wang",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Pose-Controllable_Talking_Face_Generation_by_Implicitly_Modularized_Audio-Visual_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Pose-Controllable_Talking_Face_Generation_by_Implicitly_Modularized_Audio-Visual_Representation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Pose-Controllable_Talking_Face_CVPR_2021_supplemental.zip | 2104.11116 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Hang and Sun, Yasheng and Wu, Wayne and Loy, Chen Change and Wang, Xiaogang and Liu, Ziwei},
title = {Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation},
booktitle = {Proceedings of the IEEE/CVF Conference on... | While accurate lip synchronization has been achieved for arbitrary-subject audio-driven talking face generation, the problem of how to efficiently drive the head pose remains. Previous methods rely on pre-estimated structural information such as landmarks and 3D parameters, aiming to generate personalized rhythmic move... |
Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper | Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion | [
"Ho Kei Cheng",
"Yu-Wing Tai",
"Chi-Keung Tang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Modular_Interactive_Video_CVPR_2021_supplemental.pdf | 2103.07941 | cvf | @InProceedings{Cheng_2021_CVPR,
author = {Cheng, Ho Kei and Tai, Yu-Wing and Tang, Chi-Keung},
title = {Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation modu... |
Heitz_A_Sliced_Wasserstein_Loss_for_Neural_Texture_Synthesis_CVPR_2021_paper | A Sliced Wasserstein Loss for Neural Texture Synthesis | [
"Eric Heitz",
"Kenneth Vanhoey",
"Thomas Chambon",
"Laurent Belcour"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Heitz_A_Sliced_Wasserstein_Loss_for_Neural_Texture_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Heitz_A_Sliced_Wasserstein_Loss_for_Neural_Texture_Synthesis_CVPR_2021_paper.pdf | null | 2006.07229 | cvf | @InProceedings{Heitz_2021_CVPR,
author = {Heitz, Eric and Vanhoey, Kenneth and Chambon, Thomas and Belcour, Laurent},
title = {A Sliced Wasserstein Loss for Neural Texture Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We address the problem of computing a textural loss based on the statistics extracted from the feature activations of a convolutional neural network optimized for object recognition (e.g. VGG-19). The underlying mathematical problem is the measure of the distance between two distributions in feature space. The Gram-mat... |
Truong_Learning_Accurate_Dense_Correspondences_and_When_To_Trust_Them_CVPR_2021_paper | Learning Accurate Dense Correspondences and When To Trust Them | [
"Prune Truong",
"Martin Danelljan",
"Luc Van Gool",
"Radu Timofte"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Truong_Learning_Accurate_Dense_Correspondences_and_When_To_Trust_Them_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Truong_Learning_Accurate_Dense_Correspondences_and_When_To_Trust_Them_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Truong_Learning_Accurate_Dense_CVPR_2021_supplemental.pdf | 2101.01710 | cvf | @InProceedings{Truong_2021_CVPR,
author = {Truong, Prune and Danelljan, Martin and Van Gool, Luc and Timofte, Radu},
title = {Learning Accurate Dense Correspondences and When To Trust Them},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | Establishing dense correspondences between a pair of images is an important and general problem. However, dense flow estimation is often inaccurate in the case of large displacements or homogeneous regions. For most applications and down-stream tasks, such as pose estimation, image manipulation, or 3D reconstruction, i... |
Tu_Learning_Better_Visual_Dialog_Agents_With_Pretrained_Visual-Linguistic_Representation_CVPR_2021_paper | Learning Better Visual Dialog Agents With Pretrained Visual-Linguistic Representation | [
"Tao Tu",
"Qing Ping",
"Govindarajan Thattai",
"Gokhan Tur",
"Prem Natarajan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tu_Learning_Better_Visual_Dialog_Agents_With_Pretrained_Visual-Linguistic_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tu_Learning_Better_Visual_Dialog_Agents_With_Pretrained_Visual-Linguistic_Representation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tu_Learning_Better_Visual_CVPR_2021_supplemental.pdf | 2105.11541 | cvf | @InProceedings{Tu_2021_CVPR,
author = {Tu, Tao and Ping, Qing and Thattai, Govindarajan and Tur, Gokhan and Natarajan, Prem},
title = {Learning Better Visual Dialog Agents With Pretrained Visual-Linguistic Representation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | GuessWhat?! is a visual dialog guessing game which incorporates a Questioner agent that generates a sequence of questions, while an Oracle agent answers the respective questions about a target object in an image. Based on this dialog history between the Questioner and the Oracle, a Guesser agent makes a final guess of ... |
Lamba_Restoring_Extremely_Dark_Images_in_Real_Time_CVPR_2021_paper | Restoring Extremely Dark Images in Real Time | [
"Mohit Lamba",
"Kaushik Mitra"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lamba_Restoring_Extremely_Dark_Images_in_Real_Time_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lamba_Restoring_Extremely_Dark_Images_in_Real_Time_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lamba_Restoring_Extremely_Dark_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Lamba_2021_CVPR,
author = {Lamba, Mohit and Mitra, Kaushik},
title = {Restoring Extremely Dark Images in Real Time},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {... | A practical low-light enhancement solution must be computationally fast, memory-efficient, and achieve a visually appealing restoration. Most of the existing methods target restoration quality and thus compromise on speed and memory requirements, raising concerns about their real-world deployability. We propose a new d... |
Wang_Weakly-Supervised_Instance_Segmentation_via_Class-Agnostic_Learning_With_Salient_Images_CVPR_2021_paper | Weakly-Supervised Instance Segmentation via Class-Agnostic Learning With Salient Images | [
"Xinggang Wang",
"Jiapei Feng",
"Bin Hu",
"Qi Ding",
"Longjin Ran",
"Xiaoxin Chen",
"Wenyu Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Weakly-Supervised_Instance_Segmentation_via_Class-Agnostic_Learning_With_Salient_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Weakly-Supervised_Instance_Segmentation_via_Class-Agnostic_Learning_With_Salient_Images_CVPR_2021_paper.pdf | null | 2104.01526 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Xinggang and Feng, Jiapei and Hu, Bin and Ding, Qi and Ran, Longjin and Chen, Xiaoxin and Liu, Wenyu},
title = {Weakly-Supervised Instance Segmentation via Class-Agnostic Learning With Salient Images},
booktitle = {Proceedings of the IEEE/CVF Conference ... | Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based solution for weakly-supervised instance segmentation. The BoxCaseg model is jointly trained using bo... |
Monfort_Spoken_Moments_Learning_Joint_Audio-Visual_Representations_From_Video_Descriptions_CVPR_2021_paper | Spoken Moments: Learning Joint Audio-Visual Representations From Video Descriptions | [
"Mathew Monfort",
"SouYoung Jin",
"Alexander Liu",
"David Harwath",
"Rogerio Feris",
"James Glass",
"Aude Oliva"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Monfort_Spoken_Moments_Learning_Joint_Audio-Visual_Representations_From_Video_Descriptions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Monfort_Spoken_Moments_Learning_Joint_Audio-Visual_Representations_From_Video_Descriptions_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Monfort_Spoken_Moments_Learning_CVPR_2021_supplemental.pdf | 2105.04489 | cvf | @InProceedings{Monfort_2021_CVPR,
author = {Monfort, Mathew and Jin, SouYoung and Liu, Alexander and Harwath, David and Feris, Rogerio and Glass, James and Oliva, Aude},
title = {Spoken Moments: Learning Joint Audio-Visual Representations From Video Descriptions},
booktitle = {Proceedings of the IEEE... | When people observe events, they are able to abstract key information and build concise summaries of what is happening. These summaries include contextual and semantic information describing the important high-level details (what, where, who and how) of the observed event and exclude background information that is deem... |
Zhou_Image_Restoration_for_Under-Display_Camera_CVPR_2021_paper | Image Restoration for Under-Display Camera | [
"Yuqian Zhou",
"David Ren",
"Neil Emerton",
"Sehoon Lim",
"Timothy Large"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Image_Restoration_for_Under-Display_Camera_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Image_Restoration_for_Under-Display_Camera_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Image_Restoration_for_CVPR_2021_supplemental.pdf | 2003.04857 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Yuqian and Ren, David and Emerton, Neil and Lim, Sehoon and Large, Timothy},
title = {Image Restoration for Under-Display Camera},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | The new trend of full-screen devices encourages us to position a camera behind a screen. Removing the bezel and centralizing the camera under the screen brings larger display-to-body ratio and enhances eye contact in video chat, but also causes image degradation. In this paper, we focus on a newly-defined Under-Display... |
Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper | Unbiased Mean Teacher for Cross-Domain Object Detection | [
"Jinhong Deng",
"Wen Li",
"Yuhua Chen",
"Lixin Duan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper.pdf | null | 2003.00707 | cvf | @InProceedings{Deng_2021_CVPR,
author = {Deng, Jinhong and Li, Wen and Chen, Yuhua and Duan, Lixin},
