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title string | authors string | abstract string | pdf string | arXiv string | video string | bibtex string | url string | detail_url string | tags string | supp string | dataset null | null |
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Dual Super-Resolution Learning for Semantic Segmentation | Li Wang, Dong Li, Yousong Zhu, Lu Tian, Yi Shan | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Unfolding Network for Image Super-Resolution | Kai Zhang, Luc Van Gool, Radu Timofte | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Deep_Unfolding_Network_for_Image_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10428 | null | https://openaccess.thecvf.com | 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/html/Zhang_Deep_Unfolding_Network_for_Image_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Unsupervised Learning for Intrinsic Image Decomposition From a Single Image | Yunfei Liu, Yu Li, Shaodi You, Feng Lu | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Unsupervised_Learning_for_Intrinsic_Image_Decomposition_From_a_Single_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.09930 | https://www.youtube.com/watch?v=qGszWVyDF9c | https://openaccess.thecvf.com | 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/html/Liu_Unsupervised_Learning_for_Intrinsic_Image_Decomposition_From_a_Single_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Unsupervised_Learning_for_CVPR_2020_supplemental.pdf | null | null |
COCAS: A Large-Scale Clothes Changing Person Dataset for Re-Identification | Shijie Yu, Shihua Li, Dapeng Chen, Rui Zhao, Junjie Yan, Yu Qiao | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_COCAS_A_Large-Scale_Clothes_Changing_Person_Dataset_for_Re-Identification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.07862 | null | https://openaccess.thecvf.com | 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/html/Yu_COCAS_A_Large-Scale_Clothes_Changing_Person_Dataset_for_Re-Identification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference | Thomas Verelst, Tinne Tuytelaars | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Verelst_Dynamic_Convolutions_Exploiting_Spatial_Sparsity_for_Faster_Inference_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03203 | null | https://openaccess.thecvf.com | 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/html/Verelst_Dynamic_Convolutions_Exploiting_Spatial_Sparsity_for_Faster_Inference_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Verelst_Dynamic_Convolutions_Exploiting_CVPR_2020_supplemental.pdf | null | null |
Alleviation of Gradient Exploding in GANs: Fake Can Be Real | Song Tao, Jia Wang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tao_Alleviation_of_Gradient_Exploding_in_GANs_Fake_Can_Be_Real_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.12485 | null | https://openaccess.thecvf.com | 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/html/Tao_Alleviation_of_Gradient_Exploding_in_GANs_Fake_Can_Be_Real_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tao_Alleviation_of_Gradient_CVPR_2020_supplemental.pdf | null | null |
Forward and Backward Information Retention for Accurate Binary Neural Networks | Haotong Qin, Ruihao Gong, Xianglong Liu, Mingzhu Shen, Ziran Wei, Fengwei Yu, