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0 | Dual Super-Resolution Learning for Semantic Segmentation | [
"Li Wang",
"Dong Li",
"Yousong Zhu",
"Lu Tian",
"Yi Shan"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Wang_2020_CVPR,author = {Wang, Li and Li, Dong and Zhu, Yousong and Tian, Lu and Shan, Yi},title = {Dual Super-Resolution Learning for Semantic Segmentation},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Current state-of-the-art semantic segmentation methods often apply high-resolution input to attain high performance, which brings large computation budgets and limits their applications on resource-constrained devices. In this paper, we propose a simple and flexible two-stream framework named Dual Super-Resolution Lear... | [
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1 | Deep Unfolding Network for Image Super-Resolution | [
"Kai Zhang",
"Luc Van Gool",
"Radu Timofte"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Deep_Unfolding_Network_for_Image_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Deep_Unfolding_Network_for_Image_Super-Resolution_CVPR_2020_paper.pdf | null | 2003.10428 | cvf | @InProceedings{Zhang_2020_CVPR,author = {Zhang, Kai and Van Gool, Luc and Timofte, Radu},title = {Deep Unfolding Network for Image Super-Resolution},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training. However, different from model-based methods that can handle the SISR problem with different scale factors, blur kernels... | [
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2 | Unsupervised Learning for Intrinsic Image Decomposition From a Single Image | [
"Yunfei Liu",
"Yu Li",
"Shaodi You",
"Feng Lu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Unsupervised_Learning_for_Intrinsic_Image_Decomposition_From_a_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Unsupervised_Learning_for_Intrinsic_Image_Decomposition_From_a_Single_Image_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Liu_Unsupervised_Learning_for_CVPR_2020_supplemental.pdf | 1911.09930 | cvf | @InProceedings{Liu_2020_CVPR,author = {Liu, Yunfei and Li, Yu and You, Shaodi and Lu, Feng},title = {Unsupervised Learning for Intrinsic Image Decomposition From a Single Image},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Intrinsic image decomposition, which is an essential task in computer vision, aims to infer the reflectance and shading of the scene. It is challenging since it needs to separate one image into two components. To tackle this, conventional methods introduce various priors to constrain the solution, yet with limited perf... | [
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3 | COCAS: A Large-Scale Clothes Changing Person Dataset for Re-Identification | [
"Shijie Yu",
"Shihua Li",
"Dapeng Chen",
"Rui Zhao",
"Junjie Yan",
"Yu Qiao"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_COCAS_A_Large-Scale_Clothes_Changing_Person_Dataset_for_Re-Identification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_COCAS_A_Large-Scale_Clothes_Changing_Person_Dataset_for_Re-Identification_CVPR_2020_paper.pdf | null | 2005.07862 | cvf | @InProceedings{Yu_2020_CVPR,author = {Yu, Shijie and Li, Shihua and Chen, Dapeng and Zhao, Rui and Yan, Junjie and Qiao, Yu},title = {COCAS: A Large-Scale Clothes Changing Person Dataset for Re-Identification},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {202... | Recent years have witnessed great progress in person re-identification (re-id). Several academic benchmarks such as Market1501, CUHK03 and DukeMTMC play important roles to promote the re-id research. To our best knowledge, all the existing benchmarks assume the same person will have the same clothes. While in real-worl... | [
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4 | Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference | [
"Thomas Verelst",
"Tinne Tuytelaars"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Verelst_Dynamic_Convolutions_Exploiting_Spatial_Sparsity_for_Faster_Inference_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Verelst_Dynamic_Convolutions_Exploiting_Spatial_Sparsity_for_Faster_Inference_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Verelst_Dynamic_Convolutions_Exploiting_CVPR_2020_supplemental.pdf | 1912.03203 | cvf | @InProceedings{Verelst_2020_CVPR,author = {Verelst, Thomas and Tuytelaars, Tinne},title = {Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Modern convolutional neural networks apply the same operations on every pixel in an image. However, not all image regions are equally important. To address this inefficiency, we propose a method to dynamically apply convolutions conditioned on the input image. We introduce a residual block where a small gating branch l... | [
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5 | Alleviation of Gradient Exploding in GANs: Fake Can Be Real | [
"Song Tao",
"Jia Wang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Tao_Alleviation_of_Gradient_Exploding_in_GANs_Fake_Can_Be_Real_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Tao_Alleviation_of_Gradient_Exploding_in_GANs_Fake_Can_Be_Real_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Tao_Alleviation_of_Gradient_CVPR_2020_supplemental.pdf | 1912.12485 | cvf | @InProceedings{Tao_2020_CVPR,author = {Tao, Song and Wang, Jia},title = {Alleviation of Gradient Exploding in GANs: Fake Can Be Real},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In order to alleviate the notorious mode collapse phenomenon in generative adversarial networks (GANs), we propose a novel training method of GANs in which certain fake samples are considered as real ones during the training process. This strategy can reduce the gradient value that generator receives in the region wher... | [
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6 | Forward and Backward Information Retention for Accurate Binary Neural Networks | [
"Haotong Qin",
"Ruihao Gong",
"Xianglong Liu",
"Mingzhu Shen",
"Ziran Wei",
"Fengwei Yu",
"Jingkuan Song"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Qin_Forward_and_Backward_Information_Retention_for_Accurate_Binary_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Qin_Forward_and_Backward_Information_Retention_for_Accurate_Binary_Neural_Networks_CVPR_2020_paper.pdf | null | 1909.10788 | cvf | @InProceedings{Qin_2020_CVPR,author = {Qin, Haotong and Gong, Ruihao and Liu, Xianglong and Shen, Mingzhu and Wei, Ziran and Yu, Fengwei and Song, Jingkuan},title = {Forward and Backward Information Retention for Accurate Binary Neural Networks},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a notice... | [
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7 | Cooling-Shrinking Attack: Blinding the Tracker With Imperceptible Noises | [
"Bin Yan",
"Dong Wang",
"Huchuan Lu",
"Xiaoyun Yang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Cooling-Shrinking_Attack_Blinding_the_Tracker_With_Imperceptible_Noises_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Yan_Cooling-Shrinking_Attack_Blinding_the_Tracker_With_Imperceptible_Noises_CVPR_2020_paper.pdf | null | 2003.09595 | cvf | @InProceedings{Yan_2020_CVPR,author = {Yan, Bin and Wang, Dong and Lu, Huchuan and Yang, Xiaoyun},title = {Cooling-Shrinking Attack: Blinding the Tracker With Imperceptible Noises},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Adversarial attack of CNN aims at deceiving models to misbehave by adding imperceptible perturbations to images. This feature facilitates to understand neural networks deeply and to improve the robustness of deep learning models. Although several works have focused on attacking image classifiers and object detectors, a... | [
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8 | Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution | [
"Xiaoyu Xiang",
"Yapeng Tian",
"Yulun Zhang",
"Yun Fu",
"Jan P. Allebach",
"Chenliang Xu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiang_Zooming_Slow-Mo_Fast_and_Accurate_One-Stage_Space-Time_Video_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Xiang_Zooming_Slow-Mo_Fast_and_Accurate_One-Stage_Space-Time_Video_Super-Resolution_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Xiang_Zooming_Slow-Mo_Fast_CVPR_2020_supplemental.pdf | 2002.11616 | title_snapshot | @InProceedings{Xiang_2020_CVPR,author = {Xiang, Xiaoyu and Tian, Yapeng and Zhang, Yulun and Fu, Yun and Allebach, Jan P. and Xu, Chenliang},title = {Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month ... | In this paper, we explore the space-time video super-resolution task, which aims to generate a high-resolution (HR) slow-motion video from a low frame rate (LFR), low-resolution (LR) video. A simple solution is to split it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR). However, te... | [
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9 | A Hierarchical Graph Network for 3D Object Detection on Point Clouds | [
"Jintai Chen",
"Biwen Lei",
"Qingyu Song",
"Haochao Ying",
"Danny Z. Chen",
"Jian Wu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_A_Hierarchical_Graph_Network_for_3D_Object_Detection_on_Point_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_A_Hierarchical_Graph_Network_for_3D_Object_Detection_on_Point_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Chen_2020_CVPR,author = {Chen, Jintai and Lei, Biwen and Song, Qingyu and Ying, Haochao and Chen, Danny Z. and Wu, Jian},title = {A Hierarchical Graph Network for 3D Object Detection on Point Clouds},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | 3D object detection on point clouds finds many applications. However, most known point cloud object detection methods did not adequately accommodate the characteristics (e.g., sparsity) of point clouds, and thus some key semantic information (e.g., shape information) is not well captured. In this paper, we propose a ne... | [
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10 | Online Joint Multi-Metric Adaptation From Frequent Sharing-Subset Mining for Person Re-Identification | [
"Jiahuan Zhou",
"Bing Su",
"Ying Wu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Online_Joint_Multi-Metric_Adaptation_From_Frequent_Sharing-Subset_Mining_for_Person_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Online_Joint_Multi-Metric_Adaptation_From_Frequent_Sharing-Subset_Mining_for_Person_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Zhou_2020_CVPR,author = {Zhou, Jiahuan and Su, Bing and Wu, Ying},title = {Online Joint Multi-Metric Adaptation From Frequent Sharing-Subset Mining for Person Re-Identification},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Person Re-IDentification (P-RID), as an instance-level recognition problem, still remains challenging in computer vision community. Many P-RID works aim to learn faithful and discriminative features/metrics from offline training data and directly use them for the unseen online testing data. However, their performance i... | [
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11 | Learning to Discriminate Information for Online Action Detection | [
"Hyunjun Eun",
"Jinyoung Moon",
"Jongyoul Park",
"Chanho Jung",
"Changick Kim"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.pdf | null | 1912.04461 | cvf | @InProceedings{Eun_2020_CVPR,author = {Eun, Hyunjun and Moon, Jinyoung and Park, Jongyoul and Jung, Chanho and Kim, Changick},title = {Learning to Discriminate Information for Online Action Detection},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as wel... | [
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12 | Video to Events: Recycling Video Datasets for Event Cameras | [
"Daniel Gehrig",
"Mathias Gehrig",
"Javier Hidalgo-Carrio",
"Davide Scaramuzza"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Gehrig_Video_to_Events_Recycling_Video_Datasets_for_Event_Cameras_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Gehrig_Video_to_Events_Recycling_Video_Datasets_for_Event_Cameras_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Gehrig_Video_to_Events_CVPR_2020_supplemental.zip | 1912.03095 | title_snapshot | @InProceedings{Gehrig_2020_CVPR,author = {Gehrig, Daniel and Gehrig, Mathias and Hidalgo-Carrio, Javier and Scaramuzza, Davide},title = {Video to Events: Recycling Video Datasets for Event Cameras},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high dynamic range (HDR), high temporal resolution, and no motion blur. Recently, novel learning approaches... | [
