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Cheng_Light_Field_Super-Resolution_With_Zero-Shot_Learning_CVPR_2021_paper | Light Field Super-Resolution With Zero-Shot Learning | [
"Zhen Cheng",
"Zhiwei Xiong",
"Chang Chen",
"Dong Liu",
"Zheng-Jun Zha"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Light_Field_Super-Resolution_With_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Light_Field_Super-Resolution_With_Zero-Shot_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Light_Field_Super-Resolution_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Cheng_2021_CVPR,
author = {Cheng, Zhen and Xiong, Zhiwei and Chen, Chang and Liu, Dong and Zha, Zheng-Jun},
title = {Light Field Super-Resolution With Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Deep learning provides a new avenue for light field super-resolution (SR). However, the domain gap caused by drastically different light field acquisition conditions poses a main obstacle in practice. To fill this gap, we propose a zero-shot learning framework for light field SR, which learns a mapping to super-resolve... |
Li_Spherical_Confidence_Learning_for_Face_Recognition_CVPR_2021_paper | Spherical Confidence Learning for Face Recognition | [
"Shen Li",
"Jianqing Xu",
"Xiaqing Xu",
"Pengcheng Shen",
"Shaoxin Li",
"Bryan Hooi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Spherical_Confidence_Learning_for_Face_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Spherical_Confidence_Learning_for_Face_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Spherical_Confidence_Learning_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_CVPR,
author = {Li, Shen and Xu, Jianqing and Xu, Xiaqing and Shen, Pengcheng and Li, Shaoxin and Hooi, Bryan},
title = {Spherical Confidence Learning for Face Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | An emerging line of research has found that spherical spaces better match the underlying geometry of facial images, as evidenced by the state-of-the-art facial recognition methods which benefit empirically from spherical representations. Yet, these approaches rely on deterministic embeddings and hence suffer from the f... |
Hoyer_Three_Ways_To_Improve_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_CVPR_2021_paper | Three Ways To Improve Semantic Segmentation With Self-Supervised Depth Estimation | [
"Lukas Hoyer",
"Dengxin Dai",
"Yuhua Chen",
"Adrian Koring",
"Suman Saha",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hoyer_Three_Ways_To_Improve_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hoyer_Three_Ways_To_Improve_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hoyer_Three_Ways_To_CVPR_2021_supplemental.pdf | 2012.10782 | cvf | @InProceedings{Hoyer_2021_CVPR,
author = {Hoyer, Lukas and Dai, Dengxin and Chen, Yuhua and Koring, Adrian and Saha, Suman and Van Gool, Luc},
title = {Three Ways To Improve Semantic Segmentation With Self-Supervised Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To address this issue, we present a framework for semi-supervised semantic segmentation, which is enhanced b... |
Zhang_Cross-Modal_Contrastive_Learning_for_Text-to-Image_Generation_CVPR_2021_paper | Cross-Modal Contrastive Learning for Text-to-Image Generation | [
"Han Zhang",
"Jing Yu Koh",
"Jason Baldridge",
"Honglak Lee",
"Yinfei Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Cross-Modal_Contrastive_Learning_for_Text-to-Image_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Cross-Modal_Contrastive_Learning_for_Text-to-Image_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Cross-Modal_Contrastive_Learning_CVPR_2021_supplemental.pdf | 2101.04702 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Han and Koh, Jing Yu and Baldridge, Jason and Lee, Honglak and Yang, Yinfei},
title = {Cross-Modal Contrastive Learning for Text-to-Image Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | The output of text-to-image synthesis systems should be coherent, clear, photo-realistic scenes with high semantic fidelity to their conditioned text descriptions. Our Cross-Modal Contrastive Generative Adversarial Network (XMC-GAN) addresses this challenge by maximizing the mutual information between image and text. I... |
Shi_Lifting_2D_StyleGAN_for_3D-Aware_Face_Generation_CVPR_2021_paper | Lifting 2D StyleGAN for 3D-Aware Face Generation | [
"Yichun Shi",
"Divyansh Aggarwal",
"Anil K. Jain"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Lifting_2D_StyleGAN_for_3D-Aware_Face_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_Lifting_2D_StyleGAN_for_3D-Aware_Face_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_Lifting_2D_StyleGAN_CVPR_2021_supplemental.pdf | 2011.13126 | cvf | @InProceedings{Shi_2021_CVPR,
author = {Shi, Yichun and Aggarwal, Divyansh and Jain, Anil K.},
title = {Lifting 2D StyleGAN for 3D-Aware Face Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2... | We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained StyleGAN2 for 3D-aware face generation. Our model is "3D-aware" in the sense that it is able to (1) disentangle the latent space of StyleGAN2 into texture, shape, viewpoint, lighting and (2) generate 3D components for rendering syntheti... |
Liu_iMiGUE_An_Identity-Free_Video_Dataset_for_Micro-Gesture_Understanding_and_Emotion_CVPR_2021_paper | iMiGUE: An Identity-Free Video Dataset for Micro-Gesture Understanding and Emotion Analysis | [
"Xin Liu",
"Henglin Shi",
"Haoyu Chen",
"Zitong Yu",
"Xiaobai Li",
"Guoying Zhao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_iMiGUE_An_Identity-Free_Video_Dataset_for_Micro-Gesture_Understanding_and_Emotion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_iMiGUE_An_Identity-Free_Video_Dataset_for_Micro-Gesture_Understanding_and_Emotion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_iMiGUE_An_Identity-Free_CVPR_2021_supplemental.pdf | 2107.00285 | title_snapshot | @InProceedings{Liu_2021_CVPR,
author = {Liu, Xin and Shi, Henglin and Chen, Haoyu and Yu, Zitong and Li, Xiaobai and Zhao, Guoying},
title = {iMiGUE: An Identity-Free Video Dataset for Micro-Gesture Understanding and Emotion Analysis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | We introduce a new dataset for the emotional artificial intelligence research: identity-free video dataset for micro-gesture understanding and emotion analysis (iMiGUE). Different from existing public datasets, iMiGUE focuses on nonverbal body gestures without using any identity information, while the predominant resea... |
VS_MeGA-CDA_Memory_Guided_Attention_for_Category-Aware_Unsupervised_Domain_Adaptive_Object_CVPR_2021_paper | MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection | [
"Vibashan VS",
"Vikram Gupta",
"Poojan Oza",
"Vishwanath A. Sindagi",
"Vishal M. Patel"
] | https://openaccess.thecvf.com/content/CVPR2021/html/VS_MeGA-CDA_Memory_Guided_Attention_for_Category-Aware_Unsupervised_Domain_Adaptive_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/VS_MeGA-CDA_Memory_Guided_Attention_for_Category-Aware_Unsupervised_Domain_Adaptive_Object_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/VS_MeGA-CDA_Memory_Guided_CVPR_2021_supplemental.pdf | 2103.04224 | title_snapshot | @InProceedings{VS_2021_CVPR,
author = {VS, Vibashan and Gupta, Vikram and Oza, Poojan and Sindagi, Vishwanath A. and Patel, Vishal M.},
title = {MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in performance, they typically perform category-agnostic domain alignment, thereby resulting in negative transfer of features. To overcome this is... |
Thames_Nutrition5k_Towards_Automatic_Nutritional_Understanding_of_Generic_Food_CVPR_2021_paper | Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food | [
"Quin Thames",
"Arjun Karpur",
"Wade Norris",
"Fangting Xia",
"Liviu Panait",
"Tobias Weyand",
"Jack Sim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Thames_Nutrition5k_Towards_Automatic_Nutritional_Understanding_of_Generic_Food_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Thames_Nutrition5k_Towards_Automatic_Nutritional_Understanding_of_Generic_Food_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Thames_Nutrition5k_Towards_Automatic_CVPR_2021_supplemental.pdf | 2103.03375 | cvf | @InProceedings{Thames_2021_CVPR,
author = {Thames, Quin and Karpur, Arjun and Norris, Wade and Xia, Fangting and Panait, Liviu and Weyand, Tobias and Sim, Jack},
title = {Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | Understanding the nutritional content of food from visual data is a challenging computer vision problem, with the potential to have a positive and widespread impact on public health. Studies in this area are limited to existing datasets in the field that lack sufficient diversity or labels required for training models ... |
Moseley_Extreme_Low-Light_Environment-Driven_Image_Denoising_Over_Permanently_Shadowed_Lunar_Regions_CVPR_2021_paper | Extreme Low-Light Environment-Driven Image Denoising Over Permanently Shadowed Lunar Regions With a Physical Noise Model | [
"Ben Moseley",
"Valentin Bickel",
"Ignacio G. Lopez-Francos",
"Loveneesh Rana"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Moseley_Extreme_Low-Light_Environment-Driven_Image_Denoising_Over_Permanently_Shadowed_Lunar_Regions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Moseley_Extreme_Low-Light_Environment-Driven_Image_Denoising_Over_Permanently_Shadowed_Lunar_Regions_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Moseley_Extreme_Low-Light_Environment-Driven_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Moseley_2021_CVPR,
author = {Moseley, Ben and Bickel, Valentin and Lopez-Francos, Ignacio G. and Rana, Loveneesh},
title = {Extreme Low-Light Environment-Driven Image Denoising Over Permanently Shadowed Lunar Regions With a Physical Noise Model},
booktitle = {Proceedings of the IEEE/CV... | Recently, learning-based approaches have achieved impressive results in the field of low-light image denoising. Some state of the art approaches employ a rich physical model to generate realistic training data. However, the performance of these approaches ultimately depends on the realism of the physical model, and man... |
Weng_Unsupervised_Discovery_of_the_Long-Tail_in_Instance_Segmentation_Using_Hierarchical_CVPR_2021_paper | Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision | [
"Zhenzhen Weng",
"Mehmet Giray Ogut",
"Shai Limonchik",
"Serena Yeung"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Weng_Unsupervised_Discovery_of_the_Long-Tail_in_Instance_Segmentation_Using_Hierarchical_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Weng_Unsupervised_Discovery_of_the_Long-Tail_in_Instance_Segmentation_Using_Hierarchical_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Weng_Unsupervised_Discovery_of_CVPR_2021_supplemental.pdf | 2104.01257 | cvf | @InProceedings{Weng_2021_CVPR,
author = {Weng, Zhenzhen and Ogut, Mehmet Giray and Limonchik, Shai and Yeung, Serena},
title = {Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | Instance segmentation is an active topic in computer vision that is usually solved by using supervised learning approaches over very large datasets composed of object level masks. Obtaining such a dataset for any new domain can be very expensive and time-consuming. In addition, models trained on certain annotated categ... |
Chelani_How_Privacy-Preserving_Are_Line_Clouds_Recovering_Scene_Details_From_3D_CVPR_2021_paper | How Privacy-Preserving Are Line Clouds? Recovering Scene Details From 3D Lines | [
"Kunal Chelani",
"Fredrik Kahl",
"Torsten Sattler"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chelani_How_Privacy-Preserving_Are_Line_Clouds_Recovering_Scene_Details_From_3D_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chelani_How_Privacy-Preserving_Are_Line_Clouds_Recovering_Scene_Details_From_3D_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chelani_How_Privacy-Preserving_Are_CVPR_2021_supplemental.pdf | 2103.05086 | cvf | @InProceedings{Chelani_2021_CVPR,
author = {Chelani, Kunal and Kahl, Fredrik and Sattler, Torsten},
title = {How Privacy-Preserving Are Line Clouds? Recovering Scene Details From 3D Lines},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | Visual localization is the problem of estimating the camera pose of a given image with respect to a known scene. Visual localization algorithms are a fundamental building block in advanced computer vision applications, including Mixed and Virtual Reality systems. Many algorithms used in practice represent the scene thr... |
Cheng_Multi-View_3D_Reconstruction_of_a_Texture-Less_Smooth_Surface_of_Unknown_CVPR_2021_paper | Multi-View 3D Reconstruction of a Texture-Less Smooth Surface of Unknown Generic Reflectance | [
"Ziang Cheng",
"Hongdong Li",
"Yuta Asano",
"Yinqiang Zheng",
"Imari Sato"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Multi-View_3D_Reconstruction_of_a_Texture-Less_Smooth_Surface_of_Unknown_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Multi-View_3D_Reconstruction_of_a_Texture-Less_Smooth_Surface_of_Unknown_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Multi-View_3D_Reconstruction_CVPR_2021_supplemental.pdf | 2105.11599 | cvf | @InProceedings{Cheng_2021_CVPR,
author = {Cheng, Ziang and Li, Hongdong and Asano, Yuta and Zheng, Yinqiang and Sato, Imari},
title = {Multi-View 3D Reconstruction of a Texture-Less Smooth Surface of Unknown Generic Reflectance},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | Recovering the 3D geometry of a purely texture-less object with generally unknown surface reflectance (e.g. nonLambertian) is regarded as a challenging task in multiview reconstruction. The major obstacle revolves around establishing cross-view correspondences where photometric constancy is violated. This paper propose... |
Singh_Rectification-Based_Knowledge_Retention_for_Continual_Learning_CVPR_2021_paper | Rectification-Based Knowledge Retention for Continual Learning | [
"Pravendra Singh",
"Pratik Mazumder",
"Piyush Rai",
"Vinay P. Namboodiri"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Singh_Rectification-Based_Knowledge_Retention_for_Continual_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Singh_Rectification-Based_Knowledge_Retention_for_Continual_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Singh_Rectification-Based_Knowledge_Retention_CVPR_2021_supplemental.pdf | 2103.16597 | cvf | @InProceedings{Singh_2021_CVPR,
author = {Singh, Pravendra and Mazumder, Pratik and Rai, Piyush and Namboodiri, Vinay P.},
title = {Rectification-Based Knowledge Retention for Continual Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Deep learning models suffer from catastrophic forgetting when trained in an incremental learning setting. In this work, we propose a novel approach to address the task incremental learning problem, which involves training a model on new tasks that arrive in an incremental manner. The task incremental learning problem b... |
Chen_Scale-Aware_Automatic_Augmentation_for_Object_Detection_CVPR_2021_paper | Scale-Aware Automatic Augmentation for Object Detection | [
"Yukang Chen",
"Yanwei Li",
"Tao Kong",
"Lu Qi",
"Ruihang Chu",
"Lei Li",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Scale-Aware_Automatic_Augmentation_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Scale-Aware_Automatic_Augmentation_for_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Scale-Aware_Automatic_Augmentation_CVPR_2021_supplemental.pdf | 2103.17220 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Yukang and Li, Yanwei and Kong, Tao and Qi, Lu and Chu, Ruihang and Li, Lei and Jia, Jiaya},
title = {Scale-Aware Automatic Augmentation for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scale-aware search space, where both image- and box-level augmentations are designed for maintaining scale invariance. Upon this search space, we propose a new search metric, termed Pareto Scale Balance, to facilita... |
Chang_Towards_Robust_Classification_Model_by_Counterfactual_and_Invariant_Data_Generation_CVPR_2021_paper | Towards Robust Classification Model by Counterfactual and Invariant Data Generation | [
"Chun-Hao Chang",
"George Alexandru Adam",
"Anna Goldenberg"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chang_Towards_Robust_Classification_Model_by_Counterfactual_and_Invariant_Data_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chang_Towards_Robust_Classification_Model_by_Counterfactual_and_Invariant_Data_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chang_Towards_Robust_Classification_CVPR_2021_supplemental.pdf | 2106.01127 | cvf | @InProceedings{Chang_2021_CVPR,
author = {Chang, Chun-Hao and Adam, George Alexandru and Goldenberg, Anna},
title = {Towards Robust Classification Model by Counterfactual and Invariant Data Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions. Spuriousness occurs when some features correlate with labels but are not causal; relying on such features prevents mo... |
Li_Fully_Convolutional_Networks_for_Panoptic_Segmentation_CVPR_2021_paper | Fully Convolutional Networks for Panoptic Segmentation | [
"Yanwei Li",
"Hengshuang Zhao",
"Xiaojuan Qi",
"Liwei Wang",
"Zeming Li",
"Jian Sun",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Fully_Convolutional_Networks_for_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Fully_Convolutional_Networks_for_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | 2012.00720 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Yanwei and Zhao, Hengshuang and Qi, Xiaojuan and Wang, Liwei and Li, Zeming and Sun, Jian and Jia, Jiaya},
title = {Fully Convolutional Networks for Panoptic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground things and background stuff in a unified fully convolutional pipeline. In particular, Panoptic FCN encodes each object instance or stuff ... |
Van_Horn_Benchmarking_Representation_Learning_for_Natural_World_Image_Collections_CVPR_2021_paper | Benchmarking Representation Learning for Natural World Image Collections | [
"Grant Van Horn",
"Elijah Cole",
"Sara Beery",
"Kimberly Wilber",
"Serge Belongie",
"Oisin Mac Aodha"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Van_Horn_Benchmarking_Representation_Learning_for_Natural_World_Image_Collections_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Van_Horn_Benchmarking_Representation_Learning_for_Natural_World_Image_Collections_CVPR_2021_paper.pdf | null | 2103.16483 | cvf | @InProceedings{Van_Horn_2021_CVPR,
author = {Van Horn, Grant and Cole, Elijah and Beery, Sara and Wilber, Kimberly and Belongie, Serge and Mac Aodha, Oisin},
title = {Benchmarking Representation Learning for Natural World Image Collections},
booktitle = {Proceedings of the IEEE/CVF Conference on Comp... | Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. However, to date the vast majority of these approaches have restricted themselves to training on standard benchmark datasets suc... |
Pang_PGT_A_Progressive_Method_for_Training_Models_on_Long_Videos_CVPR_2021_paper | PGT: A Progressive Method for Training Models on Long Videos | [
"Bo Pang",
"Gao Peng",
"Yizhuo Li",
"Cewu Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_PGT_A_Progressive_Method_for_Training_Models_on_Long_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pang_PGT_A_Progressive_Method_for_Training_Models_on_Long_Videos_CVPR_2021_paper.pdf | null | 2103.11313 | cvf | @InProceedings{Pang_2021_CVPR,
author = {Pang, Bo and Peng, Gao and Li, Yizhuo and Lu, Cewu},
title = {PGT: A Progressive Method for Training Models on Long Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Convolutional video models have an order of magnitude larger computational complexity than their counterpart image-level models. Constrained by computational resources, there is no model or training method that can train long video sequences end-to-end. Currently, the main-stream method is to split a raw video into cli... |
Su_Prioritized_Architecture_Sampling_With_Monto-Carlo_Tree_Search_CVPR_2021_paper | Prioritized Architecture Sampling With Monto-Carlo Tree Search | [
"Xiu Su",
"Tao Huang",
"Yanxi Li",
"Shan You",
"Fei Wang",
"Chen Qian",
"Changshui Zhang",
"Chang Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Su_Prioritized_Architecture_Sampling_With_Monto-Carlo_Tree_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Su_Prioritized_Architecture_Sampling_With_Monto-Carlo_Tree_Search_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Su_Prioritized_Architecture_Sampling_CVPR_2021_supplemental.pdf | 2103.11922 | cvf | @InProceedings{Su_2021_CVPR,
author = {Su, Xiu and Huang, Tao and Li, Yanxi and You, Shan and Wang, Fei and Qian, Chen and Zhang, Changshui and Xu, Chang},
title = {Prioritized Architecture Sampling With Monto-Carlo Tree Search},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once. However, current methods select each operation independently without considering previous layers. Besides, the historical information obtained... |
Tan_HumanGPS_Geodesic_PreServing_Feature_for_Dense_Human_Correspondences_CVPR_2021_paper | HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences | [
"Feitong Tan",
"Danhang Tang",
"Mingsong Dou",
"Kaiwen Guo",
"Rohit Pandey",
"Cem Keskin",
"Ruofei Du",
"Deqing Sun",
"Sofien Bouaziz",
"Sean Fanello",
"Ping Tan",
"Yinda Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_HumanGPS_Geodesic_PreServing_Feature_for_Dense_Human_Correspondences_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_HumanGPS_Geodesic_PreServing_Feature_for_Dense_Human_Correspondences_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_HumanGPS_Geodesic_PreServing_CVPR_2021_supplemental.zip | 2103.15573 | cvf | @InProceedings{Tan_2021_CVPR,
author = {Tan, Feitong and Tang, Danhang and Dou, Mingsong and Guo, Kaiwen and Pandey, Rohit and Keskin, Cem and Du, Ruofei and Sun, Deqing and Bouaziz, Sofien and Fanello, Sean and Tan, Ping and Zhang, Yinda},
title = {HumanGPS: Geodesic PreServing Feature for Dense Human C... | In this paper, we address the problem of building pixel-wise dense correspondences between human images under arbitrary camera viewpoints and body poses. Previous methods either assume small motions or rely on discriminative descriptors extracted from local patches, which cannot handle large motion or visually ambiguou... |
Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper | Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition | [
"Shancheng Fang",
"Hongtao Xie",
"Yuxin Wang",
"Zhendong Mao",
"Yongdong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fang_Read_Like_Humans_CVPR_2021_supplemental.pdf | 2103.06495 | cvf | @InProceedings{Fang_2021_CVPR,
author = {Fang, Shancheng and Xie, Hongtao and Wang, Yuxin and Mao, Zhendong and Zhang, Yongdong},
title = {Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively model linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from: 1) implicitly language modeling; 2) unidirectional feature rep... |
Liu_Generic_Perceptual_Loss_for_Modeling_Structured_Output_Dependencies_CVPR_2021_paper | Generic Perceptual Loss for Modeling Structured Output Dependencies | [
"Yifan Liu",
"Hao Chen",
"Yu Chen",
"Wei Yin",
"Chunhua Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Generic_Perceptual_Loss_for_Modeling_Structured_Output_Dependencies_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Generic_Perceptual_Loss_for_Modeling_Structured_Output_Dependencies_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Generic_Perceptual_Loss_CVPR_2021_supplemental.pdf | 2103.10571 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yifan and Chen, Hao and Chen, Yu and Yin, Wei and Shen, Chunhua},
title = {Generic Perceptual Loss for Modeling Structured Output Dependencies},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | The perceptual loss has been widely used as an effective loss term in image synthesis tasks including image super-resolution [16], and style transfer [14]. It was believed that the success lies in the high-level perceptual feature representations extracted from CNNs pretrained with a large set of images. Here we reveal... |
Xie_Style-Based_Point_Generator_With_Adversarial_Rendering_for_Point_Cloud_Completion_CVPR_2021_paper | Style-Based Point Generator With Adversarial Rendering for Point Cloud Completion | [
"Chulin Xie",
"Chuxin Wang",
"Bo Zhang",
"Hao Yang",
"Dong Chen",
"Fang Wen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Style-Based_Point_Generator_With_Adversarial_Rendering_for_Point_Cloud_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Style-Based_Point_Generator_With_Adversarial_Rendering_for_Point_Cloud_Completion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xie_Style-Based_Point_Generator_CVPR_2021_supplemental.pdf | 2103.02535 | cvf | @InProceedings{Xie_2021_CVPR,
author = {Xie, Chulin and Wang, Chuxin and Zhang, Bo and Yang, Hao and Chen, Dong and Wen, Fang},
title = {Style-Based Point Generator With Adversarial Rendering for Point Cloud Completion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv to fully exploit the local structures as well as the global shape in point features. Secondly, we observe that the concatenation manner used... |
Zhang_Neural_Architecture_Search_With_Random_Labels_CVPR_2021_paper | Neural Architecture Search With Random Labels | [
"Xuanyang Zhang",
"Pengfei Hou",
"Xiangyu Zhang",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Neural_Architecture_Search_With_Random_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Neural_Architecture_Search_With_Random_Labels_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Neural_Architecture_Search_CVPR_2021_supplemental.pdf | 2101.11834 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Xuanyang and Hou, Pengfei and Zhang, Xiangyu and Sun, Jian},
title = {Neural Architecture Search With Random Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
ye... | In this paper, we investigate a new variant of neural architecture search (NAS) paradigm -- searching with random labels (RLNAS). The task sounds counter-intuitive for most existing NAS algorithms since random label provides few information on the performance of each candidate architecture. Instead, we propose a novel ... |
Wu_Towards_Long-Form_Video_Understanding_CVPR_2021_paper | Towards Long-Form Video Understanding | [
"Chao-Yuan Wu",
"Philipp Krahenbuhl"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Towards_Long-Form_Video_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Towards_Long-Form_Video_Understanding_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Towards_Long-Form_Video_CVPR_2021_supplemental.pdf | 2106.11310 | cvf | @InProceedings{Wu_2021_CVPR,
author = {Wu, Chao-Yuan and Krahenbuhl, Philipp},
title = {Towards Long-Form Video Understanding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {1884... | Our world offers a never-ending stream of visual stimuli, yet today's vision systems only accurately recognize patterns within a few seconds. These systems understand the present, but fail to contextualize it in past or future events. In this paper, we study long-form video understanding. We introduce a framework for m... |
