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title string | authors string | abstract string | pdf string | arXiv string | video string | bibtex string | url string | detail_url string | tags string | supp string | dataset string | string |
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Pattern-Structure Diffusion for Multi-Task Learning | Ling Zhou, Zhen Cui, Chunyan Xu, Zhenyu Zhang, Chaoqun Wang, Tong Zhang, Jian Yang | Inspired by the observation that pattern structures high-frequently recur within intra-task also across tasks, we propose a pattern-structure diffusion (PSD) framework to mine and propagate task-specific and task-across pattern structures in the task-level space for joint depth estimation, segmentation and surface norm... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Pattern-Structure_Diffusion_for_Multi-Task_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Pattern-Structure_Diffusion_for_Multi-Task_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Pattern-Structure_Diffusion_for_Multi-Task_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
On the Acceleration of Deep Learning Model Parallelism With Staleness | An Xu, Zhouyuan Huo, Heng Huang | Training the deep convolutional neural network for computer vision problems is slow and inefficient, especially when it is large and distributed across multiple devices. The inefficiency is caused by the backpropagation algorithm's forward locking, backward locking, and update locking problems. Existing solutions for a... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_On_the_Acceleration_of_Deep_Learning_Model_Parallelism_With_Staleness_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_On_the_Acceleration_of_Deep_Learning_Model_Parallelism_With_Staleness_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_On_the_Acceleration_of_Deep_Learning_Model_Parallelism_With_Staleness_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xu_On_the_Acceleration_CVPR_2020_supplemental.pdf | null | null |
Self-Supervised Scene De-Occlusion | Xiaohang Zhan, Xingang Pan, Bo Dai, Ziwei Liu, Dahua Lin, Chen Change Loy | Natural scene understanding is a challenging task, particularly when encountering images of multiple objects that are partially occluded. This obstacle is given rise by varying object ordering and positioning. Existing scene understanding paradigms are able to parse only the visible parts, resulting in incomplete and u... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhan_Self-Supervised_Scene_De-Occlusion_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02788 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhan_Self-Supervised_Scene_De-Occlusion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhan_Self-Supervised_Scene_De-Occlusion_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhan_Self-Supervised_Scene_De-Occlusion_CVPR_2020_supplemental.pdf | null | null |
DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration | Jian Wang, Miaomiao Zhang | This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration. In contrast to existing approaches that learn spatial transformations from training data in the high dimensional imaging space, we develop a new registration network entirely in a low dime... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_DeepFLASH_An_Efficient_Network_for_Learning-Based_Medical_Image_Registration_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02097 | https://www.youtube.com/watch?v=QAIVcdnB0v4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_DeepFLASH_An_Efficient_Network_for_Learning-Based_Medical_Image_Registration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_DeepFLASH_An_Efficient_Network_for_Learning-Based_Medical_Image_Registration_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Structure Boundary Preserving Segmentation for Medical Image With Ambiguous Boundary | Hong Joo Lee, Jung Uk Kim, Sangmin Lee, Hak Gu Kim, Yong Man Ro | In this paper, we propose a novel image segmentation method to tackle two critical problems of medical image, which are (i) ambiguity of structure boundary in the medical image domain and (ii) uncertainty of the segmented region without specialized domain knowledge. To solve those two problems in automatic medical segm... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_Structure_Boundary_Preserving_Segmentation_for_Medical_Image_With_Ambiguous_Boundary_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=qF3TAxF3m1I | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Structure_Boundary_Preserving_Segmentation_for_Medical_Image_With_Ambiguous_Boundary_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Structure_Boundary_Preserving_Segmentation_for_Medical_Image_With_Ambiguous_Boundary_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lee_Structure_Boundary_Preserving_CVPR_2020_supplemental.pdf | null | null |
Residual Feature Aggregation Network for Image Super-Resolution | Jie Liu, Wenjie Zhang, Yuting Tang, Jie Tang, Gangshan Wu | Recently, very deep convolutional neural networks (CNNs) have shown great power in single image super-resolution (SISR) and achieved significant improvements against traditional methods. Among these CNN-based methods, the residual connections play a critical role in boosting the network performance. As the network dept... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Residual_Feature_Aggregation_Network_for_Image_Super-Resolution_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Residual_Feature_Aggregation_Network_for_Image_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Residual_Feature_Aggregation_Network_for_Image_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Residual_Feature_Aggregation_CVPR_2020_supplemental.pdf | null | null |
Context-Aware and Scale-Insensitive Temporal Repetition Counting | Huaidong Zhang, Xuemiao Xu, Guoqiang Han, Shengfeng He | Temporal repetition counting aims to estimate the number of cycles of a given repetitive action. Existing deep learning methods assume repetitive actions are performed in a fixed time-scale, which is invalid for the complex repetitive actions in real life. In this paper, we tailor a context-aware and scale-insensitive ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Context-Aware_and_Scale-Insensitive_Temporal_Repetition_Counting_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.08465 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Context-Aware_and_Scale-Insensitive_Temporal_Repetition_Counting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Context-Aware_and_Scale-Insensitive_Temporal_Repetition_Counting_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability | Jie Song, Yixin Chen, Jingwen Ye, Xinchao Wang, Chengchao Shen, Feng Mao, Mingli Song | Exploring the intrinsic interconnections between the knowledge encoded in PRe-trained Deep Neural Networks (PR-DNNs) of heterogeneous tasks sheds light on their mutual transferability, and consequently enables knowledge transfer from one task to another so as to reduce the training effort of the latter. In this paper, ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Song_DEPARA_Deep_Attribution_Graph_for_Deep_Knowledge_Transferability_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07496 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Song_DEPARA_Deep_Attribution_Graph_for_Deep_Knowledge_Transferability_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Song_DEPARA_Deep_Attribution_Graph_for_Deep_Knowledge_Transferability_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Song_DEPARA_Deep_Attribution_CVPR_2020_supplemental.pdf | null | null |
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition | Ziyu Liu, Hongwen Zhang, Zhenghao Chen, Zhiyong Wang, Wanli Ouyang | Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal dependency modeling are critical aspects of a powerful feature extracto... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Disentangling_and_Unifying_Graph_Convolutions_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14111 | https://www.youtube.com/watch?v=fh6ys5qWqxk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Disentangling_and_Unifying_Graph_Convolutions_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Disentangling_and_Unifying_Graph_Convolutions_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Disentangling_and_Unifying_CVPR_2020_supplemental.pdf | null | null |
Category-Level Articulated Object Pose Estimation | Xiaolong Li, He Wang, Li Yi, Leonidas J. Guibas, A. Lynn Abbott, Shuran Song | This paper addresses the task of category-level pose estimation for articulated objects from a single depth image. We present a novel category-level approach that correctly accommodates object instances previously unseen during training. We introduce Articulation-aware Normalized Coordinate Space Hierarchy (ANCSH) - a ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Category-Level_Articulated_Object_Pose_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.11913 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Category-Level_Articulated_Object_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Category-Level_Articulated_Object_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Category-Level_Articulated_Object_CVPR_2020_supplemental.pdf | null | null |
ImVoteNet: Boosting 3D Object Detection in Point Clouds With Image Votes | Charles R. Qi, Xinlei Chen, Or Litany, Leonidas J. Guibas | 3D object detection has seen quick progress thanks to advances in deep learning on point clouds. A few recent works have even shown state-of-the-art performance with just point clouds input (e.g. VoteNet). However, point cloud data have inherent limitations. They are sparse, lack color information and often suffer from... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qi_ImVoteNet_Boosting_3D_Object_Detection_in_Point_Clouds_With_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.10692 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_ImVoteNet_Boosting_3D_Object_Detection_in_Point_Clouds_With_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_ImVoteNet_Boosting_3D_Object_Detection_in_Point_Clouds_With_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Qi_ImVoteNet_Boosting_3D_CVPR_2020_supplemental.pdf | null | null |
Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations | Sven Gowal, Chongli Qin, Po-Sen Huang, Taylan Cemgil, Krishnamurthy Dvijotham, Timothy Mann, Pushmeet Kohli | Recent research has made the surprising finding that state-of-the-art deep learning models sometimes fail to generalize to small variations of the input. Adversarial training has been shown to be an effective approach to overcome this problem. However, its application has been limited to enforcing invariance to analyti... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gowal_Achieving_Robustness_in_the_Wild_via_Adversarial_Mixing_With_Disentangled_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03192 | https://www.youtube.com/watch?v=4r5ekdI33OQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gowal_Achieving_Robustness_in_the_Wild_via_Adversarial_Mixing_With_Disentangled_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gowal_Achieving_Robustness_in_the_Wild_via_Adversarial_Mixing_With_Disentangled_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gowal_Achieving_Robustness_in_CVPR_2020_supplemental.pdf | null | null |