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13 | Bundle Pooling for Polygonal Architecture Segmentation Problem | [
"Huayi Zeng",
"Kevin Joseph",
"Adam Vest",
"Yasutaka Furukawa"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Bundle_Pooling_for_Polygonal_Architecture_Segmentation_Problem_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Zeng_Bundle_Pooling_for_Polygonal_Architecture_Segmentation_Problem_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Zeng_2020_CVPR,author = {Zeng, Huayi and Joseph, Kevin and Vest, Adam and Furukawa, Yasutaka},title = {Bundle Pooling for Polygonal Architecture Segmentation Problem},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | This paper introduces a polygonal architecture segmentation problem, proposes bundle-pooling modules for line structure reasoning, and demonstrates a virtual remodeling application that produces production quality results. Given a photograph of a house with a few vanishing point candidates, we decompose the house into ... | [
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14 | Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects | [
"Kiana Ehsani",
"Shubham Tulsiani",
"Saurabh Gupta",
"Ali Farhadi",
"Abhinav Gupta"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Ehsani_Use_the_Force_Luke_Learning_to_Predict_Physical_Forces_by_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Ehsani_Use_the_Force_Luke_Learning_to_Predict_Physical_Forces_by_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Ehsani_Use_the_Force_CVPR_2020_supplemental.zip | 2003.12045 | cvf | @InProceedings{Ehsani_2020_CVPR,author = {Ehsani, Kiana and Tulsiani, Shubham and Gupta, Saurabh and Farhadi, Ali and Gupta, Abhinav},title = {Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June... | When we humans look at a video of human-object interaction, we can not only infer what is happening but we can even extract actionable information and imitate those interactions. On the other hand, current recognition or geometric approaches lack the physicality of action representation. In this paper, we take a step t... | [
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15 | Articulation-Aware Canonical Surface Mapping | [
"Nilesh Kulkarni",
"Abhinav Gupta",
"David F. Fouhey",
"Shubham Tulsiani"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Kulkarni_Articulation-Aware_Canonical_Surface_Mapping_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Kulkarni_Articulation-Aware_Canonical_Surface_Mapping_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Kulkarni_Articulation-Aware_Canonical_Surface_CVPR_2020_supplemental.pdf | 2004.00614 | cvf | @InProceedings{Kulkarni_2020_CVPR,author = {Kulkarni, Nilesh and Gupta, Abhinav and Fouhey, David F. and Tulsiani, Shubham},title = {Articulation-Aware Canonical Surface Mapping},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canonical template shape , and 2) inferring the articulation and pose of the template corresponding to the input image. While previous approaches rely on keypoint supervision fo... | [
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16 | NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks | [
"Eugene Lee",
"Chen-Yi Lee"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Lee_NeuralScale_Efficient_Scaling_CVPR_2020_supplemental.pdf | 2006.12813 | cvf | @InProceedings{Lee_2020_CVPR,author = {Lee, Eugene and Lee, Chen-Yi},title = {NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Deciding the amount of neurons during the design of a deep neural network to maximize performance is not intuitive. In this work, we attempt to search for the neuron (filter) configuration of a fixed network architecture that maximizes accuracy. Using iterative pruning methods as a proxy, we parametrize the change of t... | [
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17 | Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization | [
"Yoonsik Kim",
"Jae Woong Soh",
"Gu Yong Park",
"Nam Ik Cho"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Transfer_Learning_From_Synthetic_to_Real-Noise_Denoising_With_Adaptive_Instance_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Kim_Transfer_Learning_From_Synthetic_to_Real-Noise_Denoising_With_Adaptive_Instance_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Kim_Transfer_Learning_From_CVPR_2020_supplemental.pdf | 2002.11244 | cvf | @InProceedings{Kim_2020_CVPR,author = {Kim, Yoonsik and Soh, Jae Woong and Park, Gu Yong and Cho, Nam Ik},title = {Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {202... | Real-noise denoising is a challenging task because the statistics of real-noise do not follow the normal distribution, and they are also spatially and temporally changing. In order to cope with various and complex real-noise, we propose a well-generalized denoising architecture and a transfer learning scheme. Specifica... | [
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18 | Variational Context-Deformable ConvNets for Indoor Scene Parsing | [
"Zhitong Xiong",
"Yuan Yuan",
"Nianhui Guo",
"Qi Wang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Xiong_Variational_Context-Deformable_ConvNets_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Xiong_2020_CVPR,author = {Xiong, Zhitong and Yuan, Yuan and Guo, Nianhui and Wang, Qi},title = {Variational Context-Deformable ConvNets for Indoor Scene Parsing},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Context information is critical for image semantic segmentation. Especially in indoor scenes, the large variation of object scales makes spatial-context an important factor for improving the segmentation performance. Thus, in this paper, we propose a novel variational context-deformable (VCD) module to learn adaptive r... | [
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19 | Augmenting Colonoscopy Using Extended and Directional CycleGAN for Lossy Image Translation | [
"Shawn Mathew",
"Saad Nadeem",
"Sruti Kumari",
"Arie Kaufman"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Mathew_Augmenting_Colonoscopy_Using_Extended_and_Directional_CycleGAN_for_Lossy_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Mathew_Augmenting_Colonoscopy_Using_Extended_and_Directional_CycleGAN_for_Lossy_Image_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Mathew_Augmenting_Colonoscopy_Using_CVPR_2020_supplemental.zip | 2003.12473 | cvf | @InProceedings{Mathew_2020_CVPR,author = {Mathew, Shawn and Nadeem, Saad and Kumari, Sruti and Kaufman, Arie},title = {Augmenting Colonoscopy Using Extended and Directional CycleGAN for Lossy Image Translation},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {20... | Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopy (VC), are critical for diagnosing and ultimately removing polyps (precursors for colon cancer). The non-invasive VC is normally used to inspect a 3D reconstructed colon (from computed tomography scans) for polyps and if fou... | [
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20 | BANet: Bidirectional Aggregation Network With Occlusion Handling for Panoptic Segmentation | [
"Yifeng Chen",
"Guangchen Lin",
"Songyuan Li",
"Omar Bourahla",
"Yiming Wu",
"Fangfang Wang",
"Junyi Feng",
"Mingliang Xu",
"Xi Li"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Chen_BANet_Bidirectional_Aggregation_CVPR_2020_supplemental.zip | 2003.14031 | cvf | @InProceedings{Chen_2020_CVPR,author = {Chen, Yifeng and Lin, Guangchen and Li, Songyuan and Bourahla, Omar and Wu, Yiming and Wang, Fangfang and Feng, Junyi and Xu, Mingliang and Li, Xi},title = {BANet: Bidirectional Aggregation Network With Occlusion Handling for Panoptic Segmentation},booktitle = {IEEE/CVF Conferenc... | Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously. The typical top-down pipeline concentrates on two key issues: 1) how to effectively model the intrinsic interaction between semantic segmentation and instance segmentation,... | [
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21 | C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation | [
"Qihang Yu",
"Dong Yang",
"Holger Roth",
"Yutong Bai",
"Yixiao Zhang",
"Alan L. Yuille",
"Daguang Xu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_C2FNAS_Coarse-to-Fine_Neural_Architecture_Search_for_3D_Medical_Image_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_C2FNAS_Coarse-to-Fine_Neural_Architecture_Search_for_3D_Medical_Image_Segmentation_CVPR_2020_paper.pdf | null | 1912.09628 | cvf | @InProceedings{Yu_2020_CVPR,author = {Yu, Qihang and Yang, Dong and Roth, Holger and Bai, Yutong and Zhang, Yixiao and Yuille, Alan L. and Xu, Daguang},title = {C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | 3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose or design proper 3D networks given different task contexts. Recently, Neural Architecture Search (NAS) is proposed to solve this problem by s... | [
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22 | Seeing the World in a Bag of Chips | [
"Jeong Joon Park",
"Aleksander Holynski",
"Steven M. Seitz"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Park_Seeing_the_World_in_a_Bag_of_Chips_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Park_Seeing_the_World_in_a_Bag_of_Chips_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Park_Seeing_the_World_CVPR_2020_supplemental.pdf | 2001.04642 | cvf | @InProceedings{Park_2020_CVPR,author = {Park, Jeong Joon and Holynski, Aleksander and Seitz, Steven M.},title = {Seeing the World in a Bag of Chips},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We address the dual problems of novel view synthesis and environment reconstruction from hand-held RGBD sensors. Our contributions include 1) modeling highly specular objects, 2) modeling inter-reflections and Fresnel effects, and 3) enabling surface light field reconstruction with the same input needed to reconstruct ... | [
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23 | Cascaded Deep Video Deblurring Using Temporal Sharpness Prior | [
"Jinshan Pan",
"Haoran Bai",
"Jinhui Tang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Cascaded_Deep_Video_Deblurring_Using_Temporal_Sharpness_Prior_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Cascaded_Deep_Video_Deblurring_Using_Temporal_Sharpness_Prior_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Pan_Cascaded_Deep_Video_CVPR_2020_supplemental.pdf | 2004.02501 | cvf | @InProceedings{Pan_2020_CVPR,author = {Pan, Jinshan and Bai, Haoran and Tang, Jinhui},title = {Cascaded Deep Video Deblurring Using Temporal Sharpness Prior},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We present a simple and effective deep convolutional neural network (CNN) model for video deblurring. The proposed algorithm mainly consists of optical flow estimation from intermediate latent frames and latent frame restoration steps. It first develops a deep CNN model to estimate optical flow from intermediate latent... | [
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24 | Reflection Scene Separation From a Single Image | [
"Renjie Wan",
"Boxin Shi",
"Haoliang Li",
"Ling-Yu Duan",
"Alex C. Kot"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Wan_Reflection_Scene_Separation_From_a_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Wan_Reflection_Scene_Separation_From_a_Single_Image_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Wan_2020_CVPR,author = {Wan, Renjie and Shi, Boxin and Li, Haoliang and Duan, Ling-Yu and Kot, Alex C.},title = {Reflection Scene Separation From a Single Image},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | For images taken through glass, existing methods focus on the restoration of the background scene by regarding the reflection components as noise. However, the scene reflected by glass surface also contains important information to be recovered, especially for the surveillance or criminal investigations. In this paper,... | [
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25 | SmallBigNet: Integrating Core and Contextual Views for Video Classification | [
"Xianhang Li",
"Yali Wang",
"Zhipeng Zhou",
"Yu Qiao"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_SmallBigNet_Integrating_Core_and_Contextual_Views_for_Video_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_SmallBigNet_Integrating_Core_and_Contextual_Views_for_Video_Classification_CVPR_2020_paper.pdf | null | 2006.14582 | cvf | @InProceedings{Li_2020_CVPR,author = {Li, Xianhang and Wang, Yali and Zhou, Zhipeng and Qiao, Yu},title = {SmallBigNet: Integrating Core and Contextual Views for Video Classification},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Temporal convolution has been widely used for video classification. However, it is performed on spatio-temporal contexts in a limited view, which often weakens its capacity of learning video representation. To alleviate this problem, we propose a concise and novel SmallBig network, with the cooperation of small and big... | [
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26 | From Two Rolling Shutters to One Global Shutter | [
"Cenek Albl",
"Zuzana Kukelova",
"Viktor Larsson",
"Michal Polic",
"Tomas Pajdla",
"Konrad Schindler"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Albl_From_Two_Rolling_Shutters_to_One_Global_Shutter_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Albl_From_Two_Rolling_Shutters_to_One_Global_Shutter_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Albl_From_Two_Rolling_CVPR_2020_supplemental.pdf | 2006.01964 | cvf | @InProceedings{Albl_2020_CVPR,author = {Albl, Cenek and Kukelova, Zuzana and Larsson, Viktor and Polic, Michal and Pajdla, Tomas and Schindler, Konrad},title = {From Two Rolling Shutters to One Global Shutter},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {202... | Most consumer cameras are equipped with electronic rolling shutter, leading to image distortions when the camera moves during image capture. We explore a surprisingly simple camera configuration that makes it possible to undo the rolling shutter distortion: two cameras mounted to have different rolling shutter directio... | [
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27 | CvxNet: Learnable Convex Decomposition | [
"Boyang Deng",
"Kyle Genova",
"Soroosh Yazdani",
"Sofien Bouaziz",
"Geoffrey Hinton",
"Andrea Tagliasacchi"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.pdf | null | 1909.05736 | cvf | @InProceedings{Deng_2020_CVPR,author = {Deng, Boyang and Genova, Kyle and Yazdani, Soroosh and Bouaziz, Sofien and Hinton, Geoffrey and Tagliasacchi, Andrea},title = {CvxNet: Learnable Convex Decomposition},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Any solid object can be decomposed into a collection of convex polytopes (in short, convexes). When a small number of convexes are used, such a decomposition can be thought of as a piece-wise approximation of the geometry. This decomposition is fundamental in computer graphics, where it provides one of the most common ... | [
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28 | RoboTHOR: An Open Simulation-to-Real Embodied AI Platform | [
"Matt Deitke",
"Winson Han",
"Alvaro Herrasti",
"Aniruddha Kembhavi",
"Eric Kolve",
"Roozbeh Mottaghi",
"Jordi Salvador",
"Dustin Schwenk",
"Eli VanderBilt",
"Matthew Wallingford",
"Luca Weihs",
"Mark Yatskar",
"Ali Farhadi"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Deitke_RoboTHOR_An_Open_Simulation-to-Real_Embodied_AI_Platform_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Deitke_RoboTHOR_An_Open_Simulation-to-Real_Embodied_AI_Platform_CVPR_2020_paper.pdf | null | 2004.06799 | cvf | @InProceedings{Deitke_2020_CVPR,author = {Deitke, Matt and Han, Winson and Herrasti, Alvaro and Kembhavi, Aniruddha and Kolve, Eric and Mottaghi, Roozbeh and Salvador, Jordi and Schwenk, Dustin and VanderBilt, Eli and Wallingford, Matthew and Weihs, Luca and Yatskar, Mark and Farhadi, Ali},title = {RoboTHOR: An Open Si... | Visual recognition ecosystems (e.g. ImageNet, Pascal, COCO) have undeniably played a prevailing role in the evolution of modern computer vision. We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems. Recently, various sy... | [
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29 | Style Normalization and Restitution for Generalizable Person Re-Identification | [
"Xin Jin",
"Cuiling Lan",
"Wenjun Zeng",
"Zhibo Chen",
"Li Zhang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Jin_Style_Normalization_and_CVPR_2020_supplemental.pdf | 2005.11037 | cvf | @InProceedings{Jin_2020_CVPR,author = {Jin, Xin and Lan, Cuiling and Zeng, Wenjun and Chen, Zhibo and Zhang, Li},title = {Style Normalization and Restitution for Generalizable Person Re-Identification},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in filtering out identity-irrelevant interference and learning domain-invariant person representations. In this paper, we aim to design a gener... | [
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30 | Training Noise-Robust Deep Neural Networks via Meta-Learning | [
"Zhen Wang",
"Guosheng Hu",
"Qinghua Hu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Training_Noise-Robust_Deep_Neural_Networks_via_Meta-Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Training_Noise-Robust_Deep_Neural_Networks_via_Meta-Learning_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Wang_2020_CVPR,author = {Wang, Zhen and Hu, Guosheng and Hu, Qinghua},title = {Training Noise-Robust Deep Neural Networks via Meta-Learning},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Label noise may significantly degrade the performance of Deep Neural Networks (DNNs). To train noise-robust DNNs, Loss correction (LC) approaches have been introduced. LC approaches assume the noisy labels are corrupted from clean (ground-truth) labels by an unknown noise transition matrix T. The backbone DNNs and T ca... | [
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31 | HUMBI: A Large Multiview Dataset of Human Body Expressions | [
"Zhixuan Yu",
"Jae Shin Yoon",
"In Kyu Lee",
"Prashanth Venkatesh",
"Jaesik Park",
"Jihun Yu",
"Hyun Soo Park"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_HUMBI_A_Large_Multiview_Dataset_of_Human_Body_Expressions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_HUMBI_A_Large_Multiview_Dataset_of_Human_Body_Expressions_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Yu_HUMBI_A_Large_CVPR_2020_supplemental.zip | 1812.00281 | cvf | @InProceedings{Yu_2020_CVPR,author = {Yu, Zhixuan and Yoon, Jae Shin and Lee, In Kyu and Venkatesh, Prashanth and Park, Jaesik and Yu, Jihun and Park, Hyun Soo},title = {HUMBI: A Large Multiview Dataset of Human Body Expressions},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month ... | This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cam- eras are used to capture 772 distinctiv... | [
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32 | Towards Transferable Targeted Attack | [
"Maosen Li",
"Cheng Deng",
"Tengjiao Li",
"Junchi Yan",
"Xinbo Gao",
"Heng Huang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Towards_Transferable_Targeted_Attack_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Towards_Transferable_Targeted_Attack_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Li_Towards_Transferable_Targeted_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Li_2020_CVPR,author = {Li, Maosen and Deng, Cheng and Li, Tengjiao and Yan, Junchi and Gao, Xinbo and Huang, Heng},title = {Towards Transferable Targeted Attack},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | An intriguing property of adversarial examples is their transferability, which suggests that black-box attacks are feasible in real-world applications. Previous works mostly study the transferability on non-targeted setting. However, recent studies show that targeted adversarial examples are more difficult to transfer ... | [
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33 | Supervised Raw Video Denoising With a Benchmark Dataset on Dynamic Scenes | [
"Huanjing Yue",
"Cong Cao",
"Lei Liao",
"Ronghe Chu",
"Jingyu Yang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Yue_Supervised_Raw_Video_CVPR_2020_supplemental.pdf | 2003.14013 | cvf | @InProceedings{Yue_2020_CVPR,author = {Yue, Huanjing and Cao, Cong and Liao, Lei and Chu, Ronghe and Yang, Jingyu},title = {Supervised Raw Video Denoising With a Benchmark Dataset on Dynamic Scenes},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In recent years, the supervised learning strategy for real noisy image denoising has been emerging and has achieved promising results. In contrast, realistic noise removal for raw noisy videos is rarely studied due to the lack of noisy-clean pairs for dynamic scenes. Clean video frames for dynamic scenes cannot be capt... | [
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34 | FDA: Fourier Domain Adaptation for Semantic Segmentation | [
"Yanchao Yang",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_FDA_Fourier_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_FDA_Fourier_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2020_paper.pdf | null | 2004.05498 | cvf | @InProceedings{Yang_2020_CVPR,author = {Yang, Yanchao and Soatto, Stefano},title = {FDA: Fourier Domain Adaptation for Semantic Segmentation},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other. We illustrate the method in semantic segmentation, where densely annotated images are aplenty in one domain (synthe... | [
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35 | SGAS: Sequential Greedy Architecture Search | [
"Guohao Li",
"Guocheng Qian",
"Itzel C. Delgadillo",
"Matthias Muller",
"Ali Thabet",
"Bernard Ghanem"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_SGAS_Sequential_Greedy_Architecture_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_SGAS_Sequential_Greedy_Architecture_Search_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Li_SGAS_Sequential_Greedy_CVPR_2020_supplemental.pdf | 1912.00195 | cvf | @InProceedings{Li_2020_CVPR,author = {Li, Guohao and Qian, Guocheng and Delgadillo, Itzel C. and Muller, Matthias and Thabet, Ali and Ghanem, Bernard},title = {SGAS: Sequential Greedy Architecture Search},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, discovered architectures often fail to generalize in the final evaluation. Architectures with a higher validation accuracy during the search phas... | [
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36 | Instance Segmentation of Biological Images Using Harmonic Embeddings | [
"Victor Kulikov",
"Victor Lempitsky"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Kulikov_Instance_Segmentation_of_Biological_Images_Using_Harmonic_Embeddings_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Kulikov_Instance_Segmentation_of_Biological_Images_Using_Harmonic_Embeddings_CVPR_2020_paper.pdf | null | 1904.05257 | cvf | @InProceedings{Kulikov_2020_CVPR,author = {Kulikov, Victor and Lempitsky, Victor},title = {Instance Segmentation of Biological Images Using Harmonic Embeddings},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We present a new instance segmentation approach tailored to biological images, where instances may correspond to individual cells, organisms or plant parts. Unlike instance segmentation for user photographs or road scenes, in biological data object instances may be particularly densely packed, the appearance variation ... | [
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37 | Rethinking Zero-Shot Video Classification: End-to-End Training for Realistic Applications | [
"Biagio Brattoli",
"Joseph Tighe",
"Fedor Zhdanov",
"Pietro Perona",
"Krzysztof Chalupka"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Brattoli_Rethinking_Zero-Shot_Video_Classification_End-to-End_Training_for_Realistic_Applications_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Brattoli_Rethinking_Zero-Shot_Video_Classification_End-to-End_Training_for_Realistic_Applications_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Brattoli_Rethinking_Zero-Shot_Video_CVPR_2020_supplemental.pdf | 2003.01455 | cvf | @InProceedings{Brattoli_2020_CVPR,author = {Brattoli, Biagio and Tighe, Joseph and Zhdanov, Fedor and Perona, Pietro and Chalupka, Krzysztof},title = {Rethinking Zero-Shot Video Classification: End-to-End Training for Realistic Applications},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) proposes one solution to this problem. ZSL trains a model once, and generalizes to new tasks whose classes are not present in the training... | [