Lichy_Shape_and_Material_Capture_at_Home_CVPR_2021_paper | Shape and Material Capture at Home | [
"Daniel Lichy",
"Jiaye Wu",
"Soumyadip Sengupta",
"David W. Jacobs"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lichy_Shape_and_Material_Capture_at_Home_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lichy_Shape_and_Material_Capture_at_Home_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lichy_Shape_and_Material_CVPR_2021_supplemental.pdf | 2104.06397 | cvf | @InProceedings{Lichy_2021_CVPR,
author = {Lichy, Daniel and Wu, Jiaye and Sengupta, Soumyadip and Jacobs, David W.},
title = {Shape and Material Capture at Home},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod. We propose a simple data capture technique in which the user goes around the object, illuminating it with a flashlight and capturing only a few images. Our main technica... |
Deschaintre_Deep_Polarization_Imaging_for_3D_Shape_and_SVBRDF_Acquisition_CVPR_2021_paper | Deep Polarization Imaging for 3D Shape and SVBRDF Acquisition | [
"Valentin Deschaintre",
"Yiming Lin",
"Abhijeet Ghosh"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Deschaintre_Deep_Polarization_Imaging_for_3D_Shape_and_SVBRDF_Acquisition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Deschaintre_Deep_Polarization_Imaging_for_3D_Shape_and_SVBRDF_Acquisition_CVPR_2021_paper.pdf | null | 2105.02875 | cvf | @InProceedings{Deschaintre_2021_CVPR,
author = {Deschaintre, Valentin and Lin, Yiming and Ghosh, Abhijeet},
title = {Deep Polarization Imaging for 3D Shape and SVBRDF Acquisition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues. Unlike previous works that have exploited polarization to estimate material or object appearance under certain constraints (known shape or multiview acquisition), we lift such restrictio... |
Wang_Convolutional_Neural_Network_Pruning_With_Structural_Redundancy_Reduction_CVPR_2021_paper | Convolutional Neural Network Pruning With Structural Redundancy Reduction | [
"Zi Wang",
"Chengcheng Li",
"Xiangyang Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Convolutional_Neural_Network_Pruning_With_Structural_Redundancy_Reduction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Convolutional_Neural_Network_Pruning_With_Structural_Redundancy_Reduction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Convolutional_Neural_Network_CVPR_2021_supplemental.pdf | 2104.03438 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Zi and Li, Chengcheng and Wang, Xiangyang},
title = {Convolutional Neural Network Pruning With Structural Redundancy Reduction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | Convolutional neural network (CNN) pruning has become one of the most successful network compression approaches in recent years. Existing works on network pruning usually focus on removing the least important filters in the network to achieve compact architectures. In this study, we claim that identifying structural re... |
Kobayashi_T-vMF_Similarity_for_Regularizing_Intra-Class_Feature_Distribution_CVPR_2021_paper | T-vMF Similarity for Regularizing Intra-Class Feature Distribution | [
"Takumi Kobayashi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kobayashi_T-vMF_Similarity_for_Regularizing_Intra-Class_Feature_Distribution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kobayashi_T-vMF_Similarity_for_Regularizing_Intra-Class_Feature_Distribution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kobayashi_T-vMF_Similarity_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Kobayashi_2021_CVPR,
author = {Kobayashi, Takumi},
title = {T-vMF Similarity for Regularizing Intra-Class Feature Distribution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
p... | Deep convolutional neural networks (CNNs) leverage large-scale training dataset to produce remarkable performance on various image classification tasks. It, however, is difficult to effectively train the CNNs on some realistic learning situations such as regarding class imbalance, small-scale and label noises. Regulari... |
Li_Surrogate_Gradient_Field_for_Latent_Space_Manipulation_CVPR_2021_paper | Surrogate Gradient Field for Latent Space Manipulation | [
"Minjun Li",
"Yanghua Jin",
"Huachun Zhu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Surrogate_Gradient_Field_for_Latent_Space_Manipulation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Surrogate_Gradient_Field_for_Latent_Space_Manipulation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Surrogate_Gradient_Field_CVPR_2021_supplemental.pdf | 2104.09065 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Minjun and Jin, Yanghua and Zhu, Huachun},
title = {Surrogate Gradient Field for Latent Space Manipulation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | Generative adversarial networks (GANs) can generate high-quality images from sampled latent codes. Recent works attempt to edit an image by manipulating its underlying latent code, but rarely go beyond the basic task of attribute adjustment. We propose the first method that enables manipulation with multidimensional co... |
Fan_SCF-Net_Learning_Spatial_Contextual_Features_for_Large-Scale_Point_Cloud_Segmentation_CVPR_2021_paper | SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation | [
"Siqi Fan",
"Qiulei Dong",
"Fenghua Zhu",
"Yisheng Lv",
"Peijun Ye",
"Fei-Yue Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_SCF-Net_Learning_Spatial_Contextual_Features_for_Large-Scale_Point_Cloud_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_SCF-Net_Learning_Spatial_Contextual_Features_for_Large-Scale_Point_Cloud_Segmentation_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Fan_2021_CVPR,
author = {Fan, Siqi and Dong, Qiulei and Zhu, Fenghua and Lv, Yisheng and Ye, Peijun and Wang, Fei-Yue},
title = {SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | How to learn effective features from large-scale point clouds for semantic segmentation has attracted increasing attention in recent years. Addressing this problem, we propose a learnable module that learns Spatial Contextual Features from large-scale point clouds, called SCF in this paper. The proposed module mainly c... |
Banani_UnsupervisedRR_Unsupervised_Point_Cloud_Registration_via_Differentiable_Rendering_CVPR_2021_paper | UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering | [
"Mohamed El Banani",
"Luya Gao",
"Justin Johnson"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Banani_UnsupervisedRR_Unsupervised_Point_Cloud_Registration_via_Differentiable_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Banani_UnsupervisedRR_Unsupervised_Point_Cloud_Registration_via_Differentiable_Rendering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Banani_UnsupervisedRR_Unsupervised_Point_CVPR_2021_supplemental.pdf | 2102.11870 | title_snapshot | @InProceedings{El_Banani_2021_CVPR,
author = {El Banani, Mohamed and Gao, Luya and Johnson, Justin},
title = {UnsupervisedR\&R: Unsupervised Point Cloud Registration via Differentiable Rendering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Aligning partial views of a scene into a single whole is essential to understanding one's environment and is a key component of numerous robotics tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems that can outperform traditional methods by leveraging pose supervision. However, with the risin... |
Shi_ZeroScatter_Domain_Transfer_for_Long_Distance_Imaging_and_Vision_Through_CVPR_2021_paper | ZeroScatter: Domain Transfer for Long Distance Imaging and Vision Through Scattering Media | [
"Zheng Shi",
"Ethan Tseng",
"Mario Bijelic",
"Werner Ritter",
"Felix Heide"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_ZeroScatter_Domain_Transfer_for_Long_Distance_Imaging_and_Vision_Through_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_ZeroScatter_Domain_Transfer_for_Long_Distance_Imaging_and_Vision_Through_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_ZeroScatter_Domain_Transfer_CVPR_2021_supplemental.zip | 2102.05847 | cvf | @InProceedings{Shi_2021_CVPR,
author = {Shi, Zheng and Tseng, Ethan and Bijelic, Mario and Ritter, Werner and Heide, Felix},
title = {ZeroScatter: Domain Transfer for Long Distance Imaging and Vision Through Scattering Media},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | Adverse weather conditions, including snow, rain, and fog, pose a major challenge for both human and computer vision. Handling these environmental conditions is essential for safe decision making, especially in autonomous vehicles, robotics, and drones. Most of today's supervised imaging and vision approaches, however,... |
Yang_Defending_Multimodal_Fusion_Models_Against_Single-Source_Adversaries_CVPR_2021_paper | Defending Multimodal Fusion Models Against Single-Source Adversaries | [
"Karren Yang",
"Wan-Yi Lin",
"Manash Barman",
"Filipe Condessa",
"Zico Kolter"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Defending_Multimodal_Fusion_Models_Against_Single-Source_Adversaries_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Defending_Multimodal_Fusion_Models_Against_Single-Source_Adversaries_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Defending_Multimodal_Fusion_CVPR_2021_supplemental.pdf | 2206.12714 | title_snapshot | @InProceedings{Yang_2021_CVPR,
author = {Yang, Karren and Lin, Wan-Yi and Barman, Manash and Condessa, Filipe and Kolter, Zico},
title = {Defending Multimodal Fusion Models Against Single-Source Adversaries},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Beyond achieving high performance across many vision tasks, multimodal models are expected to be robust to single-source faults due to the availability of redundant information between modalities. In this paper, we investigate the robustness of multimodal neural networks against worst-case (i.e., adversarial) perturbat... |
Mitsuzumi_Generalized_Domain_Adaptation_CVPR_2021_paper | Generalized Domain Adaptation | [
"Yu Mitsuzumi",
"Go Irie",
"Daiki Ikami",
"Takashi Shibata"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mitsuzumi_Generalized_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mitsuzumi_Generalized_Domain_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mitsuzumi_Generalized_Domain_Adaptation_CVPR_2021_supplemental.pdf | 2106.01656 | cvf | @InProceedings{Mitsuzumi_2021_CVPR,
author = {Mitsuzumi, Yu and Irie, Go and Ikami, Daiki and Shibata, Takashi},
title = {Generalized Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {20... | Many variants of unsupervised domain adaptation (UDA) problems have been proposed and solved individually. Its side effect is that a method that works for one variant is often ineffective for or not even applicable to another, which has prevented practical applications. In this paper, we give a general representation o... |
Patel_AGORA_Avatars_in_Geography_Optimized_for_Regression_Analysis_CVPR_2021_paper | AGORA: Avatars in Geography Optimized for Regression Analysis | [
"Priyanka Patel",
"Chun-Hao P. Huang",
"Joachim Tesch",
"David T. Hoffmann",
"Shashank Tripathi",
"Michael J. Black"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Patel_AGORA_Avatars_in_Geography_Optimized_for_Regression_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Patel_AGORA_Avatars_in_Geography_Optimized_for_Regression_Analysis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Patel_AGORA_Avatars_in_CVPR_2021_supplemental.pdf | 2104.14643 | cvf | @InProceedings{Patel_2021_CVPR,
author = {Patel, Priyanka and Huang, Chun-Hao P. and Tesch, Joachim and Hoffmann, David T. and Tripathi, Shashank and Black, Michael J.},
title = {AGORA: Avatars in Geography Optimized for Regression Analysis},
booktitle = {Proceedings of the IEEE/CVF Conference on Com... | While the accuracy of 3D human pose estimation from images has steadily improved on benchmark datasets, the best methods still fail in many real-world scenarios. This suggests that there is a domain gap between current datasets and common scenes containing people. To obtain ground-truth 3D pose, current datasets limit ... |
Liu_Exploring_and_Distilling_Posterior_and_Prior_Knowledge_for_Radiology_Report_CVPR_2021_paper | Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation | [
"Fenglin Liu",
"Xian Wu",
"Shen Ge",
"Wei Fan",
"Yuexian Zou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Exploring_and_Distilling_Posterior_and_Prior_Knowledge_for_Radiology_Report_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Exploring_and_Distilling_Posterior_and_Prior_Knowledge_for_Radiology_Report_CVPR_2021_paper.pdf | null | 2106.06963 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Fenglin and Wu, Xian and Ge, Shen and Fan, Wei and Zou, Yuexian},
title = {Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Automatically generating radiology reports can improve current clinical practice in diagnostic radiology. On one hand, it can relieve radiologists from the heavy burden of report writing; On the other hand, it can remind radiologists of abnormalities and avoid the misdiagnosis and missed diagnosis. Yet, this task remai... |
Parra_Rotation_Coordinate_Descent_for_Fast_Globally_Optimal_Rotation_Averaging_CVPR_2021_paper | Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging | [
"Alvaro Parra",
"Shin-Fang Chng",
"Tat-Jun Chin",
"Anders Eriksson",