title = {Unbiased Mean Teacher for Cross-Domain Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distinctive domains. In this paper, we propose a new Unbiased Mean Teacher (UMT) model for cross-domain object detection. We reveal that there often ... |
Duarte_How2Sign_A_Large-Scale_Multimodal_Dataset_for_Continuous_American_Sign_Language_CVPR_2021_paper | How2Sign: A Large-Scale Multimodal Dataset for Continuous American Sign Language | [
"Amanda Duarte",
"Shruti Palaskar",
"Lucas Ventura",
"Deepti Ghadiyaram",
"Kenneth DeHaan",
"Florian Metze",
"Jordi Torres",
"Xavier Giro-i-Nieto"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Duarte_How2Sign_A_Large-Scale_Multimodal_Dataset_for_Continuous_American_Sign_Language_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Duarte_How2Sign_A_Large-Scale_Multimodal_Dataset_for_Continuous_American_Sign_Language_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duarte_How2Sign_A_Large-Scale_CVPR_2021_supplemental.pdf | 2008.08143 | cvf | @InProceedings{Duarte_2021_CVPR,
author = {Duarte, Amanda and Palaskar, Shruti and Ventura, Lucas and Ghadiyaram, Deepti and DeHaan, Kenneth and Metze, Florian and Torres, Jordi and Giro-i-Nieto, Xavier},
title = {How2Sign: A Large-Scale Multimodal Dataset for Continuous American Sign Language},
book... | One of the factors that have hindered progress in the areas of sign language recognition, translation, and production is the absence of large annotated datasets. Towards this end, we introduce How2Sign, a multimodal and multiview continuous American Sign Language (ASL) dataset, consisting of a parallel corpus of more t... |
Chen_Indoor_Lighting_Estimation_Using_an_Event_Camera_CVPR_2021_paper | Indoor Lighting Estimation Using an Event Camera | [
"Zehao Chen",
"Qian Zheng",
"Peisong Niu",
"Huajin Tang",
"Gang Pan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Indoor_Lighting_Estimation_Using_an_Event_Camera_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Indoor_Lighting_Estimation_Using_an_Event_Camera_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Indoor_Lighting_Estimation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Zehao and Zheng, Qian and Niu, Peisong and Tang, Huajin and Pan, Gang},
title = {Indoor Lighting Estimation Using an Event Camera},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | Image-based methods for indoor lighting estimation suffer from the problem of intensity-distance ambiguity. This paper introduces a novel setup to help alleviate the ambiguity based on the event camera. We further demonstrate that estimating the distance of a light source becomes a well-posed problem under this setup, ... |
Chen_Shot_Contrastive_Self-Supervised_Learning_for_Scene_Boundary_Detection_CVPR_2021_paper | Shot Contrastive Self-Supervised Learning for Scene Boundary Detection | [
"Shixing Chen",
"Xiaohan Nie",
"David Fan",
"Dongqing Zhang",
"Vimal Bhat",
"Raffay Hamid"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Shot_Contrastive_Self-Supervised_Learning_for_Scene_Boundary_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Shot_Contrastive_Self-Supervised_Learning_for_Scene_Boundary_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Shot_Contrastive_Self-Supervised_CVPR_2021_supplemental.pdf | 2104.13537 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Shixing and Nie, Xiaohan and Fan, David and Zhang, Dongqing and Bhat, Vimal and Hamid, Raffay},
title = {Shot Contrastive Self-Supervised Learning for Scene Boundary Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Scenes play a crucial role in breaking the storyline of movies and TV episodes into semantically cohesive parts. However, given their complex temporal structure, finding scene boundaries can be a challenging task requiring large amounts of labeled training data. To address this challenge, we present a self-supervised s... |
Haurum_Sewer-ML_A_Multi-Label_Sewer_Defect_Classification_Dataset_and_Benchmark_CVPR_2021_paper | Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark | [
"Joakim Bruslund Haurum",
"Thomas B. Moeslund"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Haurum_Sewer-ML_A_Multi-Label_Sewer_Defect_Classification_Dataset_and_Benchmark_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Haurum_Sewer-ML_A_Multi-Label_Sewer_Defect_Classification_Dataset_and_Benchmark_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Haurum_Sewer-ML_A_Multi-Label_CVPR_2021_supplemental.pdf | 2103.10895 | title_snapshot | @InProceedings{Haurum_2021_CVPR,
author = {Haurum, Joakim Bruslund and Moeslund, Thomas B.},
title = {Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of thi... |
Yao_Joint-DetNAS_Upgrade_Your_Detector_With_NAS_Pruning_and_Dynamic_Distillation_CVPR_2021_paper | Joint-DetNAS: Upgrade Your Detector With NAS, Pruning and Dynamic Distillation | [
"Lewei Yao",
"Renjie Pi",
"Hang Xu",