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38 | A Multigrid Method for Efficiently Training Video Models | [
"Chao-Yuan Wu",
"Ross Girshick",
"Kaiming He",
"Christoph Feichtenhofer",
"Philipp Krahenbuhl"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_A_Multigrid_Method_for_Efficiently_Training_Video_Models_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Wu_A_Multigrid_Method_for_Efficiently_Training_Video_Models_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Wu_A_Multigrid_Method_CVPR_2020_supplemental.pdf | 1912.00998 | cvf | @InProceedings{Wu_2020_CVPR,author = {Wu, Chao-Yuan and Girshick, Ross and He, Kaiming and Feichtenhofer, Christoph and Krahenbuhl, Philipp},title = {A Multigrid Method for Efficiently Training Video Models},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}... | Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training has used a fixed mini-batch ... | [
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39 | Attention-Aware Multi-View Stereo | [
"Keyang Luo",
"Tao Guan",
"Lili Ju",
"Yuesong Wang",
"Zhuo Chen",
"Yawei Luo"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Attention-Aware_Multi-View_Stereo_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Luo_Attention-Aware_Multi-View_Stereo_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Luo_2020_CVPR,author = {Luo, Keyang and Guan, Tao and Ju, Lili and Wang, Yuesong and Chen, Zhuo and Luo, Yawei},title = {Attention-Aware Multi-View Stereo},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Multi-view stereo is a crucial task in computer vision, that requires accurate and robust photo-consistency among input images for depth estimation. Recent studies have shown that learning-based feature matching and confidence regularization can play a vital role in this task. Nevertheless, how to design good matching ... | [
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40 | PPDM: Parallel Point Detection and Matching for Real-Time Human-Object Interaction Detection | [
"Yue Liao",
"Si Liu",
"Fei Wang",
"Yanjie Chen",
"Chen Qian",
"Jiashi Feng"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Liao_PPDM_Parallel_Point_Detection_and_Matching_for_Real-Time_Human-Object_Interaction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Liao_PPDM_Parallel_Point_Detection_and_Matching_for_Real-Time_Human-Object_Interaction_CVPR_2020_paper.pdf | null | 1912.12898 | cvf | @InProceedings{Liao_2020_CVPR,author = {Liao, Yue and Liu, Si and Wang, Fei and Chen, Yanjie and Qian, Chen and Feng, Jiashi},title = {PPDM: Parallel Point Detection and Matching for Real-Time Human-Object Interaction Detection},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | We propose a single-stage Human-Object Interaction (HOI) detection method that has outperformed all existing methods on HICO-DET dataset at 37 fps on a single Titan XP GPU. It is the first real-time HOI detection method. Conventional HOI detection methods are composed of two stages, i.e., human-object proposals generat... | [
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41 | PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models | [
"Sachit Menon",
"Alexandru Damian",
"Shijia Hu",
"Nikhil Ravi",
"Cynthia Rudin"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Menon_PULSE_Self-Supervised_Photo_Upsampling_via_Latent_Space_Exploration_of_Generative_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Menon_PULSE_Self-Supervised_Photo_Upsampling_via_Latent_Space_Exploration_of_Generative_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Menon_PULSE_Self-Supervised_Photo_CVPR_2020_supplemental.pdf | 2003.03808 | cvf | @InProceedings{Menon_2020_CVPR,author = {Menon, Sachit and Damian, Alexandru and Hu, Shijia and Ravi, Nikhil and Rudin, Cynthia},title = {PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | The primary aim of single-image super-resolution is to construct a high-resolution (HR) image from a corresponding low-resolution (LR) input. In previous approaches, which have generally been supervised, the training objective typically measures a pixel-wise average distance between the super-resolved (SR) and HR image... | [
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42 | Discrete Model Compression With Resource Constraint for Deep Neural Networks | [
"Shangqian Gao",
"Feihu Huang",
"Jian Pei",
"Heng Huang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Discrete_Model_Compression_With_Resource_Constraint_for_Deep_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Gao_Discrete_Model_Compression_With_Resource_Constraint_for_Deep_Neural_Networks_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Gao_Discrete_Model_Compression_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Gao_2020_CVPR,author = {Gao, Shangqian and Huang, Feihu and Pei, Jian and Huang, Heng},title = {Discrete Model Compression With Resource Constraint for Deep Neural Networks},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In this paper, we target to address the problem of compression and acceleration of Convolutional Neural Networks (CNNs). Specifically, we propose a novel structural pruning method to obtain a compact CNN with strong discriminative power. To find such networks, we propose an efficient discrete optimization method to dir... | [
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43 | GhostNet: More Features From Cheap Operations | [
"Kai Han",
"Yunhe Wang",
"Qi Tian",
"Jianyuan Guo",
"Chunjing Xu",
"Chang Xu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_GhostNet_More_Features_From_Cheap_Operations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Han_GhostNet_More_Features_From_Cheap_Operations_CVPR_2020_paper.pdf | null | 1911.11907 | cvf | @InProceedings{Han_2020_CVPR,author = {Han, Kai and Wang, Yunhe and Tian, Qi and Guo, Jianyuan and Xu, Chunjing and Xu, Chang},title = {GhostNet: More Features From Cheap Operations},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost m... | [
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44 | SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization | [
"Yue Jiang",
"Dantong Ji",
"Zhizhong Han",
"Matthias Zwicker"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_SDFDiff_Differentiable_Rendering_of_Signed_Distance_Fields_for_3D_Shape_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_SDFDiff_Differentiable_Rendering_of_Signed_Distance_Fields_for_3D_Shape_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Jiang_SDFDiff_Differentiable_Rendering_CVPR_2020_supplemental.zip | 1912.07109 | cvf | @InProceedings{Jiang_2020_CVPR,author = {Jiang, Yue and Ji, Dantong and Han, Zhizhong and Zwicker, Matthias},title = {SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs). Compared to other representations, SDFs have the advantage that they can represent shapes with arbitrary topology, and that they guarantee watertight surfa... | [
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45 | Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image | [
"Yuhui Quan",
"Mingqin Chen",
"Tongyao Pang",
"Hui Ji"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Quan_Self2Self_With_Dropout_Learning_Self-Supervised_Denoising_From_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Quan_Self2Self_With_Dropout_Learning_Self-Supervised_Denoising_From_Single_Image_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Quan_Self2Self_With_Dropout_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Quan_2020_CVPR,author = {Quan, Yuhui and Chen, Mingqin and Pang, Tongyao and Ji, Hui},title = {Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In last few years, supervised deep learning has emerged as one powerful tool for image denoising, which trains a denoising network over an external dataset of noisy/clean image pairs. However, the requirement on a high-quality training dataset limits the broad applicability of the denoising networks. Recently, there ha... | [
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46 | A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image | [
"Yuyu Guo",
"Lei Bi",
"Euijoon Ahn",
"Dagan Feng",
"Qian Wang",
"Jinman Kim"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_A_Spatiotemporal_Volumetric_Interpolation_Network_for_4D_Dynamic_Medical_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_A_Spatiotemporal_Volumetric_Interpolation_Network_for_4D_Dynamic_Medical_Image_CVPR_2020_paper.pdf | null | 2002.12680 | cvf | @InProceedings{Guo_2020_CVPR,author = {Guo, Yuyu and Bi, Lei and Ahn, Euijoon and Feng, Dagan and Wang, Qian and Kim, Jinman},title = {A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | Dynamic medical images are often limited in its application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce the volume samples in the dynamic sequence by interpolating the volumes between the acquired samples. However, these methods are limited to ... | [
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47 | Where Am I Looking At? Joint Location and Orientation Estimation by Cross-View Matching | [
"Yujiao Shi",
"Xin Yu",
"Dylan Campbell",
"Hongdong Li"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Where_Am_I_Looking_At_Joint_Location_and_Orientation_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_Where_Am_I_Looking_At_Joint_Location_and_Orientation_Estimation_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Shi_Where_Am_I_CVPR_2020_supplemental.pdf | 2005.03860 | cvf | @InProceedings{Shi_2020_CVPR,author = {Shi, Yujiao and Yu, Xin and Campbell, Dylan and Li, Hongdong},title = {Where Am I Looking At? Joint Location and Orientation Estimation by Cross-View Matching},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Cross-view geo-localization is the problem of estimating the position and orientation (latitude, longitude and azimuth angle) of a camera at ground level given a large-scale database of geo-tagged aerial (eg., satellite) images. Existing approaches treat the task as a pure location estimation problem by learning discri... | [
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48 | Towards Large Yet Imperceptible Adversarial Image Perturbations With Perceptual Color Distance | [
"Zhengyu Zhao",
"Zhuoran Liu",
"Martha Larson"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Towards_Large_Yet_Imperceptible_Adversarial_Image_Perturbations_With_Perceptual_Color_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhao_Towards_Large_Yet_Imperceptible_Adversarial_Image_Perturbations_With_Perceptual_Color_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Zhao_Towards_Large_Yet_CVPR_2020_supplemental.pdf | 1911.02466 | cvf | @InProceedings{Zhao_2020_CVPR,author = {Zhao, Zhengyu and Liu, Zhuoran and Larson, Martha},title = {Towards Large Yet Imperceptible Adversarial Image Perturbations With Perceptual Color Distance},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | The success of image perturbations that are designed to fool image classifier is assessed in terms of both adversarial effect and visual imperceptibility. The conventional assumption on imperceptibility is that perturbations should strive for tight Lp-norm bounds in RGB space. In this work, we drop this assumption by p... | [
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49 | Assessing Image Quality Issues for Real-World Problems | [
"Tai-Yin Chiu",
"Yinan Zhao",
"Danna Gurari"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Chiu_Assessing_Image_Quality_Issues_for_Real-World_Problems_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Chiu_Assessing_Image_Quality_Issues_for_Real-World_Problems_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Chiu_Assessing_Image_Quality_CVPR_2020_supplemental.pdf | 2003.12511 | cvf | @InProceedings{Chiu_2020_CVPR,author = {Chiu, Tai-Yin and Zhao, Yinan and Gurari, Danna},title = {Assessing Image Quality Issues for Real-World Problems},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We introduce a new large-scale dataset that links the assessment of image quality issues to two practical vision tasks: image captioning and visual question answering. First, we identify for 39,181 images taken by people who are blind whether each is sufficient quality to recognize the content as well as what quality f... | [