Deep Non-Line-of-Sight Reconstruction | Javier Grau Chopite, Matthias B. Hullin, Michael Wand, Julian Iseringhausen | The recent years have seen a surge of interest in methods for imaging beyond the direct line of sight. The most prominent techniques rely on time-resolved optical impulse responses, obtained by illuminating a diffuse wall with an ultrashort light pulse and observing multi-bounce indirect reflections with an ultrafast t... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chopite_Deep_Non-Line-of-Sight_Reconstruction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.09067 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chopite_Deep_Non-Line-of-Sight_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chopite_Deep_Non-Line-of-Sight_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chopite_Deep_Non-Line-of-Sight_Reconstruction_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution | Lei Zhang, Jiangtao Nie, Wei Wei, Yanning Zhang, Shengcai Liao, Ling Shao | The key for fusion based hyperspectral image (HSI) super-resolution (SR) is to infer the posteriori of a latent HSI using appropriate image prior and likelihood that depends on degeneration. However, in practice the priors of high-dimensional HSIs can be extremely complicated and the degeneration is often unknown. Cons... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=VmEkrRmdnVE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Unsupervised_Adaptation_Learning_for_Hyperspectral_Imagery_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Unsupervised_Adaptation_Learning_CVPR_2020_supplemental.pdf | null | null |
Joint Demosaicing and Denoising With Self Guidance | Lin Liu, Xu Jia, Jianzhuang Liu, Qi Tian | Usually located at the very early stages of the computational photography pipeline, demosaicing and denoising play important parts in the modern camera image processing. Recently, some neural networks have shown the effectiveness in joint demosaicing and denoising (JDD). Most of them first decompose a Bayer raw image i... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Joint_Demosaicing_and_Denoising_With_Self_Guidance_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Joint_Demosaicing_and_Denoising_With_Self_Guidance_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Joint_Demosaicing_and_Denoising_With_Self_Guidance_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SpSequenceNet: Semantic Segmentation Network on 4D Point Clouds | Hanyu Shi, Guosheng Lin, Hao Wang, Tzu-Yi Hung, Zhenhua Wang | Point clouds are useful in many applications like autonomous driving and robotics as they provide natural 3D information of the surrounding environments. While there are extensive research on 3D point clouds, scene understanding on 4D point clouds, a series of consecutive 3D point clouds frames, is an emerging topic an... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shi_SpSequenceNet_Semantic_Segmentation_Network_on_4D_Point_Clouds_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_SpSequenceNet_Semantic_Segmentation_Network_on_4D_Point_Clouds_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_SpSequenceNet_Semantic_Segmentation_Network_on_4D_Point_Clouds_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data | Wanyu Lin, Zhaolin Gao, Baochun Li | Graph-based semi-supervised learning has been shown to be one of the most effective classification approaches, as it can exploit connectivity patterns between labeled and unlabeled samples to improve learning performance. However, we show that existing techniques perform poorly when labeled data are severely limited. T... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Shoestring_Graph-Based_Semi-Supervised_Classification_With_Severely_Limited_Labeled_Data_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=gwy92d6IO1Q | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Shoestring_Graph-Based_Semi-Supervised_Classification_With_Severely_Limited_Labeled_Data_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Shoestring_Graph-Based_Semi-Supervised_Classification_With_Severely_Limited_Labeled_Data_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_Shoestring_Graph-Based_Semi-Supervised_CVPR_2020_supplemental.pdf | null | null |
Distilling Image Dehazing With Heterogeneous Task Imitation | Ming Hong, Yuan Xie, Cuihua Li, Yanyun Qu | State-of-the-art deep dehazing models are often difficult in training. Knowledge distillation paves a way to train a student network assisted by a teacher network. However, most knowledge distill methods are used for image classification and segmentation as well as object detection, and few investigate distilling image... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hong_Distilling_Image_Dehazing_With_Heterogeneous_Task_Imitation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hong_Distilling_Image_Dehazing_With_Heterogeneous_Task_Imitation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hong_Distilling_Image_Dehazing_With_Heterogeneous_Task_Imitation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Photometric Stereo via Discrete Hypothesis-and-Test Search | Kenji Enomoto, Michael Waechter, Kiriakos N. Kutulakos, Yasuyuki Matsushita | In this paper, we consider the problem of estimating surface normals of a scene with spatially varying, general BRDFs observed by a static camera under varying, known, distant illumination. Unlike previous approaches that are mostly based on continuous local optimization, we cast the problem as a discrete hypothesis-an... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Enomoto_Photometric_Stereo_via_Discrete_Hypothesis-and-Test_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Enomoto_Photometric_Stereo_via_Discrete_Hypothesis-and-Test_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Enomoto_Photometric_Stereo_via_Discrete_Hypothesis-and-Test_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Enomoto_Photometric_Stereo_via_CVPR_2020_supplemental.pdf | null | null |
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks | Sungjoon Choi, Sanghoon Hong, Kyungjae Lee, Sungbin Lim | In this paper, we focus on weakly supervised learning with noisy training data for both classification and regression problems. We assume that the training outputs are collected from a mixture of a target and correlated noise distributions. Our proposed method simultaneously estimates the target distribution and the qu... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Choi_Task_Agnostic_Robust_Learning_on_Corrupt_Outputs_by_Correlation-Guided_Mixture_CVPR_2020_paper.pdf | http://arxiv.org/abs/1805.06431 | https://www.youtube.com/watch?v=RvFKk3I3nwk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_Task_Agnostic_Robust_Learning_on_Corrupt_Outputs_by_Correlation-Guided_Mixture_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_Task_Agnostic_Robust_Learning_on_Corrupt_Outputs_by_Correlation-Guided_Mixture_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Choi_Task_Agnostic_Robust_CVPR_2020_supplemental.pdf | null | null |
Multi-Path Region Mining for Weakly Supervised 3D Semantic Segmentation on Point Clouds | Jiacheng Wei, Guosheng Lin, Kim-Hui Yap, Tzu-Yi Hung, Lihua Xie | Point clouds provide intrinsic geometric information and surface context for scene understanding. Existing methods for point cloud segmentation require a large amount of fully labeled data. Using advanced depth sensors, collection of large scale 3D dataset is no longer a cumbersome process. However, manually producing ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wei_Multi-Path_Region_Mining_for_Weakly_Supervised_3D_Semantic_Segmentation_on_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13035 | https://www.youtube.com/watch?v=V87_y6dPyRQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Multi-Path_Region_Mining_for_Weakly_Supervised_3D_Semantic_Segmentation_on_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Multi-Path_Region_Mining_for_Weakly_Supervised_3D_Semantic_Segmentation_on_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Single-Step Adversarial Training With Dropout Scheduling | Vivek B.S., R. Venkatesh Babu | Deep learning models have shown impressive performance across a spectrum of computer vision applications including medical diagnosis and autonomous driving. One of the major concerns that these models face is their susceptibility to adversarial attacks. Realizing the importance of this issue, more researchers are worki... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/B.S._Single-Step_Adversarial_Training_With_Dropout_Scheduling_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/B.S._Single-Step_Adversarial_Training_With_Dropout_Scheduling_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/B.S._Single-Step_Adversarial_Training_With_Dropout_Scheduling_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/B.S._Single-Step_Adversarial_Training_CVPR_2020_supplemental.pdf | null | null |
Online Depth Learning Against Forgetting in Monocular Videos | Zhenyu Zhang, Stephane Lathuiliere, Elisa Ricci, Nicu Sebe, Yan Yan, Jian Yang | Online depth learning is the problem of consistently adapting a depth estimation model to handle a continuously changing environment. This problem is challenging due to the network easily overfits on the current environment and forgets its past experiences. To address such problem, this paper presents a novel Learning ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Online_Depth_Learning_Against_Forgetting_in_Monocular_Videos_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Online_Depth_Learning_Against_Forgetting_in_Monocular_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Online_Depth_Learning_Against_Forgetting_in_Monocular_Videos_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Online_Depth_Learning_CVPR_2020_supplemental.pdf | null | null |
Neuromorphic Camera Guided High Dynamic Range Imaging | Jin Han, Chu Zhou, Peiqi Duan, Yehui Tang, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi | Reconstruction of high dynamic range image from a single low dynamic range image captured by a frame-based conventional camera, which suffers from over- or under-exposure, is an ill-posed problem. In contrast, recent neuromorphic cameras are able to record high dynamic range scenes in the form of an intensity map, with... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Han_Neuromorphic_Camera_Guided_High_Dynamic_Range_Imaging_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_Neuromorphic_Camera_Guided_High_Dynamic_Range_Imaging_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_Neuromorphic_Camera_Guided_High_Dynamic_Range_Imaging_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Han_Neuromorphic_Camera_Guided_CVPR_2020_supplemental.pdf | null | null |
Reconstruct Locally, Localize Globally: A Model Free Method for Object Pose Estimation | Ming Cai, Ian Reid | Six degree-of-freedom pose estimation of a known object in a single image is a long-standing computer vision objective. It is classically posed as a correspondence problem between a known geometric model, such as a CAD model, and image locations. If a CAD model is not available, it is possible to use multi-view visual ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cai_Reconstruct_Locally_Localize_Globally_A_Model_Free_Method_for_Object_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=Gf7bTrNIcqw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Reconstruct_Locally_Localize_Globally_A_Model_Free_Method_for_Object_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Reconstruct_Locally_Localize_Globally_A_Model_Free_Method_for_Object_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cai_Reconstruct_Locally_Localize_CVPR_2020_supplemental.pdf | null | null |
Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution | Yuesong Nan, Hui Ji | Most existing non-blind image deconvolution methods assume that the given blurring kernel is error-free. In practice, blurring kernel often is estimated via some blind deblurring algorithm which is not exactly the truth. Also, the convolution model is only an approximation to practical blurring effect. It is known that... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Nan_Deep_Learning_for_Handling_Kernelmodel_Uncertainty_in_Image_Deconvolution_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Nan_Deep_Learning_for_Handling_Kernelmodel_Uncertainty_in_Image_Deconvolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Nan_Deep_Learning_for_Handling_Kernelmodel_Uncertainty_in_Image_Deconvolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Nan_Deep_Learning_for_CVPR_2020_supplemental.pdf | null | null |
VPLNet: Deep Single View Normal Estimation With Vanishing Points and Lines | Rui Wang, David Geraghty, Kevin Matzen, Richard Szeliski, Jan-Michael Frahm | We present a novel single-view surface normal estimation method that combines traditional line and vanishing point analysis with a deep learning approach. Starting from a color image and a Manhattan line map, we use a deep neural network to regress on a dense normal map, and a dense Manhattan label map that identifies ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_VPLNet_Deep_Single_View_Normal_Estimation_With_Vanishing_Points_and_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_VPLNet_Deep_Single_View_Normal_Estimation_With_Vanishing_Points_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_VPLNet_Deep_Single_View_Normal_Estimation_With_Vanishing_Points_and_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Intra- and Inter-Action Understanding via Temporal Action Parsing | Dian Shao, Yue Zhao, Bo Dai, Dahua Lin | Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features. While these methods have demonstrated remarkable performance on standard benchmarks, we are still in need of a better understanding as to how the videos, in particular their i... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shao_Intra-_and_Inter-Action_Understanding_via_Temporal_Action_Parsing_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shao_Intra-_and_Inter-Action_Understanding_via_Temporal_Action_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shao_Intra-_and_Inter-Action_Understanding_via_Temporal_Action_Parsing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shao_Intra-_and_Inter-Action_CVPR_2020_supplemental.zip | null | null |
Graph-Guided Architecture Search for Real-Time Semantic Segmentation | Peiwen Lin, Peng Sun, Guangliang Cheng, Sirui Xie, Xi Li, Jianping Shi | Designing a lightweight semantic segmentation network often requires researchers to find a trade-off between performance and speed, which is always empirical due to the limited interpretability of neural networks. In order to release researchers from these tedious mechanical trials, we propose a Graph-guided Architectu... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Graph-Guided_Architecture_Search_for_Real-Time_Semantic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.06793 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Graph-Guided_Architecture_Search_for_Real-Time_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Graph-Guided_Architecture_Search_for_Real-Time_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_Graph-Guided_Architecture_Search_CVPR_2020_supplemental.pdf | null | null |
Polarized Reflection Removal With Perfect Alignment in the Wild | Chenyang Lei, Xuhua Huang, Mengdi Zhang, Qiong Yan, Wenxiu Sun, Qifeng Chen | We present a novel formulation to removing reflection from polarized images in the wild. We first identify the misalignment issues of existing reflection removal datasets where the collected reflection-free images are not perfectly aligned with input mixed images due to glass refraction. Then we build a new dataset wit... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lei_Polarized_Reflection_Removal_With_Perfect_Alignment_in_the_Wild_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12789 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lei_Polarized_Reflection_Removal_With_Perfect_Alignment_in_the_Wild_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lei_Polarized_Reflection_Removal_With_Perfect_Alignment_in_the_Wild_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Blur Aware Calibration of Multi-Focus Plenoptic Camera | Mathieu Labussiere, Celine Teuliere, Frederic Bernardin, Omar Ait-Aider | This paper presents a novel calibration algorithm for Multi-Focus Plenoptic Cameras (MFPCs) using raw images only. The design of such cameras is usually complex and relies on precise placement of optic elements. Several calibration procedures have been proposed to retrieve the camera parameters but relying on simplifie... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Labussiere_Blur_Aware_Calibration_of_Multi-Focus_Plenoptic_Camera_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07745 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Labussiere_Blur_Aware_Calibration_of_Multi-Focus_Plenoptic_Camera_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Labussiere_Blur_Aware_Calibration_of_Multi-Focus_Plenoptic_Camera_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Labussiere_Blur_Aware_Calibration_CVPR_2020_supplemental.pdf | null | null |
Single-Shot Monocular RGB-D Imaging Using Uneven Double Refraction | Andreas Meuleman, Seung-Hwan Baek, Felix Heide, Min H. Kim | Cameras that capture color and depth information have become an essential imaging modality for applications in robotics, autonomous driving, virtual, and augmented reality. Existing RGB-D cameras rely on multiple sensors or active illumination with specialized sensors. In this work, we propose a method for monocular si... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Meuleman_Single-Shot_Monocular_RGB-D_Imaging_Using_Uneven_Double_Refraction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Meuleman_Single-Shot_Monocular_RGB-D_Imaging_Using_Uneven_Double_Refraction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Meuleman_Single-Shot_Monocular_RGB-D_Imaging_Using_Uneven_Double_Refraction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Meuleman_Single-Shot_Monocular_RGB-D_CVPR_2020_supplemental.zip | null | null |
Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings | Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger | We introduce a powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images. Student networks are trained to regress the output of a descriptive teacher network that was pretrained on a large dataset of patches from nat... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bergmann_Uninformed_Students_Student-Teacher_Anomaly_Detection_With_Discriminative_Latent_Embeddings_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.02357 | https://www.youtube.com/watch?v=pozNR-dgzOM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bergmann_Uninformed_Students_Student-Teacher_Anomaly_Detection_With_Discriminative_Latent_Embeddings_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bergmann_Uninformed_Students_Student-Teacher_Anomaly_Detection_With_Discriminative_Latent_Embeddings_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bergmann_Uninformed_Students_Student-Teacher_CVPR_2020_supplemental.pdf | null | null |
Learning to Restore Low-Light Images via Decomposition-and-Enhancement | Ke Xu, Xin Yang, Baocai Yin, Rynson W.H. Lau | Low-light images typically suffer from two problems. First, they have low visibility (i.e., small pixel values). Second, noise becomes significant and disrupts the image content, due to low signal-to-noise ratio. Most existing lowlight image enhancement methods, however, learn from noise-negligible datasets. They rely ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Learning_to_Restore_Low-Light_Images_via_Decomposition-and-Enhancement_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Learning_to_Restore_Low-Light_Images_via_Decomposition-and-Enhancement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Learning_to_Restore_Low-Light_Images_via_Decomposition-and-Enhancement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xu_Learning_to_Restore_CVPR_2020_supplemental.pdf | null | null |
HRank: Filter Pruning Using High-Rank Feature Map | Mingbao Lin, Rongrong Ji, Yan Wang, Yichen Zhang, Baochang Zhang, Yonghong Tian, Ling Shao | Neural network pruning offers a promising prospect to facilitate deploying deep neural networks on resource-limited devices. However, existing methods are still challenged by the training inefficiency and labor cost in pruning designs, due to missing theoretical guidance of non-salient network components. In this paper... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_HRank_Filter_Pruning_Using_High-Rank_Feature_Map_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10179 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_HRank_Filter_Pruning_Using_High-Rank_Feature_Map_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_HRank_Filter_Pruning_Using_High-Rank_Feature_Map_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
AANet: Adaptive Aggregation Network for Efficient Stereo Matching | Haofei Xu, Juyong Zhang | Despite the remarkable progress made by learning based stereo matching algorithms, one key challenge remains unsolved. Current state-of-the-art stereo models are mostly based on costly 3D convolutions, the cubic computational complexity and high memory consumption make it quite expensive to deploy in real-world applica... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_AANet_Adaptive_Aggregation_Network_for_Efficient_Stereo_Matching_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.09548 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_AANet_Adaptive_Aggregation_Network_for_Efficient_Stereo_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_AANet_Adaptive_Aggregation_Network_for_Efficient_Stereo_Matching_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xu_AANet_Adaptive_Aggregation_CVPR_2020_supplemental.pdf | null | null |
Unbiased Scene Graph Generation From Biased Training | Kaihua Tang, Yulei Niu, Jianqiang Huang, Jiaxin Shi, Hanwang Zhang | Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e.g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach". Given such SGG, the down-stream tasks such as VQA can hardly infer better scene structures than merely a bag of objects. ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tang_Unbiased_Scene_Graph_Generation_From_Biased_Training_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11949 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Unbiased_Scene_Graph_Generation_From_Biased_Training_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Unbiased_Scene_Graph_Generation_From_Biased_Training_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tang_Unbiased_Scene_Graph_CVPR_2020_supplemental.pdf | null | null |
