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Li_Bipartite_Graph_Network_With_Adaptive_Message_Passing_for_Unbiased_Scene_CVPR_2021_paper | Bipartite Graph Network With Adaptive Message Passing for Unbiased Scene Graph Generation | [
"Rongjie Li",
"Songyang Zhang",
"Bo Wan",
"Xuming He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Bipartite_Graph_Network_With_Adaptive_Message_Passing_for_Unbiased_Scene_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Bipartite_Graph_Network_With_Adaptive_Message_Passing_for_Unbiased_Scene_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Bipartite_Graph_Network_CVPR_2021_supplemental.pdf | 2104.00308 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Rongjie and Zhang, Songyang and Wan, Bo and He, Xuming},
title = {Bipartite Graph Network With Adaptive Message Passing for Unbiased Scene Graph Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Scene graph generation is an important visual understanding task with a broad range of vision applications. Despite recent tremendous progress, it remains challenging due to the intrinsic long-tailed class distribution and large intra-class variation. To address these issues, we introduce a novel confidence-aware bipar... |
Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper | Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps | [
"Yuk Heo",
"Yeong Jun Koh",
"Chang-Su Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Heo_Guided_Interactive_Video_CVPR_2021_supplemental.zip | 2104.10386 | cvf | @InProceedings{Heo_2021_CVPR,
author = {Heo, Yuk and Koh, Yeong Jun and Kim, Chang-Su},
title = {Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagatio... |
Shu_Learning_Spatial-Semantic_Relationship_for_Facial_Attribute_Recognition_With_Limited_Labeled_CVPR_2021_paper | Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data | [
"Ying Shu",
"Yan Yan",
"Si Chen",
"Jing-Hao Xue",
"Chunhua Shen",
"Hanzi Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shu_Learning_Spatial-Semantic_Relationship_for_Facial_Attribute_Recognition_With_Limited_Labeled_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shu_Learning_Spatial-Semantic_Relationship_for_Facial_Attribute_Recognition_With_Limited_Labeled_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Shu_2021_CVPR,
author = {Shu, Ying and Yan, Yan and Chen, Si and Xue, Jing-Hao and Shen, Chunhua and Wang, Hanzi},
title = {Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data},
booktitle = {Proceedings of the IEEE/CVF Conference on Compute... | Recent advances in deep learning have demonstrated excellent results for Facial Attribute Recognition (FAR), typically trained with large-scale labeled data. However, in many real-world FAR applications, only limited labeled data are available, leading to remarkable deterioration in performance for most existing deep l... |
Zhou_Decoupled_Dynamic_Filter_Networks_CVPR_2021_paper | Decoupled Dynamic Filter Networks | [
"Jingkai Zhou",
"Varun Jampani",
"Zhixiong Pi",
"Qiong Liu",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Decoupled_Dynamic_Filter_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Decoupled_Dynamic_Filter_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Decoupled_Dynamic_Filter_CVPR_2021_supplemental.pdf | 2104.14107 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Jingkai and Jampani, Varun and Pi, Zhixiong and Liu, Qiong and Yang, Ming-Hsuan},
title = {Decoupled Dynamic Filter Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | Convolution is one of the basic building blocks of CNN architectures. Despite its common use, standard convolution has two main shortcomings: Content-agnostic and Computation-heavy. Dynamic filters are content-adaptive, while further increasing the computational overhead. Depth-wise convolution is a lightweight variant... |
Siarohin_Motion_Representations_for_Articulated_Animation_CVPR_2021_paper | Motion Representations for Articulated Animation | [
"Aliaksandr Siarohin",
"Oliver J. Woodford",
"Jian Ren",
"Menglei Chai",
"Sergey Tulyakov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Siarohin_Motion_Representations_for_Articulated_Animation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Siarohin_Motion_Representations_for_Articulated_Animation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Siarohin_Motion_Representations_for_CVPR_2021_supplemental.pdf | 2104.11280 | cvf | @InProceedings{Siarohin_2021_CVPR,
author = {Siarohin, Aliaksandr and Woodford, Oliver J. and Ren, Jian and Chai, Menglei and Tulyakov, Sergey},
title = {Motion Representations for Articulated Animation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | We propose novel motion representations for animating articulated objects consisting of distinct parts. In a completely unsupervised manner, our method identifies object parts, tracks them in a driving video, and infers their motions by considering their principal axes. In contrast to the previous keypoint-based works,... |
Lanchantin_General_Multi-Label_Image_Classification_With_Transformers_CVPR_2021_paper | General Multi-Label Image Classification With Transformers | [
"Jack Lanchantin",
"Tianlu Wang",
"Vicente Ordonez",
"Yanjun Qi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lanchantin_General_Multi-Label_Image_Classification_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lanchantin_General_Multi-Label_Image_Classification_With_Transformers_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lanchantin_General_Multi-Label_Image_CVPR_2021_supplemental.pdf | 2011.14027 | cvf | @InProceedings{Lanchantin_2021_CVPR,
author = {Lanchantin, Jack and Wang, Tianlu and Ordonez, Vicente and Qi, Yanjun},
title = {General Multi-Label Image Classification With Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit the comp... |
Muller_On_Self-Contact_and_Human_Pose_CVPR_2021_paper | On Self-Contact and Human Pose | [
"Lea Muller",
"Ahmed A. A. Osman",
"Siyu Tang",
"Chun-Hao P. Huang",
"Michael J. Black"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Muller_On_Self-Contact_and_Human_Pose_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Muller_On_Self-Contact_and_Human_Pose_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Muller_On_Self-Contact_and_CVPR_2021_supplemental.pdf | 2104.03176 | cvf | @InProceedings{Muller_2021_CVPR,
author = {Muller, Lea and Osman, Ahmed A. A. and Tang, Siyu and Huang, Chun-Hao P. and Black, Michael J.},
title = {On Self-Contact and Human Pose},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | People touch their face 23 times an hour, they cross their arms and legs, put their hands on their hips, etc. While many images of people contain some form of self-contact, current 3D human pose and shape (HPS) regression methods typically fail to estimate this contact. To address this, we develop new datasets and meth... |
Yin_Center-Based_3D_Object_Detection_and_Tracking_CVPR_2021_paper | Center-Based 3D Object Detection and Tracking | [
"Tianwei Yin",
"Xingyi Zhou",
"Philipp Krahenbuhl"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Center-Based_3D_Object_Detection_and_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Center-Based_3D_Object_Detection_and_Tracking_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_Center-Based_3D_Object_CVPR_2021_supplemental.pdf | 2006.11275 | cvf | @InProceedings{Yin_2021_CVPR,
author = {Yin, Tianwei and Zhou, Xingyi and Krahenbuhl, Philipp},
title = {Center-Based 3D Object Detection and Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {202... | Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating a... |
Zhu_Prototype_Augmentation_and_Self-Supervision_for_Incremental_Learning_CVPR_2021_paper | Prototype Augmentation and Self-Supervision for Incremental Learning | [
"Fei Zhu",
"Xu-Yao Zhang",
"Chuang Wang",
"Fei Yin",
"Cheng-Lin Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Prototype_Augmentation_and_Self-Supervision_for_Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Prototype_Augmentation_and_Self-Supervision_for_Incremental_Learning_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Fei and Zhang, Xu-Yao and Wang, Chuang and Yin, Fei and Liu, Cheng-Lin},
title = {Prototype Augmentation and Self-Supervision for Incremental Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally. Recently, various incremental learning methods have been proposed, and some approaches achieved acceptable performance relying on stored data or complex generative... |
Popovic_CompositeTasking_Understanding_Images_by_Spatial_Composition_of_Tasks_CVPR_2021_paper | CompositeTasking: Understanding Images by Spatial Composition of Tasks | [
"Nikola Popovic",
"Danda Pani Paudel",
"Thomas Probst",
"Guolei Sun",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Popovic_CompositeTasking_Understanding_Images_by_Spatial_Composition_of_Tasks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Popovic_CompositeTasking_Understanding_Images_by_Spatial_Composition_of_Tasks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Popovic_CompositeTasking_Understanding_Images_CVPR_2021_supplemental.pdf | 2012.09030 | cvf | @InProceedings{Popovic_2021_CVPR,
author = {Popovic, Nikola and Paudel, Danda Pani and Probst, Thomas and Sun, Guolei and Van Gool, Luc},
title = {CompositeTasking: Understanding Images by Spatial Composition of Tasks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | We define the concept of CompositeTasking as the fusion of multiple, spatially distributed tasks, for various aspects of image understanding. Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network. ... |
Li_Searching_for_Fast_Model_Families_on_Datacenter_Accelerators_CVPR_2021_paper | Searching for Fast Model Families on Datacenter Accelerators | [
"Sheng Li",
"Mingxing Tan",
"Ruoming Pang",
"Andrew Li",
"Liqun Cheng",
"Quoc V. Le",
"Norman P. Jouppi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Searching_for_Fast_Model_Families_on_Datacenter_Accelerators_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Searching_for_Fast_Model_Families_on_Datacenter_Accelerators_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Searching_for_Fast_CVPR_2021_supplemental.pdf | 2102.05610 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Sheng and Tan, Mingxing and Pang, Ruoming and Li, Andrew and Cheng, Liqun and Le, Quoc V. and Jouppi, Norman P.},
title = {Searching for Fast Model Families on Datacenter Accelerators},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the emerging datacenter (... |
Kim_Task-Aware_Variational_Adversarial_Active_Learning_CVPR_2021_paper | Task-Aware Variational Adversarial Active Learning | [
"Kwanyoung Kim",
"Dongwon Park",
"Kwang In Kim",
"Se Young Chun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Task-Aware_Variational_Adversarial_Active_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Task-Aware_Variational_Adversarial_Active_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Task-Aware_Variational_Adversarial_CVPR_2021_supplemental.pdf | 2002.04709 | cvf | @InProceedings{Kim_2021_CVPR,
author = {Kim, Kwanyoung and Park, Dongwon and Kim, Kwang In and Chun, Se Young},
title = {Task-Aware Variational Adversarial Active Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques. Active learning (AL) tackles this by querying the most informative samples to be annotated among unlabeled pool. Two promising directions for AL that have been recently explored are... |
Amir_Understanding_and_Simplifying_Perceptual_Distances_CVPR_2021_paper | Understanding and Simplifying Perceptual Distances | [
"Dan Amir",
"Yair Weiss"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Amir_Understanding_and_Simplifying_Perceptual_Distances_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Amir_Understanding_and_Simplifying_Perceptual_Distances_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Amir_Understanding_and_Simplifying_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Amir_2021_CVPR,
author = {Amir, Dan and Weiss, Yair},
title = {Understanding and Simplifying Perceptual Distances},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {1... | Perceptual metrics based on features of deep Convolutional Neural Networks (CNNs) have shown remarkable success when used as loss functions in a range of computer vision problems and significantly outperform classical losses such as L1 or L2 in pixel space. The source of this success remains somewhat mysterious, especi... |
Chen_Class-Aware_Robust_Adversarial_Training_for_Object_Detection_CVPR_2021_paper | Class-Aware Robust Adversarial Training for Object Detection | [