"Ian Reid"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Parra_Rotation_Coordinate_Descent_for_Fast_Globally_Optimal_Rotation_Averaging_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Parra_Rotation_Coordinate_Descent_for_Fast_Globally_Optimal_Rotation_Averaging_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Parra_Rotation_Coordinate_Descent_CVPR_2021_supplemental.pdf | 2103.08292 | cvf | @InProceedings{Parra_2021_CVPR,
author = {Parra, Alvaro and Chng, Shin-Fang and Chin, Tat-Jun and Eriksson, Anders and Reid, Ian},
title = {Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programming (SDP) relaxation. However, generic solvers for SDP are rather slow in practice, even on rotation averaging instances of moderate size, thu... |
Cai_Extreme_Rotation_Estimation_Using_Dense_Correlation_Volumes_CVPR_2021_paper | Extreme Rotation Estimation Using Dense Correlation Volumes | [
"Ruojin Cai",
"Bharath Hariharan",
"Noah Snavely",
"Hadar Averbuch-Elor"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Extreme_Rotation_Estimation_Using_Dense_Correlation_Volumes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Extreme_Rotation_Estimation_Using_Dense_Correlation_Volumes_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cai_Extreme_Rotation_Estimation_CVPR_2021_supplemental.pdf | 2104.13530 | cvf | @InProceedings{Cai_2021_CVPR,
author = {Cai, Ruojin and Hariharan, Bharath and Snavely, Noah and Averbuch-Elor, Hadar},
title = {Extreme Rotation Estimation Using Dense Correlation Volumes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap. We observe that, even when images do not overlap, there may be rich hidden cues as to their geometric relationship, such as light source directions, vanishing points, an... |
Gu_Capsule_Network_Is_Not_More_Robust_Than_Convolutional_Network_CVPR_2021_paper | Capsule Network Is Not More Robust Than Convolutional Network | [
"Jindong Gu",
"Volker Tresp",
"Han Hu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gu_Capsule_Network_Is_Not_More_Robust_Than_Convolutional_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gu_Capsule_Network_Is_Not_More_Robust_Than_Convolutional_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gu_Capsule_Network_Is_CVPR_2021_supplemental.pdf | 2103.15459 | cvf | @InProceedings{Gu_2021_CVPR,
author = {Gu, Jindong and Tresp, Volker and Hu, Han},
title = {Capsule Network Is Not More Robust Than Convolutional Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {... | The Capsule Network is widely believed to be more robust than Convolutional Networks. However, there lack comprehensive comparisons between these two networks, and it is also unknown which components in the CapsNet affect its robustness. In this paper, we first carefully examine the special designs in CapsNet differing... |
Diao_BASARBlack-Box_Attack_on_Skeletal_Action_Recognition_CVPR_2021_paper | BASAR:Black-Box Attack on Skeletal Action Recognition | [
"Yunfeng Diao",
"Tianjia Shao",
"Yong-Liang Yang",
"Kun Zhou",
"He Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Diao_BASARBlack-Box_Attack_on_Skeletal_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Diao_BASARBlack-Box_Attack_on_Skeletal_Action_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Diao_BASARBlack-Box_Attack_on_CVPR_2021_supplemental.zip | 2103.05266 | cvf | @InProceedings{Diao_2021_CVPR,
author = {Diao, Yunfeng and Shao, Tianjia and Yang, Yong-Liang and Zhou, Kun and Wang, He},
title = {BASAR:Black-Box Attack on Skeletal Action Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | Skeletal motion plays a vital role in human activity recognition as either an independent data source or a complement. The robustness of skeleton-based activity recognizers has been questioned recently, which shows that they are vulnerable to adversarial attacks when the full-knowledge of the recognizer is accessible t... |
Eckart_Self-Supervised_Learning_on_3D_Point_Clouds_by_Learning_Discrete_Generative_CVPR_2021_paper | Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models | [
"Benjamin Eckart",
"Wentao Yuan",
"Chao Liu",
"Jan Kautz"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Eckart_Self-Supervised_Learning_on_3D_Point_Clouds_by_Learning_Discrete_Generative_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Eckart_Self-Supervised_Learning_on_3D_Point_Clouds_by_Learning_Discrete_Generative_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Eckart_Self-Supervised_Learning_on_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Eckart_2021_CVPR,
author = {Eckart, Benjamin and Yuan, Wentao and Liu, Chao and Kautz, Jan},
title = {Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | While recent pre-training tasks on 2D images have proven very successful for transfer learning, pre-training for 3D data remains challenging. In this work, we introduce a general method for 3D self-supervised representation learning that 1) remains agnostic to the underlying neural network architecture, and 2) specific... |
Yifan_Iso-Points_Optimizing_Neural_Implicit_Surfaces_With_Hybrid_Representations_CVPR_2021_paper | Iso-Points: Optimizing Neural Implicit Surfaces With Hybrid Representations | [
"Wang Yifan",
"Shihao Wu",
"Cengiz Oztireli",
"Olga Sorkine-Hornung"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yifan_Iso-Points_Optimizing_Neural_Implicit_Surfaces_With_Hybrid_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yifan_Iso-Points_Optimizing_Neural_Implicit_Surfaces_With_Hybrid_Representations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yifan_Iso-Points_Optimizing_Neural_CVPR_2021_supplemental.pdf | 2012.06434 | title_snapshot | @InProceedings{Yifan_2021_CVPR,
author = {Yifan, Wang and Wu, Shihao and Oztireli, Cengiz and Sorkine-Hornung, Olga},
title = {Iso-Points: Optimizing Neural Implicit Surfaces With Hybrid Representations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Neural implicit functions have emerged as a powerful representation for surfaces in 3D. Such a function can encode a high quality surface with intricate details into the parameters of a deep neural network. However, optimizing for the parameters for accurate and robust reconstructions remains a challenge especially whe... |
Hu_Dense_Relation_Distillation_With_Context-Aware_Aggregation_for_Few-Shot_Object_Detection_CVPR_2021_paper | Dense Relation Distillation With Context-Aware Aggregation for Few-Shot Object Detection | [
"Hanzhe Hu",
"Shuai Bai",
"Aoxue Li",
"Jinshi Cui",
"Liwei Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Dense_Relation_Distillation_With_Context-Aware_Aggregation_for_Few-Shot_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Dense_Relation_Distillation_With_Context-Aware_Aggregation_for_Few-Shot_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Dense_Relation_Distillation_CVPR_2021_supplemental.pdf | 2103.17115 | cvf | @InProceedings{Hu_2021_CVPR,
author = {Hu, Hanzhe and Bai, Shuai and Li, Aoxue and Cui, Jinshi and Wang, Liwei},
title = {Dense Relation Distillation With Context-Aware Aggregation for Few-Shot Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | Conventional deep learning based methods for object detection require a large amount of bounding box annotations for training, which is expensive to obtain such high quality annotated data. Few-shot object detection, which learns to adapt to novel classes with only a few annotated examples, is very challenging since th... |
Zou_End-to-End_Human_Object_Interaction_Detection_With_HOI_Transformer_CVPR_2021_paper | End-to-End Human Object Interaction Detection With HOI Transformer | [
"Cheng Zou",
"Bohan Wang",
"Yue Hu",
"Junqi Liu",
"Qian Wu",
"Yu Zhao",
"Boxun Li",
"Chenguang Zhang",
"Chi Zhang",
"Yichen Wei",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_End-to-End_Human_Object_Interaction_Detection_With_HOI_Transformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zou_End-to-End_Human_Object_Interaction_Detection_With_HOI_Transformer_CVPR_2021_paper.pdf | null | 2103.04503 | cvf | @InProceedings{Zou_2021_CVPR,
author = {Zou, Cheng and Wang, Bohan and Hu, Yue and Liu, Junqi and Wu, Qian and Zhao, Yu and Li, Boxun and Zhang, Chenguang and Zhang, Chi and Wei, Yichen and Sun, Jian},
title = {End-to-End Human Object Interaction Detection With HOI Transformer},
booktitle = {Proceedi... | We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner. Current approaches either decouple HOI task into separated stages of object detection and interaction classification or introduce surrogate interaction problem. In contrast, our method, named HOI Transformer, streamli... |
Bhardwaj_How_Does_Topology_Influence_Gradient_Propagation_and_Model_Performance_of_CVPR_2021_paper | How Does Topology Influence Gradient Propagation and Model Performance of Deep Networks With DenseNet-Type Skip Connections? | [
"Kartikeya Bhardwaj",
"Guihong Li",
"Radu Marculescu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bhardwaj_How_Does_Topology_Influence_Gradient_Propagation_and_Model_Performance_of_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhardwaj_How_Does_Topology_Influence_Gradient_Propagation_and_Model_Performance_of_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhardwaj_How_Does_Topology_CVPR_2021_supplemental.pdf | 1910.00780 | cvf | @InProceedings{Bhardwaj_2021_CVPR,
author = {Bhardwaj, Kartikeya and Li, Guihong and Marculescu, Radu},
title = {How Does Topology Influence Gradient Propagation and Model Performance of Deep Networks With DenseNet-Type Skip Connections?},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | DenseNets introduce concatenation-type skip connections that achieve state-of-the-art accuracy in several computer vision tasks. In this paper, we reveal that the topology of the concatenation-type skip connections is closely related to the gradient propagation which, in turn, enables a predictable behavior of DNNs' te... |
Liu_Multi-Shot_Temporal_Event_Localization_A_Benchmark_CVPR_2021_paper | Multi-Shot Temporal Event Localization: A Benchmark | [
"Xiaolong Liu",
"Yao Hu",
"Song Bai",
"Fei Ding",
"Xiang Bai",
"Philip H. S. Torr"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Multi-Shot_Temporal_Event_Localization_A_Benchmark_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Multi-Shot_Temporal_Event_Localization_A_Benchmark_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Multi-Shot_Temporal_Event_CVPR_2021_supplemental.pdf | 2012.09434 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Xiaolong and Hu, Yao and Bai, Song and Ding, Fei and Bai, Xiang and Torr, Philip H. S.},
title = {Multi-Shot Temporal Event Localization: A Benchmark},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Current developments in temporal event or action localization usually target actions captured by a single camera. However, extensive events or actions in the wild may be captured as a sequence of shots by multiple cameras at different positions. In this paper, we propose a new and challenging task called multi-shot tem... |
Zhang_We_Are_More_Than_Our_Joints_Predicting_How_3D_Bodies_CVPR_2021_paper | We Are More Than Our Joints: Predicting How 3D Bodies Move | [
"Yan Zhang",
"Michael J. Black",
"Siyu Tang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_We_Are_More_Than_Our_Joints_Predicting_How_3D_Bodies_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_We_Are_More_Than_Our_Joints_Predicting_How_3D_Bodies_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_We_Are_More_CVPR_2021_supplemental.pdf | 2012.00619 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Yan and Black, Michael J. and Tang, Siyu},
title = {We Are More Than Our Joints: Predicting How 3D Bodies Move},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | A key step towards understanding human behavior is the prediction of 3D human motion. Successful solutions have many applications in human tracking, HCI, and graphics. Most previous work focuses on predicting a time series of future 3D joint locations given a sequence 3D joints from the past. This Euclidean formulation... |
Shaham_Spatially-Adaptive_Pixelwise_Networks_for_Fast_Image_Translation_CVPR_2021_paper | Spatially-Adaptive Pixelwise Networks for Fast Image Translation | [
"Tamar Rott Shaham",
"Michael Gharbi",
"Richard Zhang",
"Eli Shechtman",
"Tomer Michaeli"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shaham_Spatially-Adaptive_Pixelwise_Networks_for_Fast_Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shaham_Spatially-Adaptive_Pixelwise_Networks_for_Fast_Image_Translation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shaham_Spatially-Adaptive_Pixelwise_Networks_CVPR_2021_supplemental.pdf | 2012.02992 | cvf | @InProceedings{Shaham_2021_CVPR,
author = {Shaham, Tamar Rott and Gharbi, Michael and Zhang, Richard and Shechtman, Eli and Michaeli, Tomer},
title = {Spatially-Adaptive Pixelwise Networks for Fast Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | We introduce a new generator architecture, aimed at fast and efficient high-resolution image-to-image translation. We design the generator to be an extremely lightweight function of the full-resolution image. In fact, we use pixel-wise networks; that is, each pixel is processed independently of others, through a compos... |
Li_PointFlow_Flowing_Semantics_Through_Points_for_Aerial_Image_Segmentation_CVPR_2021_paper | PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation | [
"Xiangtai Li",
"Hao He",
"Xia Li",
"Duo Li",