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50 | Adaptive Dilated Network With Self-Correction Supervision for Counting | [
"Shuai Bai",
"Zhiqun He",
"Yu Qiao",
"Hanzhe Hu",
"Wei Wu",
"Junjie Yan"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Bai_Adaptive_Dilated_Network_With_Self-Correction_Supervision_for_Counting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Bai_Adaptive_Dilated_Network_With_Self-Correction_Supervision_for_Counting_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Bai_Adaptive_Dilated_Network_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Bai_2020_CVPR,author = {Bai, Shuai and He, Zhiqun and Qiao, Yu and Hu, Hanzhe and Wu, Wei and Yan, Junjie},title = {Adaptive Dilated Network With Self-Correction Supervision for Counting},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | The counting problem aims to estimate the number of objects in images. Due to large scale variation and labeling deviations, it remains a challenging task. The static density map supervised learning framework is widely used in existing methods, which uses the Gaussian kernel to generate a density map as the learning ta... | [
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51 | Camouflaged Object Detection | [
"Deng-Ping Fan",
"Ge-Peng Ji",
"Guolei Sun",
"Ming-Ming Cheng",
"Jianbing Shen",
"Ling Shao"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Fan_Camouflaged_Object_Detection_CVPR_2020_supplemental.zip | null | null | @InProceedings{Fan_2020_CVPR,author = {Fan, Deng-Ping and Ji, Ge-Peng and Sun, Guolei and Cheng, Ming-Ming and Shen, Jianbing and Shao, Ling},title = {Camouflaged Object Detection},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We present a comprehensive study on a new task named camouflaged object detection (COD), which aims to identify objects that are "seamlessly" embedded in their surroundings. The high intrinsic similarities between the target object and the background make COD far more challenging than the traditional object detection t... | [
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52 | Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve | [
"Thomas Schops",
"Viktor Larsson",
"Marc Pollefeys",
"Torsten Sattler"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Schops_Why_Having_10000_Parameters_in_Your_Camera_Model_Is_Better_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Schops_Why_Having_10000_Parameters_in_Your_Camera_Model_Is_Better_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Schops_Why_Having_10000_CVPR_2020_supplemental.pdf | 1912.02908 | title_snapshot | @InProceedings{Schops_2020_CVPR,author = {Schops, Thomas and Larsson, Viktor and Pollefeys, Marc and Sattler, Torsten},title = {Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their ... | [
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53 | BiDet: An Efficient Binarized Object Detector | [
"Ziwei Wang",
"Ziyi Wu",
"Jiwen Lu",
"Jie Zhou"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_BiDet_An_Efficient_Binarized_Object_Detector_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_BiDet_An_Efficient_Binarized_Object_Detector_CVPR_2020_paper.pdf | null | 2003.03961 | cvf | @InProceedings{Wang_2020_CVPR,author = {Wang, Ziwei and Wu, Ziyi and Lu, Jiwen and Zhou, Jie},title = {BiDet: An Efficient Binarized Object Detector},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In this paper, we propose a binarized neural network learning method called BiDet for efficient object detection. Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in ... | [
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54 | Searching for Actions on the Hyperbole | [
"Teng Long",
"Pascal Mettes",
"Heng Tao Shen",
"Cees G. M. Snoek"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Long_Searching_for_Actions_on_the_Hyperbole_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Long_Searching_for_Actions_on_the_Hyperbole_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Long_Searching_for_Actions_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Long_2020_CVPR,author = {Long, Teng and Mettes, Pascal and Shen, Heng Tao and Snoek, Cees G. M.},title = {Searching for Actions on the Hyperbole},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In this paper, we introduce hierarchical action search. Starting from the observation that hierarchies are mostly ignored in the action literature, we retrieve not only individual actions but also relevant and related actions, given an action name or video example as input. We propose a hyperbolic action network, which... | [
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55 | SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans | [
"Angela Dai",
"Christian Diller",
"Matthias Niessner"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Dai_SG-NN_Sparse_Generative_Neural_Networks_for_Self-Supervised_Scene_Completion_of_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Dai_SG-NN_Sparse_Generative_Neural_Networks_for_Self-Supervised_Scene_Completion_of_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Dai_SG-NN_Sparse_Generative_CVPR_2020_supplemental.pdf | 1912.00036 | title_snapshot | @InProceedings{Dai_2020_CVPR,author = {Dai, Angela and Diller, Christian and Niessner, Matthias},title = {SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We present a novel approach that converts partial and noisy RGB-D scans into high-quality 3D scene reconstructions by inferring unobserved scene geometry. Our approach is fully self-supervised and can hence be trained solely on incomplete, real-world scans. To achieve, self-supervision, we remove frames from a given (i... | [
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56 | Stereoscopic Flash and No-Flash Photography for Shape and Albedo Recovery | [
"Xu Cao",
"Michael Waechter",
"Boxin Shi",
"Ye Gao",
"Bo Zheng",
"Yasuyuki Matsushita"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Stereoscopic_Flash_and_No-Flash_Photography_for_Shape_and_Albedo_Recovery_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_Stereoscopic_Flash_and_No-Flash_Photography_for_Shape_and_Albedo_Recovery_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Cao_Stereoscopic_Flash_and_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Cao_2020_CVPR,author = {Cao, Xu and Waechter, Michael and Shi, Boxin and Gao, Ye and Zheng, Bo and Matsushita, Yasuyuki},title = {Stereoscopic Flash and No-Flash Photography for Shape and Albedo Recovery},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | We present a minimal imaging setup that harnesses both geometric and photometric approaches for shape and albedo recovery. We adopt a stereo camera and a flashlight to capture a stereo image pair and a flash/no-flash pair. From the stereo image pair, we recover a rough shape that captures low-frequency shape variation ... | [
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57 | What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation | [
"Jiahua Dong",
"Yang Cong",
"Gan Sun",
"Bineng Zhong",
"Xiaowei Xu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_What_Can_Be_Transferred_Unsupervised_Domain_Adaptation_for_Endoscopic_Lesions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Dong_What_Can_Be_Transferred_Unsupervised_Domain_Adaptation_for_Endoscopic_Lesions_CVPR_2020_paper.pdf | null | 2004.11500 | cvf | @InProceedings{Dong_2020_CVPR,author = {Dong, Jiahua and Cong, Yang and Sun, Gan and Zhong, Bineng and Xu, Xiaowei},title = {What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},yea... | Unsupervised domain adaptation has attracted growing research attention on semantic segmentation. However, 1) most existing models cannot be directly applied into lesions transfer of medical images, due to the diverse appearances of same lesion among different datasets; 2) equal attention has been paid into all semanti... | [
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58 | Learning to Generate 3D Training Data Through Hybrid Gradient | [
"Dawei Yang",
"Jia Deng"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_to_Generate_3D_Training_Data_Through_Hybrid_Gradient_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Learning_to_Generate_3D_Training_Data_Through_Hybrid_Gradient_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Yang_Learning_to_Generate_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Yang_2020_CVPR,author = {Yang, Dawei and Deng, Jia},title = {Learning to Generate 3D Training Data Through Hybrid Gradient},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train a network to perform well on real images, because a graphics-based generation pipeline requires numerous design decisions such as the selection of 3D shapes and ... | [
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59 | On Joint Estimation of Pose, Geometry and svBRDF From a Handheld Scanner | [
"Carolin Schmitt",
"Simon Donne",
"Gernot Riegler",
"Vladlen Koltun",
"Andreas Geiger"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Schmitt_On_Joint_Estimation_of_Pose_Geometry_and_svBRDF_From_a_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Schmitt_On_Joint_Estimation_of_Pose_Geometry_and_svBRDF_From_a_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Schmitt_2020_CVPR,author = {Schmitt, Carolin and Donne, Simon and Riegler, Gernot and Koltun, Vladlen and Geiger, Andreas},title = {On Joint Estimation of Pose, Geometry and svBRDF From a Handheld Scanner},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},... | We propose a novel formulation for joint recovery of camera pose, object geometry and spatially-varying BRDF. The input to our approach is a sequence of RGB-D images captured by a mobile, hand-held scanner that actively illuminates the scene with point light sources. Compared to previous works that jointly estimate geo... | [
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60 | Synchronizing Probability Measures on Rotations via Optimal Transport | [
"Tolga Birdal",
"Michael Arbel",
"Umut Simsekli",
"Leonidas J. Guibas"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Birdal_Synchronizing_Probability_Measures_on_Rotations_via_Optimal_Transport_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Birdal_Synchronizing_Probability_Measures_on_Rotations_via_Optimal_Transport_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Birdal_Synchronizing_Probability_Measures_CVPR_2020_supplemental.pdf | 2004.00663 | cvf | @InProceedings{Birdal_2020_CVPR,author = {Birdal, Tolga and Arbel, Michael and Simsekli, Umut and Guibas, Leonidas J.},title = {Synchronizing Probability Measures on Rotations via Optimal Transport},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We introduce a new paradigm, `measure synchronization', for synchronizing graphs with measure-valued edges. We formulate this problem as maximization of the cycle-consistency in the space of probability measures over relative rotations. In particular, we aim at estimating marginal distributions of absolute orientations... | [
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61 | Camera Trace Erasing | [
"Chang Chen",
"Zhiwei Xiong",
"Xiaoming Liu",
"Feng Wu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Camera_Trace_Erasing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Camera_Trace_Erasing_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Chen_Camera_Trace_Erasing_CVPR_2020_supplemental.pdf | 2003.06951 | cvf | @InProceedings{Chen_2020_CVPR,author = {Chen, Chang and Xiong, Zhiwei and Liu, Xiaoming and Wu, Feng},title = {Camera Trace Erasing},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Camera trace is a unique noise produced in digital imaging process. Most existing forensic methods analyze camera trace to identify image origins. In this paper, we address a new low-level vision problem, camera trace erasing, to reveal the weakness of trace-based forensic methods. A comprehensive investigation on exis... | [