"Pin-Chun Chen",
"Bo-Han Kung",
"Jun-Cheng Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Class-Aware_Robust_Adversarial_Training_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Class-Aware_Robust_Adversarial_Training_for_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Class-Aware_Robust_Adversarial_CVPR_2021_supplemental.pdf | 2103.16148 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Pin-Chun and Kung, Bo-Han and Chen, Jun-Cheng},
title = {Class-Aware Robust Adversarial Training for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
ye... | Object detection is an important computer vision task with plenty of real-world applications; therefore, how to enhance its robustness against adversarial attacks has emerged as a crucial issue. However, most of the previous defense methods focused on the classification task and had few analysis in the context of the o... |
Cui_Bayesian_Nested_Neural_Networks_for_Uncertainty_Calibration_and_Adaptive_Compression_CVPR_2021_paper | Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression | [
"Yufei Cui",
"Ziquan Liu",
"Qiao Li",
"Antoni B. Chan",
"Chun Jason Xue"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cui_Bayesian_Nested_Neural_Networks_for_Uncertainty_Calibration_and_Adaptive_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cui_Bayesian_Nested_Neural_Networks_for_Uncertainty_Calibration_and_Adaptive_Compression_CVPR_2021_paper.pdf | null | 2101.11353 | cvf | @InProceedings{Cui_2021_CVPR,
author = {Cui, Yufei and Liu, Ziquan and Li, Qiao and Chan, Antoni B. and Xue, Chun Jason},
title = {Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | Nested networks or slimmable networks are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. Recent studies have focused on a "nested dropout" layer, which is able to order the nodes of a layer by importance during training, thus generating a nes... |
Kar_Fast_Bayesian_Uncertainty_Estimation_and_Reduction_of_Batch_Normalized_Single_CVPR_2021_paper | Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network | [
"Aupendu Kar",
"Prabir Kumar Biswas"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kar_Fast_Bayesian_Uncertainty_Estimation_and_Reduction_of_Batch_Normalized_Single_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kar_Fast_Bayesian_Uncertainty_Estimation_and_Reduction_of_Batch_Normalized_Single_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kar_Fast_Bayesian_Uncertainty_CVPR_2021_supplemental.pdf | 1903.09410 | cvf | @InProceedings{Kar_2021_CVPR,
author = {Kar, Aupendu and Biswas, Prabir Kumar},
title = {Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Convolutional neural network (CNN) has achieved unprecedented success in image super-resolution tasks in recent years. However, the network's performance depends on the distribution of the training sets and degrades on out-of-distribution samples. This paper adopts a Bayesian approach for estimating uncertainty associa... |
Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper | Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers | [
"Apratim Bhattacharyya",
"Daniel Olmeda Reino",
"Mario Fritz",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_CVPR_2021_supplemental.pdf | 2106.12442 | title_snapshot | @InProceedings{Bhattacharyya_2021_CVPR,
author = {Bhattacharyya, Apratim and Reino, Daniel Olmeda and Fritz, Mario and Schiele, Bernt},
title = {Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collision... |
Ding_RepVGG_Making_VGG-Style_ConvNets_Great_Again_CVPR_2021_paper | RepVGG: Making VGG-Style ConvNets Great Again | [
"Xiaohan Ding",
"Xiangyu Zhang",
"Ningning Ma",
"Jungong Han",
"Guiguang Ding",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_RepVGG_Making_VGG-Style_ConvNets_Great_Again_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_RepVGG_Making_VGG-Style_ConvNets_Great_Again_CVPR_2021_paper.pdf | null | 2101.03697 | cvf | @InProceedings{Ding_2021_CVPR,
author = {Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian},
title = {RepVGG: Making VGG-Style ConvNets Great Again},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized... |
Fu_Partial_Feature_Selection_and_Alignment_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper | Partial Feature Selection and Alignment for Multi-Source Domain Adaptation | [
"Yangye Fu",
"Ming Zhang",
"Xing Xu",
"Zuo Cao",
"Chao Ma",
"Yanli Ji",
"Kai Zuo",
"Huimin Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Partial_Feature_Selection_and_Alignment_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Partial_Feature_Selection_and_Alignment_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Fu_2021_CVPR,
author = {Fu, Yangye and Zhang, Ming and Xu, Xing and Cao, Zuo and Ma, Chao and Ji, Yanli and Zuo, Kai and Lu, Huimin},
title = {Partial Feature Selection and Alignment for Multi-Source Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vi... | Multi-Source Domain Adaptation (MSDA), which dedicates to transfer the knowledge learned from multiple source domains to an unlabeled target domain, has drawn increasing attention in the research community. By assuming that the source and target domains share consistent key feature representations and identical label s... |
Guo_Multi-Institutional_Collaborations_for_Improving_Deep_Learning-Based_Magnetic_Resonance_Image_Reconstruction_CVPR_2021_paper | Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning | [
"Pengfei Guo",
"Puyang Wang",
"Jinyuan Zhou",
"Shanshan Jiang",
"Vishal M. Patel"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Multi-Institutional_Collaborations_for_Improving_Deep_Learning-Based_Magnetic_Resonance_Image_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Multi-Institutional_Collaborations_for_Improving_Deep_Learning-Based_Magnetic_Resonance_Image_Reconstruction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guo_Multi-Institutional_Collaborations_for_CVPR_2021_supplemental.pdf | 2103.02148 | cvf | @InProceedings{Guo_2021_CVPR,
author = {Guo, Pengfei and Wang, Puyang and Zhou, Jinyuan and Jiang, Shanshan and Patel, Vishal M.},
title = {Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning},
booktitle = {Proceedings ... | Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is d... |
Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper | UAV-Human: A Large Benchmark for Human Behavior Understanding With Unmanned Aerial Vehicles | [
"Tianjiao Li",
"Jun Liu",
"Wei Zhang",
"Yun Ni",
"Wenqian Wang",
"Zhiheng Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper.pdf | null | 2104.00946 | title_snapshot | @InProceedings{Li_2021_CVPR,
author = {Li, Tianjiao and Liu, Jun and Zhang, Wei and Ni, Yun and Wang, Wenqian and Li, Zhiheng},
title = {UAV-Human: A Large Benchmark for Human Behavior Understanding With Unmanned Aerial Vehicles},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models. However, existing benchmarks have limitati... |
Meyer_An_Alternative_Probabilistic_Interpretation_of_the_Huber_Loss_CVPR_2021_paper | An Alternative Probabilistic Interpretation of the Huber Loss | [
"Gregory P. Meyer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Meyer_An_Alternative_Probabilistic_Interpretation_of_the_Huber_Loss_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Meyer_An_Alternative_Probabilistic_Interpretation_of_the_Huber_Loss_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meyer_An_Alternative_Probabilistic_CVPR_2021_supplemental.pdf | 1911.02088 | cvf | @InProceedings{Meyer_2021_CVPR,
author = {Meyer, Gregory P.},
title = {An Alternative Probabilistic Interpretation of the Huber Loss},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | The Huber loss is a robust loss function used for a wide range of regression tasks. To utilize the Huber loss, a parameter that controls the transitions from a quadratic function to an absolute value function needs to be selected. We believe the standard probabilistic interpretation that relates the Huber loss to the H... |
Feng_Siamese_Natural_Language_Tracker_Tracking_by_Natural_Language_Descriptions_With_CVPR_2021_paper | Siamese Natural Language Tracker: Tracking by Natural Language Descriptions With Siamese Trackers | [
"Qi Feng",
"Vitaly Ablavsky",
"Qinxun Bai",
"Stan Sclaroff"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Siamese_Natural_Language_Tracker_Tracking_by_Natural_Language_Descriptions_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Siamese_Natural_Language_Tracker_Tracking_by_Natural_Language_Descriptions_With_CVPR_2021_paper.pdf | null | 1912.02048 | cvf | @InProceedings{Feng_2021_CVPR,
author = {Feng, Qi and Ablavsky, Vitaly and Bai, Qinxun and Sclaroff, Stan},
title = {Siamese Natural Language Tracker: Tracking by Natural Language Descriptions With Siamese Trackers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) specification task. The proposed SNLT is applicable to a wide range of Siamese trackers, providing a new class of baselines for the tracking by NL task and promising futu... |
Xu_Discrimination-Aware_Mechanism_for_Fine-Grained_Representation_Learning_CVPR_2021_paper | Discrimination-Aware Mechanism for Fine-Grained Representation Learning | [
"Furong Xu",
"Meng Wang",
"Wei Zhang",
"Yuan Cheng",
"Wei Chu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Discrimination-Aware_Mechanism_for_Fine-Grained_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Discrimination-Aware_Mechanism_for_Fine-Grained_Representation_Learning_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Xu_2021_CVPR,
author = {Xu, Furong and Wang, Meng and Zhang, Wei and Cheng, Yuan and Chu, Wei},
title = {Discrimination-Aware Mechanism for Fine-Grained Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Recently, with the emergence of retrieval requirements for certain individual in the same superclass, e.g., birds, persons, cars, fine-grained recognition task has attracted a significant amount of attention from academia and industry. In fine-grained recognition scenario, the inter-class differences are quite diverse ... |
Bang_Rainbow_Memory_Continual_Learning_With_a_Memory_of_Diverse_Samples_CVPR_2021_paper | Rainbow Memory: Continual Learning With a Memory of Diverse Samples | [
"Jihwan Bang",
"Heesu Kim",
"YoungJoon Yoo",
"Jung-Woo Ha",
"Jonghyun Choi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bang_Rainbow_Memory_Continual_Learning_With_a_Memory_of_Diverse_Samples_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bang_Rainbow_Memory_Continual_Learning_With_a_Memory_of_Diverse_Samples_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bang_Rainbow_Memory_Continual_CVPR_2021_supplemental.pdf | 2103.17230 | cvf | @InProceedings{Bang_2021_CVPR,
author = {Bang, Jihwan and Kim, Heesu and Yoo, YoungJoon and Ha, Jung-Woo and Choi, Jonghyun},
title = {Rainbow Memory: Continual Learning With a Memory of Diverse Samples},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realistic rather artificial. Instead, we focus on 'blurry' task boundary; where tasks shares classes and is more realistic and practical. To address... |
Chang_Learning_Discriminative_Prototypes_With_Dynamic_Time_Warping_CVPR_2021_paper | Learning Discriminative Prototypes With Dynamic Time Warping | [
"Xiaobin Chang",
"Frederick Tung",
"Greg Mori"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chang_Learning_Discriminative_Prototypes_With_Dynamic_Time_Warping_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chang_Learning_Discriminative_Prototypes_With_Dynamic_Time_Warping_CVPR_2021_paper.pdf | null | 2103.09458 | cvf | @InProceedings{Chang_2021_CVPR,
author = {Chang, Xiaobin and Tung, Frederick and Mori, Greg},
title = {Learning Discriminative Prototypes With Dynamic Time Warping},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Dynamic Time Warping (DTW) is widely used for temporal data processing. However, existing methods can neither learn the discriminative prototypes of different classes nor exploit such prototypes for further analysis. We propose Discriminative Prototype DTW (DP-DTW), a novel method to learn class-specific discriminative... |
Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper | Deep Implicit Moving Least-Squares Functions for 3D Reconstruction | [
"Shi-Lin Liu",
"Hao-Xiang Guo",
"Hao Pan",
"Peng-Shuai Wang",
"Xin Tong",
"Yang Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Deep_Implicit_Moving_CVPR_2021_supplemental.pdf | 2103.12266 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Shi-Lin and Guo, Hao-Xiang and Pan, Hao and Wang, Peng-Shuai and Tong, Xin and Liu, Yang},