"Wei Zhang",
"Zhenguo Li",
"Tong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Joint-DetNAS_Upgrade_Your_Detector_With_NAS_Pruning_and_Dynamic_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yao_Joint-DetNAS_Upgrade_Your_Detector_With_NAS_Pruning_and_Dynamic_Distillation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yao_Joint-DetNAS_Upgrade_Your_CVPR_2021_supplemental.pdf | 2105.12971 | title_snapshot | @InProceedings{Yao_2021_CVPR,
author = {Yao, Lewei and Pi, Renjie and Xu, Hang and Zhang, Wei and Li, Zhenguo and Zhang, Tong},
title = {Joint-DetNAS: Upgrade Your Detector With NAS, Pruning and Dynamic Distillation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead of naively pipelining these techniques, our Joint-DetNAS optimizes them jointly. The algorithm consists of two core processes: student morphi... |
Cheng_Back-Tracing_Representative_Points_for_Voting-Based_3D_Object_Detection_in_Point_CVPR_2021_paper | Back-Tracing Representative Points for Voting-Based 3D Object Detection in Point Clouds | [
"Bowen Cheng",
"Lu Sheng",
"Shaoshuai Shi",
"Ming Yang",
"Dong Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Back-Tracing_Representative_Points_for_Voting-Based_3D_Object_Detection_in_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Back-Tracing_Representative_Points_for_Voting-Based_3D_Object_Detection_in_Point_CVPR_2021_paper.pdf | null | 2104.06114 | cvf | @InProceedings{Cheng_2021_CVPR,
author = {Cheng, Bowen and Sheng, Lu and Shi, Shaoshuai and Yang, Ming and Xu, Dong},
title = {Back-Tracing Representative Points for Voting-Based 3D Object Detection in Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | 3D object detection in point clouds is a challenging vision task that benefits various applications for understanding the 3D visual world. Lots of recent research focuses on how to exploit end-to-end trainable Hough voting for generating object proposals. However, the current voting strategy can only receive partial vo... |
Liang_High-Resolution_Photorealistic_Image_Translation_in_Real-Time_A_Laplacian_Pyramid_Translation_CVPR_2021_paper | High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network | [
"Jie Liang",
"Hui Zeng",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_High-Resolution_Photorealistic_Image_Translation_in_Real-Time_A_Laplacian_Pyramid_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_High-Resolution_Photorealistic_Image_Translation_in_Real-Time_A_Laplacian_Pyramid_Translation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liang_High-Resolution_Photorealistic_Image_CVPR_2021_supplemental.pdf | 2105.09188 | title_snapshot | @InProceedings{Liang_2021_CVPR,
author = {Liang, Jie and Zeng, Hui and Zhang, Lei},
title = {High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | Existing image-to-image translation (I2IT) methods are either constrained to low-resolution images or long inference time due to their heavy computational burden on the convolution of high-resolution feature maps. In this paper, we focus on speeding-up the high-resolution photorealistic I2IT tasks based on closed-form ... |
Wang_End-to-End_Video_Instance_Segmentation_With_Transformers_CVPR_2021_paper | End-to-End Video Instance Segmentation With Transformers | [
"Yuqing Wang",
"Zhaoliang Xu",
"Xinlong Wang",
"Chunhua Shen",
"Baoshan Cheng",
"Hao Shen",
"Huaxia Xia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_End-to-End_Video_Instance_Segmentation_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_End-to-End_Video_Instance_Segmentation_With_Transformers_CVPR_2021_paper.pdf | null | 2011.14503 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Yuqing and Xu, Zhaoliang and Wang, Xinlong and Shen, Chunhua and Cheng, Baoshan and Shen, Hao and Xia, Huaxia},
title = {End-to-End Video Instance Segmentation With Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | Video instance segmentation (VIS) is the task that requires simultaneously classifying, segmenting and tracking object instances of interest in video. Recent methods typically develop sophisticated pipelines to tackle this task. Here, we propose a new video instance segmentation framework built upon Transformers, terme... |
Que_VoxelContext-Net_An_Octree_Based_Framework_for_Point_Cloud_Compression_CVPR_2021_paper | VoxelContext-Net: An Octree Based Framework for Point Cloud Compression | [
"Zizheng Que",
"Guo Lu",
"Dong Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Que_VoxelContext-Net_An_Octree_Based_Framework_for_Point_Cloud_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Que_VoxelContext-Net_An_Octree_Based_Framework_for_Point_Cloud_Compression_CVPR_2021_paper.pdf | null | 2105.02158 | title_snapshot | @InProceedings{Que_2021_CVPR,
author = {Que, Zizheng and Lu, Guo and Xu, Dong},