"Guangliang Cheng",
"Jianping Shi",
"Lubin Weng",
"Yunhai Tong",
"Zhouchen Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_PointFlow_Flowing_Semantics_Through_Points_for_Aerial_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_PointFlow_Flowing_Semantics_Through_Points_for_Aerial_Image_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_PointFlow_Flowing_Semantics_CVPR_2021_supplemental.pdf | 2103.06564 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Xiangtai and He, Hao and Li, Xia and Li, Duo and Cheng, Guangliang and Shi, Jianping and Weng, Lubin and Tong, Yunhai and Lin, Zhouchen},
title = {PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation},
booktitle = {Proceedings of the IEE... | Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two critical issues: The one is an extremely foreground-background imbalanced distribution and the other is multiple small objects along with co... |
Zhang_Deep_Stable_Learning_for_Out-of-Distribution_Generalization_CVPR_2021_paper | Deep Stable Learning for Out-of-Distribution Generalization | [
"Xingxuan Zhang",
"Peng Cui",
"Renzhe Xu",
"Linjun Zhou",
"Yue He",
"Zheyan Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Deep_Stable_Learning_for_Out-of-Distribution_Generalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Deep_Stable_Learning_for_Out-of-Distribution_Generalization_CVPR_2021_paper.pdf | null | 2104.07876 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Xingxuan and Cui, Peng and Xu, Renzhe and Zhou, Linjun and He, Yue and Shen, Zheyan},
title = {Deep Stable Learning for Out-of-Distribution Generalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Therefore, eliminating the impact of distribution shifts between training and testing data is crucial for building performance-promising deep ... |
Shi_Continual_Learning_via_Bit-Level_Information_Preserving_CVPR_2021_paper | Continual Learning via Bit-Level Information Preserving | [
"Yujun Shi",
"Li Yuan",
"Yunpeng Chen",
"Jiashi Feng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Continual_Learning_via_Bit-Level_Information_Preserving_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_Continual_Learning_via_Bit-Level_Information_Preserving_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_Continual_Learning_via_CVPR_2021_supplemental.pdf | 2105.04444 | cvf | @InProceedings{Shi_2021_CVPR,
author = {Shi, Yujun and Yuan, Li and Chen, Yunpeng and Feng, Jiashi},
title = {Continual Learning via Bit-Level Information Preserving},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | Continual learning tackles the setting of learning different tasks sequentially. Despite the lots of previous solutions, most of them still suffer significant forgetting or expensive memory cost. In this work, targeted at these problems, we first study the continual learning process through the lens of information theo... |
Bhunia_Vectorization_and_Rasterization_Self-Supervised_Learning_for_Sketch_and_Handwriting_CVPR_2021_paper | Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting | [
"Ayan Kumar Bhunia",
"Pinaki Nath Chowdhury",
"Yongxin Yang",
"Timothy M. Hospedales",
"Tao Xiang",
"Yi-Zhe Song"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bhunia_Vectorization_and_Rasterization_Self-Supervised_Learning_for_Sketch_and_Handwriting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhunia_Vectorization_and_Rasterization_Self-Supervised_Learning_for_Sketch_and_Handwriting_CVPR_2021_paper.pdf | null | 2103.13716 | cvf | @InProceedings{Bhunia_2021_CVPR,
author = {Bhunia, Ayan Kumar and Chowdhury, Pinaki Nath and Yang, Yongxin and Hospedales, Timothy M. and Xiang, Tao and Song, Yi-Zhe},
title = {Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting},
booktitle = {Proceedings of the IEEE/... | Self-supervised learning has gained prominence due to its efficacy at learning powerful representations from unlabelled data that achieve excellent performance on many challenging downstream tasks. However, supervision-free pre-text tasks are challenging to design and usually modality specific. Although there is a rich... |
Peng_Generating_Diverse_Structure_for_Image_Inpainting_With_Hierarchical_VQ-VAE_CVPR_2021_paper | Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE | [
"Jialun Peng",
"Dong Liu",
"Songcen Xu",
"Houqiang Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Peng_Generating_Diverse_Structure_for_Image_Inpainting_With_Hierarchical_VQ-VAE_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Peng_Generating_Diverse_Structure_for_Image_Inpainting_With_Hierarchical_VQ-VAE_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Peng_Generating_Diverse_Structure_CVPR_2021_supplemental.pdf | 2103.10022 | cvf | @InProceedings{Peng_2021_CVPR,
author = {Peng, Jialun and Liu, Dong and Xu, Songcen and Li, Houqiang},
title = {Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Given an incomplete image without additional constraint, image inpainting natively allows for multiple solutions as long as they appear plausible. Recently, multiple-solution inpainting methods have been proposed and shown the potential of generating diverse results. However, these methods have difficulty in ensuring t... |
Ji_Refine_Myself_by_Teaching_Myself_Feature_Refinement_via_Self-Knowledge_Distillation_CVPR_2021_paper | Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation | [
"Mingi Ji",
"Seungjae Shin",
"Seunghyun Hwang",
"Gibeom Park",
"Il-Chul Moon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Refine_Myself_by_Teaching_Myself_Feature_Refinement_via_Self-Knowledge_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ji_Refine_Myself_by_Teaching_Myself_Feature_Refinement_via_Self-Knowledge_Distillation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ji_Refine_Myself_by_CVPR_2021_supplemental.zip | 2103.08273 | cvf | @InProceedings{Ji_2021_CVPR,
author = {Ji, Mingi and Shin, Seungjae and Hwang, Seunghyun and Park, Gibeom and Moon, Il-Chul},
title = {Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | Knowledge distillation is a method of transferring the knowledge from a pretrained complex teacher model to a student model, so a smaller network can replace a large teacher network at the deployment stage. To reduce the necessity of training a large teacher model, the recent literatures introduced a self-knowledge dis... |
Shi_Self-Supervised_Visibility_Learning_for_Novel_View_Synthesis_CVPR_2021_paper | Self-Supervised Visibility Learning for Novel View Synthesis | [
"Yujiao Shi",
"Hongdong Li",
"Xin Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Self-Supervised_Visibility_Learning_for_Novel_View_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_Self-Supervised_Visibility_Learning_for_Novel_View_Synthesis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_Self-Supervised_Visibility_Learning_CVPR_2021_supplemental.pdf | 2103.15407 | cvf | @InProceedings{Shi_2021_CVPR,
author = {Shi, Yujiao and Li, Hongdong and Yu, Xin},
title = {Self-Supervised Visibility Learning for Novel View Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2... | We address the problem of novel view synthesis (NVS) from a few sparse source view images. Conventional image-based rendering methods estimate scene geometry and synthesize novel views in two separate steps. However, erroneous geometry estimation will decrease NVS performance as view synthesis highly depends on the qua... |
Lin_End-to-End_Human_Pose_and_Mesh_Reconstruction_with_Transformers_CVPR_2021_paper | End-to-End Human Pose and Mesh Reconstruction with Transformers | [
"Kevin Lin",
"Lijuan Wang",
"Zicheng Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_End-to-End_Human_Pose_and_Mesh_Reconstruction_with_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_End-to-End_Human_Pose_and_Mesh_Reconstruction_with_Transformers_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_End-to-End_Human_Pose_CVPR_2021_supplemental.pdf | 2012.09760 | cvf | @InProceedings{Lin_2021_CVPR,
author = {Lin, Kevin and Wang, Lijuan and Liu, Zicheng},
title = {End-to-End Human Pose and Mesh Reconstruction with Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We present a new method, called MEsh TRansfOrmer (METRO), to reconstruct 3D human pose and mesh vertices from a single image. Our method uses a transformer encoder to jointly model vertex-vertex and vertex-joint interactions, and outputs 3D joint coordinates and mesh vertices simultaneously. Compared to existing techni... |
Ma_CapsuleRRT_Relationships-Aware_Regression_Tracking_via_Capsules_CVPR_2021_paper | CapsuleRRT: Relationships-Aware Regression Tracking via Capsules | [
"Ding Ma",
"Xiangqian Wu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_CapsuleRRT_Relationships-Aware_Regression_Tracking_via_Capsules_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_CapsuleRRT_Relationships-Aware_Regression_Tracking_via_Capsules_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Ma_2021_CVPR,
author = {Ma, Ding and Wu, Xiangqian},
title = {CapsuleRRT: Relationships-Aware Regression Tracking via Capsules},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
p... | Regression tracking has gained more and more attention thanks to its easy-to-implement characteristics, while existing regression trackers rarely consider the relationships between the object parts and the complete object. This would ultimately result in drift from the target object when missing some parts of the targe... |
Chi_Test-Time_Fast_Adaptation_for_Dynamic_Scene_Deblurring_via_Meta-Auxiliary_Learning_CVPR_2021_paper | Test-Time Fast Adaptation for Dynamic Scene Deblurring via Meta-Auxiliary Learning | [
"Zhixiang Chi",
"Yang Wang",
"Yuanhao Yu",
"Jin Tang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chi_Test-Time_Fast_Adaptation_for_Dynamic_Scene_Deblurring_via_Meta-Auxiliary_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chi_Test-Time_Fast_Adaptation_for_Dynamic_Scene_Deblurring_via_Meta-Auxiliary_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chi_Test-Time_Fast_Adaptation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chi_2021_CVPR,
author = {Chi, Zhixiang and Wang, Yang and Yu, Yuanhao and Tang, Jin},
title = {Test-Time Fast Adaptation for Dynamic Scene Deblurring via Meta-Auxiliary Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | In this paper, we tackle the problem of dynamic scene deblurring. Most existing deep end-to-end learning approaches adopt the same generic model for all unseen test images. These solutions are sub-optimal, as they fail to utilize the internal information within a specific image. On the other hand, a self-supervised app... |
Lin_Anycost_GANs_for_Interactive_Image_Synthesis_and_Editing_CVPR_2021_paper | Anycost GANs for Interactive Image Synthesis and Editing | [
"Ji Lin",
"Richard Zhang",
"Frieder Ganz",
"Song Han",
"Jun-Yan Zhu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Anycost_GANs_for_Interactive_Image_Synthesis_and_Editing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Anycost_GANs_for_Interactive_Image_Synthesis_and_Editing_CVPR_2021_paper.pdf | null | 2103.03243 | cvf | @InProceedings{Lin_2021_CVPR,
author = {Lin, Ji and Zhang, Richard and Ganz, Frieder and Han, Song and Zhu, Jun-Yan},
title = {Anycost GANs for Interactive Image Synthesis and Editing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Generative adversarial networks (GANs) have enabled photorealistic image synthesis and editing. However, due to the high computational cost of large-scale generators (e.g., StyleGAN2), it usually takes seconds to see the results of a single edit on edge devices, prohibiting interactive user experience. In this paper, i... |
Suo_TrafficSim_Learning_To_Simulate_Realistic_Multi-Agent_Behaviors_CVPR_2021_paper | TrafficSim: Learning To Simulate Realistic Multi-Agent Behaviors | [
"Simon Suo",
"Sebastian Regalado",
"Sergio Casas",
"Raquel Urtasun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Suo_TrafficSim_Learning_To_Simulate_Realistic_Multi-Agent_Behaviors_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Suo_TrafficSim_Learning_To_Simulate_Realistic_Multi-Agent_Behaviors_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Suo_TrafficSim_Learning_To_CVPR_2021_supplemental.zip | 2101.06557 | cvf | @InProceedings{Suo_2021_CVPR,
author = {Suo, Simon and Regalado, Sebastian and Casas, Sergio and Urtasun, Raquel},
title = {TrafficSim: Learning To Simulate Realistic Multi-Agent Behaviors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | Simulation has the potential to massively scale evaluation of self-driving systems, enabling rapid development as well as safe deployment. Bridging the gap between simulation and the real world requires realistic multi-agent behaviors. Existing simulation environments rely on heuristic-based models that directly encode... |
Cheng_Monocular_3D_Multi-Person_Pose_Estimation_by_Integrating_Top-Down_and_Bottom-Up_CVPR_2021_paper | Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks | [
"Yu Cheng",
"Bo Wang",
"Bo Yang",
"Robby T. Tan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Monocular_3D_Multi-Person_Pose_Estimation_by_Integrating_Top-Down_and_Bottom-Up_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Monocular_3D_Multi-Person_Pose_Estimation_by_Integrating_Top-Down_and_Bottom-Up_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Monocular_3D_Multi-Person_CVPR_2021_supplemental.pdf | 2104.01797 | cvf | @InProceedings{Cheng_2021_CVPR,