A Semi-Supervised Assessor of Neural Architectures | Yehui Tang, Yunhe Wang, Yixing Xu, Hanting Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, Chang Xu | Neural architecture search (NAS) aims to automatically design deep neural networks of satisfactory performance. Wherein, architecture performance predictor is critical to efficiently value an intermediate neural architecture. But for the training of this predictor, a number of neural architectures and their correspondi... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tang_A_Semi-Supervised_Assessor_of_Neural_Architectures_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.06821 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_A_Semi-Supervised_Assessor_of_Neural_Architectures_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_A_Semi-Supervised_Assessor_of_Neural_Architectures_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Proxy Anchor Loss for Deep Metric Learning | Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak | Existing metric learning losses can be categorized into two classes: pair-based and proxy-based losses. The former class can leverage fine-grained semantic relations between data points, but slows convergence in general due to its high training complexity. In contrast, the latter class enables fast and reliable converg... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Proxy_Anchor_Loss_for_Deep_Metric_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13911 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Proxy_Anchor_Loss_for_Deep_Metric_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Proxy_Anchor_Loss_for_Deep_Metric_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs | Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash, Heiko Hoffmann | The unprecedented success of deep neural networks in many applications has made these networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs). We introduce the concept of Univ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kolouri_Universal_Litmus_Patterns_Revealing_Backdoor_Attacks_in_CNNs_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.10842 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kolouri_Universal_Litmus_Patterns_Revealing_Backdoor_Attacks_in_CNNs_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kolouri_Universal_Litmus_Patterns_Revealing_Backdoor_Attacks_in_CNNs_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes | Rundi Wu, Yixin Zhuang, Kai Xu, Hao Zhang, Baoquan Chen | We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly. The input to our network is a 3D shape segmented into parts, where each part is first encoded into a feature representation using a part autoencoder. The core component of PQ-NET is a sequence-to-sequence o... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_PQ-NET_A_Generative_Part_Seq2Seq_Network_for_3D_Shapes_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_PQ-NET_A_Generative_Part_Seq2Seq_Network_for_3D_Shapes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_PQ-NET_A_Generative_Part_Seq2Seq_Network_for_3D_Shapes_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_PQ-NET_A_Generative_CVPR_2020_supplemental.pdf | null | null |
Extreme Relative Pose Network Under Hybrid Representations | Zhenpei Yang, Siming Yan, Qixing Huang | In this paper, we introduce a novel RGB-D based relative pose estimation approach that is suitable for small-overlapping or non-overlapping scans and can output multiple relative poses. Our method performs scene completion and matches the completed scans. However, instead of using a fixed representation for completion,... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Extreme_Relative_Pose_Network_Under_Hybrid_Representations_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.11695 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Extreme_Relative_Pose_Network_Under_Hybrid_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Extreme_Relative_Pose_Network_Under_Hybrid_Representations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Extreme_Relative_Pose_CVPR_2020_supplemental.pdf | null | null |
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline | Yu-Lun Liu, Wei-Sheng Lai, Yu-Sheng Chen, Yi-Lung Kao, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang | Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-exposed regions caused by quantization and saturation of camera sensors. In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Single-Image_HDR_Reconstruction_by_Learning_to_Reverse_the_Camera_Pipeline_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01179 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Single-Image_HDR_Reconstruction_by_Learning_to_Reverse_the_Camera_Pipeline_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Single-Image_HDR_Reconstruction_by_Learning_to_Reverse_the_Camera_Pipeline_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Single-Image_HDR_Reconstruction_CVPR_2020_supplemental.pdf | null | null |
Learning to Observe: Approximating Human Perceptual Thresholds for Detection of Suprathreshold Image Transformations | Alan Dolhasz, Carlo Harvey, Ian Williams | Many tasks in computer vision are often calibrated and evaluated relative to human perception. In this paper, we propose to directly approximate the perceptual function performed by human observers completing a visual detection task. Specifically, we present a novel methodology for learning to detect image transformati... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dolhasz_Learning_to_Observe_Approximating_Human_Perceptual_Thresholds_for_Detection_of_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.06433 | https://www.youtube.com/watch?v=BpzUgPANkp4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dolhasz_Learning_to_Observe_Approximating_Human_Perceptual_Thresholds_for_Detection_of_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dolhasz_Learning_to_Observe_Approximating_Human_Perceptual_Thresholds_for_Detection_of_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Dolhasz_Learning_to_Observe_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/333 | null |
MEBOW: Monocular Estimation of Body Orientation in the Wild | Chenyan Wu, Yukun Chen, Jiajia Luo, Che-Chun Su, Anuja Dawane, Bikramjot Hanzra, Zhuo Deng, Bilan Liu, James Z. Wang, Cheng-hao Kuo | Body orientation estimation provides crucial visual cues in many applications, including robotics and autonomous driving. It is particularly desirable when 3-D pose estimation is difficult to infer due to poor image resolution, occlusion or indistinguishable body parts. We present COCO-MEBOW (Monocular Estimation of Bo... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_MEBOW_Monocular_Estimation_of_Body_Orientation_in_the_Wild_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_MEBOW_Monocular_Estimation_of_Body_Orientation_in_the_Wild_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_MEBOW_Monocular_Estimation_of_Body_Orientation_in_the_Wild_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_MEBOW_Monocular_Estimation_CVPR_2020_supplemental.pdf | null | null |
Depth Sensing Beyond LiDAR Range | Kai Zhang, Jiaxin Xie, Noah Snavely, Qifeng Chen | Depth sensing is a critical component of autonomous driving technologies, but today's LiDAR- or stereo camera- based solutions have limited range. We seek to increase the maximum range of self-driving vehicles' depth perception modules for the sake of better safety. To that end, we propose a novel three-camera system t... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Depth_Sensing_Beyond_LiDAR_Range_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03048 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Depth_Sensing_Beyond_LiDAR_Range_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Depth_Sensing_Beyond_LiDAR_Range_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
BlendedMVS: A Large-Scale Dataset for Generalized Multi-View Stereo Networks | Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, Long Quan | While deep learning has recently achieved great success on multi-view stereo (MVS), limited training data makes the trained model hard to be generalized to unseen scenarios. Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners an... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yao_BlendedMVS_A_Large-Scale_Dataset_for_Generalized_Multi-View_Stereo_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.10127 | https://www.youtube.com/watch?v=3mMuSwpjd8A | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yao_BlendedMVS_A_Large-Scale_Dataset_for_Generalized_Multi-View_Stereo_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yao_BlendedMVS_A_Large-Scale_Dataset_for_Generalized_Multi-View_Stereo_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yao_BlendedMVS_A_Large-Scale_CVPR_2020_supplemental.pdf | null | null |
Learning to Detect Important People in Unlabelled Images for Semi-Supervised Important People Detection | Fa-Ting Hong, Wei-Hong Li, Wei-Shi Zheng | Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern. However, existing methods rely heavily on supervised learning using large quantities of annotated image samples, which ar... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hong_Learning_to_Detect_Important_People_in_Unlabelled_Images_for_Semi-Supervised_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07568 | https://www.youtube.com/watch?v=9pJnBTRAZfQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hong_Learning_to_Detect_Important_People_in_Unlabelled_Images_for_Semi-Supervised_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hong_Learning_to_Detect_Important_People_in_Unlabelled_Images_for_Semi-Supervised_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hong_Learning_to_Detect_CVPR_2020_supplemental.pdf | null | null |
Fixed-Point Back-Propagation Training | Xishan Zhang, Shaoli Liu, Rui Zhang, Chang Liu, Di Huang, Shiyi Zhou, Jiaming Guo, Qi Guo, Zidong Du, Tian Zhi, Yunji Chen | Recent emerged quantization technique (i.e., using low bit-width fixed-point data instead of high bit-width floating-point data) has been applied to inference of deep neural networks for fast and efficient execution. However, directly applying quantization in training can cause significant accuracy loss, thus remaining... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Fixed-Point_Back-Propagation_Training_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Fixed-Point_Back-Propagation_Training_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Fixed-Point_Back-Propagation_Training_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Fixed-Point_Back-Propagation_Training_CVPR_2020_supplemental.pdf | null | null |
Zero-Assignment Constraint for Graph Matching With Outliers | Fudong Wang, Nan Xue, Jin-Gang Yu, Gui-Song Xia | Graph matching (GM), as a longstanding problem in computer vision and pattern recognition, still suffers from numerous cluttered outliers in practical applications. To address this issue, we present the zero-assignment constraint (ZAC) for approaching the graph matching problem in the presence of outliers. The underlyi... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Zero-Assignment_Constraint_for_Graph_Matching_With_Outliers_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11928 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Zero-Assignment_Constraint_for_Graph_Matching_With_Outliers_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Zero-Assignment_Constraint_for_Graph_Matching_With_Outliers_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Zero-Assignment_Constraint_for_CVPR_2020_supplemental.pdf | null | null |