title = {VoxelContext-Net: An Octree Based Framework for Point Cloud Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | In this paper, we propose a two-stage deep learning framework called VoxelContext-Net for both static and dynamic point cloud compression. Taking advantages of both octree based methods and voxel based schemes, our approach employs the voxel context to compress the octree structured data. Specifically, we first extract... |
Zhu_A_Second-Order_Approach_to_Learning_With_Instance-Dependent_Label_Noise_CVPR_2021_paper | A Second-Order Approach to Learning With Instance-Dependent Label Noise | [
"Zhaowei Zhu",
"Tongliang Liu",
"Yang Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_A_Second-Order_Approach_to_Learning_With_Instance-Dependent_Label_Noise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_A_Second-Order_Approach_to_Learning_With_Instance-Dependent_Label_Noise_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_A_Second-Order_Approach_CVPR_2021_supplemental.pdf | 2012.11854 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Zhaowei and Liu, Tongliang and Liu, Yang},
title = {A Second-Order Approach to Learning With Instance-Dependent Label Noise},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | The presence of label noise often misleads the training of deep neural networks. Departing from the recent literature which largely assumes the label noise rate is only determined by the true label class, the errors in human-annotated labels are more likely to be dependent on the difficulty levels of tasks, resulting i... |
Ao_SpinNet_Learning_a_General_Surface_Descriptor_for_3D_Point_Cloud_CVPR_2021_paper | SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration | [
"Sheng Ao",
"Qingyong Hu",
"Bo Yang",
"Andrew Markham",
"Yulan Guo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ao_SpinNet_Learning_a_General_Surface_Descriptor_for_3D_Point_Cloud_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ao_SpinNet_Learning_a_General_Surface_Descriptor_for_3D_Point_Cloud_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ao_SpinNet_Learning_a_CVPR_2021_supplemental.pdf | 2011.12149 | cvf | @InProceedings{Ao_2021_CVPR,
author = {Ao, Sheng and Hu, Qingyong and Yang, Bo and Markham, Andrew and Guo, Yulan},
title = {SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this pape... |
Huang_FSDR_Frequency_Space_Domain_Randomization_for_Domain_Generalization_CVPR_2021_paper | FSDR: Frequency Space Domain Randomization for Domain Generalization | [
"Jiaxing Huang",
"Dayan Guan",
"Aoran Xiao",
"Shijian Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_FSDR_Frequency_Space_Domain_Randomization_for_Domain_Generalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_FSDR_Frequency_Space_Domain_Randomization_for_Domain_Generalization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_FSDR_Frequency_Space_CVPR_2021_supplemental.pdf | 2103.02370 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Jiaxing and Guan, Dayan and Xiao, Aoran and Lu, Shijian},
title = {FSDR: Frequency Space Domain Randomization for Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Domain generalization aims to learn a generalizable model from a `known' source domain for various `unknown' target domains. It has been studied widely by domain randomization that transfers source images to different styles in spatial space for learning domain-agnostic features. However, most existing randomization me... |
Chen_DualAST_Dual_Style-Learning_Networks_for_Artistic_Style_Transfer_CVPR_2021_paper | DualAST: Dual Style-Learning Networks for Artistic Style Transfer | [
"Haibo Chen",
"Lei Zhao",
"Zhizhong Wang",
"Huiming Zhang",
"Zhiwen Zuo",
"Ailin Li",
"Wei Xing",
"Dongming Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_DualAST_Dual_Style-Learning_Networks_for_Artistic_Style_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_DualAST_Dual_Style-Learning_Networks_for_Artistic_Style_Transfer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_DualAST_Dual_Style-Learning_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Haibo and Zhao, Lei and Wang, Zhizhong and Zhang, Huiming and Zuo, Zhiwen and Li, Ailin and Xing, Wei and Lu, Dongming},
title = {DualAST: Dual Style-Learning Networks for Artistic Style Transfer},
booktitle = {Proceedings of the IEEE/CVF Conference on C... | Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles. Existing methods learn styles from either a single style example or a collection of artworks. Accordingly, the stylization results are either inferior in visual quality or limited in style control... |
Dai_Learning_a_Proposal_Classifier_for_Multiple_Object_Tracking_CVPR_2021_paper | Learning a Proposal Classifier for Multiple Object Tracking | [
"Peng Dai",
"Renliang Weng",
"Wongun Choi",
"Changshui Zhang",
"Zhangping He",