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62 | Robust 3D Self-Portraits in Seconds | [
"Zhe Li",
"Tao Yu",
"Chuanyu Pan",
"Zerong Zheng",
"Yebin Liu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Robust_3D_Self-Portraits_in_Seconds_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Robust_3D_Self-Portraits_in_Seconds_CVPR_2020_paper.pdf | null | 2004.02460 | cvf | @InProceedings{Li_2020_CVPR,author = {Li, Zhe and Yu, Tao and Pan, Chuanyu and Zheng, Zerong and Liu, Yebin},title = {Robust 3D Self-Portraits in Seconds},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In this paper, we propose an efficient method for robust 3D self-portraits using a single RGBD camera. Benefiting from the proposed PIFusion and lightweight bundle adjustment algorithm, our method can generate detailed 3D self-portraits in seconds and shows the ability to handle subjects wearing extremely loose clothes... | [
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63 | Instance Shadow Detection | [
"Tianyu Wang",
"Xiaowei Hu",
"Qiong Wang",
"Pheng-Ann Heng",
"Chi-Wing Fu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Instance_Shadow_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Instance_Shadow_Detection_CVPR_2020_paper.pdf | null | 1911.07034 | cvf | @InProceedings{Wang_2020_CVPR,author = {Wang, Tianyu and Hu, Xiaowei and Wang, Qiong and Heng, Pheng-Ann and Fu, Chi-Wing},title = {Instance Shadow Detection},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, ... | [
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64 | MemNAS: Memory-Efficient Neural Architecture Search With Grow-Trim Learning | [
"Peiye Liu",
"Bo Wu",
"Huadong Ma",
"Mingoo Seok"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Liu_2020_CVPR,author = {Liu, Peiye and Wu, Bo and Ma, Huadong and Seok, Mingoo},title = {MemNAS: Memory-Efficient Neural Architecture Search With Grow-Trim Learning},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Recent studies on automatic neural architecture search techniques have demonstrated significant performance, competitive to or even better than hand-crafted neural architectures. However, most of the existing search approaches tend to use residual structures and a concatenation connection between shallow and deep featu... | [
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65 | Deep Distance Transform for Tubular Structure Segmentation in CT Scans | [
"Yan Wang",
"Xu Wei",
"Fengze Liu",
"Jieneng Chen",
"Yuyin Zhou",
"Wei Shen",
"Elliot K. Fishman",
"Alan L. Yuille"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Wang_Deep_Distance_Transform_CVPR_2020_supplemental.pdf | 1912.03383 | cvf | @InProceedings{Wang_2020_CVPR,author = {Wang, Yan and Wei, Xu and Liu, Fengze and Chen, Jieneng and Zhou, Yuyin and Shen, Wei and Fishman, Elliot K. and Yuille, Alan L.},title = {Deep Distance Transform for Tubular Structure Segmentation in CT Scans},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recog... | Tubular structure segmentation in medical images, e.g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases. But automatic tubular structure segmentation in CT scans is a challenging problem, due to issues such as poor contrast, noise and c... | [
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66 | FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding | [
"Dian Shao",
"Yue Zhao",
"Bo Dai",
"Dahua Lin"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Shao_FineGym_A_Hierarchical_CVPR_2020_supplemental.zip | 2004.06704 | cvf | @InProceedings{Shao_2020_CVPR,author = {Shao, Dian and Zhao, Yue and Dai, Bo and Lin, Dahua},title = {FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | On public benchmarks, current action recognition techniques have achieved great success. However, when used in real-world applications, e.g. sport analysis, which requires the capability of parsing an activity into phases and differentiating between subtly different actions, their performances remain far from being sat... | [
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67 | What Does Plate Glass Reveal About Camera Calibration? | [
"Qian Zheng",
"Jinnan Chen",
"Zhan Lu",
"Boxin Shi",
"Xudong Jiang",
"Kim-Hui Yap",
"Ling-Yu Duan",
"Alex C. Kot"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Zheng_2020_CVPR,author = {Zheng, Qian and Chen, Jinnan and Lu, Zhan and Shi, Boxin and Jiang, Xudong and Yap, Kim-Hui and Duan, Ling-Yu and Kot, Alex C.},title = {What Does Plate Glass Reveal About Camera Calibration?},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},mo... | This paper aims to calibrate the orientation of glass and the field of view of the camera from a single reflection-contaminated image. We show how a reflective amplitude coefficient map can be used as a calibration cue. Different from existing methods, the proposed solution is free from image contents. To reduce the im... | [
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68 | One Man's Trash Is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples | [
"Chang Xiao",
"Changxi Zheng"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_One_Mans_Trash_Is_Another_Mans_Treasure_Resisting_Adversarial_Examples_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Xiao_One_Mans_Trash_Is_Another_Mans_Treasure_Resisting_Adversarial_Examples_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Xiao_One_Mans_Trash_CVPR_2020_supplemental.pdf | 1911.11219 | title_snapshot | @InProceedings{Xiao_2020_CVPR,author = {Xiao, Chang and Zheng, Changxi},title = {One Man's Trash Is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Modern image classification systems are often built on deep neural networks, which suffer from adversarial examples--images with deliberately crafted, imperceptible noise to mislead the network's classification. To defend against adversarial examples, a plausible idea is to obfuscate the network's gradient with respect... | [
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69 | Image Processing Using Multi-Code GAN Prior | [
"Jinjin Gu",
"Yujun Shen",
"Bolei Zhou"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.pdf | null | 1912.07116 | cvf | @InProceedings{Gu_2020_CVPR,author = {Gu, Jinjin and Shen, Yujun and Zhou, Bolei},title = {Image Processing Using Multi-Code GAN Prior},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Despite the success of Generative Adversarial Networks (GANs) in image synthesis, applying trained GAN models to real image processing remains challenging. Previous methods typically invert a target image back to the latent space either by back-propagation or by learning an additional encoder. However, the reconstructi... | [
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70 | ColorFool: Semantic Adversarial Colorization | [
"Ali Shahin Shamsabadi",
"Ricardo Sanchez-Matilla",
"Andrea Cavallaro"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Shamsabadi_ColorFool_Semantic_Adversarial_Colorization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Shamsabadi_ColorFool_Semantic_Adversarial_Colorization_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Shamsabadi_ColorFool_Semantic_Adversarial_CVPR_2020_supplemental.zip | 1911.10891 | title_snapshot | @InProceedings{Shamsabadi_2020_CVPR,author = {Shamsabadi, Ali Shahin and Sanchez-Matilla, Ricardo and Cavallaro, Andrea},title = {ColorFool: Semantic Adversarial Colorization},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Adversarial attacks that generate small Lp norm perturbations to mislead classifiers have limited success in black-box settings and with unseen classifiers. These attacks are also not robust to defenses that use denoising filters and to adversarial training procedures. Instead, adversarial attacks that generate unrestr... | [
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71 | Bi3D: Stereo Depth Estimation via Binary Classifications | [
"Abhishek Badki",
"Alejandro Troccoli",
"Kihwan Kim",
"Jan Kautz",
"Pradeep Sen",
"Orazio Gallo"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Badki_Bi3D_Stereo_Depth_CVPR_2020_supplemental.zip | 2005.07274 | cvf | @InProceedings{Badki_2020_CVPR,author = {Badki, Abhishek and Troccoli, Alejandro and Kim, Kihwan and Kautz, Jan and Sen, Pradeep and Gallo, Orazio},title = {Bi3D: Stereo Depth Estimation via Binary Classifications},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | Stereo-based depth estimation is a cornerstone of computer vision, with state-of-the-art methods delivering accurate results in real time. For several applications such as autonomous navigation, however, it may be useful to trade accuracy for lower latency. We present Bi3D, a method that estimates depth via a series of... | [
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72 | D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry | [
"Nan Yang",
"Lukas von Stumberg",
"Rui Wang",
"Daniel Cremers"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Yang_D3VO_Deep_Depth_CVPR_2020_supplemental.pdf | 2003.01060 | cvf | @InProceedings{Yang_2020_CVPR,author = {Yang, Nan and von Stumberg, Lukas and Wang, Rui and Cremers, Daniel},title = {D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. In particular, it aligns t... | [
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73 | Fantastic Answers and Where to Find Them: Immersive Question-Directed Visual Attention | [
"Ming Jiang",
"Shi Chen",
"Jinhui Yang",
"Qi Zhao"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Fantastic_Answers_and_Where_to_Find_Them_Immersive_Question-Directed_Visual_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Fantastic_Answers_and_Where_to_Find_Them_Immersive_Question-Directed_Visual_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Jiang_Fantastic_Answers_and_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Jiang_2020_CVPR,author = {Jiang, Ming and Chen, Shi and Yang, Jinhui and Zhao, Qi},title = {Fantastic Answers and Where to Find Them: Immersive Question-Directed Visual Attention},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | While most visual attention studies focus on bottom-up attention with restricted field-of-view, real-life situations are filled with embodied vision tasks. The role of attention is more significant in the latter due to the information overload, and attention to the most important regions is critical to the success of t... | [
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74 | Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction | [
"Maosen Li",
"Siheng Chen",
"Yangheng Zhao",
"Ya Zhang",
"Yanfeng Wang",
"Qi Tian"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Dynamic_Multiscale_Graph_Neural_Networks_for_3D_Skeleton_Based_Human_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Dynamic_Multiscale_Graph_Neural_Networks_for_3D_Skeleton_Based_Human_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Li_Dynamic_Multiscale_Graph_CVPR_2020_supplemental.zip | 2003.08802 | cvf | @InProceedings{Li_2020_CVPR,author = {Li, Maosen and Chen, Siheng and Zhao, Yangheng and Zhang, Ya and Wang, Yanfeng and Tian, Qi},title = {Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = ... | We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning. This multiscale graph is adaptive during training and dynamic acr... | [
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75 | Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes From a Single Image | [
"Yinyu Nie",
"Xiaoguang Han",
"Shihui Guo",
"Yujian Zheng",
"Jian Chang",
"Jian Jun Zhang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Nie_Total3DUnderstanding_Joint_Layout_Object_Pose_and_Mesh_Reconstruction_for_Indoor_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Nie_Total3DUnderstanding_Joint_Layout_Object_Pose_and_Mesh_Reconstruction_for_Indoor_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Nie_Total3DUnderstanding_Joint_Layout_CVPR_2020_supplemental.pdf | 2002.12212 | cvf | @InProceedings{Nie_2020_CVPR,author = {Nie, Yinyu and Han, Xiaoguang and Guo, Shihui and Zheng, Yujian and Chang, Jian and Zhang, Jian Jun},title = {Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes From a Single Image},booktitle = {IEEE/CVF Conference on Computer Vision and Patt... | Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction. Existing works either address one part of this problem or focus on independent objects. In this paper, we bridge the gap between understanding and reconstruction, and propose an end-to-end solution to jointly reconstr... | [