AvatarMe: Realistically Renderable 3D Facial Reconstruction "In-the-Wild" | Alexandros Lattas, Stylianos Moschoglou, Baris Gecer, Stylianos Ploumpis, Vasileios Triantafyllou, Abhijeet Ghosh, Stefanos Zafeiriou | Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image. Nevertheless, to the best of our knowledge, th... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lattas_AvatarMe_Realistically_Renderable_3D_Facial_Reconstruction_In-the-Wild_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13845 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lattas_AvatarMe_Realistically_Renderable_3D_Facial_Reconstruction_In-the-Wild_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lattas_AvatarMe_Realistically_Renderable_3D_Facial_Reconstruction_In-the-Wild_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Generalized Product Quantization Network for Semi-Supervised Image Retrieval | Young Kyun Jang, Nam Ik Cho | Image retrieval methods that employ hashing or vector quantization have achieved great success by taking advantage of deep learning. However, these approaches do not meet expectations unless expensive label information is sufficient. To resolve this issue, we propose the first quantization-based semi-supervised image r... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jang_Generalized_Product_Quantization_Network_for_Semi-Supervised_Image_Retrieval_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11281 | https://www.youtube.com/watch?v=SblaP2hkUpA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jang_Generalized_Product_Quantization_Network_for_Semi-Supervised_Image_Retrieval_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jang_Generalized_Product_Quantization_Network_for_Semi-Supervised_Image_Retrieval_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jang_Generalized_Product_Quantization_CVPR_2020_supplemental.pdf | null | null |
Extremely Dense Point Correspondences Using a Learned Feature Descriptor | Xingtong Liu, Yiping Zheng, Benjamin Killeen, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath | High-quality 3D reconstructions from endoscopy video play an important role in many clinical applications, including surgical navigation where they enable direct video-CT registration. While many methods exist for general multi-view 3D reconstruction, these methods often fail to deliver satisfactory performance on endo... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Extremely_Dense_Point_Correspondences_Using_a_Learned_Feature_Descriptor_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00619 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Extremely_Dense_Point_Correspondences_Using_a_Learned_Feature_Descriptor_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Extremely_Dense_Point_Correspondences_Using_a_Learned_Feature_Descriptor_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Gate-Shift Networks for Video Action Recognition | Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz | Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large number of parameters and computations involved, they may under-perform in the lack of sufficiently large datasets for training them at scale. In ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sudhakaran_Gate-Shift_Networks_for_Video_Action_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00381 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sudhakaran_Gate-Shift_Networks_for_Video_Action_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sudhakaran_Gate-Shift_Networks_for_Video_Action_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sudhakaran_Gate-Shift_Networks_for_CVPR_2020_supplemental.zip | null | null |
CRNet: Cross-Reference Networks for Few-Shot Segmentation | Weide Liu, Chi Zhang, Guosheng Lin, Fayao Liu | Over the past few years, state-of-the-art image segmentation algorithms are based on deep convolutional neural networks. To render a deep network with the ability to understand a concept, humans need to collect a large amount of pixel-level annotated data to train the models, which is time-consuming and tedious. Recent... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_CRNet_Cross-Reference_Networks_for_Few-Shot_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10658 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_CRNet_Cross-Reference_Networks_for_Few-Shot_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_CRNet_Cross-Reference_Networks_for_Few-Shot_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Space-Time-Aware Multi-Resolution Video Enhancement | Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita | We consider the problem of space-time super-resolution (ST-SR): increasing spatial resolution of video frames and simultaneously interpolating frames to increase the frame rate. Modern approaches handle these axes one at a time. In contrast, our proposed model called STARnet super-resolves jointly in space and time. Th... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Haris_Space-Time-Aware_Multi-Resolution_Video_Enhancement_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13170 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Haris_Space-Time-Aware_Multi-Resolution_Video_Enhancement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Haris_Space-Time-Aware_Multi-Resolution_Video_Enhancement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Haris_Space-Time-Aware_Multi-Resolution_Video_CVPR_2020_supplemental.pdf | null | null |
METAL: Minimum Effort Temporal Activity Localization in Untrimmed Videos | Da Zhang, Xiyang Dai, Yuan-Fang Wang | Existing Temporal Activity Localization (TAL) methods largely adopt strong supervision for model training, which requires (1) vast amounts of untrimmed videos per each activity category and (2) accurate segment-level boundary annotations (start time and end time) for every instance. This poses a critical restriction to... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_METAL_Minimum_Effort_Temporal_Activity_Localization_in_Untrimmed_Videos_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_METAL_Minimum_Effort_Temporal_Activity_Localization_in_Untrimmed_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_METAL_Minimum_Effort_Temporal_Activity_Localization_in_Untrimmed_Videos_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Image Demoireing with Learnable Bandpass Filters | Bolun Zheng, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis | Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. In this paper, we propose a novel multiscale bandpass convolutional neural network (MBCNN) to address this problem. As an end-to-end solution, MBCNN respectively solves the two sub-problems. For texture restoration,... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Image_Demoireing_with_Learnable_Bandpass_Filters_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00406 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Image_Demoireing_with_Learnable_Bandpass_Filters_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Image_Demoireing_with_Learnable_Bandpass_Filters_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Active Vision for Early Recognition of Human Actions | Boyu Wang, Lihan Huang, Minh Hoai | We propose a method for early recognition of human actions, one that can take advantages of multiple cameras while satisfying the constraints due to limited communication bandwidth and processing power. Our method considers multiple cameras, and at each time step, it will decide the best camera to use so that a confide... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Active_Vision_for_Early_Recognition_of_Human_Actions_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Active_Vision_for_Early_Recognition_of_Human_Actions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Active_Vision_for_Early_Recognition_of_Human_Actions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Active_Vision_for_CVPR_2020_supplemental.pdf | null | null |
Weakly-Supervised Action Localization by Generative Attention Modeling | Baifeng Shi, Qi Dai, Yadong Mu, Jingdong Wang | Weakly-supervised temporal action localization is a problem of learning an action localization model with only video-level action labeling available. The general framework largely relies on the classification activation, which employs an attention model to identify the action-related frames and then categorizes them in... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shi_Weakly-Supervised_Action_Localization_by_Generative_Attention_Modeling_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12424 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Weakly-Supervised_Action_Localization_by_Generative_Attention_Modeling_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Weakly-Supervised_Action_Localization_by_Generative_Attention_Modeling_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization | Lanqing Hu, Meina Kan, Shiguang Shan, Xilin Chen | Domain adaptation attempts to boost the performance on a target domain by borrowing knowledge from a well established source domain. To handle the distribution gap between two domains, the prominent approaches endeavor to extract domain-invariant features. It is known that after a perfect domain alignment the domain-in... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Unsupervised_Domain_Adaptation_With_Hierarchical_Gradient_Synchronization_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Unsupervised_Domain_Adaptation_With_Hierarchical_Gradient_Synchronization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Unsupervised_Domain_Adaptation_With_Hierarchical_Gradient_Synchronization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hu_Unsupervised_Domain_Adaptation_CVPR_2020_supplemental.pdf | null | null |
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects From a Single RGB Image | Despoina Paschalidou, Luc Van Gool, Andreas Geiger | Humans perceive the 3D world as a set of distinct objects that are characterized by various low-level (geometry, reflectance) and high-level (connectivity, adjacency, symmetry) properties. Recent methods based on convolutional neural networks (CNNs) demonstrated impressive progress in 3D reconstruction, even when using... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Paschalidou_Learning_Unsupervised_Hierarchical_Part_Decomposition_of_3D_Objects_From_a_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01176 | https://www.youtube.com/watch?v=Wy6PDTS4_JQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Paschalidou_Learning_Unsupervised_Hierarchical_Part_Decomposition_of_3D_Objects_From_a_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Paschalidou_Learning_Unsupervised_Hierarchical_Part_Decomposition_of_3D_Objects_From_a_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Paschalidou_Learning_Unsupervised_Hierarchical_CVPR_2020_supplemental.pdf | null | null |