"Wei Ding"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_Learning_a_Proposal_Classifier_for_Multiple_Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Learning_a_Proposal_Classifier_for_Multiple_Object_Tracking_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_Learning_a_Proposal_CVPR_2021_supplemental.pdf | 2103.07889 | cvf | @InProceedings{Dai_2021_CVPR,
author = {Dai, Peng and Weng, Renliang and Choi, Wongun and Zhang, Changshui and He, Zhangping and Ding, Wei},
title = {Learning a Proposal Classifier for Multiple Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-association problem in an end-to-end fashion. In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposa... |
Zhao_Multi-Attentional_Deepfake_Detection_CVPR_2021_paper | Multi-Attentional Deepfake Detection | [
"Hanqing Zhao",
"Wenbo Zhou",
"Dongdong Chen",
"Tianyi Wei",
"Weiming Zhang",
"Nenghai Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Multi-Attentional_Deepfake_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Multi-Attentional_Deepfake_Detection_CVPR_2021_paper.pdf | null | 2103.02406 | cvf | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Hanqing and Zhou, Wenbo and Chen, Dongdong and Wei, Tianyi and Zhang, Weiming and Yu, Nenghai},
title = {Multi-Attentional Deepfake Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | Face forgery by deepfake is widely spread over the internet and has raised severe societal concerns. Recently, how to detect such forgery contents has become a hot research topic and many deepfake detection methods have been proposed. Most of them model deepfake detection as a vanilla binary classification problem, i.e... |
Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_Line_Description_and_Detection_CVPR_2021_paper | SOLD2: Self-Supervised Occlusion-Aware Line Description and Detection | [
"Remi Pautrat",
"Juan-Ting Lin",
"Viktor Larsson",
"Martin R. Oswald",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_Line_Description_and_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_Line_Description_and_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_CVPR_2021_supplemental.pdf | 2104.03362 | cvf | @InProceedings{Pautrat_2021_CVPR,
author = {Pautrat, Remi and Lin, Juan-Ting and Larsson, Viktor and Oswald, Martin R. and Pollefeys, Marc},
title = {SOLD2: Self-Supervised Occlusion-Aware Line Description and Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | Compared to feature point detection and description, detecting and matching line segments offer additional challenges. Yet, line features represent a promising complement to points for multi-view tasks. Lines are indeed well-defined by the image gradient, frequently appear even in poorly textured areas and offer robust... |
Choi_Shared_Cross-Modal_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2021_paper | Shared Cross-Modal Trajectory Prediction for Autonomous Driving | [
"Chiho Choi",
"Joon Hee Choi",
"Jiachen Li",
"Srikanth Malla"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Shared_Cross-Modal_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_Shared_Cross-Modal_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_Shared_Cross-Modal_Trajectory_CVPR_2021_supplemental.zip | 2004.00202 | title_snapshot | @InProceedings{Choi_2021_CVPR,
author = {Choi, Chiho and Choi, Joon Hee and Li, Jiachen and Malla, Srikanth},
title = {Shared Cross-Modal Trajectory Prediction for Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Predicting future trajectories of traffic agents in highly interactive environments is an essential and challenging problem for the safe operation of autonomous driving systems. On the basis of the fact that self-driving vehicles are equipped with various types of sensors (e.g., LiDAR scanner, RGB camera, radar, etc.),... |
Wen_Cycle4Completion_Unpaired_Point_Cloud_Completion_Using_Cycle_Transformation_With_Missing_CVPR_2021_paper | Cycle4Completion: Unpaired Point Cloud Completion Using Cycle Transformation With Missing Region Coding | [
"Xin Wen",
"Zhizhong Han",
"Yan-Pei Cao",
"Pengfei Wan",
"Wen Zheng",
"Yu-Shen Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Cycle4Completion_Unpaired_Point_Cloud_Completion_Using_Cycle_Transformation_With_Missing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Cycle4Completion_Unpaired_Point_Cloud_Completion_Using_Cycle_Transformation_With_Missing_CVPR_2021_paper.pdf | null | 2103.07838 | cvf | @InProceedings{Wen_2021_CVPR,
author = {Wen, Xin and Han, Zhizhong and Cao, Yan-Pei and Wan, Pengfei and Zheng, Wen and Liu, Yu-Shen},
title = {Cycle4Completion: Unpaired Point Cloud Completion Using Cycle Transformation With Missing Region Coding},
booktitle = {Proceedings of the IEEE/CVF Conference... | In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired completion methods merely focus on the learning of geometric correspondence from incomplete shapes to complete shapes, and ignore the learning i... |
Lu_CGA-Net_Category_Guided_Aggregation_for_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper | CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation | [
"Tao Lu",
"Limin Wang",
"Gangshan Wu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_CGA-Net_Category_Guided_Aggregation_for_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_CGA-Net_Category_Guided_Aggregation_for_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Lu_2021_CVPR,
author = {Lu, Tao and Wang, Limin and Wu, Gangshan},
title = {CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | Previous point cloud semantic segmentation networks use the same process to aggregate features from neighbors of the same category and different categories. However, the joint area between two objects usually only occupies a small percentage in the whole scene. Thus the networks are well-trained for aggregating feature... |
Douillard_PLOP_Learning_Without_Forgetting_for_Continual_Semantic_Segmentation_CVPR_2021_paper | PLOP: Learning Without Forgetting for Continual Semantic Segmentation | [
"Arthur Douillard",
"Yifu Chen",
"Arnaud Dapogny",
"Matthieu Cord"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Douillard_PLOP_Learning_Without_Forgetting_for_Continual_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Douillard_PLOP_Learning_Without_Forgetting_for_Continual_Semantic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Douillard_PLOP_Learning_Without_CVPR_2021_supplemental.pdf | 2011.11390 | cvf | @InProceedings{Douillard_2021_CVPR,
author = {Douillard, Arthur and Chen, Yifu and Dapogny, Arnaud and Cord, Matthieu},
title = {PLOP: Learning Without Forgetting for Continual Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segmentation (CSS) is an emerging trend that consists in updating an old model by sequentially adding new c... |
Manandhar_Magic_Layouts_Structural_Prior_for_Component_Detection_in_User_Interface_CVPR_2021_paper | Magic Layouts: Structural Prior for Component Detection in User Interface Designs | [
"Dipu Manandhar",
"Hailin Jin",
"John Collomosse"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Manandhar_Magic_Layouts_Structural_Prior_for_Component_Detection_in_User_Interface_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Manandhar_Magic_Layouts_Structural_Prior_for_Component_Detection_in_User_Interface_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Manandhar_Magic_Layouts_Structural_CVPR_2021_supplemental.zip | 2106.07615 | cvf | @InProceedings{Manandhar_2021_CVPR,
author = {Manandhar, Dipu and Jin, Hailin and Collomosse, John},
title = {Magic Layouts: Structural Prior for Component Detection in User Interface Designs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We present Magic Layouts; a method for parsing screenshots or hand-drawn sketches of user interface (UI) layouts. Our core contribution is to extend existing detectors to exploit a learned structural prior for UI designs, enabling robust detection of UI components; buttons, text boxes and similar. Specifically we learn... |
Wei_MetaAlign_Coordinating_Domain_Alignment_and_Classification_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper | MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation | [
"Guoqiang Wei",
"Cuiling Lan",
"Wenjun Zeng",
"Zhibo Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_MetaAlign_Coordinating_Domain_Alignment_and_Classification_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_MetaAlign_Coordinating_Domain_Alignment_and_Classification_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_MetaAlign_Coordinating_Domain_CVPR_2021_supplemental.pdf | 2103.13575 | cvf | @InProceedings{Wei_2021_CVPR,
author = {Wei, Guoqiang and Lan, Cuiling and Zeng, Wenjun and Chen, Zhibo},
title = {MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning or by explicitly aligning their statistics. However, the optimization objective of such domain alignment is generally not coordinated with t... |
Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper | Neural Prototype Trees for Interpretable Fine-Grained Image Recognition | [
"Meike Nauta",
"Ron van Bree",
"Christin Seifert"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nauta_Neural_Prototype_Trees_CVPR_2021_supplemental.zip | 2012.02046 | cvf | @InProceedings{Nauta_2021_CVPR,
author = {Nauta, Meike and van Bree, Ron and Seifert, Christin},
title = {Neural Prototype Trees for Interpretable Fine-Grained Image Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate such models. We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image r... |
Bo_Hardness_Sampling_for_Self-Training_Based_Transductive_Zero-Shot_Learning_CVPR_2021_paper | Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning | [
"Liu Bo",
"Qiulei Dong",
"Zhanyi Hu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bo_Hardness_Sampling_for_Self-Training_Based_Transductive_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bo_Hardness_Sampling_for_Self-Training_Based_Transductive_Zero-Shot_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bo_Hardness_Sampling_for_CVPR_2021_supplemental.pdf | 2106.00264 | cvf | @InProceedings{Bo_2021_CVPR,