author = {Cheng, Yu and Wang, Bo and Yang, Bo and Tan, Robby T.},
title = {Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | In monocular video 3D multi-person pose estimation, inter-person occlusion and close interactions can cause human detection to be erroneous and human-joints grouping to be unreliable. Existing top-down methods rely on human detection and thus suffer from these problems. Existing bottom-up methods do not use human detec... |
Xiao_Space-Time_Distillation_for_Video_Super-Resolution_CVPR_2021_paper | Space-Time Distillation for Video Super-Resolution | [
"Zeyu Xiao",
"Xueyang Fu",
"Jie Huang",
"Zhen Cheng",
"Zhiwei Xiong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xiao_Space-Time_Distillation_for_Video_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_Space-Time_Distillation_for_Video_Super-Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiao_Space-Time_Distillation_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Xiao_2021_CVPR,
author = {Xiao, Zeyu and Fu, Xueyang and Huang, Jie and Cheng, Zhen and Xiong, Zhiwei},
title = {Space-Time Distillation for Video Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | Compact video super-resolution (VSR) networks can be easily deployed on resource-limited devices, e.g., smart-phones and wearable devices, but have considerable performance gaps compared with complicated VSR networks that require a large amount of computing resources. In this paper, we aim to improve the performance of... |
Morgado_Robust_Audio-Visual_Instance_Discrimination_CVPR_2021_paper | Robust Audio-Visual Instance Discrimination | [
"Pedro Morgado",
"Ishan Misra",
"Nuno Vasconcelos"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Morgado_Robust_Audio-Visual_Instance_Discrimination_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Morgado_Robust_Audio-Visual_Instance_Discrimination_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Morgado_Robust_Audio-Visual_Instance_CVPR_2021_supplemental.pdf | 2103.15916 | cvf | @InProceedings{Morgado_2021_CVPR,
author = {Morgado, Pedro and Misra, Ishan and Vasconcelos, Nuno},
title = {Robust Audio-Visual Instance Discrimination},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2... | We present a self-supervised learning method to learn audio and video representations. Prior work uses the natural correspondence between audio and video to define a standard cross-modal instance discrimination task, where a model is trained to match representations from the two modalities. However, the standard approa... |
Gao_High-Fidelity_and_Arbitrary_Face_Editing_CVPR_2021_paper | High-Fidelity and Arbitrary Face Editing | [
"Yue Gao",
"Fangyun Wei",
"Jianmin Bao",
"Shuyang Gu",
"Dong Chen",
"Fang Wen",
"Zhouhui Lian"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_High-Fidelity_and_Arbitrary_Face_Editing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_High-Fidelity_and_Arbitrary_Face_Editing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gao_High-Fidelity_and_Arbitrary_CVPR_2021_supplemental.pdf | 2103.15814 | cvf | @InProceedings{Gao_2021_CVPR,
author = {Gao, Yue and Wei, Fangyun and Bao, Jianmin and Gu, Shuyang and Chen, Dong and Wen, Fang and Lian, Zhouhui},
title = {High-Fidelity and Arbitrary Face Editing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Cycle consistency is widely used for face editing. However, we observe that the generator tends to find a tricky way to hide information from the original image to satisfy the constraint of cycle consistency, making it impossible to maintain the rich details (e.g., wrinkles and moles) of nonediting areas. In this work,... |
Zhang_Explicit_Knowledge_Incorporation_for_Visual_Reasoning_CVPR_2021_paper | Explicit Knowledge Incorporation for Visual Reasoning | [
"Yifeng Zhang",
"Ming Jiang",
"Qi Zhao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Explicit_Knowledge_Incorporation_for_Visual_Reasoning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Explicit_Knowledge_Incorporation_for_Visual_Reasoning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Explicit_Knowledge_Incorporation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Yifeng and Jiang, Ming and Zhao, Qi},
title = {Explicit Knowledge Incorporation for Visual Reasoning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | Existing explainable and explicit visual reasoning methods only perform reasoning based on visual evidence but do not take into account knowledge beyond what is in the visual scene. To addresses the knowledge gap between visual reasoning methods and the semantic complexity of real-world images, we present the first exp... |
Wu_Progressive_Unsupervised_Learning_for_Visual_Object_Tracking_CVPR_2021_paper | Progressive Unsupervised Learning for Visual Object Tracking | [
"Qiangqiang Wu",
"Jia Wan",
"Antoni B. Chan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Progressive_Unsupervised_Learning_for_Visual_Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Progressive_Unsupervised_Learning_for_Visual_Object_Tracking_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Progressive_Unsupervised_Learning_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wu_2021_CVPR,
author = {Wu, Qiangqiang and Wan, Jia and Chan, Antoni B.},
title = {Progressive Unsupervised Learning for Visual Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking. Specifically, we first learn a background discrimination (BD) model that effectively distinguishes an object from background in a contrastive learning way. We ... |
Jia_IoU_Attack_Towards_Temporally_Coherent_Black-Box_Adversarial_Attack_for_Visual_CVPR_2021_paper | IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking | [
"Shuai Jia",
"Yibing Song",
"Chao Ma",
"Xiaokang Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jia_IoU_Attack_Towards_Temporally_Coherent_Black-Box_Adversarial_Attack_for_Visual_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jia_IoU_Attack_Towards_Temporally_Coherent_Black-Box_Adversarial_Attack_for_Visual_CVPR_2021_paper.pdf | null | 2103.14938 | cvf | @InProceedings{Jia_2021_CVPR,
author = {Jia, Shuai and Song, Yibing and Ma, Chao and Yang, Xiaokang},
title = {IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Adversarial attack arises due to the vulnerability of deep neural networks to perceive input samples injected with imperceptible perturbations. Recently, adversarial attack has been applied to visual object tracking to evaluate the robustness of deep trackers. Assuming that the model structures of deep trackers are kno... |
Gao_Deep_Graph_Matching_Under_Quadratic_Constraint_CVPR_2021_paper | Deep Graph Matching Under Quadratic Constraint | [
"Quankai Gao",
"Fudong Wang",
"Nan Xue",
"Jin-Gang Yu",
"Gui-Song Xia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Deep_Graph_Matching_Under_Quadratic_Constraint_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Deep_Graph_Matching_Under_Quadratic_Constraint_CVPR_2021_paper.pdf | null | 2103.06643 | cvf | @InProceedings{Gao_2021_CVPR,
author = {Gao, Quankai and Wang, Fudong and Xue, Nan and Yu, Jin-Gang and Xia, Gui-Song},
title = {Deep Graph Matching Under Quadratic Constraint},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes. However, one main limitation with existing deep graph matching (DGM) methods lies in their ignorance of explicit constraint of grap... |
Zhang_Multi-Label_Activity_Recognition_Using_Activity-Specific_Features_and_Activity_Correlations_CVPR_2021_paper | Multi-Label Activity Recognition Using Activity-Specific Features and Activity Correlations | [
"Yanyi Zhang",
"Xinyu Li",
"Ivan Marsic"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Multi-Label_Activity_Recognition_Using_Activity-Specific_Features_and_Activity_Correlations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Multi-Label_Activity_Recognition_Using_Activity-Specific_Features_and_Activity_Correlations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Multi-Label_Activity_Recognition_CVPR_2021_supplemental.pdf | 2009.07420 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Yanyi and Li, Xinyu and Marsic, Ivan},
title = {Multi-Label Activity Recognition Using Activity-Specific Features and Activity Correlations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | Multi-label activity recognition is designed for recognizing multiple activities that are performed simultaneously or sequentially in each video. Most recent activity recognition networks focus on single-activities, that assume only one activity in each video. These networks extract shared features for all the activiti... |
Jafarian_Learning_High_Fidelity_Depths_of_Dressed_Humans_by_Watching_Social_CVPR_2021_paper | Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos | [
"Yasamin Jafarian",
"Hyun Soo Park"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jafarian_Learning_High_Fidelity_Depths_of_Dressed_Humans_by_Watching_Social_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jafarian_Learning_High_Fidelity_Depths_of_Dressed_Humans_by_Watching_Social_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jafarian_Learning_High_Fidelity_CVPR_2021_supplemental.zip | 2103.03319 | cvf | @InProceedings{Jafarian_2021_CVPR,
author = {Jafarian, Yasamin and Park, Hyun Soo},
title = {Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | A key challenge of learning the geometry of dressed humans lies in the limited availability of the ground truth data (e.g., 3D scanned models), which results in the performance degradation of 3D human reconstruction when applying to real world imagery. We address this challenge by leveraging a new data resource: a numb... |
Zhao_Unpaired_Image-to-Image_Translation_via_Latent_Energy_Transport_CVPR_2021_paper | Unpaired Image-to-Image Translation via Latent Energy Transport | [
"Yang Zhao",
"Changyou Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Unpaired_Image-to-Image_Translation_via_Latent_Energy_Transport_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Unpaired_Image-to-Image_Translation_via_Latent_Energy_Transport_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Unpaired_Image-to-Image_Translation_CVPR_2021_supplemental.pdf | 2012.00649 | cvf | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Yang and Chen, Changyou},
title = {Unpaired Image-to-Image Translation via Latent Energy Transport},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in the ambient image space, which could be computationally expensive and challenging to train. In this paper, we propose to deploy an energy-bas... |
Hong_VLN_BERT_A_Recurrent_Vision-and-Language_BERT_for_Navigation_CVPR_2021_paper | VLN BERT: A Recurrent Vision-and-Language BERT for Navigation | [
"Yicong Hong",
"Qi Wu",
"Yuankai Qi",
"Cristian Rodriguez-Opazo",
"Stephen Gould"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_VLN_BERT_A_Recurrent_Vision-and-Language_BERT_for_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_VLN_BERT_A_Recurrent_Vision-and-Language_BERT_for_Navigation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_VLN_BERT_A_CVPR_2021_supplemental.pdf | 2011.13922 | title_judge | @InProceedings{Hong_2021_CVPR,
author = {Hong, Yicong and Wu, Qi and Qi, Yuankai and Rodriguez-Opazo, Cristian and Gould, Stephen},
title = {VLN BERT: A Recurrent Vision-and-Language BERT for Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Accuracy of many visiolinguistic tasks has benefited significantly from the application of vision-and-language (V&L) BERT. However, its application for the task of vision-and-language navigation (VLN) remains limited. One reason for this is the difficulty adapting the BERT architecture to the partially observable Marko... |
Liu_Content-Aware_GAN_Compression_CVPR_2021_paper | Content-Aware GAN Compression | [
"Yuchen Liu",
"Zhixin Shu",
"Yijun Li",
"Zhe Lin",
"Federico Perazzi",
"Sun-Yuan Kung"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Content-Aware_GAN_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Content-Aware_GAN_Compression_CVPR_2021_paper.pdf | null | 2104.02244 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yuchen and Shu, Zhixin and Li, Yijun and Lin, Zhe and Perazzi, Federico and Kung, Sun-Yuan},
title = {Content-Aware GAN Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in various image generation and synthesis tasks, yet their notoriously high computational cost hinders their efficient deployment on edge devices. Directly applying generic compression approaches yields poor results on GANs, which motivates a nu... |
Byun_FBI-Denoiser_Fast_Blind_Image_Denoiser_for_Poisson-Gaussian_Noise_CVPR_2021_paper | FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise | [
"Jaeseok Byun",
"Sungmin Cha",
"Taesup Moon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Byun_FBI-Denoiser_Fast_Blind_Image_Denoiser_for_Poisson-Gaussian_Noise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Byun_FBI-Denoiser_Fast_Blind_Image_Denoiser_for_Poisson-Gaussian_Noise_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Byun_FBI-Denoiser_Fast_Blind_CVPR_2021_supplemental.pdf | 2105.10967 | title_snapshot | @InProceedings{Byun_2021_CVPR,
author = {Byun, Jaeseok and Cha, Sungmin and Moon, Taesup},
title = {FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We consider the challenging blind denoising problem for Poisson-Gaussian noise, in which no additional information about clean images or noise level parameters is available. Particularly, when only "single" noisy images are available for training a denoiser, the denoising performance of existing methods was not satisfa... |