Light Field Spatial Super-Resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization | Jing Jin, Junhui Hou, Jie Chen, Sam Kwong | Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution as the limited sampling resources have to be shared with the angular dimension. LF spatial super-resolution (SR) thus becomes an indispensable part of the LF camera processing pipeline. The high-dimensionality characteristi... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jin_Light_Field_Spatial_Super-Resolution_via_Deep_Combinatorial_Geometry_Embedding_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02215 | https://www.youtube.com/watch?v=Nhf1Z7N8kDg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jin_Light_Field_Spatial_Super-Resolution_via_Deep_Combinatorial_Geometry_Embedding_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jin_Light_Field_Spatial_Super-Resolution_via_Deep_Combinatorial_Geometry_Embedding_and_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Auto-Encoding Twin-Bottleneck Hashing | Yuming Shen, Jie Qin, Jiaxin Chen, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, Ling Shao | Conventional unsupervised hashing methods usually take advantage of similarity graphs, which are either pre-computed in the high-dimensional space or obtained from random anchor points. On the one hand, existing methods uncouple the procedures of hash function learning and graph construction. On the other hand, graphs ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shen_Auto-Encoding_Twin-Bottleneck_Hashing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11930 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Auto-Encoding_Twin-Bottleneck_Hashing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Auto-Encoding_Twin-Bottleneck_Hashing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Super Resolve Intensity Images From Events | S. Mohammad Mostafavi I., Jonghyun Choi, Kuk-Jin Yoon | An event camera detects per-pixel intensity difference and produces asynchronous event stream with low latency, high dynamic range, and low power consumption. As a trade-off, the event camera has low spatial resolution. We propose an end-to-end network to reconstruct high resolution, high dynamic range (HDR) images dir... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/I._Learning_to_Super_Resolve_Intensity_Images_From_Events_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01196 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/I._Learning_to_Super_Resolve_Intensity_Images_From_Events_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/I._Learning_to_Super_Resolve_Intensity_Images_From_Events_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/I._Learning_to_Super_CVPR_2020_supplemental.zip | null | null |
FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction | Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, Xun Cao | In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D mo... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_FaceScape_A_Large-Scale_High_Quality_3D_Face_Dataset_and_Detailed_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13989 | https://www.youtube.com/watch?v=uctztOT8ov4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_FaceScape_A_Large-Scale_High_Quality_3D_Face_Dataset_and_Detailed_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_FaceScape_A_Large-Scale_High_Quality_3D_Face_Dataset_and_Detailed_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_FaceScape_A_Large-Scale_CVPR_2020_supplemental.zip | https://cove.thecvf.com/datasets/343 | null |
Cross-View Tracking for Multi-Human 3D Pose Estimation at Over 100 FPS | Long Chen, Haizhou Ai, Rui Chen, Zijie Zhuang, Shuang Liu | Estimating 3D poses of multiple humans in real-time is a classic but still challenging task in computer vision. Its major difficulty lies in the ambiguity in cross-view association of 2D poses and the huge state space when there are multiple people in multiple views. In this paper, we present a novel solution for multi... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Cross-View_Tracking_for_Multi-Human_3D_Pose_Estimation_at_Over_100_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03972 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Cross-View_Tracking_for_Multi-Human_3D_Pose_Estimation_at_Over_100_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Cross-View_Tracking_for_Multi-Human_3D_Pose_Estimation_at_Over_100_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Cross-View_Tracking_for_CVPR_2020_supplemental.pdf | null | null |
ADINet: Attribute Driven Incremental Network for Retinal Image Classification | Qier Meng, Satoh Shin'ichi | Retinal diseases encompass a variety of types, including different diseases and severity levels. Training a model with different types of disease is impractical. Dynamically training a model is necessary when a patient with a new disease appears. Deep learning techniques have stood out in recent years, but they suffer ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Meng_ADINet_Attribute_Driven_Incremental_Network_for_Retinal_Image_Classification_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Meng_ADINet_Attribute_Driven_Incremental_Network_for_Retinal_Image_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Meng_ADINet_Attribute_Driven_Incremental_Network_for_Retinal_Image_Classification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Meng_ADINet_Attribute_Driven_CVPR_2020_supplemental.pdf | null | null |
Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery | Zhuo Zheng, Yanfei Zhong, Junjue Wang, Ailong Ma | Geospatial object segmentation, as a particular semantic segmentation task, always faces with larger-scale variation, larger intra-class variance of background, and foreground-background imbalance in the high spatial resolution (HSR) remote sensing imagery. However, general semantic segmentation methods mainly focus on... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Foreground-Aware_Relation_Network_for_Geospatial_Object_Segmentation_in_High_Spatial_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=wwYE1FR1AWQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Foreground-Aware_Relation_Network_for_Geospatial_Object_Segmentation_in_High_Spatial_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Foreground-Aware_Relation_Network_for_Geospatial_Object_Segmentation_in_High_Spatial_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zheng_Foreground-Aware_Relation_Network_CVPR_2020_supplemental.pdf | null | null |
Single-View View Synthesis With Multiplane Images | Richard Tucker, Noah Snavely | A recent strand of work in view synthesis uses deep learning to generate multiplane images--a camera-centric, layered 3D representation--given two or more input images at known viewpoints. We apply this representation to single-view view synthesis, a problem which is more challenging but has potentially much wider appl... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tucker_Single-View_View_Synthesis_With_Multiplane_Images_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.11364 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tucker_Single-View_View_Synthesis_With_Multiplane_Images_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tucker_Single-View_View_Synthesis_With_Multiplane_Images_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tucker_Single-View_View_Synthesis_CVPR_2020_supplemental.zip | null | null |
Select, Supplement and Focus for RGB-D Saliency Detection | Miao Zhang, Weisong Ren, Yongri Piao, Zhengkun Rong, Huchuan Lu | Depth data containing a preponderance of discriminative power in location have been proven beneficial for accurate saliency prediction. However, RGB-D saliency detection methods are also negatively influenced by randomly distributed erroneous or missing regions on the depth map or along the object boundaries. This offe... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Towards Discriminability and Diversity: Batch Nuclear-Norm Maximization Under Label Insufficient Situations | Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, Qi Tian | The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary with high data density. A common solution is to directly minimize the Shannon Entropy, but the side effect caused by entropy minimization, i.... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cui_Towards_Discriminability_and_Diversity_Batch_Nuclear-Norm_Maximization_Under_Label_Insufficient_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12237 | https://www.youtube.com/watch?v=n8Y5O3JFdgY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cui_Towards_Discriminability_and_Diversity_Batch_Nuclear-Norm_Maximization_Under_Label_Insufficient_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cui_Towards_Discriminability_and_Diversity_Batch_Nuclear-Norm_Maximization_Under_Label_Insufficient_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cui_Towards_Discriminability_and_CVPR_2020_supplemental.pdf | null | null |
4D Association Graph for Realtime Multi-Person Motion Capture Using Multiple Video Cameras | Yuxiang Zhang, Liang An, Tao Yu, Xiu Li, Kun Li, Yebin Liu | his paper contributes a novel realtime multi-person motion capture algorithm using multiview video inputs. Due to the heavy occlusions and closely interacting motions in each view, joint optimization on the multiview images and multiple temporal frames is indispensable, which brings up the essential challenge of realti... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_4D_Association_Graph_for_Realtime_Multi-Person_Motion_Capture_Using_Multiple_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12625 | https://www.youtube.com/watch?v=PgWaul71tzE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_4D_Association_Graph_for_Realtime_Multi-Person_Motion_Capture_Using_Multiple_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_4D_Association_Graph_for_Realtime_Multi-Person_Motion_Capture_Using_Multiple_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation | Lijun Wang, Jianming Zhang, Oliver Wang, Zhe Lin, Huchuan Lu | Monocular depth estimation is an ill-posed problem, and as such critically relies on scene priors and semantics. Due to its complexity, we propose a deep neural network model based on a semantic divide-and-conquer approach. Our model decomposes a scene into semantic segments, such as object instances and background stu... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_SDC-Depth_Semantic_Divide-and-Conquer_Network_for_Monocular_Depth_Estimation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=PGE5OqoqP5U | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_SDC-Depth_Semantic_Divide-and-Conquer_Network_for_Monocular_Depth_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_SDC-Depth_Semantic_Divide-and-Conquer_Network_for_Monocular_Depth_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Meshlet Priors for 3D Mesh Reconstruction | Abhishek Badki, Orazio Gallo, Jan Kautz, Pradeep Sen | Estimating a mesh from an unordered set of sparse, noisy 3D points is a challenging problem that requires to carefully select priors. Existing hand-crafted priors, such as smoothness regularizers, impose an undesirable trade-off between attenuating noise and preserving local detail. Recent deep-learning approaches prod... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Badki_Meshlet_Priors_for_3D_Mesh_Reconstruction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.01744 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Badki_Meshlet_Priors_for_3D_Mesh_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Badki_Meshlet_Priors_for_3D_Mesh_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Badki_Meshlet_Priors_for_CVPR_2020_supplemental.pdf | null | null |