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76 | GPS-Net: Graph Property Sensing Network for Scene Graph Generation | [
"Xin Lin",
"Changxing Ding",
"Jinquan Zeng",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_GPS-Net_Graph_Property_Sensing_Network_for_Scene_Graph_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_GPS-Net_Graph_Property_Sensing_Network_for_Scene_Graph_Generation_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Lin_GPS-Net_Graph_Property_CVPR_2020_supplemental.pdf | 2003.12962 | title_snapshot | @InProceedings{Lin_2020_CVPR,author = {Lin, Xin and Ding, Changxing and Zeng, Jinquan and Tao, Dacheng},title = {GPS-Net: Graph Property Sensing Network for Scene Graph Generation},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Scene graph generation (SGG) aims to detect objects in an image along with their pairwise relationships. There are three key properties of scene graph that have been underexplored in recent works: namely, the edge direction information, the difference in priority between nodes, and the long-tailed distribution of relat... | [
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77 | Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes | [
"Zhengqin Li",
"Yu-Ying Yeh",
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Through_the_Looking_Glass_Neural_3D_Reconstruction_of_Transparent_Shapes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Through_the_Looking_Glass_Neural_3D_Reconstruction_of_Transparent_Shapes_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Li_Through_the_Looking_CVPR_2020_supplemental.zip | 2004.10904 | cvf | @InProceedings{Li_2020_CVPR,author = {Li, Zhengqin and Yeh, Yu-Ying and Chandraker, Manmohan},title = {Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem. Complex light paths induced by refraction and reflection have prevented both traditional and deep multiview stereo from solving this challenge. We propose a physically-based network to recover 3D... | [
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78 | Recursive Social Behavior Graph for Trajectory Prediction | [
"Jianhua Sun",
"Qinhong Jiang",
"Cewu Lu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Recursive_Social_Behavior_Graph_for_Trajectory_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Recursive_Social_Behavior_Graph_for_Trajectory_Prediction_CVPR_2020_paper.pdf | null | 2004.10402 | cvf | @InProceedings{Sun_2020_CVPR,author = {Sun, Jianhua and Jiang, Qinhong and Lu, Cewu},title = {Recursive Social Behavior Graph for Trajectory Prediction},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Social interaction is an important topic in human trajectory prediction to generate plausible paths. In this paper, we present a novel insight of group-based social interaction model to explore relationships among pedestrians. We recursively extract social representations supervised by group-based annotations and formu... | [
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79 | Attention Scaling for Crowd Counting | [
"Xiaoheng Jiang",
"Li Zhang",
"Mingliang Xu",
"Tianzhu Zhang",
"Pei Lv",
"Bing Zhou",
"Xin Yang",
"Yanwei Pang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Attention_Scaling_for_Crowd_Counting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Attention_Scaling_for_Crowd_Counting_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Jiang_2020_CVPR,author = {Jiang, Xiaoheng and Zhang, Li and Xu, Mingliang and Zhang, Tianzhu and Lv, Pei and Zhou, Bing and Yang, Xin and Pang, Yanwei},title = {Attention Scaling for Crowd Counting},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | Convolutional Neural Network (CNN) based methods generally take crowd counting as a regression task by outputting crowd densities. They learn the mapping between image contents and crowd density distributions. Though having achieved promising results, these data-driven counting networks are prone to overestimate or und... | [
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80 | FocalMix: Semi-Supervised Learning for 3D Medical Image Detection | [
"Dong Wang",
"Yuan Zhang",
"Kexin Zhang",
"Liwei Wang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_FocalMix_Semi-Supervised_Learning_for_3D_Medical_Image_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_FocalMix_Semi-Supervised_Learning_for_3D_Medical_Image_Detection_CVPR_2020_paper.pdf | null | 2003.09108 | cvf | @InProceedings{Wang_2020_CVPR,author = {Wang, Dong and Zhang, Yuan and Zhang, Kexin and Wang, Liwei},title = {FocalMix: Semi-Supervised Learning for 3D Medical Image Detection},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Applying artificial intelligence techniques in medical imaging is one of the most promising areas in medicine. However, most of the recent success in this area highly relies on large amounts of carefully annotated data, whereas annotating medical images is a costly process. In this paper, we propose a novel method, cal... | [
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81 | Bi-Directional Relationship Inferring Network for Referring Image Segmentation | [
"Zhiwei Hu",
"Guang Feng",
"Jiayu Sun",
"Lihe Zhang",
"Huchuan Lu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Hu_2020_CVPR,author = {Hu, Zhiwei and Feng, Guang and Sun, Jiayu and Zhang, Lihe and Lu, Huchuan},title = {Bi-Directional Relationship Inferring Network for Referring Image Segmentation},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Most existing methods do not explicitly formulate the mutual guidance between vision and language. In this work, we propose a bi-directional relationship inferring network (BRINet) to model the dependencies of cross-modal information. In detail, the vision-guided linguistic attention is used to learn the adaptive lingu... | [
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82 | FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation | [
"Matias Tassano",
"Julie Delon",
"Thomas Veit"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Tassano_FastDVDnet_Towards_Real-Time_CVPR_2020_supplemental.pdf | 1907.01361 | cvf | @InProceedings{Tassano_2020_CVPR,author = {Tassano, Matias and Delon, Julie and Veit, Thomas},title = {FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Until recently, video denoising with neural networks had been a largely under explored domain, and existing methods could not compete with the performance of the best patch-based methods. The app... | [
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83 | Composed Query Image Retrieval Using Locally Bounded Features | [
"Mehrdad Hosseinzadeh",
"Yang Wang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Hosseinzadeh_Composed_Query_Image_Retrieval_Using_Locally_Bounded_Features_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Hosseinzadeh_Composed_Query_Image_Retrieval_Using_Locally_Bounded_Features_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Hosseinzadeh_2020_CVPR,author = {Hosseinzadeh, Mehrdad and Wang, Yang},title = {Composed Query Image Retrieval Using Locally Bounded Features},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Composed query image retrieval is a new problem where the query consists of an image together with a requested modification expressed via a textual sentence. The goal is then to retrieve the images that are generally similar to the query image, but differ according to the requested modification. Previous methods usuall... | [
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84 | Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring | [
"Yuesong Nan",
"Yuhui Quan",
"Hui Ji"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Nan_Variational-EM-Based_Deep_Learning_for_Noise-Blind_Image_Deblurring_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Nan_Variational-EM-Based_Deep_Learning_for_Noise-Blind_Image_Deblurring_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Nan_Variational-EM-Based_Deep_Learning_CVPR_2020_supplemental.pdf | null | null | @InProceedings{Nan_2020_CVPR,author = {Nan, Yuesong and Quan, Yuhui and Ji, Hui},title = {Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Non-blind deblurring is an important problem encountered in many image restoration tasks. The focus of non-blind deblurring is on how to suppress noise magnification during deblurring. In practice, it often happens that the noise level of input image is unknown and varies among different images. This paper aims at deve... | [
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85 | Central Similarity Quantization for Efficient Image and Video Retrieval | [
"Li Yuan",
"Tao Wang",
"Xiaopeng Zhang",
"Francis EH Tay",
"Zequn Jie",
"Wei Liu",
"Jiashi Feng"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Central_Similarity_Quantization_for_Efficient_Image_and_Video_Retrieval_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Yuan_Central_Similarity_Quantization_for_Efficient_Image_and_Video_Retrieval_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Yuan_Central_Similarity_Quantization_CVPR_2020_supplemental.pdf | 1908.00347 | cvf | @InProceedings{Yuan_2020_CVPR,author = {Yuan, Li and Wang, Tao and Zhang, Xiaopeng and Tay, Francis EH and Jie, Zequn and Liu, Wei and Feng, Jiashi},title = {Central Similarity Quantization for Efficient Image and Video Retrieval},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month... | Existing data-dependent hashing methods usually learn hash functions from pairwise or triplet data relationships, which only capture the data similarity locally, and often suffer from low learning efficiency and low collision rate. In this work, we propose a new global similarity metric, termed as central similarity, w... | [
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86 | Taking a Deeper Look at Co-Salient Object Detection | [
"Deng-Ping Fan",
"Zheng Lin",
"Ge-Peng Ji",
"Dingwen Zhang",
"Huazhu Fu",
"Ming-Ming Cheng"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Taking_a_Deeper_Look_at_Co-Salient_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_Taking_a_Deeper_Look_at_Co-Salient_Object_Detection_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Fan_Taking_a_Deeper_CVPR_2020_supplemental.zip | null | null | @InProceedings{Fan_2020_CVPR,author = {Fan, Deng-Ping and Lin, Zheng and Ji, Ge-Peng and Zhang, Dingwen and Fu, Huazhu and Cheng, Ming-Ming},title = {Taking a Deeper Look at Co-Salient Object Detection},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Co-salient object detection (CoSOD) is a newly emerging and rapidly growing branch of salient object detection (SOD), which aims to detect the co-occurring salient objects in multiple images. However, existing CoSOD datasets often have a serious data bias, which assumes that each group of images contains salient object... | [
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87 | Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics | [
"Yuezun Li",
"Xin Yang",
"Pu Sun",
"Honggang Qi",
"Siwei Lyu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Celeb-DF_A_Large-Scale_Challenging_Dataset_for_DeepFake_Forensics_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Celeb-DF_A_Large-Scale_Challenging_Dataset_for_DeepFake_Forensics_CVPR_2020_paper.pdf | null | 1909.12962 | title_snapshot | @InProceedings{Li_2020_CVPR,author = {Li, Yuezun and Yang, Xin and Sun, Pu and Qi, Honggang and Lyu, Siwei},title = {Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for datasets of DeepFake videos. However, current DeepFake datasets suffer from low visual quality and do n... | [
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88 | TEA: Temporal Excitation and Aggregation for Action Recognition | [
"Yan Li",
"Bin Ji",
"Xintian Shi",
"Jianguo Zhang",
"Bin Kang",