Wang_Hijack-GAN_Unintended-Use_of_Pretrained_Black-Box_GANs_CVPR_2021_paper | Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs | [
"Hui-Po Wang",
"Ning Yu",
"Mario Fritz"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Hijack-GAN_Unintended-Use_of_Pretrained_Black-Box_GANs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Hijack-GAN_Unintended-Use_of_Pretrained_Black-Box_GANs_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Hijack-GAN_Unintended-Use_of_CVPR_2021_supplemental.pdf | 2011.14107 | title_snapshot | @InProceedings{Wang_2021_CVPR,
author = {Wang, Hui-Po and Yu, Ning and Fritz, Mario},
title = {Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {20... | While Generative Adversarial Networks (GANs) show increasing performance and the level of realism is becoming indistinguishable from natural images, this also comes with high demands on data and computation. We show that state-of-the-art GAN models -- such as they are being publicly released by researchers and industry... |
Li_LiDAR_R-CNN_An_Efficient_and_Universal_3D_Object_Detector_CVPR_2021_paper | LiDAR R-CNN: An Efficient and Universal 3D Object Detector | [
"Zhichao Li",
"Feng Wang",
"Naiyan Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_LiDAR_R-CNN_An_Efficient_and_Universal_3D_Object_Detector_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_LiDAR_R-CNN_An_Efficient_and_Universal_3D_Object_Detector_CVPR_2021_paper.pdf | null | 2103.15297 | title_snapshot | @InProceedings{Li_2021_CVPR,
author = {Li, Zhichao and Wang, Feng and Wang, Naiyan},
title = {LiDAR R-CNN: An Efficient and Universal 3D Object Detector},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2... | LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving. In this paper, we present LiDAR R-CNN, a second stage detector that can generally improve any existing 3D detector. To fulfill the real-time and high precision requirement in practice, we resort to point-based approach ... |
Xu_Line_Segment_Detection_Using_Transformers_Without_Edges_CVPR_2021_paper | Line Segment Detection Using Transformers Without Edges | [
"Yifan Xu",
"Weijian Xu",
"David Cheung",
"Zhuowen Tu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Line_Segment_Detection_Using_Transformers_Without_Edges_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Line_Segment_Detection_Using_Transformers_Without_Edges_CVPR_2021_paper.pdf | null | 2101.01909 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Yifan and Xu, Weijian and Cheung, David and Tu, Zhuowen},
title = {Line Segment Detection Using Transformers Without Edges},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LETR), takes advantages of having integrated tokenized queries, a sel... |
Ling_Region-Aware_Adaptive_Instance_Normalization_for_Image_Harmonization_CVPR_2021_paper | Region-Aware Adaptive Instance Normalization for Image Harmonization | [
"Jun Ling",
"Han Xue",
"Li Song",
"Rong Xie",
"Xiao Gu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ling_Region-Aware_Adaptive_Instance_Normalization_for_Image_Harmonization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ling_Region-Aware_Adaptive_Instance_Normalization_for_Image_Harmonization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ling_Region-Aware_Adaptive_Instance_CVPR_2021_supplemental.pdf | 2106.02853 | cvf | @InProceedings{Ling_2021_CVPR,
author = {Ling, Jun and Xue, Han and Song, Li and Xie, Rong and Gu, Xiao},
title = {Region-Aware Adaptive Instance Normalization for Image Harmonization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Image composition plays a common but important role in photo editing. To acquire photo-realistic composite images, one must adjust the appearance and visual style of the foreground to be compatible with the background. Existing deep learning methods for harmonizing composite images directly learn an image mapping netwo... |
Zhang_Learning_Tensor_Low-Rank_Prior_for_Hyperspectral_Image_Reconstruction_CVPR_2021_paper | Learning Tensor Low-Rank Prior for Hyperspectral Image Reconstruction | [
"Shipeng Zhang",
"Lizhi Wang",
"Lei Zhang",
"Hua Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_Tensor_Low-Rank_Prior_for_Hyperspectral_Image_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_Tensor_Low-Rank_Prior_for_Hyperspectral_Image_Reconstruction_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Shipeng and Wang, Lizhi and Zhang, Lei and Huang, Hua},
title = {Learning Tensor Low-Rank Prior for Hyperspectral Image Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Snapshot hyperspectral imaging has been developed to capture the spectral information of dynamic scenes. In this paper, we propose a deep neural network by learning the tensor low-rank prior of hyperspectral images (HSI) in the feature domain to promote the reconstruction quality. Our method is inspired by the canonica... |
Kaneko_Unsupervised_Learning_of_Depth_and_Depth-of-Field_Effect_From_Natural_Images_CVPR_2021_paper | Unsupervised Learning of Depth and Depth-of-Field Effect From Natural Images With Aperture Rendering Generative Adversarial Networks | [
"Takuhiro Kaneko"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kaneko_Unsupervised_Learning_of_Depth_and_Depth-of-Field_Effect_From_Natural_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kaneko_Unsupervised_Learning_of_Depth_and_Depth-of-Field_Effect_From_Natural_Images_CVPR_2021_paper.pdf | null | 2106.13041 | title_snapshot | @InProceedings{Kaneko_2021_CVPR,
author = {Kaneko, Takuhiro},
title = {Unsupervised Learning of Depth and Depth-of-Field Effect From Natural Images With Aperture Rendering Generative Adversarial Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Understanding the 3D world from 2D projected natural images is a fundamental challenge in computer vision and graphics. Recently, an unsupervised learning approach has garnered considerable attention owing to its advantages in data collection. However, to mitigate training limitations, typical methods need to impose as... |
Zhao_Sign-Agnostic_Implicit_Learning_of_Surface_Self-Similarities_for_Shape_Modeling_and_CVPR_2021_paper | Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction From Raw Point Clouds | [
"Wenbin Zhao",
"Jiabao Lei",
"Yuxin Wen",
"Jianguo Zhang",
"Kui Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Sign-Agnostic_Implicit_Learning_of_Surface_Self-Similarities_for_Shape_Modeling_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Sign-Agnostic_Implicit_Learning_of_Surface_Self-Similarities_for_Shape_Modeling_and_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Sign-Agnostic_Implicit_Learning_CVPR_2021_supplemental.pdf | 2012.07498 | cvf | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Wenbin and Lei, Jiabao and Wen, Yuxin and Zhang, Jianguo and Jia, Kui},
title = {Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction From Raw Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | Shape modeling and reconstruction from raw point clouds of objects stand as a fundamental challenge in vision and graphics research. Classical methods consider analytic shape priors; however, their performance is degraded when the scanned points deviate from the ideal conditions of cleanness and completeness. Important... |
Wang_Towards_More_Flexible_and_Accurate_Object_Tracking_With_Natural_Language_CVPR_2021_paper | Towards More Flexible and Accurate Object Tracking With Natural Language: Algorithms and Benchmark | [
"Xiao Wang",
"Xiujun Shu",
"Zhipeng Zhang",
"Bo Jiang",
"Yaowei Wang",
"Yonghong Tian",
"Feng Wu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Towards_More_Flexible_and_Accurate_Object_Tracking_With_Natural_Language_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Towards_More_Flexible_and_Accurate_Object_Tracking_With_Natural_Language_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Towards_More_Flexible_CVPR_2021_supplemental.pdf | 2103.16746 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Xiao and Shu, Xiujun and Zhang, Zhipeng and Jiang, Bo and Wang, Yaowei and Tian, Yonghong and Wu, Feng},
title = {Towards More Flexible and Accurate Object Tracking With Natural Language: Algorithms and Benchmark},
booktitle = {Proceedings of the IEEE/CV... | Tracking by natural language specification is a new rising research topic that aims at locating the target object in the video sequence based on its language description. Compared with traditional bounding box (BBox) based tracking, this setting guides object tracking with high-level semantic information, addresses the... |
Simon_On_Learning_the_Geodesic_Path_for_Incremental_Learning_CVPR_2021_paper | On Learning the Geodesic Path for Incremental Learning | [
"Christian Simon",
"Piotr Koniusz",
"Mehrtash Harandi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Simon_On_Learning_the_Geodesic_Path_for_Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Simon_On_Learning_the_Geodesic_Path_for_Incremental_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Simon_On_Learning_the_CVPR_2021_supplemental.pdf | 2104.08572 | cvf | @InProceedings{Simon_2021_CVPR,
author = {Simon, Christian and Koniusz, Piotr and Harandi, Mehrtash},
title = {On Learning the Geodesic Path for Incremental Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | Neural networks notoriously suffer from the problem of catastrophic forgetting, the phenomenon of forgetting the past knowledge when acquiring new knowledge. Overcoming catastrophic forgetting is of significant importance to emulate the process of "incremental learning", where the model is capable of learning from sequ... |
Chen_The_Lottery_Tickets_Hypothesis_for_Supervised_and_Self-Supervised_Pre-Training_in_CVPR_2021_paper | The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models | [
"Tianlong Chen",
"Jonathan Frankle",
"Shiyu Chang",
"Sijia Liu",
"Yang Zhang",
"Michael Carbin",
"Zhangyang Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_The_Lottery_Tickets_Hypothesis_for_Supervised_and_Self-Supervised_Pre-Training_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_The_Lottery_Tickets_Hypothesis_for_Supervised_and_Self-Supervised_Pre-Training_in_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_The_Lottery_Tickets_CVPR_2021_supplemental.pdf | 2012.06908 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Tianlong and Frankle, Jonathan and Chang, Shiyu and Liu, Sijia and Zhang, Yang and Carbin, Michael and Wang, Zhangyang},
title = {The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models},
booktitle = {Proc... | The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervised pre-training such as simCLR and MoCo. Pre-trained weights often boost a wide range of downstream tasks including classification, detectio... |
Sun_Iterative_Shrinking_for_Referring_Expression_Grounding_Using_Deep_Reinforcement_Learning_CVPR_2021_paper | Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning | [
"Mingjie Sun",
"Jimin Xiao",
"Eng Gee Lim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Iterative_Shrinking_for_Referring_Expression_Grounding_Using_Deep_Reinforcement_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Iterative_Shrinking_for_Referring_Expression_Grounding_Using_Deep_Reinforcement_Learning_CVPR_2021_paper.pdf | null | 2103.05187 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Mingjie and Xiao, Jimin and Lim, Eng Gee},
title = {Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | In this paper, we are tackling the proposal-free referring expression grounding task, aiming at localizing the target object according to a query sentence, without relying on off-the-shelf object proposals. Existing proposal-free methods employ a query-image matching branch to select the highest-score point in the imag... |
Ma_Simulating_Unknown_Target_Models_for_Query-Efficient_Black-Box_Attacks_CVPR_2021_paper | Simulating Unknown Target Models for Query-Efficient Black-Box Attacks | [
"Chen Ma",
"Li Chen",
"Jun-Hai Yong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Simulating_Unknown_Target_Models_for_Query-Efficient_Black-Box_Attacks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Simulating_Unknown_Target_Models_for_Query-Efficient_Black-Box_Attacks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_Simulating_Unknown_Target_CVPR_2021_supplemental.pdf | 2009.00960 | cvf | @InProceedings{Ma_2021_CVPR,
author = {Ma, Chen and Chen, Li and Yong, Jun-Hai},
title = {Simulating Unknown Target Models for Query-Efficient Black-Box Attacks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Many adversarial attacks have been proposed to investigate the security issues of deep neural networks. In the black-box setting, current model stealing attacks train a substitute model to counterfeit the functionality of the target model. However, the training requires querying the target model. Consequently, the quer... |
Luo_Diffusion_Probabilistic_Models_for_3D_Point_Cloud_Generation_CVPR_2021_paper | Diffusion Probabilistic Models for 3D Point Cloud Generation | [
"Shitong Luo",
"Wei Hu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Diffusion_Probabilistic_Models_for_3D_Point_Cloud_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Diffusion_Probabilistic_Models_for_3D_Point_Cloud_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Diffusion_Probabilistic_Models_CVPR_2021_supplemental.pdf | 2103.01458 | cvf | @InProceedings{Luo_2021_CVPR,