title = {Deep Implicit Moving Least-Squares Functions for 3D Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | Point set is a flexible and lightweight representation widely used for 3D deep learning. However, their discrete nature prevents them from representing continuous and fine geometry, posing a major issue for learning-based shape generation. In this work, we turn the discrete point sets into smooth surfaces by introducin... |
Lee_Video_Prediction_Recalling_Long-Term_Motion_Context_via_Memory_Alignment_Learning_CVPR_2021_paper | Video Prediction Recalling Long-Term Motion Context via Memory Alignment Learning | [
"Sangmin Lee",
"Hak Gu Kim",
"Dae Hwi Choi",
"Hyung-Il Kim",
"Yong Man Ro"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Video_Prediction_Recalling_Long-Term_Motion_Context_via_Memory_Alignment_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Video_Prediction_Recalling_Long-Term_Motion_Context_via_Memory_Alignment_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Video_Prediction_Recalling_CVPR_2021_supplemental.pdf | 2104.00924 | cvf | @InProceedings{Lee_2021_CVPR,
author = {Lee, Sangmin and Kim, Hak Gu and Choi, Dae Hwi and Kim, Hyung-Il and Ro, Yong Man},
title = {Video Prediction Recalling Long-Term Motion Context via Memory Alignment Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bottlenecks arising when dealing with the long-term motion context a... |
Wang_Automatic_Vertebra_Localization_and_Identification_in_CT_by_Spine_Rectification_CVPR_2021_paper | Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-Constrained Optimization | [
"Fakai Wang",
"Kang Zheng",
"Le Lu",
"Jing Xiao",
"Min Wu",
"Shun Miao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Automatic_Vertebra_Localization_and_Identification_in_CT_by_Spine_Rectification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Automatic_Vertebra_Localization_and_Identification_in_CT_by_Spine_Rectification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Automatic_Vertebra_Localization_CVPR_2021_supplemental.pdf | 2012.07947 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Fakai and Zheng, Kang and Lu, Le and Xiao, Jing and Wu, Min and Miao, Shun},
title = {Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-Constrained Optimization},
booktitle = {Proceedings of the IEEE/CVF Con... | Accurate vertebra localization and identification are required in many clinical applications of spine disorder diagnosis and surgery planning. However, significant challenges are posed in this task by highly varying pathologies (such as vertebral compression fracture, scoliosis, and vertebral fixation) and imaging cond... |
Wu_MotionRNN_A_Flexible_Model_for_Video_Prediction_With_Spacetime-Varying_Motions_CVPR_2021_paper | MotionRNN: A Flexible Model for Video Prediction With Spacetime-Varying Motions | [
"Haixu Wu",
"Zhiyu Yao",
"Jianmin Wang",
"Mingsheng Long"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_MotionRNN_A_Flexible_Model_for_Video_Prediction_With_Spacetime-Varying_Motions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_MotionRNN_A_Flexible_Model_for_Video_Prediction_With_Spacetime-Varying_Motions_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_MotionRNN_A_Flexible_CVPR_2021_supplemental.pdf | 2103.02243 | cvf | @InProceedings{Wu_2021_CVPR,
author = {Wu, Haixu and Yao, Zhiyu and Wang, Jianmin and Long, Mingsheng},
title = {MotionRNN: A Flexible Model for Video Prediction With Spacetime-Varying Motions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | This paper tackles video prediction from a new dimension of predicting spacetime-varying motions that are incessantly changing across both space and time. Prior methods mainly capture the temporal state transitions but overlook the complex spatiotemporal variations of the motion itself, making them difficult to adapt t... |
Huang_MOS_Towards_Scaling_Out-of-Distribution_Detection_for_Large_Semantic_Space_CVPR_2021_paper | MOS: Towards Scaling Out-of-Distribution Detection for Large Semantic Space | [
"Rui Huang",
"Yixuan Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MOS_Towards_Scaling_Out-of-Distribution_Detection_for_Large_Semantic_Space_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_MOS_Towards_Scaling_Out-of-Distribution_Detection_for_Large_Semantic_Space_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_MOS_Towards_Scaling_CVPR_2021_supplemental.pdf | 2105.01879 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Rui and Li, Yixuan},
title = {MOS: Towards Scaling Out-of-Distribution Detection for Large Semantic Space},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = ... | Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Existing solutions are mainly driven by small datasets, with low resolution and very few class labels (e.g., CIFAR). As a result, OOD detection for large-scale image classification tasks rem... |
Sadhu_Visual_Semantic_Role_Labeling_for_Video_Understanding_CVPR_2021_paper | Visual Semantic Role Labeling for Video Understanding | [
"Arka Sadhu",
"Tanmay Gupta",
"Mark Yatskar",
"Ram Nevatia",
"Aniruddha Kembhavi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sadhu_Visual_Semantic_Role_Labeling_for_Video_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sadhu_Visual_Semantic_Role_Labeling_for_Video_Understanding_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sadhu_Visual_Semantic_Role_CVPR_2021_supplemental.pdf | 2104.00990 | cvf | @InProceedings{Sadhu_2021_CVPR,
author = {Sadhu, Arka and Gupta, Tanmay and Yatskar, Mark and Nevatia, Ram and Kembhavi, Aniruddha},
title = {Visual Semantic Role Labeling for Video Understanding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | We propose a new framework for understanding and representing related salient events in a video using visual semantic role labeling. We represent videos as a set of related events, wherein each event consists of a verb and multiple entities that fulfill various roles relevant to that event. To study the challenging tas... |
Wang_SwiftNet_Real-Time_Video_Object_Segmentation_CVPR_2021_paper | SwiftNet: Real-Time Video Object Segmentation | [
"Haochen Wang",
"Xiaolong Jiang",
"Haibing Ren",
"Yao Hu",
"Song Bai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_SwiftNet_Real-Time_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_SwiftNet_Real-Time_Video_Object_Segmentation_CVPR_2021_paper.pdf | null | 2102.04604 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Haochen and Jiang, Xiaolong and Ren, Haibing and Hu, Yao and Bai, Song},
title = {SwiftNet: Real-Time Video Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | In this work we present SwiftNet for real-time semi-supervised video object segmentation (one-shot VOS), which reports 77.8% J&F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance. We achieve this by elaborately compressing spatiotemporal redundancy in m... |
Han_Contrastive_Embedding_for_Generalized_Zero-Shot_Learning_CVPR_2021_paper | Contrastive Embedding for Generalized Zero-Shot Learning | [
"Zongyan Han",
"Zhenyong Fu",
"Shuo Chen",
"Jian Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Han_Contrastive_Embedding_for_Generalized_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Han_Contrastive_Embedding_for_Generalized_Zero-Shot_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Han_Contrastive_Embedding_for_CVPR_2021_supplemental.pdf | 2103.16173 | cvf | @InProceedings{Han_2021_CVPR,
author = {Han, Zongyan and Fu, Zhenyong and Chen, Shuo and Yang, Jian},
title = {Contrastive Embedding for Generalized Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance pr... |
Benny_Scale-Localized_Abstract_Reasoning_CVPR_2021_paper | Scale-Localized Abstract Reasoning | [
"Yaniv Benny",
"Niv Pekar",
"Lior Wolf"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Benny_Scale-Localized_Abstract_Reasoning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Benny_Scale-Localized_Abstract_Reasoning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Benny_Scale-Localized_Abstract_Reasoning_CVPR_2021_supplemental.pdf | 2009.09405 | cvf | @InProceedings{Benny_2021_CVPR,
author = {Benny, Yaniv and Pekar, Niv and Wolf, Lior},
title = {Scale-Localized Abstract Reasoning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = ... | We consider the abstract relational reasoning task, which is commonly used as an intelligence test. Since some patterns have spatial rationales, while others are only semantic, we propose a multi-scale architecture that processes each query in multiple resolutions. We show that indeed different rules are solved by diff... |
Fu_Transferable_Query_Selection_for_Active_Domain_Adaptation_CVPR_2021_paper | Transferable Query Selection for Active Domain Adaptation | [
"Bo Fu",
"Zhangjie Cao",
"Jianmin Wang",
"Mingsheng Long"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Transferable_Query_Selection_for_Active_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Transferable_Query_Selection_for_Active_Domain_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_Transferable_Query_Selection_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Fu_2021_CVPR,
author = {Fu, Bo and Cao, Zhangjie and Wang, Jianmin and Long, Mingsheng},
title = {Transferable Query Selection for Active Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Unsupervised domain adaptation (UDA) enables transferring knowledge from a related source domain to a fully unlabeled target domain. Despite the significant advances in UDA, the performance gap remains quite large between UDA and supervised learning with fully labeled target data. Active domain adaptation (ADA) mitigat... |
Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper | CLCC: Contrastive Learning for Color Constancy | [
"Yi-Chen Lo",
"Chia-Che Chang",
"Hsuan-Chao Chiu",
"Yu-Hao Huang",
"Chia-Ping Chen",
"Yu-Lin Chang",
"Kevin Jou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper.pdf | null | 2106.04989 | cvf | @InProceedings{Lo_2021_CVPR,
author = {Lo, Yi-Chen and Chang, Chia-Che and Chiu, Hsuan-Chao and Huang, Yu-Hao and Chen, Chia-Ping and Chang, Yu-Lin and Jou, Kevin},
title = {CLCC: Contrastive Learning for Color Constancy},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | In this paper, we present CLCC, a novel contrastive learning framework for color constancy. Contrastive learning has been applied for learning high-quality visual representations for image classification. One key aspect to yield useful representations for image classification is to design illuminant invariant augmentat... |
Wang_Dual_Attention_Suppression_Attack_Generate_Adversarial_Camouflage_in_Physical_World_CVPR_2021_paper | Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World | [
"Jiakai Wang",
"Aishan Liu",
"Zixin Yin",
"Shunchang Liu",
"Shiyu Tang",
"Xianglong Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Dual_Attention_Suppression_Attack_Generate_Adversarial_Camouflage_in_Physical_World_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Dual_Attention_Suppression_Attack_Generate_Adversarial_Camouflage_in_Physical_World_CVPR_2021_paper.pdf | null | 2103.01050 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Jiakai and Liu, Aishan and Yin, Zixin and Liu, Shunchang and Tang, Shiyu and Liu, Xianglong},
title = {Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer ... | Deep learning models are vulnerable to adversarial examples. As a more threatening type for practical deep learning systems, physical adversarial examples have received extensive research attention in recent years. However, without exploiting the intrinsic characteristics such as model-agnostic and human-specific patte... |
Guo_Long-Tailed_Multi-Label_Visual_Recognition_by_Collaborative_Training_on_Uniform_and_CVPR_2021_paper | Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings | [
"Hao Guo",
"Song Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Long-Tailed_Multi-Label_Visual_Recognition_by_Collaborative_Training_on_Uniform_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Long-Tailed_Multi-Label_Visual_Recognition_by_Collaborative_Training_on_Uniform_and_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Guo_2021_CVPR,
author = {Guo, Hao and Wang, Song},
title = {Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Long-tailed data distribution is common in many multi-label visual recognition tasks and the direct use of these data for training usually leads to relatively low performance on tail classes. While re-balanced data sampling can improve the performance on tail classes, it may also hurt the performance on head classes in... |
Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper | 3D Object Detection With Pointformer | [
"Xuran Pan",
"Zhuofan Xia",
"Shiji Song",
"Li Erran Li",
"Gao Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pan_3D_Object_Detection_CVPR_2021_supplemental.pdf | 2012.11409 | cvf | @InProceedings{Pan_2021_CVPR,
author = {Pan, Xuran and Xia, Zhuofan and Song, Shiji and Li, Li Erran and Huang, Gao},
title = {3D Object Detection With Pointformer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer backbone designed for 3D point clouds to learn features effectively. Specifically, a Local Transformer module is employed to model interacti... |
Jung_Fair_Feature_Distillation_for_Visual_Recognition_CVPR_2021_paper | Fair Feature Distillation for Visual Recognition | [
"Sangwon Jung",
"Donggyu Lee",
"Taeeon Park",
"Taesup Moon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jung_Fair_Feature_Distillation_for_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jung_Fair_Feature_Distillation_for_Visual_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jung_Fair_Feature_Distillation_CVPR_2021_supplemental.pdf | 2106.04411 | cvf | @InProceedings{Jung_2021_CVPR,
author = {Jung, Sangwon and Lee, Donggyu and Park, Taeeon and Moon, Taesup},
title = {Fair Feature Distillation for Visual Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | Fairness is becoming an increasingly crucial issue for computer vision, especially in the human-related decision systems. However, achieving algorithmic fairness, which makes a model produce indiscriminative outcomes against protected groups, is still an unresolved problem. In this paper, we devise a systematic approac... |
Zhang_Diversifying_Sample_Generation_for_Accurate_Data-Free_Quantization_CVPR_2021_paper | Diversifying Sample Generation for Accurate Data-Free Quantization | [
"Xiangguo Zhang",
"Haotong Qin",
"Yifu Ding",
"Ruihao Gong",
"Qinghua Yan",
"Renshuai Tao",
"Yuhang Li",
"Fengwei Yu",
"Xianglong Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Diversifying_Sample_Generation_for_Accurate_Data-Free_Quantization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Diversifying_Sample_Generation_for_Accurate_Data-Free_Quantization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Diversifying_Sample_Generation_CVPR_2021_supplemental.pdf | 2103.01049 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Xiangguo and Qin, Haotong and Ding, Yifu and Gong, Ruihao and Yan, Qinghua and Tao, Renshuai and Li, Yuhang and Yu, Fengwei and Liu, Xianglong},
title = {Diversifying Sample Generation for Accurate Data-Free Quantization},
booktitle = {Proceedings of t... | Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical and promising solution. It synthesizes data for calibrating the quantized model according to the batch normalization (BN) statistics of FP3... |
Duke_SSTVOS_Sparse_Spatiotemporal_Transformers_for_Video_Object_Segmentation_CVPR_2021_paper | SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation | [
"Brendan Duke",
"Abdalla Ahmed",
"Christian Wolf",
"Parham Aarabi",
"Graham W. Taylor"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Duke_SSTVOS_Sparse_Spatiotemporal_Transformers_for_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Duke_SSTVOS_Sparse_Spatiotemporal_Transformers_for_Video_Object_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duke_SSTVOS_Sparse_Spatiotemporal_CVPR_2021_supplemental.pdf | 2101.08833 | cvf | @InProceedings{Duke_2021_CVPR,
author = {Duke, Brendan and Ahmed, Abdalla and Wolf, Christian and Aarabi, Parham and Taylor, Graham W.},
title = {SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable, end-to-end method for VOS called Sparse Spatiotemporal Transformers (SST). SST extracts per-pixel representations for each object in a vid... |
Xu_Inferring_CAD_Modeling_Sequences_Using_Zone_Graphs_CVPR_2021_paper | Inferring CAD Modeling Sequences Using Zone Graphs | [
"Xianghao Xu",
"Wenzhe Peng",
"Chin-Yi Cheng",
"Karl D.D. Willis",
"Daniel Ritchie"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Inferring_CAD_Modeling_Sequences_Using_Zone_Graphs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Inferring_CAD_Modeling_Sequences_Using_Zone_Graphs_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Inferring_CAD_Modeling_CVPR_2021_supplemental.pdf | 2104.03900 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Xianghao and Peng, Wenzhe and Cheng, Chin-Yi and Willis, Karl D.D. and Ritchie, Daniel},
title = {Inferring CAD Modeling Sequences Using Zone Graphs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | In computer-aided design (CAD), the ability to "reverse engineer" the modeling steps used to create 3D shapes is a long-sought-after goal. This process can be decomposed into two sub-problems: converting an input mesh or point cloud into a boundary representation (or B-rep), and then inferring modeling operations which... |
Shen_Closed-Form_Factorization_of_Latent_Semantics_in_GANs_CVPR_2021_paper | Closed-Form Factorization of Latent Semantics in GANs | [
"Yujun Shen",
"Bolei Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Closed-Form_Factorization_of_Latent_Semantics_in_GANs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Closed-Form_Factorization_of_Latent_Semantics_in_GANs_CVPR_2021_paper.pdf | null | 2007.06600 | cvf | @InProceedings{Shen_2021_CVPR,
author = {Shen, Yujun and Zhou, Bolei},
title = {Closed-Form Factorization of Latent Semantics in GANs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In order to identify such latent dimensions for image editing, previous methods typically annotate a collection of synthesized samples and train linear class... |
Kothari_Weakly-Supervised_Physically_Unconstrained_Gaze_Estimation_CVPR_2021_paper | Weakly-Supervised Physically Unconstrained Gaze Estimation | [
"Rakshit Kothari",
"Shalini De Mello",
"Umar Iqbal",
"Wonmin Byeon",
"Seonwook Park",
"Jan Kautz"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kothari_Weakly-Supervised_Physically_Unconstrained_Gaze_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kothari_Weakly-Supervised_Physically_Unconstrained_Gaze_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kothari_Weakly-Supervised_Physically_Unconstrained_CVPR_2021_supplemental.pdf | 2105.09803 | cvf | @InProceedings{Kothari_2021_CVPR,
author = {Kothari, Rakshit and De Mello, Shalini and Iqbal, Umar and Byeon, Wonmin and Park, Seonwook and Kautz, Jan},
title = {Weakly-Supervised Physically Unconstrained Gaze Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios. In contrast, videos of human interactions in unconstrained environments are abundantly available and can be much more easily annotated with frame-level activity label... |
Yang_A_Circular-Structured_Representation_for_Visual_Emotion_Distribution_Learning_CVPR_2021_paper | A Circular-Structured Representation for Visual Emotion Distribution Learning | [
"Jingyuan Yang",
"Jie Li",
"Leida Li",
"Xiumei Wang",
"Xinbo Gao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_A_Circular-Structured_Representation_for_Visual_Emotion_Distribution_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_A_Circular-Structured_Representation_for_Visual_Emotion_Distribution_Learning_CVPR_2021_paper.pdf | null | 2106.12450 | title_snapshot | @InProceedings{Yang_2021_CVPR,
author = {Yang, Jingyuan and Li, Jie and Li, Leida and Wang, Xiumei and Gao, Xinbo},
title = {A Circular-Structured Representation for Visual Emotion Distribution Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks. Since human emotions are ambiguous and subjective, it is more reasonable to address VEA in a label distribution learning (LDL) paradigm rather than a single-label classification task. Diff... |
Desai_VirTex_Learning_Visual_Representations_From_Textual_Annotations_CVPR_2021_paper | VirTex: Learning Visual Representations From Textual Annotations | [
"Karan Desai",
"Justin Johnson"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Desai_VirTex_Learning_Visual_Representations_From_Textual_Annotations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Desai_VirTex_Learning_Visual_Representations_From_Textual_Annotations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Desai_VirTex_Learning_Visual_CVPR_2021_supplemental.pdf | 2006.06666 | cvf | @InProceedings{Desai_2021_CVPR,
author = {Desai, Karan and Johnson, Justin},
title = {VirTex: Learning Visual Representations From Textual Annotations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {202... | The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, we aim to learn high-quality visual representations fr... |
Lu_MASA-SR_Matching_Acceleration_and_Spatial_Adaptation_for_Reference-Based_Image_Super-Resolution_CVPR_2021_paper | MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution | [
"Liying Lu",
"Wenbo Li",
"Xin Tao",
"Jiangbo Lu",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_MASA-SR_Matching_Acceleration_and_Spatial_Adaptation_for_Reference-Based_Image_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_MASA-SR_Matching_Acceleration_and_Spatial_Adaptation_for_Reference-Based_Image_Super-Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_MASA-SR_Matching_Acceleration_CVPR_2021_supplemental.pdf | 2106.02299 | title_snapshot | @InProceedings{Lu_2021_CVPR,
author = {Lu, Liying and Li, Wenbo and Tao, Xin and Lu, Jiangbo and Jia, Jiaya},
title = {MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | Reference-based image super-resolution (RefSR) has shown promising success in recovering high-frequency details by utilizing an external reference image (Ref). In this task, texture details are transferred from the Ref image to the low-resolution (LR) image according to their point- or patch-wise correspondence. Theref... |
Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper | Spatiotemporal Contrastive Video Representation Learning | [
"Rui Qian",
"Tianjian Meng",
"Boqing Gong",
"Ming-Hsuan Yang",
"Huisheng Wang",
"Serge Belongie",
"Yin Cui"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qian_Spatiotemporal_Contrastive_Video_CVPR_2021_supplemental.pdf | 2008.03800 | cvf | @InProceedings{Qian_2021_CVPR,
author = {Qian, Rui and Meng, Tianjian and Gong, Boqing and Yang, Ming-Hsuan and Wang, Huisheng and Belongie, Serge and Cui, Yin},
title = {Spatiotemporal Contrastive Video Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips... |
Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper | Scaled-YOLOv4: Scaling Cross Stage Partial Network | [
"Chien-Yao Wang",
"Alexey Bochkovskiy",
"Hong-Yuan Mark Liao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Scaled-YOLOv4_Scaling_Cross_CVPR_2021_supplemental.pdf | 2011.08036 | title_snapshot | @InProceedings{Wang_2021_CVPR,
author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
title = {Scaled-YOLOv4: Scaling Cross Stage Partial Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy. We propose a network scaling approach that modifies not only the depth, width, resolution, but also structure of the netw... |
Jaume_Quantifying_Explainers_of_Graph_Neural_Networks_in_Computational_Pathology_CVPR_2021_paper | Quantifying Explainers of Graph Neural Networks in Computational Pathology | [
"Guillaume Jaume",
"Pushpak Pati",
"Behzad Bozorgtabar",
"Antonio Foncubierta",
"Anna Maria Anniciello",
"Florinda Feroce",
"Tilman Rau",
"Jean-Philippe Thiran",
"Maria Gabrani",
"Orcun Goksel"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jaume_Quantifying_Explainers_of_Graph_Neural_Networks_in_Computational_Pathology_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jaume_Quantifying_Explainers_of_Graph_Neural_Networks_in_Computational_Pathology_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jaume_Quantifying_Explainers_of_CVPR_2021_supplemental.pdf | 2011.12646 | cvf | @InProceedings{Jaume_2021_CVPR,
author = {Jaume, Guillaume and Pati, Pushpak and Bozorgtabar, Behzad and Foncubierta, Antonio and Anniciello, Anna Maria and Feroce, Florinda and Rau, Tilman and Thiran, Jean-Philippe and Gabrani, Maria and Goksel, Orcun},
title = {Quantifying Explainers of Graph Neural Ne... | Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists. In th... |
Taha_Knowledge_Evolution_in_Neural_Networks_CVPR_2021_paper | Knowledge Evolution in Neural Networks | [
"Ahmed Taha",
"Abhinav Shrivastava",
"Larry S. Davis"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Taha_Knowledge_Evolution_in_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Taha_Knowledge_Evolution_in_Neural_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Taha_Knowledge_Evolution_in_CVPR_2021_supplemental.pdf | 2103.05152 | cvf | @InProceedings{Taha_2021_CVPR,