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking | Hongjun Wang, Guangrun Wang, Ya Li, Dongyu Zhang, Liang Lin | The success of DNNs has driven the extensive applications of person re-identification (ReID) into a new era. However, whether ReID inherits the vulnerability of DNNs remains unexplored. To examine the robustness of ReID systems is rather important because the insecurity of ReID systems may cause severe losses, e.g., th... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Transferable_Controllable_and_Inconspicuous_Adversarial_Attacks_on_Person_Re-identification_With_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04199 | https://www.youtube.com/watch?v=R1wx1DVMsNI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Transferable_Controllable_and_Inconspicuous_Adversarial_Attacks_on_Person_Re-identification_With_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Transferable_Controllable_and_Inconspicuous_Adversarial_Attacks_on_Person_Re-identification_With_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Transferable_Controllable_and_CVPR_2020_supplemental.pdf | null | null |
More Grounded Image Captioning by Distilling Image-Text Matching Model | Yuanen Zhou, Meng Wang, Daqing Liu, Zhenzhen Hu, Hanwang Zhang | Visual attention not only improves the performance of image captioners, but also serves as a visual interpretation to qualitatively measure the caption rationality and model transparency. Specifically, we expect that a captioner can fix its attentive gaze on the correct objects while generating the corresponding words.... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_More_Grounded_Image_Captioning_by_Distilling_Image-Text_Matching_Model_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00390 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_More_Grounded_Image_Captioning_by_Distilling_Image-Text_Matching_Model_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_More_Grounded_Image_Captioning_by_Distilling_Image-Text_Matching_Model_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Leveraging Photometric Consistency Over Time for Sparsely Supervised Hand-Object Reconstruction | Yana Hasson, Bugra Tekin, Federica Bogo, Ivan Laptev, Marc Pollefeys, Cordelia Schmid | Modeling hand-object manipulations is essential for understanding how humans interact with their environment. While of practical importance, estimating the pose of hands and objects during interactions is challenging due to the large mutual occlusions that occur during manipulation. Recent efforts have been directed to... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hasson_Leveraging_Photometric_Consistency_Over_Time_for_Sparsely_Supervised_Hand-Object_Reconstruction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.13449 | https://www.youtube.com/watch?v=PNwjsAN0ztw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hasson_Leveraging_Photometric_Consistency_Over_Time_for_Sparsely_Supervised_Hand-Object_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hasson_Leveraging_Photometric_Consistency_Over_Time_for_Sparsely_Supervised_Hand-Object_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hasson_Leveraging_Photometric_Consistency_CVPR_2020_supplemental.pdf | null | null |
Scene Recomposition by Learning-Based ICP | Hamid Izadinia, Steven M. Seitz | By moving a depth sensor around a room, we compute a 3D CAD model of the environment, capturing the room shape and contents such as chairs, desks, sofas, and tables. Rather than reconstructing geometry, we match, place, and align each object in the scene to thousands of CAD models of objects. In addition to the fully a... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Izadinia_Scene_Recomposition_by_Learning-Based_ICP_CVPR_2020_paper.pdf | http://arxiv.org/abs/1812.05583 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Izadinia_Scene_Recomposition_by_Learning-Based_ICP_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Izadinia_Scene_Recomposition_by_Learning-Based_ICP_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection | Keren Fu, Deng-Ping Fan, Ge-Peng Ji, Qijun Zhao | This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited am... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fu_JL-DCF_Joint_Learning_and_Densely-Cooperative_Fusion_Framework_for_RGB-D_Salient_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=3V6mUtcSvmE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fu_JL-DCF_Joint_Learning_and_Densely-Cooperative_Fusion_Framework_for_RGB-D_Salient_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fu_JL-DCF_Joint_Learning_and_Densely-Cooperative_Fusion_Framework_for_RGB-D_Salient_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition | Linchao Zhu, Yi Yang | There have been increasing interests in modeling long-tailed data. Unlike artificially collected datasets, long-tailed data are naturally existed in the real-world and thus more realistic. To deal with the class imbalance problem, we introduce an Inflated Episodic Memory (IEM) for long-tailed visual recognition. First,... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_Inflated_Episodic_Memory_With_Region_Self-Attention_for_Long-Tailed_Visual_Recognition_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=UHVXOjaos3s | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Inflated_Episodic_Memory_With_Region_Self-Attention_for_Long-Tailed_Visual_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Inflated_Episodic_Memory_With_Region_Self-Attention_for_Long-Tailed_Visual_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Select Base Classes for Few-Shot Classification | Linjun Zhou, Peng Cui, Xu Jia, Shiqiang Yang, Qi Tian | Few-shot learning has attracted intensive research attention in recent years. Many methods have been proposed to generalize a model learned from provided base classes to novel classes, but no previous work studies how to select base classes, or even whether different base classes will result in different generalization... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Learning_to_Select_Base_Classes_for_Few-Shot_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00315 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Learning_to_Select_Base_Classes_for_Few-Shot_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Learning_to_Select_Base_Classes_for_Few-Shot_Classification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_Learning_to_Select_CVPR_2020_supplemental.pdf | null | null |
Attention-Based Context Aware Reasoning for Situation Recognition | Thilini Cooray, Ngai-Man Cheung, Wei Lu | Situation Recognition (SR) is a fine-grained action recognition task where the model is expected to not only predict the salient action of the image, but also predict values of all associated semantic roles of the action. Predicting semantic roles is very challenging: a vast variety of possibilities can be the match fo... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cooray_Attention-Based_Context_Aware_Reasoning_for_Situation_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cooray_Attention-Based_Context_Aware_Reasoning_for_Situation_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cooray_Attention-Based_Context_Aware_Reasoning_for_Situation_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cooray_Attention-Based_Context_Aware_CVPR_2020_supplemental.pdf | null | null |
Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations | Jeet Mohapatra, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel | Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task. While current verification methods mainly focus on the l_p-norm threat model of the input instances, robustness verification against semantic adversarial attacks inducing large l_p-norm perturbations, such as c... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mohapatra_Towards_Verifying_Robustness_of_Neural_Networks_Against_A_Family_of_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=Sy274_4IAGk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mohapatra_Towards_Verifying_Robustness_of_Neural_Networks_Against_A_Family_of_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mohapatra_Towards_Verifying_Robustness_of_Neural_Networks_Against_A_Family_of_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mohapatra_Towards_Verifying_Robustness_CVPR_2020_supplemental.pdf | null | null |
Background Matting: The World Is Your Green Screen | Soumyadip Sengupta, Vivek Jayaram, Brian Curless, Steven M. Seitz, Ira Kemelmacher-Shlizerman | We propose a method for creating a matte - the per-pixel foreground color and alpha - of a person by taking photos or videos in an everyday setting with a handheld camera. Most existing matting methods require a green screen background or a manually created trimap to produce a good matte. Automatic, trimap-free methods... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sengupta_Background_Matting_The_World_Is_Your_Green_Screen_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00626 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sengupta_Background_Matting_The_World_Is_Your_Green_Screen_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sengupta_Background_Matting_The_World_Is_Your_Green_Screen_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sengupta_Background_Matting_The_CVPR_2020_supplemental.pdf | null | null |
Learning to Simulate Dynamic Environments With GameGAN | Seung Wook Kim, Yuhao Zhou, Jonah Philion, Antonio Torralba, Sanja Fidler | Simulation is a crucial component of any robotic system. In order to simulate correctly, we need to write complex rules of the environment: how dynamic agents behave, and how the actions of each of the agents affect the behavior of others. In this paper, we aim to learn a simulator by simply watching an agent interact ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Learning_to_Simulate_Dynamic_Environments_With_GameGAN_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.12126 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Learning_to_Simulate_Dynamic_Environments_With_GameGAN_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Learning_to_Simulate_Dynamic_Environments_With_GameGAN_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kim_Learning_to_Simulate_CVPR_2020_supplemental.zip | null | null |
Active 3D Motion Visualization Based on Spatiotemporal Light-Ray Integration | Fumihiko Sakaue, Jun Sato | In this paper, we propose a method of visualizing 3D motion with zero latency. This method achieves motion visualization by projecting special high-frequency light patterns on moving objects without using any feedback mechanisms. For this objective, we focus on the time integration of light rays in the sensing system o... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sakaue_Active_3D_Motion_Visualization_Based_on_Spatiotemporal_Light-Ray_Integration_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=In2Iv3vsWC4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sakaue_Active_3D_Motion_Visualization_Based_on_Spatiotemporal_Light-Ray_Integration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sakaue_Active_3D_Motion_Visualization_Based_on_Spatiotemporal_Light-Ray_Integration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sakaue_Active_3D_Motion_CVPR_2020_supplemental.zip | null | null |