author = {Luo, Shitong and Hu, Wei},
title = {Diffusion Probabilistic Models for 3D Point Cloud Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation. Inspired by the diffusion process in non-equilibrium thermodynamics, we view points in point clouds as particles in a thermodynamic system ... |
Pan_Dual_Pixel_Exploration_Simultaneous_Depth_Estimation_and_Image_Restoration_CVPR_2021_paper | Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration | [
"Liyuan Pan",
"Shah Chowdhury",
"Richard Hartley",
"Miaomiao Liu",
"Hongguang Zhang",
"Hongdong Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_Dual_Pixel_Exploration_Simultaneous_Depth_Estimation_and_Image_Restoration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_Dual_Pixel_Exploration_Simultaneous_Depth_Estimation_and_Image_Restoration_CVPR_2021_paper.pdf | null | 2012.00301 | cvf | @InProceedings{Pan_2021_CVPR,
author = {Pan, Liyuan and Chowdhury, Shah and Hartley, Richard and Liu, Miaomiao and Zhang, Hongguang and Li, Hongdong},
title = {Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer... | The dual-pixel (DP) hardware works by splitting each pixel in half and creating an image pair in a single snapshot. Several works estimate depth/inverse depth by treating the DP pair as a stereo pair. However, dual-pixel disparity only occurs in image regions with the defocus blur. The heavy defocus blur in DP pairs af... |
Kapishnikov_Guided_Integrated_Gradients_An_Adaptive_Path_Method_for_Removing_Noise_CVPR_2021_paper | Guided Integrated Gradients: An Adaptive Path Method for Removing Noise | [
"Andrei Kapishnikov",
"Subhashini Venugopalan",
"Besim Avci",
"Ben Wedin",
"Michael Terry",
"Tolga Bolukbasi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kapishnikov_Guided_Integrated_Gradients_An_Adaptive_Path_Method_for_Removing_Noise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kapishnikov_Guided_Integrated_Gradients_An_Adaptive_Path_Method_for_Removing_Noise_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kapishnikov_Guided_Integrated_Gradients_CVPR_2021_supplemental.zip | 2106.09788 | cvf | @InProceedings{Kapishnikov_2021_CVPR,
author = {Kapishnikov, Andrei and Venugopalan, Subhashini and Avci, Besim and Wedin, Ben and Terry, Michael and Bolukbasi, Tolga},
title = {Guided Integrated Gradients: An Adaptive Path Method for Removing Noise},
booktitle = {Proceedings of the IEEE/CVF Conferen... | Integrated Gradients (IG) is a commonly used feature attribution method for deep neural networks. While IG has many desirable properties, the method often produces spurious/noisy pixel attributions in regions that are not related to the predicted class when applied to visual models. While this has been previously noted... |
Liu_Spatiotemporal_Registration_for_Event-Based_Visual_Odometry_CVPR_2021_paper | Spatiotemporal Registration for Event-Based Visual Odometry | [
"Daqi Liu",
"Alvaro Parra",
"Tat-Jun Chin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Spatiotemporal_Registration_for_Event-Based_Visual_Odometry_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Spatiotemporal_Registration_for_Event-Based_Visual_Odometry_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Spatiotemporal_Registration_for_CVPR_2021_supplemental.pdf | 2103.05955 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Daqi and Parra, Alvaro and Chin, Tat-Jun},
title = {Spatiotemporal Registration for Event-Based Visual Odometry},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | A useful application of event sensing is visual odometry, especially in settings that require high-temporal resolution. The state-of-the-art method of contrast maximisation recovers the motion from a batch of events by maximising the contrast of the image of warped events. However, the cost scales with image resolution... |
Li_Temporal_Action_Segmentation_From_Timestamp_Supervision_CVPR_2021_paper | Temporal Action Segmentation From Timestamp Supervision | [
"Zhe Li",
"Yazan Abu Farha",
"Jurgen Gall"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Temporal_Action_Segmentation_From_Timestamp_Supervision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Temporal_Action_Segmentation_From_Timestamp_Supervision_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Temporal_Action_Segmentation_CVPR_2021_supplemental.pdf | 2103.06669 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Zhe and Abu Farha, Yazan and Gall, Jurgen},
title = {Temporal Action Segmentation From Timestamp Supervision},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {20... | Temporal action segmentation approaches have been very successful recently. However, annotating videos with frame-wise labels to train such models is very expensive and time consuming. While weakly supervised methods trained using only ordered action lists require less annotation effort, the performance is still worse ... |
Truong_Data-Free_Model_Extraction_CVPR_2021_paper | Data-Free Model Extraction | [
"Jean-Baptiste Truong",
"Pratyush Maini",
"Robert J. Walls",
"Nicolas Papernot"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Truong_Data-Free_Model_Extraction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Truong_Data-Free_Model_Extraction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Truong_Data-Free_Model_Extraction_CVPR_2021_supplemental.pdf | 2011.14779 | cvf | @InProceedings{Truong_2021_CVPR,
author = {Truong, Jean-Baptiste and Maini, Pratyush and Walls, Robert J. and Papernot, Nicolas},
title = {Data-Free Model Extraction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | Current model extraction attacks assume that the adversary has access to a surrogate dataset with characteristics similar to the proprietary data used to train the victim model. This requirement precludes the use of existing model extraction techniques on valuable models, such as those trained on rare or hard to acquir... |
Wang_PointAugmenting_Cross-Modal_Augmentation_for_3D_Object_Detection_CVPR_2021_paper | PointAugmenting: Cross-Modal Augmentation for 3D Object Detection | [
"Chunwei Wang",
"Chao Ma",
"Ming Zhu",
"Xiaokang Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PointAugmenting_Cross-Modal_Augmentation_for_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_PointAugmenting_Cross-Modal_Augmentation_for_3D_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_PointAugmenting_Cross-Modal_Augmentation_CVPR_2021_supplemental.zip | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Chunwei and Ma, Chao and Zhu, Ming and Yang, Xiaokang},
title = {PointAugmenting: Cross-Modal Augmentation for 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Camera and LiDAR are two complementary sensors for 3D object detection in the autonomous driving context. Camera provides rich texture and color cues while LiDAR specializes in relative distance sensing. The challenge of 3D object detection lies in effectively fusing 2D camera images with 3D LiDAR points. In this paper... |
Chen_Learning_Feature_Aggregation_for_Deep_3D_Morphable_Models_CVPR_2021_paper | Learning Feature Aggregation for Deep 3D Morphable Models | [
"Zhixiang Chen",
"Tae-Kyun Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_Feature_Aggregation_for_Deep_3D_Morphable_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_Feature_Aggregation_for_Deep_3D_Morphable_Models_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Learning_Feature_Aggregation_CVPR_2021_supplemental.pdf | 2105.02173 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Zhixiang and Kim, Tae-Kyun},
title = {Learning Feature Aggregation for Deep 3D Morphable Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | 3D morphable models are widely used for the shape representation of an object class in computer vision and graphics applications. In this work, we focus on deep 3D morphable models that directly apply deep learning on 3D mesh data with a hierarchical structure to capture information at multiple scales. While great effo... |
Valverde_There_Is_More_Than_Meets_the_Eye_Self-Supervised_Multi-Object_Detection_CVPR_2021_paper | There Is More Than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking With Sound by Distilling Multimodal Knowledge | [
"Francisco Rivera Valverde",
"Juana Valeria Hurtado",
"Abhinav Valada"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Valverde_There_Is_More_Than_Meets_the_Eye_Self-Supervised_Multi-Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Valverde_There_Is_More_Than_Meets_the_Eye_Self-Supervised_Multi-Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Valverde_There_Is_More_CVPR_2021_supplemental.pdf | 2103.01353 | cvf | @InProceedings{Valverde_2021_CVPR,
author = {Valverde, Francisco Rivera and Hurtado, Juana Valeria and Valada, Abhinav},
title = {There Is More Than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking With Sound by Distilling Multimodal Knowledge},
booktitle = {Proceedings of the IEEE/... | Attributes of sound inherent to objects can provide valuable cues to learn rich representations for object detection and tracking. Furthermore, the co-occurrence of audiovisual events in videos can be exploited to localize objects over the image field by solely monitoring the sound in the environment. Thus far, this ha... |
Rebain_DeRF_Decomposed_Radiance_Fields_CVPR_2021_paper | DeRF: Decomposed Radiance Fields | [
"Daniel Rebain",
"Wei Jiang",
"Soroosh Yazdani",
"Ke Li",
"Kwang Moo Yi",
"Andrea Tagliasacchi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Rebain_DeRF_Decomposed_Radiance_Fields_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Rebain_DeRF_Decomposed_Radiance_Fields_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rebain_DeRF_Decomposed_Radiance_CVPR_2021_supplemental.zip | 2011.12490 | cvf | @InProceedings{Rebain_2021_CVPR,
author = {Rebain, Daniel and Jiang, Wei and Yazdani, Soroosh and Li, Ke and Yi, Kwang Moo and Tagliasacchi, Andrea},
title = {DeRF: Decomposed Radiance Fields},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | With the advent of Neural Radiance Fields (NeRF), neural networks can now render novel views of a 3D scene with quality that fools the human eye. Yet, generating these images is very computationally intensive, limiting their applicability in practical scenarios. In this paper, we propose a technique based on spatial de... |
Zheng_Group-aware_Label_Transfer_for_Domain_Adaptive_Person_Re-identification_CVPR_2021_paper | Group-aware Label Transfer for Domain Adaptive Person Re-identification | [
"Kecheng Zheng",
"Wu Liu",
"Lingxiao He",
"Tao Mei",
"Jiebo Luo",
"Zheng-Jun Zha"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Group-aware_Label_Transfer_for_Domain_Adaptive_Person_Re-identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Group-aware_Label_Transfer_for_Domain_Adaptive_Person_Re-identification_CVPR_2021_paper.pdf | null | 2103.12366 | cvf | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Kecheng and Liu, Wu and He, Lingxiao and Mei, Tao and Luo, Jiebo and Zha, Zheng-Jun},
title = {Group-aware Label Transfer for Domain Adaptive Person Re-identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any further annotations. Most successful UDA-ReID approaches combine clustering-based pseudo-label prediction with representation learning and perfo... |
Zhang_MR_Image_Super-Resolution_With_Squeeze_and_Excitation_Reasoning_Attention_Network_CVPR_2021_paper | MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network | [
"Yulun Zhang",
"Kai Li",
"Kunpeng Li",
"Yun Fu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_MR_Image_Super-Resolution_With_Squeeze_and_Excitation_Reasoning_Attention_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_MR_Image_Super-Resolution_With_Squeeze_and_Excitation_Reasoning_Attention_Network_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Yulun and Li, Kai and Li, Kunpeng and Fu, Yun},
title = {MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed information for reliable diagnosis and quantitative image analyses. Deep convolutional neural networks (CNNs) have shown promising ability for MR image super-resolution (SR) given low-resolution (LR) MR images. The LR MR images usuall... |
Punnakkal_BABEL_Bodies_Action_and_Behavior_With_English_Labels_CVPR_2021_paper | BABEL: Bodies, Action and Behavior With English Labels | [
"Abhinanda R. Punnakkal",
"Arjun Chandrasekaran",
"Nikos Athanasiou",
"Alejandra Quiros-Ramirez",
"Michael J. Black"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Punnakkal_BABEL_Bodies_Action_and_Behavior_With_English_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Punnakkal_BABEL_Bodies_Action_and_Behavior_With_English_Labels_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Punnakkal_BABEL_Bodies_Action_CVPR_2021_supplemental.zip | 2106.09696 | cvf | @InProceedings{Punnakkal_2021_CVPR,
author = {Punnakkal, Abhinanda R. and Chandrasekaran, Arjun and Athanasiou, Nikos and Quiros-Ramirez, Alejandra and Black, Michael J.},
title = {BABEL: Bodies, Action and Behavior With English Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer... | Understanding the semantics of human movement -- the what, how and why of the movement -- is an important problem that requires datasets of human actions with semantic labels. Existing datasets take one of two approaches. Large-scale video datasets contain many action labels but do not contain ground-truth 3D human mot... |
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