"Limin Wang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_TEA_Temporal_Excitation_and_Aggregation_for_Action_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_TEA_Temporal_Excitation_and_Aggregation_for_Action_Recognition_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Li_TEA_Temporal_Excitation_CVPR_2020_supplemental.pdf | 2004.01398 | cvf | @InProceedings{Li_2020_CVPR,author = {Li, Yan and Ji, Bin and Shi, Xintian and Zhang, Jianguo and Kang, Bin and Wang, Limin},title = {TEA: Temporal Excitation and Aggregation for Action Recognition},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Temporal modeling is key for action recognition in videos. It normally considers both short-range motions and long-range aggregations. In this paper, we propose a Temporal Excitation and Aggregation (TEA) block, including a motion excitation (ME) module and a multiple temporal aggregation (MTA) module, specifically des... | [
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89 | Unsupervised Person Re-Identification via Softened Similarity Learning | [
"Yutian Lin",
"Lingxi Xie",
"Yu Wu",
"Chenggang Yan",
"Qi Tian"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.pdf | null | 2004.03547 | cvf | @InProceedings{Lin_2020_CVPR,author = {Lin, Yutian and Xie, Lingxi and Wu, Yu and Yan, Chenggang and Tian, Qi},title = {Unsupervised Person Re-Identification via Softened Similarity Learning},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Person re-identification (re-ID) is an important topic in computer vision. This paper studies the unsupervised setting of re-ID, which does not require any labeled information and thus is freely deployed to new scenarios. There are very few studies under this setting, and one of the best approach till now used iterativ... | [
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90 | Frequency Domain Compact 3D Convolutional Neural Networks | [
"Hanting Chen",
"Yunhe Wang",
"Han Shu",
"Yehui Tang",
"Chunjing Xu",
"Boxin Shi",
"Chao Xu",
"Qi Tian",
"Chang Xu"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Frequency_Domain_Compact_3D_Convolutional_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Frequency_Domain_Compact_3D_Convolutional_Neural_Networks_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Chen_2020_CVPR,author = {Chen, Hanting and Wang, Yunhe and Shu, Han and Tang, Yehui and Xu, Chunjing and Shi, Boxin and Xu, Chao and Tian, Qi and Xu, Chang},title = {Frequency Domain Compact 3D Convolutional Neural Networks},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | This paper studies the compression and acceleration of 3-dimensional convolutional neural networks (3D CNNs). To reduce the memory cost and computational complexity of deep neural networks, a number of algorithms have been explored by discovering redundant parameters in pre-trained networks. However, most of existing m... | [
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91 | Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve | [
"Sen Jia",
"Neil D. B. Bruce"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Jia_Revisiting_Saliency_Metrics_Farthest-Neighbor_Area_Under_Curve_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Jia_Revisiting_Saliency_Metrics_Farthest-Neighbor_Area_Under_Curve_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Jia_Revisiting_Saliency_Metrics_CVPR_2020_supplemental.pdf | 2002.10540 | cvf | @InProceedings{Jia_2020_CVPR,author = {Jia, Sen and Bruce, Neil D. B.},title = {Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In this paper, we propose a new metric to address the long-standing problem of center bias in saliency evaluation. We first show that distribution-based metrics cannot measure saliency performance across datasets due to ambiguity in the choice of standard deviation, especially for Convolutional Neural Networks. Therefo... | [
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92 | Structured Compression by Weight Encryption for Unstructured Pruning and Quantization | [
"Se Jung Kwon",
"Dongsoo Lee",
"Byeongwook Kim",
"Parichay Kapoor",
"Baeseong Park",
"Gu-Yeon Wei"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Kwon_Structured_Compression_by_Weight_Encryption_for_Unstructured_Pruning_and_Quantization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Kwon_Structured_Compression_by_Weight_Encryption_for_Unstructured_Pruning_and_Quantization_CVPR_2020_paper.pdf | null | 1905.10138 | cvf | @InProceedings{Kwon_2020_CVPR,author = {Kwon, Se Jung and Lee, Dongsoo and Kim, Byeongwook and Kapoor, Parichay and Park, Baeseong and Wei, Gu-Yeon},title = {Structured Compression by Weight Encryption for Unstructured Pruning and Quantization},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model size reduction, achieving performance enhancement on devices is, however, still challenging mainly due to the irregular representations of spa... | [
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93 | ARCH: Animatable Reconstruction of Clothed Humans | [
"Zeng Huang",
"Yuanlu Xu",
"Christoph Lassner",
"Hao Li",
"Tony Tung"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_ARCH_Animatable_Reconstruction_of_Clothed_Humans_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_ARCH_Animatable_Reconstruction_of_Clothed_Humans_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Huang_ARCH_Animatable_Reconstruction_CVPR_2020_supplemental.zip | 2004.04572 | cvf | @InProceedings{Huang_2020_CVPR,author = {Huang, Zeng and Xu, Yuanlu and Lassner, Christoph and Li, Hao and Tung, Tony},title = {ARCH: Animatable Reconstruction of Clothed Humans},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. Existing approaches to digitize 3D humans struggle to handle pose variations and recover details. Also, they do not produce ... | [
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94 | Deep Implicit Volume Compression | [
"Danhang Tang",
"Saurabh Singh",
"Philip A. Chou",
"Christian Hane",
"Mingsong Dou",
"Sean Fanello",
"Jonathan Taylor",
"Philip Davidson",
"Onur G. Guleryuz",
"Yinda Zhang",
"Shahram Izadi",
"Andrea Tagliasacchi",
"Sofien Bouaziz",
"Cem Keskin"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Deep_Implicit_Volume_Compression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Tang_Deep_Implicit_Volume_Compression_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Tang_Deep_Implicit_Volume_CVPR_2020_supplemental.zip | 2005.08877 | cvf | @InProceedings{Tang_2020_CVPR,author = {Tang, Danhang and Singh, Saurabh and Chou, Philip A. and Hane, Christian and Dou, Mingsong and Fanello, Sean and Taylor, Jonathan and Davidson, Philip and Guleryuz, Onur G. and Zhang, Yinda and Izadi, Shahram and Tagliasacchi, Andrea and Bouaziz, Sofien and Keskin, Cem},title = {... | We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method relies on a block-based neural network architecture trained end-to-end, achieving state-of-the-art rate-distortion trade-off. To prevent topol... | [
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95 | Multi-Domain Learning for Accurate and Few-Shot Color Constancy | [
"Jin Xiao",
"Shuhang Gu",
"Lei Zhang"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_Multi-Domain_Learning_for_Accurate_and_Few-Shot_Color_Constancy_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Xiao_Multi-Domain_Learning_for_Accurate_and_Few-Shot_Color_Constancy_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Xiao_2020_CVPR,author = {Xiao, Jin and Gu, Shuhang and Zhang, Lei},title = {Multi-Domain Learning for Accurate and Few-Shot Color Constancy},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Color constancy is an important process in camera pipeline to remove the color bias of captured image caused by scene illumination. Recently, significant improvements in color constancy accuracy have been achieved by using deep neural networks (DNNs). However, existing DNNbased color constancy methods learn distinct ma... | [
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96 | Computing the Testing Error Without a Testing Set | [
"Ciprian A. Corneanu",
"Sergio Escalera",
"Aleix M. Martinez"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Corneanu_Computing_the_Testing_Error_Without_a_Testing_Set_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Corneanu_Computing_the_Testing_Error_Without_a_Testing_Set_CVPR_2020_paper.pdf | null | 2005.00450 | cvf | @InProceedings{Corneanu_2020_CVPR,author = {Corneanu, Ciprian A. and Escalera, Sergio and Martinez, Aleix M.},title = {Computing the Testing Error Without a Testing Set},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (accuracy) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mos... | [
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97 | Conditional Channel Gated Networks for Task-Aware Continual Learning | [
"Davide Abati",
"Jakub Tomczak",
"Tijmen Blankevoort",
"Simone Calderara",
"Rita Cucchiara",
"Babak Ehteshami Bejnordi"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Abati_Conditional_Channel_Gated_Networks_for_Task-Aware_Continual_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Abati_Conditional_Channel_Gated_Networks_for_Task-Aware_Continual_Learning_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Abati_Conditional_Channel_Gated_CVPR_2020_supplemental.pdf | 2004.00070 | cvf | @InProceedings{Abati_2020_CVPR,author = {Abati, Davide and Tomczak, Jakub and Blankevoort, Tijmen and Calderara, Simone and Cucchiara, Rita and Bejnordi, Babak Ehteshami},title = {Conditional Channel Gated Networks for Task-Aware Continual Learning},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Convolutional Neural Networks experience catastrophic forgetting when optimized on a sequence of learning problems: as they meet the objective of the current training examples, their performance on previous tasks drops drastically. In this work, we introduce a novel framework to tackle this problem with conditional com... | [
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98 | Polishing Decision-Based Adversarial Noise With a Customized Sampling | [
"Yucheng Shi",
"Yahong Han",
"Qi Tian"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Polishing_Decision-Based_Adversarial_Noise_With_a_Customized_Sampling_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_Polishing_Decision-Based_Adversarial_Noise_With_a_Customized_Sampling_CVPR_2020_paper.pdf | null | null | null | @InProceedings{Shi_2020_CVPR,author = {Shi, Yucheng and Han, Yahong and Tian, Qi},title = {Polishing Decision-Based Adversarial Noise With a Customized Sampling},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2020}} | As an effective black-box adversarial attack, decision-based methods polish adversarial noise by querying the target model. Among them, boundary attack is widely applied due to its powerful noise compression capability, especially when combined with transfer-based methods. Boundary attack splits the noise compression i... | [
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99 | G2L-Net: Global to Local Network for Real-Time 6D Pose Estimation With Embedding Vector Features | [
"Wei Chen",
"Xi Jia",
"Hyung Jin Chang",
"Jinming Duan",
"Ales Leonardis"
] | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_G2L-Net_Global_to_Local_Network_for_Real-Time_6D_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_G2L-Net_Global_to_Local_Network_for_Real-Time_6D_Pose_Estimation_CVPR_2020_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Chen_G2L-Net_Global_to_CVPR_2020_supplemental.pdf | 2003.11089 | title_snapshot | @InProceedings{Chen_2020_CVPR,author = {Chen, Wei and Jia, Xi and Chang, Hyung Jin and Duan, Jinming and Leonardis, Ales},title = {G2L-Net: Global to Local Network for Real-Time 6D Pose Estimation With Embedding Vector Features},booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | In this paper, we propose a novel real-time 6D object pose estimation framework, named G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and-conquer fashion. Specifically, our network consists of three steps. First, we extract the coarse object point cloud from the RGB-D image by 2D detecti... | [
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