author = {Bo, Liu and Dong, Qiulei and Hu, Zhanyi},
title = {Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Transductive zero-shot learning (T-ZSL) which could alleviate the domain shift problem in existing ZSL works, has received much attention recently. However, an open problem in T-ZSL: how to effectively make use of unseen-class samples for training, still remains. Addressing this problem, we first empirically analyze th... |
Li_Hilbert_Sinkhorn_Divergence_for_Optimal_Transport_CVPR_2021_paper | Hilbert Sinkhorn Divergence for Optimal Transport | [
"Qian Li",
"Zhichao Wang",
"Gang Li",
"Jun Pang",
"Guandong Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Hilbert_Sinkhorn_Divergence_for_Optimal_Transport_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Hilbert_Sinkhorn_Divergence_for_Optimal_Transport_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Hilbert_Sinkhorn_Divergence_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_CVPR,
author = {Li, Qian and Wang, Zhichao and Li, Gang and Pang, Jun and Xu, Guandong},
title = {Hilbert Sinkhorn Divergence for Optimal Transport},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Sinkhorn divergence has become a very popular metric to compare probability distributions in optimal transport. However, most works resort to Sinkhorn divergence in Euclidean space, which greatly blocks their applications in complex data with nonlinear structure. It is therefore of theoretical demand to empower Sinkhor... |
Van_Etten_The_Multi-Temporal_Urban_Development_SpaceNet_Dataset_CVPR_2021_paper | The Multi-Temporal Urban Development SpaceNet Dataset | [
"Adam Van Etten",
"Daniel Hogan",
"Jesus Martinez Manso",
"Jacob Shermeyer",
"Nicholas Weir",
"Ryan Lewis"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Van_Etten_The_Multi-Temporal_Urban_Development_SpaceNet_Dataset_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Van_Etten_The_Multi-Temporal_Urban_Development_SpaceNet_Dataset_CVPR_2021_paper.pdf | null | 2102.04420 | title_snapshot | @InProceedings{Van_Etten_2021_CVPR,
author = {Van Etten, Adam and Hogan, Daniel and Manso, Jesus Martinez and Shermeyer, Jacob and Weir, Nicholas and Lewis, Ryan},
title = {The Multi-Temporal Urban Development SpaceNet Dataset},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 231 United Nations Sustainable Development Goals Indicators, but the World Bank estimates that ov... |
Dai_FBNetV3_Joint_Architecture-Recipe_Search_Using_Predictor_Pretraining_CVPR_2021_paper | FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining | [
"Xiaoliang Dai",
"Alvin Wan",
"Peizhao Zhang",
"Bichen Wu",
"Zijian He",
"Zhen Wei",
"Kan Chen",
"Yuandong Tian",
"Matthew Yu",
"Peter Vajda",
"Joseph E. Gonzalez"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_FBNetV3_Joint_Architecture-Recipe_Search_Using_Predictor_Pretraining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_FBNetV3_Joint_Architecture-Recipe_Search_Using_Predictor_Pretraining_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_FBNetV3_Joint_Architecture-Recipe_CVPR_2021_supplemental.pdf | 2006.02049 | cvf | @InProceedings{Dai_2021_CVPR,
author = {Dai, Xiaoliang and Wan, Alvin and Zhang, Peizhao and Wu, Bichen and He, Zijian and Wei, Zhen and Chen, Kan and Tian, Yuandong and Yu, Matthew and Vajda, Peter and Gonzalez, Joseph E.},
title = {FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining},... | Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. However, previous NAS methods search for architectures under one set of training hyper-parameters (i.e., a training recipe), overlooking superior architecture-recipe combinations. To addre... |
Guo_Intrinsic_Image_Harmonization_CVPR_2021_paper | Intrinsic Image Harmonization | [
"Zonghui Guo",
"Haiyong Zheng",
"Yufeng Jiang",
"Zhaorui Gu",
"Bing Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Intrinsic_Image_Harmonization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Intrinsic_Image_Harmonization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guo_Intrinsic_Image_Harmonization_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Guo_2021_CVPR,
author = {Guo, Zonghui and Zheng, Haiyong and Jiang, Yufeng and Gu, Zhaorui and Zheng, Bing},
title = {Intrinsic Image Harmonization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year... | Compositing an image usually inevitably suffers from inharmony problem that is mainly caused by incompatibility of foreground and background from two different images with distinct surfaces and lights, corresponding to material-dependent and light-dependent characteristics, namely, reflectance and illumination intrinsi... |
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