CVPR
Collection
Accepted papers for CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition), one dataset per year. • 14 items • Updated
paper_id uint32 0 1.47k | title stringlengths 9 140 | authors listlengths 1 23 | cvf_url stringlengths 86 189 | pdf_url stringlengths 87 190 | supp_url stringlengths 102 144 ⌀ | arxiv_id stringlengths 10 12 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 213 648 | abstract large_stringlengths 455 2.39k | embedding listlengths 768 768 |
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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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