author = {Taha, Ahmed and Shrivastava, Abhinav and Davis, Larry S.},
title = {Knowledge Evolution in Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},... | Deep learning relies on the availability of a large corpus of data (labeled or unlabeled). Thus, one challenging unsettled question is: how to train a deep network on a relatively small dataset? To tackle this question, we propose an evolution-inspired training approach to boost performance on relatively small datasets... |
Huang_Revisiting_Knowledge_Distillation_An_Inheritance_and_Exploration_Framework_CVPR_2021_paper | Revisiting Knowledge Distillation: An Inheritance and Exploration Framework | [
"Zhen Huang",
"Xu Shen",
"Jun Xing",
"Tongliang Liu",
"Xinmei Tian",
"Houqiang Li",
"Bing Deng",
"Jianqiang Huang",
"Xian-Sheng Hua"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Revisiting_Knowledge_Distillation_An_Inheritance_and_Exploration_Framework_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Revisiting_Knowledge_Distillation_An_Inheritance_and_Exploration_Framework_CVPR_2021_paper.pdf | null | 2107.00181 | title_snapshot | @InProceedings{Huang_2021_CVPR,
author = {Huang, Zhen and Shen, Xu and Xing, Jun and Liu, Tongliang and Tian, Xinmei and Li, Houqiang and Deng, Bing and Huang, Jianqiang and Hua, Xian-Sheng},
title = {Revisiting Knowledge Distillation: An Inheritance and Exploration Framework},
booktitle = {Proceedin... | Knowledge Distillation (KD) is a popular technique to transfer knowledge from a teacher model or ensemble to a student model. Its success is generally attributed to the privileged information on similarities/consistency between the class distributions or intermediate feature representations of the teacher model and the... |
Sarfraz_Temporally-Weighted_Hierarchical_Clustering_for_Unsupervised_Action_Segmentation_CVPR_2021_paper | Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation | [
"Saquib Sarfraz",
"Naila Murray",
"Vivek Sharma",
"Ali Diba",
"Luc Van Gool",
"Rainer Stiefelhagen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sarfraz_Temporally-Weighted_Hierarchical_Clustering_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sarfraz_Temporally-Weighted_Hierarchical_Clustering_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.pdf | null | 2103.11264 | cvf | @InProceedings{Sarfraz_2021_CVPR,
author = {Sarfraz, Saquib and Murray, Naila and Sharma, Vivek and Diba, Ali and Van Gool, Luc and Stiefelhagen, Rainer},
title = {Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks. For this and other video understanding tasks, supervised approaches have achieved encouraging performance but require a high volume of detailed, fra... |
Stone_SMURF_Self-Teaching_Multi-Frame_Unsupervised_RAFT_With_Full-Image_Warping_CVPR_2021_paper | SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping | [
"Austin Stone",
"Daniel Maurer",
"Alper Ayvaci",
"Anelia Angelova",
"Rico Jonschkowski"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Stone_SMURF_Self-Teaching_Multi-Frame_Unsupervised_RAFT_With_Full-Image_Warping_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Stone_SMURF_Self-Teaching_Multi-Frame_Unsupervised_RAFT_With_Full-Image_Warping_CVPR_2021_paper.pdf | null | 2105.07014 | cvf | @InProceedings{Stone_2021_CVPR,
author = {Stone, Austin and Maurer, Daniel and Ayvaci, Alper and Angelova, Anelia and Jonschkowski, Rico},
title = {SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | We present SMURF, a method for unsupervised learning of optical flow that improves state of the art on all benchmarks by 36% to 40% and even outperforms several supervised approaches such as PWC-Net and FlowNet2. Our method integrates architecture improvements from supervised optical flow, i.e. the RAFT model, with new... |
Wang_Glancing_at_the_Patch_Anomaly_Localization_With_Global_and_Local_CVPR_2021_paper | Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison | [
"Shenzhi Wang",
"Liwei Wu",
"Lei Cui",
"Yujun Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Glancing_at_the_Patch_Anomaly_Localization_With_Global_and_Local_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Glancing_at_the_Patch_Anomaly_Localization_With_Global_and_Local_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Glancing_at_the_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Shenzhi and Wu, Liwei and Cui, Lei and Shen, Yujun},
title = {Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Anomaly localization, with the purpose to segment the anomalous regions within images, is challenging due to the large variety of anomaly types. Existing methods typically train deep models by treating the entire image as a whole yet put little effort into learning the local distribution, which is vital for this pixel-... |
Yang_Single-View_3D_Object_Reconstruction_From_Shape_Priors_in_Memory_CVPR_2021_paper | Single-View 3D Object Reconstruction From Shape Priors in Memory | [
"Shuo Yang",
"Min Xu",
"Haozhe Xie",
"Stuart Perry",
"Jiahao Xia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Single-View_3D_Object_Reconstruction_From_Shape_Priors_in_Memory_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Single-View_3D_Object_Reconstruction_From_Shape_Priors_in_Memory_CVPR_2021_paper.pdf | null | 2003.03711 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Shuo and Xu, Min and Xie, Haozhe and Perry, Stuart and Xia, Jiahao},
title = {Single-View 3D Object Reconstruction From Shape Priors in Memory},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | Existing methods for single-view 3D object reconstruction directly learn to transform image features into 3D representations. However, these methods are vulnerable to images containing noisy backgrounds and heavy occlusions because the extracted image features do not contain enough information to reconstruct high-quali... |
Piergiovanni_Recognizing_Actions_in_Videos_From_Unseen_Viewpoints_CVPR_2021_paper | Recognizing Actions in Videos From Unseen Viewpoints | [
"AJ Piergiovanni",
"Michael S. Ryoo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Piergiovanni_Recognizing_Actions_in_Videos_From_Unseen_Viewpoints_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Piergiovanni_Recognizing_Actions_in_Videos_From_Unseen_Viewpoints_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Piergiovanni_Recognizing_Actions_in_CVPR_2021_supplemental.pdf | 2103.16516 | cvf | @InProceedings{Piergiovanni_2021_CVPR,
author = {Piergiovanni, AJ and Ryoo, Michael S.},
title = {Recognizing Actions in Videos From Unseen Viewpoints},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {202... | Standard methods for video recognition use large CNNs designed to capture spatio-temporal data. However, training these models requires a large amount of labeled training data, containing a wide variety of actions, scenes, settings and camera viewpoints. In this paper, we show that current convolutional neural network ... |
Chen_Perceptual_Indistinguishability-Net_PI-Net_Facial_Image_Obfuscation_With_Manipulable_Semantics_CVPR_2021_paper | Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation With Manipulable Semantics | [
"Jia-Wei Chen",
"Li-Ju Chen",
"Chia-Mu Yu",
"Chun-Shien Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Perceptual_Indistinguishability-Net_PI-Net_Facial_Image_Obfuscation_With_Manipulable_Semantics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Perceptual_Indistinguishability-Net_PI-Net_Facial_Image_Obfuscation_With_Manipulable_Semantics_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Perceptual_Indistinguishability-Net_PI-Net_CVPR_2021_supplemental.pdf | 2104.01753 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Jia-Wei and Chen, Li-Ju and Yu, Chia-Mu and Lu, Chun-Shien},
title = {Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation With Manipulable Semantics},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | With the growing use of camera devices, the industry has many image datasets that provide more opportunities for collaboration between the machine learning community and industry. However, the sensitive information in the datasets discourages data owners from releasing these datasets. Despite recent research devoted to... |
Chai_To_the_Point_Efficient_3D_Object_Detection_in_the_Range_CVPR_2021_paper | To the Point: Efficient 3D Object Detection in the Range Image With Graph Convolution Kernels | [
"Yuning Chai",
"Pei Sun",
"Jiquan Ngiam",
"Weiyue Wang",
"Benjamin Caine",
"Vijay Vasudevan",
"Xiao Zhang",
"Dragomir Anguelov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chai_To_the_Point_Efficient_3D_Object_Detection_in_the_Range_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chai_To_the_Point_Efficient_3D_Object_Detection_in_the_Range_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chai_To_the_Point_CVPR_2021_supplemental.pdf | 2106.13381 | title_snapshot | @InProceedings{Chai_2021_CVPR,
author = {Chai, Yuning and Sun, Pei and Ngiam, Jiquan and Wang, Weiyue and Caine, Benjamin and Vasudevan, Vijay and Zhang, Xiao and Anguelov, Dragomir},
title = {To the Point: Efficient 3D Object Detection in the Range Image With Graph Convolution Kernels},
booktitle = ... | 3D object detection is vital for many robotics applications. For tasks where a 2D perspective range image exists, we propose to learn a 3D representation directly from this range image view. To this end, we designed a 2D convolutional network architecture that carries the 3D spherical coordinates of each pixel througho... |
Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper | Coarse-To-Fine Domain Adaptive Semantic Segmentation With Photometric Alignment and Category-Center Regularization | [
"Haoyu Ma",
"Xiangru Lin",
"Zifeng Wu",
"Yizhou Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_Coarse-To-Fine_Domain_Adaptive_CVPR_2021_supplemental.pdf | 2103.13041 | cvf | @InProceedings{Ma_2021_CVPR,
author = {Ma, Haoyu and Lin, Xiangru and Wu, Zifeng and Yu, Yizhou},
title = {Coarse-To-Fine Domain Adaptive Semantic Segmentation With Photometric Alignment and Category-Center Regularization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works. However, the domain shifts/discrepancies problem in this task compromise the final segmentation performance. Based on our observation, the main causes of the domain shifts... |
Taherkhani_Self-Supervised_Wasserstein_Pseudo-Labeling_for_Semi-Supervised_Image_Classification_CVPR_2021_paper | Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification | [
"Fariborz Taherkhani",
"Ali Dabouei",
"Sobhan Soleymani",
"Jeremy Dawson",
"Nasser M. Nasrabadi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Taherkhani_Self-Supervised_Wasserstein_Pseudo-Labeling_for_Semi-Supervised_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Taherkhani_Self-Supervised_Wasserstein_Pseudo-Labeling_for_Semi-Supervised_Image_Classification_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Taherkhani_2021_CVPR,
author = {Taherkhani, Fariborz and Dabouei, Ali and Soleymani, Sobhan and Dawson, Jeremy and Nasrabadi, Nasser M.},
title = {Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification},
booktitle = {Proceedings of the IEEE/CVF Conference ... | The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task. The basic premise in our method is that the discrepancy between two discrete empirical measures (e.g., clusters) ... |
Jang_MeanShift_Extremely_Fast_Mode-Seeking_With_Applications_to_Segmentation_and_Object_CVPR_2021_paper | MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking | [
"Jennifer Jang",
"Heinrich Jiang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jang_MeanShift_Extremely_Fast_Mode-Seeking_With_Applications_to_Segmentation_and_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jang_MeanShift_Extremely_Fast_Mode-Seeking_With_Applications_to_Segmentation_and_Object_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jang_MeanShift_Extremely_Fast_CVPR_2021_supplemental.pdf | 2104.00303 | title_snapshot | @InProceedings{Jang_2021_CVPR,
author = {Jang, Jennifer and Jiang, Heinrich},
title = {MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | MeanShift is a popular mode-seeking clustering algorithm used in a wide range of applications in machine learning. However, it is known to be prohibitively slow, with quadratic runtime per iteration. We propose MeanShift++, an extremely fast mode-seeking algorithm based on MeanShift that uses a grid-based approach to s... |