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation From a Blackbox Model | Dongdong Wang, Yandong Li, Liqiang Wang, Boqing Gong | We study how to train a student deep neural network for visual recognition by distilling knowledge from a blackbox teacher model in a data-efficient manner. Progress on this problem can significantly reduce the dependence on large-scale datasets for learning high-performing visual recognition models. There are two majo... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Neural_Networks_Are_More_Productive_Teachers_Than_Human_Raters_Active_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13960 | https://www.youtube.com/watch?v=yBO8olcWHvE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Neural_Networks_Are_More_Productive_Teachers_Than_Human_Raters_Active_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Neural_Networks_Are_More_Productive_Teachers_Than_Human_Raters_Active_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Oops! Predicting Unintentional Action in Video | Dave Epstein, Boyuan Chen, Carl Vondrick | From just a short glance at a video, we can often tell whether a person's action is intentional or not. Can we train a model to recognize this? We introduce a dataset of in-the-wild videos of unintentional action, as well as a suite of tasks for recognizing, localizing, and anticipating its onset. We train a supervised... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Epstein_Oops_Predicting_Unintentional_Action_in_Video_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11206 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Epstein_Oops_Predicting_Unintentional_Action_in_Video_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Epstein_Oops_Predicting_Unintentional_Action_in_Video_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition | Pengfei Zhang, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jianru Xue, Nanning Zheng | Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feedforward neural networks to model the 3D coordinates of joints without considering the computational efficiency. In this paper, we propose a ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Semantics-Guided_Neural_Networks_for_Efficient_Skeleton-Based_Human_Action_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.01189 | https://www.youtube.com/watch?v=ytT0pnuyktE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Semantics-Guided_Neural_Networks_for_Efficient_Skeleton-Based_Human_Action_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Semantics-Guided_Neural_Networks_for_Efficient_Skeleton-Based_Human_Action_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization | Saehyung Lee, Hyungyu Lee, Sungroh Yoon | Adversarial examples cause neural networks to produce incorrect outputs with high confidence. Although adversarial training is one of the most effective forms of defense against adversarial examples, unfortunately, a large gap exists between test accuracy and training accuracy in adversarial training. In this paper, we... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_Adversarial_Vertex_Mixup_Toward_Better_Adversarially_Robust_Generalization_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.02484 | https://www.youtube.com/watch?v=nxryZ6gCA0M | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Adversarial_Vertex_Mixup_Toward_Better_Adversarially_Robust_Generalization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Adversarial_Vertex_Mixup_Toward_Better_Adversarially_Robust_Generalization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lee_Adversarial_Vertex_Mixup_CVPR_2020_supplemental.pdf | null | null |
Learning to Autofocus | Charles Herrmann, Richard Strong Bowen, Neal Wadhwa, Rahul Garg, Qiurui He, Jonathan T. Barron, Ramin Zabih | Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a realistic dataset of sufficient size for effective learning. Our dataset is labeled with per-pixel depths obtained from multi-view stereo, follow... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Herrmann_Learning_to_Autofocus_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.12260 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Herrmann_Learning_to_Autofocus_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Herrmann_Learning_to_Autofocus_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Herrmann_Learning_to_Autofocus_CVPR_2020_supplemental.pdf | null | null |
Boosting the Transferability of Adversarial Samples via Attention | Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, Michael R. Lyu, Yu-Wing Tai | The widespread deployment of deep models necessitates the assessment of model vulnerability in practice, especially for safety- and security-sensitive domains such as autonomous driving and medical diagnosis. Transfer-based attacks against image classifiers thus elicit mounting interest, where attackers are required to... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Boosting_the_Transferability_of_Adversarial_Samples_via_Attention_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Boosting_the_Transferability_of_Adversarial_Samples_via_Attention_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Boosting_the_Transferability_of_Adversarial_Samples_via_Attention_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Single Image Reflection Removal Through Cascaded Refinement | Chao Li, Yixiao Yang, Kun He, Stephen Lin, John E. Hopcroft | We address the problem of removing undesirable reflections from a single image captured through a glass surface, which is an ill-posed, challenging but practically important problem for photo enhancement. Inspired by iterative structure reduction for hidden community detection in social networks, we propose an Iterativ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Single_Image_Reflection_Removal_Through_Cascaded_Refinement_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.06634 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Single_Image_Reflection_Removal_Through_Cascaded_Refinement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Single_Image_Reflection_Removal_Through_Cascaded_Refinement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Single_Image_Reflection_CVPR_2020_supplemental.pdf | null | null |
Distilled Semantics for Comprehensive Scene Understanding from Videos | Fabio Tosi, Filippo Aleotti, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Luigi Di Stefano, Stefano Mattoccia | Whole understanding of the surroundings is paramount to autonomous systems. Recent works have shown that deep neural networks can learn geometry (depth) and motion (optical flow) from a monocular video without any explicit supervision from ground truth annotations, particularly hard to source for these two tasks. In th... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tosi_Distilled_Semantics_for_Comprehensive_Scene_Understanding_from_Videos_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14030 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tosi_Distilled_Semantics_for_Comprehensive_Scene_Understanding_from_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tosi_Distilled_Semantics_for_Comprehensive_Scene_Understanding_from_Videos_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tosi_Distilled_Semantics_for_CVPR_2020_supplemental.pdf | null | null |
Multi-Dimensional Pruning: A Unified Framework for Model Compression | Jinyang Guo, Wanli Ouyang, Dong Xu | In this work, we propose a unified model compression framework called Multi-Dimensional Pruning (MDP) to simultaneously compress the convolutional neural networks (CNNs) on multiple dimensions. In contrast to the existing model compression methods that only aim to reduce the redundancy along either the spatial/spatial-... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Multi-Dimensional_Pruning_A_Unified_Framework_for_Model_Compression_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Multi-Dimensional_Pruning_A_Unified_Framework_for_Model_Compression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Multi-Dimensional_Pruning_A_Unified_Framework_for_Model_Compression_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Varicolored Image De-Hazing | Akshay Dudhane, Kuldeep M. Biradar, Prashant W. Patil, Praful Hambarde, Subrahmanyam Murala | The quality of images captured in bad weather is often affected by chromatic casts and low visibility due to the presence of atmospheric particles. Restoration of the color balance is often ignored in most of the existing image de-hazing methods. In this paper, we propose a varicolored end-to-end image de-hazing networ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dudhane_Varicolored_Image_De-Hazing_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dudhane_Varicolored_Image_De-Hazing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dudhane_Varicolored_Image_De-Hazing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Visually Imbalanced Stereo Matching | Yicun Liu, Jimmy Ren, Jiawei Zhang, Jianbo Liu, Mude Lin | Understanding of human vision system (HVS) has inspired many computer vision algorithms. Stereo matching, which borrows the idea from human stereopsis, has been extensively studied in the existing literature. However, scant attention has been drawn on a typical scenario where binocular inputs are qualitatively differen... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Visually_Imbalanced_Stereo_Matching_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Visually_Imbalanced_Stereo_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Visually_Imbalanced_Stereo_Matching_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Defending Against Model Stealing Attacks With Adaptive Misinformation | Sanjay Kariyappa, Moinuddin K. Qureshi | Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which allows a data-limited adversary with no knowledge of the training dataset to clone the functionality of a target model, just by using black-box query access. Such attacks are typically carried out by querying the target model using inputs that... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kariyappa_Defending_Against_Model_Stealing_Attacks_With_Adaptive_Misinformation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07100 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kariyappa_Defending_Against_Model_Stealing_Attacks_With_Adaptive_Misinformation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kariyappa_Defending_Against_Model_Stealing_Attacks_With_Adaptive_Misinformation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kariyappa_Defending_Against_Model_CVPR_2020_supplemental.pdf | null | null |
Semi-Supervised Learning for Few-Shot Image-to-Image Translation | Yaxing Wang, Salman Khan, Abel Gonzalez-Garcia, Joost van de Weijer, Fahad Shahbaz Khan | In the last few years, unpaired image-to-image translation has witnessed Remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image ranslation, red... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Semi-Supervised_Learning_for_Few-Shot_Image-to-Image_Translation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Semi-Supervised_Learning_for_Few-Shot_Image-to-Image_Translation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Semi-Supervised_Learning_for_Few-Shot_Image-to-Image_Translation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Semi-Supervised_Learning_for_CVPR_2020_supplemental.pdf | null | null |
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