Yu_PCLs_Geometry-Aware_Neural_Reconstruction_of_3D_Pose_With_Perspective_Crop_CVPR_2021_paper | PCLs: Geometry-Aware Neural Reconstruction of 3D Pose With Perspective Crop Layers | [
"Frank Yu",
"Mathieu Salzmann",
"Pascal Fua",
"Helge Rhodin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_PCLs_Geometry-Aware_Neural_Reconstruction_of_3D_Pose_With_Perspective_Crop_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_PCLs_Geometry-Aware_Neural_Reconstruction_of_3D_Pose_With_Perspective_Crop_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_PCLs_Geometry-Aware_Neural_CVPR_2021_supplemental.zip | 2011.13607 | cvf | @InProceedings{Yu_2021_CVPR,
author = {Yu, Frank and Salzmann, Mathieu and Fua, Pascal and Rhodin, Helge},
title = {PCLs: Geometry-Aware Neural Reconstruction of 3D Pose With Perspective Crop Layers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Local processing is an essential feature of CNNs and other neural network architectures -- it is one of the reasons why they work so well on images where relevant information is, to a large extent, local. However, perspective effects stemming from the projection in a conventional camera vary for different global positi... |
Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper | Partially View-Aligned Representation Learning With Noise-Robust Contrastive Loss | [
"Mouxing Yang",
"Yunfan Li",
"Zhenyu Huang",
"Zitao Liu",
"Peng Hu",
"Xi Peng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Partially_View-Aligned_Representation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yang_2021_CVPR,
author = {Yang, Mouxing and Li, Yunfan and Huang, Zhenyu and Liu, Zitao and Hu, Peng and Peng, Xi},
title = {Partially View-Aligned Representation Learning With Noise-Robust Contrastive Loss},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | In real-world applications, it is common that only a portion of data is aligned across views due to spatial, temporal, or spatiotemporal asynchronism, thus leading to the so-called Partially View-aligned Problem (PVP). To solve such a less-touched problem without the help of labels, we propose simultaneously learning r... |
Yenamandra_i3DMM_Deep_Implicit_3D_Morphable_Model_of_Human_Heads_CVPR_2021_paper | i3DMM: Deep Implicit 3D Morphable Model of Human Heads | [
"Tarun Yenamandra",
"Ayush Tewari",
"Florian Bernard",
"Hans-Peter Seidel",
"Mohamed Elgharib",
"Daniel Cremers",
"Christian Theobalt"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yenamandra_i3DMM_Deep_Implicit_3D_Morphable_Model_of_Human_Heads_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yenamandra_i3DMM_Deep_Implicit_3D_Morphable_Model_of_Human_Heads_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yenamandra_i3DMM_Deep_Implicit_CVPR_2021_supplemental.pdf | 2011.14143 | cvf | @InProceedings{Yenamandra_2021_CVPR,
author = {Yenamandra, Tarun and Tewari, Ayush and Bernard, Florian and Seidel, Hans-Peter and Elgharib, Mohamed and Cremers, Daniel and Theobalt, Christian},
title = {i3DMM: Deep Implicit 3D Morphable Model of Human Heads},
booktitle = {Proceedings of the IEEE/CVF... | We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expressions of the frontal face, but also models the entire head, including hair. We collect a new dataset consisting of 64 people with differen... |
Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper | Searching by Generating: Flexible and Efficient One-Shot NAS With Architecture Generator | [
"Sian-Yao Huang",
"Wei-Ta Chu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Searching_by_Generating_CVPR_2021_supplemental.pdf | 2103.07289 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Sian-Yao and Chu, Wei-Ta},
title = {Searching by Generating: Flexible and Efficient One-Shot NAS With Architecture Generator},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | In one-shot NAS, sub-networks need to be searched from the supernet to meet different hardware constraints. However, the search cost is high and N times of searches are needed for N different constraints. In this work, we propose a novel search strategy called architecture generator to search sub-networks by generating... |
Yang_Discovering_Interpretable_Latent_Space_Directions_of_GANs_Beyond_Binary_Attributes_CVPR_2021_paper | Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes | [
"Huiting Yang",
"Liangyu Chai",
"Qiang Wen",
"Shuang Zhao",
"Zixun Sun",
"Shengfeng He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Discovering_Interpretable_Latent_Space_Directions_of_GANs_Beyond_Binary_Attributes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Discovering_Interpretable_Latent_Space_Directions_of_GANs_Beyond_Binary_Attributes_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Discovering_Interpretable_Latent_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yang_2021_CVPR,
author = {Yang, Huiting and Chai, Liangyu and Wen, Qiang and Zhao, Shuang and Sun, Zixun and He, Shengfeng},
title = {Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | Generative adversarial networks (GANs) learn to map noise latent vectors to high-fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond to human interpretab... |
He_ForgeryNet_A_Versatile_Benchmark_for_Comprehensive_Forgery_Analysis_CVPR_2021_paper | ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis | [
"Yinan He",
"Bei Gan",
"Siyu Chen",
"Yichun Zhou",
"Guojun Yin",
"Luchuan Song",
"Lu Sheng",
"Jing Shao",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/He_ForgeryNet_A_Versatile_Benchmark_for_Comprehensive_Forgery_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/He_ForgeryNet_A_Versatile_Benchmark_for_Comprehensive_Forgery_Analysis_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_ForgeryNet_A_Versatile_CVPR_2021_supplemental.pdf | 2103.05630 | cvf | @InProceedings{He_2021_CVPR,
author = {He, Yinan and Gan, Bei and Chen, Siyu and Zhou, Yichun and Yin, Guojun and Song, Luchuan and Sheng, Lu and Shao, Jing and Liu, Ziwei},
title = {ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis},
booktitle = {Proceedings of the IEEE/CVF Confer... | The rapid progress of photorealistic synthesis techniques has reached at a critical point where the boundary between real and manipulated images starts to blur. Thus, benchmarking and advancing digital forgery analysis have become a pressing issue. However, existing face forgery datasets either have limited diversity o... |
Lee_Blocks-World_Cameras_CVPR_2021_paper | Blocks-World Cameras | [
"Jongho Lee",
"Mohit Gupta"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Blocks-World_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Blocks-World_Cameras_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Blocks-World_Cameras_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Lee_2021_CVPR,
author = {Lee, Jongho and Gupta, Mohit},
title = {Blocks-World Cameras},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {11412-11422}
} | For several vision and robotics applications, 3D geometry of man-made environments such as indoor scenes can be represented with a small number of dominant planes. However, conventional 3D vision techniques typically first acquire dense 3D point clouds before estimating the compact piece-wise planar representations (e.... |
Su_The_Affective_Growth_of_Computer_Vision_CVPR_2021_paper | The Affective Growth of Computer Vision | [
"Norman Makoto Su",
"David J. Crandall"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Su_The_Affective_Growth_of_Computer_Vision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Su_The_Affective_Growth_of_Computer_Vision_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Su_2021_CVPR,
author = {Su, Norman Makoto and Crandall, David J.},
title = {The Affective Growth of Computer Vision},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = ... | The success of deep learning has led to intense growth and interest in computer vision, along with concerns about its potential impact on society. Yet we know little about how these changes have affected the people that research and practice computer vision: we as a community spend so much effort trying to replicate th... |
Pu_Lifelong_Person_Re-Identification_via_Adaptive_Knowledge_Accumulation_CVPR_2021_paper | Lifelong Person Re-Identification via Adaptive Knowledge Accumulation | [
"Nan Pu",
"Wei Chen",
"Yu Liu",
"Erwin M. Bakker",
"Michael S. Lew"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pu_Lifelong_Person_Re-Identification_via_Adaptive_Knowledge_Accumulation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pu_Lifelong_Person_Re-Identification_via_Adaptive_Knowledge_Accumulation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pu_Lifelong_Person_Re-Identification_CVPR_2021_supplemental.zip | 2103.12462 | cvf | @InProceedings{Pu_2021_CVPR,
author = {Pu, Nan and Chen, Wei and Liu, Yu and Bakker, Erwin M. and Lew, Michael S.},
title = {Lifelong Person Re-Identification via Adaptive Knowledge Accumulation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Person ReID methods always learn through a stationary domain that is fixed by the choice of a given dataset. In many contexts (e.g., lifelong learning), those methods are ineffective because the domain is continually changing in which case incremental learning over multiple domains is required potentially. In this work... |
Lu_Omnimatte_Associating_Objects_and_Their_Effects_in_Video_CVPR_2021_paper | Omnimatte: Associating Objects and Their Effects in Video | [
"Erika Lu",
"Forrester Cole",
"Tali Dekel",
"Andrew Zisserman",
"William T. Freeman",
"Michael Rubinstein"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Omnimatte_Associating_Objects_and_Their_Effects_in_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Omnimatte_Associating_Objects_and_Their_Effects_in_Video_CVPR_2021_paper.pdf | null | 2105.06993 | cvf | @InProceedings{Lu_2021_CVPR,
author = {Lu, Erika and Cole, Forrester and Dekel, Tali and Zisserman, Andrew and Freeman, William T. and Rubinstein, Michael},
title = {Omnimatte: Associating Objects and Their Effects in Video},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | Computer vision has become increasingly better at segmenting objects in images and videos; however, scene effects related to the objects -- shadows, reflections, generated smoke, etc. -- are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improv... |
Hou_Detecting_Human-Object_Interaction_via_Fabricated_Compositional_Learning_CVPR_2021_paper | Detecting Human-Object Interaction via Fabricated Compositional Learning | [
"Zhi Hou",
"Baosheng Yu",
"Yu Qiao",
"Xiaojiang Peng",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Detecting_Human-Object_Interaction_via_Fabricated_Compositional_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Detecting_Human-Object_Interaction_via_Fabricated_Compositional_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hou_Detecting_Human-Object_Interaction_CVPR_2021_supplemental.pdf | 2103.08214 | cvf | @InProceedings{Hou_2021_CVPR,
author = {Hou, Zhi and Yu, Baosheng and Qiao, Yu and Peng, Xiaojiang and Tao, Dacheng},
title = {Detecting Human-Object Interaction via Fabricated Compositional Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Human-Object Interaction (HOI) detection, inferring the relationships between human and objects from images/videos, is a fundamental task for high-level scene understanding. However, HOI detection usually suffers from the open long-tailed nature of interactions with objects, while human has extremely powerful compositi... |
Cheng_Memory-Efficient_Network_for_Large-Scale_Video_Compressive_Sensing_CVPR_2021_paper | Memory-Efficient Network for Large-Scale Video Compressive Sensing | [
"Ziheng Cheng",
"Bo Chen",
"Guanliang Liu",
"Hao Zhang",
"Ruiying Lu",
"Zhengjue Wang",
"Xin Yuan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Memory-Efficient_Network_for_Large-Scale_Video_Compressive_Sensing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Memory-Efficient_Network_for_Large-Scale_Video_Compressive_Sensing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Memory-Efficient_Network_for_CVPR_2021_supplemental.pdf | 2103.03089 | cvf | @InProceedings{Cheng_2021_CVPR,
author = {Cheng, Ziheng and Chen, Bo and Liu, Guanliang and Zhang, Hao and Lu, Ruiying and Wang, Zhengjue and Yuan, Xin},
title = {Memory-Efficient Network for Large-Scale Video Compressive Sensing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Video snapshot compressive imaging (SCI) captures a sequence of video frames in a single shot using a 2D detector. The underlying principle is that during one exposure time, different masks are imposed on the high-speed scene to form a compressed measurement. With the knowledge of masks, optimization algorithms or deep... |
Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper | Deep Optimized Priors for 3D Shape Modeling and Reconstruction | [
"Mingyue Yang",
"Yuxin Wen",
"Weikai Chen",
"Yongwei Chen",
"Kui Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Deep_Optimized_Priors_CVPR_2021_supplemental.zip | 2012.07241 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Mingyue and Wen, Yuxin and Chen, Weikai and Chen, Yongwei and Jia, Kui},
title = {Deep Optimized Priors for 3D Shape Modeling and Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Many learning-based approaches have difficulty scaling to unseen data, as the generality of its learned prior is limited to the scale and variations of the training samples. This holds particularly true with 3D learning tasks, given the sparsity of 3D datasets available. We introduce a new learning framework for 3D mod... |
Hou_Affordance_Transfer_Learning_for_Human-Object_Interaction_Detection_CVPR_2021_paper | Affordance Transfer Learning for Human-Object Interaction Detection | [
"Zhi Hou",
"Baosheng Yu",
"Yu Qiao",
"Xiaojiang Peng",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Affordance_Transfer_Learning_for_Human-Object_Interaction_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Affordance_Transfer_Learning_for_Human-Object_Interaction_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hou_Affordance_Transfer_Learning_CVPR_2021_supplemental.pdf | 2104.02867 | cvf | @InProceedings{Hou_2021_CVPR,
author = {Hou, Zhi and Yu, Baosheng and Qiao, Yu and Peng, Xiaojiang and Tao, Dacheng},
title = {Affordance Transfer Learning for Human-Object Interaction Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Reasoning the human-object interactions (HOI) is essential for deeper scene understanding, while object affordances (or functionalities) are of great importance for human to discover unseen HOIs with novel objects. Inspired by this, we introduce an affordance transfer learning approach to jointly detect HOIs with novel... |
Yang_DSC-PoseNet_Learning_6DoF_Object_Pose_Estimation_via_Dual-Scale_Consistency_CVPR_2021_paper | DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-Scale Consistency | [
"Zongxin Yang",
"Xin Yu",
"Yi Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_DSC-PoseNet_Learning_6DoF_Object_Pose_Estimation_via_Dual-Scale_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_DSC-PoseNet_Learning_6DoF_Object_Pose_Estimation_via_Dual-Scale_Consistency_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_DSC-PoseNet_Learning_6DoF_CVPR_2021_supplemental.pdf | 2104.03658 | title_snapshot | @InProceedings{Yang_2021_CVPR,
author = {Yang, Zongxin and Yu, Xin and Yang, Yi},
title = {DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-Scale Consistency},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Compared to 2D object bounding-box labeling, it is very difficult for humans to annotate 3D object poses, especially when depth images of scenes are unavailable. This paper investigates whether we can estimate the object poses effectively when only RGB images and 2D object annotations are given. To this end, we present... |
Cai_Rethinking_Graph_Neural_Architecture_Search_From_Message-Passing_CVPR_2021_paper | Rethinking Graph Neural Architecture Search From Message-Passing | [
"Shaofei Cai",
"Liang Li",
"Jincan Deng",
"Beichen Zhang",
"Zheng-Jun Zha",
"Li Su",
"Qingming Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Rethinking_Graph_Neural_Architecture_Search_From_Message-Passing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Rethinking_Graph_Neural_Architecture_Search_From_Message-Passing_CVPR_2021_paper.pdf | null | 2103.14282 | cvf | @InProceedings{Cai_2021_CVPR,
author = {Cai, Shaofei and Li, Liang and Deng, Jincan and Zhang, Beichen and Zha, Zheng-Jun and Su, Li and Huang, Qingming},
title = {Rethinking Graph Neural Architecture Search From Message-Passing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore different message-passing mechanisms, and require manual enumeration to determine the proper message-passing depth. Inspired by the strong search... |
Jing_Locate_Then_Segment_A_Strong_Pipeline_for_Referring_Image_Segmentation_CVPR_2021_paper | Locate Then Segment: A Strong Pipeline for Referring Image Segmentation | [
"Ya Jing",
"Tao Kong",
"Wei Wang",
"Liang Wang",
"Lei Li",
"Tieniu Tan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Locate_Then_Segment_A_Strong_Pipeline_for_Referring_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Locate_Then_Segment_A_Strong_Pipeline_for_Referring_Image_Segmentation_CVPR_2021_paper.pdf | null | 2103.16284 | cvf | @InProceedings{Jing_2021_CVPR,
author = {Jing, Ya and Kong, Tao and Wang, Wei and Wang, Liang and Li, Lei and Tan, Tieniu},
title = {Locate Then Segment: A Strong Pipeline for Referring Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Referring image segmentation aims to segment the objects referred by a natural language expression. Previous methods usually focus on designing an implicit and recurrent feature interaction mechanism to fuse the visual-linguistic features to directly generate the final segmentation mask without explicitly modeling the ... |
Rizve_Exploring_Complementary_Strengths_of_Invariant_and_Equivariant_Representations_for_Few-Shot_CVPR_2021_paper | Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning | [
"Mamshad Nayeem Rizve",
"Salman Khan",
"Fahad Shahbaz Khan",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Rizve_Exploring_Complementary_Strengths_of_Invariant_and_Equivariant_Representations_for_Few-Shot_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Rizve_Exploring_Complementary_Strengths_of_Invariant_and_Equivariant_Representations_for_Few-Shot_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rizve_Exploring_Complementary_Strengths_CVPR_2021_supplemental.pdf | 2103.01315 | cvf | @InProceedings{Rizve_2021_CVPR,
author = {Rizve, Mamshad Nayeem and Khan, Salman and Khan, Fahad Shahbaz and Shah, Mubarak},
title = {Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer ... | In many real-world problems, collecting a large number of labeled samples is infeasible. Few-shot learning (FSL) is the dominant approach to address this issue, where the objective is to quickly adapt to novel categories in presence of a limited number of samples. FSL tasks have been predominantly solved by leveraging ... |
Richardson_Encoding_in_Style_A_StyleGAN_Encoder_for_Image-to-Image_Translation_CVPR_2021_paper | Encoding in Style: A StyleGAN Encoder for Image-to-Image Translation | [
"Elad Richardson",
"Yuval Alaluf",
"Or Patashnik",
"Yotam Nitzan",
"Yaniv Azar",
"Stav Shapiro",
"Daniel Cohen-Or"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Richardson_Encoding_in_Style_A_StyleGAN_Encoder_for_Image-to-Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Richardson_Encoding_in_Style_A_StyleGAN_Encoder_for_Image-to-Image_Translation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Richardson_Encoding_in_Style_CVPR_2021_supplemental.pdf | 2008.00951 | cvf | @InProceedings{Richardson_2021_CVPR,
author = {Richardson, Elad and Alaluf, Yuval and Patashnik, Or and Nitzan, Yotam and Azar, Yaniv and Shapiro, Stav and Cohen-Or, Daniel},
title = {Encoding in Style: A StyleGAN Encoder for Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Confe... | We present a generic image-to-image translation framework, pixel2style2pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the extended W+ latent space. We first show that our encoder can directl... |
Chen_Towards_Bridging_Event_Captioner_and_Sentence_Localizer_for_Weakly_Supervised_CVPR_2021_paper | Towards Bridging Event Captioner and Sentence Localizer for Weakly Supervised Dense Event Captioning | [
"Shaoxiang Chen",
"Yu-Gang Jiang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Towards_Bridging_Event_Captioner_and_Sentence_Localizer_for_Weakly_Supervised_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Towards_Bridging_Event_Captioner_and_Sentence_Localizer_for_Weakly_Supervised_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Towards_Bridging_Event_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Shaoxiang and Jiang, Yu-Gang},
title = {Towards Bridging Event Captioner and Sentence Localizer for Weakly Supervised Dense Event Captioning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | Dense Event Captioning (DEC) aims to jointly localize and describe multiple events of interest in untrimmed videos, which is an advancement of the conventional video captioning task (generating a single sentence description for a trimmed video). Weakly Supervised Dense Event Captioning (WS-DEC) goes one step further by... |
Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper | DER: Dynamically Expandable Representation for Class Incremental Learning | [
"Shipeng Yan",
"Jiangwei Xie",
"Xuming He"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_DER_Dynamically_Expandable_CVPR_2021_supplemental.pdf | 2103.16788 | cvf | @InProceedings{Yan_2021_CVPR,
author = {Yan, Shipeng and Xie, Jiangwei and He, Xuming},
title = {DER: Dynamically Expandable Representation for Class Incremental Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We address the problem of class incremental learning, which is a core step towards achieving adaptive vision intelligence. In particular, we consider the task setting of incremental learning with limited memory and aim to achieve a better stability-plasticity trade-off. To this end, we propose a novel two-stage learnin... |
Bukchin_Fine-Grained_Angular_Contrastive_Learning_With_Coarse_Labels_CVPR_2021_paper | Fine-Grained Angular Contrastive Learning With Coarse Labels | [
"Guy Bukchin",
"Eli Schwartz",
"Kate Saenko",
"Ori Shahar",
"Rogerio Feris",
"Raja Giryes",
"Leonid Karlinsky"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bukchin_Fine-Grained_Angular_Contrastive_Learning_With_Coarse_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bukchin_Fine-Grained_Angular_Contrastive_Learning_With_Coarse_Labels_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bukchin_Fine-Grained_Angular_Contrastive_CVPR_2021_supplemental.zip | 2012.03515 | cvf | @InProceedings{Bukchin_2021_CVPR,
author = {Bukchin, Guy and Schwartz, Eli and Saenko, Kate and Shahar, Ori and Feris, Rogerio and Giryes, Raja and Karlinsky, Leonid},
title = {Fine-Grained Angular Contrastive Learning With Coarse Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This adaptivity to unseen classes is especially important for many practical applications where the pre-trained label space cannot remain fixed ... |
Fukao_Polarimetric_Normal_Stereo_CVPR_2021_paper | Polarimetric Normal Stereo | [
"Yoshiki Fukao",
"Ryo Kawahara",
"Shohei Nobuhara",
"Ko Nishino"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fukao_Polarimetric_Normal_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fukao_Polarimetric_Normal_Stereo_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fukao_Polarimetric_Normal_Stereo_CVPR_2021_supplemental.zip | null | null | @InProceedings{Fukao_2021_CVPR,
author = {Fukao, Yoshiki and Kawahara, Ryo and Nobuhara, Shohei and Nishino, Ko},
title = {Polarimetric Normal Stereo},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | We introduce a novel method for recovering per-pixel surface normals from a pair of polarization cameras. Unlike past methods that use polarimetric observations as auxiliary features for correspondence matching, we fully integrate them in cost volume construction and filtering to directly recover per-pixel surface norm... |
Tang_Manifold_Regularized_Dynamic_Network_Pruning_CVPR_2021_paper | Manifold Regularized Dynamic Network Pruning | [
"Yehui Tang",
"Yunhe Wang",
"Yixing Xu",
"Yiping Deng",
"Chao Xu",
"Dacheng Tao",
"Chang Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Manifold_Regularized_Dynamic_Network_Pruning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Manifold_Regularized_Dynamic_Network_Pruning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Manifold_Regularized_Dynamic_CVPR_2021_supplemental.pdf | 2103.05861 | cvf | @InProceedings{Tang_2021_CVPR,
author = {Tang, Yehui and Wang, Yunhe and Xu, Yixing and Deng, Yiping and Xu, Chao and Tao, Dacheng and Xu, Chang},
title = {Manifold Regularized Dynamic Network Pruning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared with conventional methods, the recently developed dynamic pruning methods determine redundant filters variant to each input instance which achi... |
Xu_ViPNAS_Efficient_Video_Pose_Estimation_via_Neural_Architecture_Search_CVPR_2021_paper | ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search | [
"Lumin Xu",
"Yingda Guan",
"Sheng Jin",
"Wentao Liu",
"Chen Qian",
"Ping Luo",
"Wanli Ouyang",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_ViPNAS_Efficient_Video_Pose_Estimation_via_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_ViPNAS_Efficient_Video_Pose_Estimation_via_Neural_Architecture_Search_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_ViPNAS_Efficient_Video_CVPR_2021_supplemental.pdf | 2105.10154 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Lumin and Guan, Yingda and Jin, Sheng and Liu, Wentao and Qian, Chen and Luo, Ping and Ouyang, Wanli and Wang, Xiaogang},
title = {ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Human pose estimation has achieved significant progress in recent years. However, most of the recent methods focus on improving accuracy using complicated models and ignoring real-time efficiency. To achieve a better trade-off between accuracy and efficiency, we propose a novel neural architecture search (NAS) method, ... |
Shu_Open_Domain_Generalization_with_Domain-Augmented_Meta-Learning_CVPR_2021_paper | Open Domain Generalization with Domain-Augmented Meta-Learning | [
"Yang Shu",
"Zhangjie Cao",
"Chenyu Wang",
"Jianmin Wang",
"Mingsheng Long"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shu_Open_Domain_Generalization_with_Domain-Augmented_Meta-Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shu_Open_Domain_Generalization_with_Domain-Augmented_Meta-Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shu_Open_Domain_Generalization_CVPR_2021_supplemental.pdf | 2104.03620 | cvf | @InProceedings{Shu_2021_CVPR,
author = {Shu, Yang and Cao, Zhangjie and Wang, Chenyu and Wang, Jianmin and Long, Mingsheng},
title = {Open Domain Generalization with Domain-Augmented Meta-Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated data are unavailable. We study the problem of learning from different source domains to achieve high performance on an unknown target domain, ... |
Ye_DeepTag_An_Unsupervised_Deep_Learning_Method_for_Motion_Tracking_on_CVPR_2021_paper | DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images | [
"Meng Ye",
"Mikael Kanski",
"Dong Yang",
"Qi Chang",
"Zhennan Yan",
"Qiaoying Huang",
"Leon Axel",
"Dimitris Metaxas"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_DeepTag_An_Unsupervised_Deep_Learning_Method_for_Motion_Tracking_on_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_DeepTag_An_Unsupervised_Deep_Learning_Method_for_Motion_Tracking_on_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ye_DeepTag_An_Unsupervised_CVPR_2021_supplemental.zip | 2103.02772 | cvf | @InProceedings{Ye_2021_CVPR,
author = {Ye, Meng and Kanski, Mikael and Yang, Dong and Chang, Qi and Yan, Zhennan and Huang, Qiaoying and Axel, Leon and Metaxas, Dimitris},
title = {DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images},
booktit... | Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation. However, this technique has not been widely used in clinical diagnosis, as a result of the difficulty of motion tracking encountered with t-MRI images. In this paper, we propose a n... |
Shi_Learning_by_Planning_Language-Guided_Global_Image_Editing_CVPR_2021_paper | Learning by Planning: Language-Guided Global Image Editing | [
"Jing Shi",
"Ning Xu",
"Yihang Xu",
"Trung Bui",
"Franck Dernoncourt",
"Chenliang Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Learning_by_Planning_Language-Guided_Global_Image_Editing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_Learning_by_Planning_Language-Guided_Global_Image_Editing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_Learning_by_Planning_CVPR_2021_supplemental.pdf | 2106.13156 | title_snapshot | @InProceedings{Shi_2021_CVPR,
author = {Shi, Jing and Xu, Ning and Xu, Yihang and Bui, Trung and Dernoncourt, Franck and Xu, Chenliang},
title = {Learning by Planning: Language-Guided Global Image Editing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Recently, language-guided global image editing draws increasing attention with growing application potentials. However, previous GAN-based methods are not only confined to domain-specific, low-resolution data but also lacking in interpretability. To overcome the collective difficulties, we develop a text-to-operation m... |
Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper | Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation | [
"Subhankar Roy",
"Evgeny Krivosheev",
"Zhun Zhong",
"Nicu Sebe",
"Elisa Ricci"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Roy_Curriculum_Graph_Co-Teaching_CVPR_2021_supplemental.pdf | 2104.00808 | cvf | @InProceedings{Roy_2021_CVPR,
author = {Roy, Subhankar and Krivosheev, Evgeny and Zhong, Zhun and Sebe, Nicu and Ricci, Elisa},
title = {Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains. We identify two key aspects that can help to alleviate multiple domain-shif... |
Kaya_Uncalibrated_Neural_Inverse_Rendering_for_Photometric_Stereo_of_General_Surfaces_CVPR_2021_paper | Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces | [
"Berk Kaya",
"Suryansh Kumar",
"Carlos Oliveira",
"Vittorio Ferrari",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kaya_Uncalibrated_Neural_Inverse_Rendering_for_Photometric_Stereo_of_General_Surfaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kaya_Uncalibrated_Neural_Inverse_Rendering_for_Photometric_Stereo_of_General_Surfaces_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kaya_Uncalibrated_Neural_Inverse_CVPR_2021_supplemental.pdf | 2012.06777 | cvf | @InProceedings{Kaya_2021_CVPR,
author = {Kaya, Berk and Kumar, Suryansh and Oliveira, Carlos and Ferrari, Vittorio and Van Gool, Luc},
title = {Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it is challenging to pr... |
Wu_Improving_the_Transferability_of_Adversarial_Samples_With_Adversarial_Transformations_CVPR_2021_paper | Improving the Transferability of Adversarial Samples With Adversarial Transformations | [
"Weibin Wu",
"Yuxin Su",
"Michael R. Lyu",
"Irwin King"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Improving_the_Transferability_of_Adversarial_Samples_With_Adversarial_Transformations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Improving_the_Transferability_of_Adversarial_Samples_With_Adversarial_Transformations_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Wu_2021_CVPR,
author = {Wu, Weibin and Su, Yuxin and Lyu, Michael R. and King, Irwin},
title = {Improving the Transferability of Adversarial Samples With Adversarial Transformations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Although deep neural networks (DNNs) have achieved tremendous performance in diverse vision challenges, they are surprisingly susceptible to adversarial examples, which are born of intentionally perturbing benign samples in a human-imperceptible fashion. It thus poses security concerns on the deployment of DNNs in prac... |
Wang_Self-Supervised_Learning_for_Semi-Supervised_Temporal_Action_Proposal_CVPR_2021_paper | Self-Supervised Learning for Semi-Supervised Temporal Action Proposal | [
"Xiang Wang",
"Shiwei Zhang",
"Zhiwu Qing",
"Yuanjie Shao",
"Changxin Gao",
"Nong Sang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Self-Supervised_Learning_for_Semi-Supervised_Temporal_Action_Proposal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Self-Supervised_Learning_for_Semi-Supervised_Temporal_Action_Proposal_CVPR_2021_paper.pdf | null | 2104.03214 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Xiang and Zhang, Shiwei and Qing, Zhiwu and Shao, Yuanjie and Gao, Changxin and Sang, Nong},
title = {Self-Supervised Learning for Semi-Supervised Temporal Action Proposal},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | Self-supervised learning presents a remarkable performance to utilize unlabeled data for various video tasks. In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action proposal generation. Particularly, we design a Self-supervised Semi-supervised Temporal Action Proposal... |
Jiang_Learning_Compositional_Representation_for_4D_Captures_With_Neural_ODE_CVPR_2021_paper | Learning Compositional Representation for 4D Captures With Neural ODE | [
"Boyan Jiang",
"Yinda Zhang",
"Xingkui Wei",
"Xiangyang Xue",
"Yanwei Fu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Learning_Compositional_Representation_for_4D_Captures_With_Neural_ODE_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiang_Learning_Compositional_Representation_for_4D_Captures_With_Neural_ODE_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jiang_Learning_Compositional_Representation_CVPR_2021_supplemental.pdf | 2103.08271 | cvf | @InProceedings{Jiang_2021_CVPR,
author = {Jiang, Boyan and Zhang, Yinda and Wei, Xingkui and Xue, Xiangyang and Fu, Yanwei},
title = {Learning Compositional Representation for 4D Captures With Neural ODE},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Learning based representation has become the key to the success of many computer vision systems. While many 3D representations have been proposed, it is still an unaddressed problem how to represent a dynamically changing 3D object. In this paper, we introduce a compositional representation for 4D captures, i.e. a defo... |
Qiu_Effective_Snapshot_Compressive-Spectral_Imaging_via_Deep_Denoising_and_Total_Variation_CVPR_2021_paper | Effective Snapshot Compressive-Spectral Imaging via Deep Denoising and Total Variation Priors | [
"Haiquan Qiu",
"Yao Wang",
"Deyu Meng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Effective_Snapshot_Compressive-Spectral_Imaging_via_Deep_Denoising_and_Total_Variation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_Effective_Snapshot_Compressive-Spectral_Imaging_via_Deep_Denoising_and_Total_Variation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiu_Effective_Snapshot_Compressive-Spectral_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Qiu_2021_CVPR,
author = {Qiu, Haiquan and Wang, Yao and Meng, Deyu},
title = {Effective Snapshot Compressive-Spectral Imaging via Deep Denoising and Total Variation Priors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Snapshot compressive imaging (SCI) is a new type of compressive imaging system that compresses multiple frames of images into a single snapshot measurement, which enjoys low cost, low bandwidth, and high-speed sensing rate. By applying the existing SCI methods to deal with hyperspectral images, however, could not fully... |
Yu_LAFEAT_Piercing_Through_Adversarial_Defenses_With_Latent_Features_CVPR_2021_paper | LAFEAT: Piercing Through Adversarial Defenses With Latent Features | [
"Yunrui Yu",
"Xitong Gao",
"Cheng-Zhong Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_LAFEAT_Piercing_Through_Adversarial_Defenses_With_Latent_Features_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_LAFEAT_Piercing_Through_Adversarial_Defenses_With_Latent_Features_CVPR_2021_paper.pdf | null | 2104.09284 | cvf | @InProceedings{Yu_2021_CVPR,
author = {Yu, Yunrui and Gao, Xitong and Xu, Cheng-Zhong},
title = {LAFEAT: Piercing Through Adversarial Defenses With Latent Features},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This presents a great challenge in making CNNs robust against such attacks. An influx of new defense techniques have been proposed to this end. I... |
Kim_Exploiting_Spatial_Dimensions_of_Latent_in_GAN_for_Real-Time_Image_CVPR_2021_paper | Exploiting Spatial Dimensions of Latent in GAN for Real-Time Image Editing | [
"Hyunsu Kim",
"Yunjey Choi",
"Junho Kim",
"Sungjoo Yoo",
"Youngjung Uh"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Exploiting_Spatial_Dimensions_of_Latent_in_GAN_for_Real-Time_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Exploiting_Spatial_Dimensions_of_Latent_in_GAN_for_Real-Time_Image_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Exploiting_Spatial_Dimensions_CVPR_2021_supplemental.pdf | 2104.14754 | cvf | @InProceedings{Kim_2021_CVPR,
author = {Kim, Hyunsu and Choi, Yunjey and Kim, Junho and Yoo, Sungjoo and Uh, Youngjung},
title = {Exploiting Spatial Dimensions of Latent in GAN for Real-Time Image Editing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embeddin... |
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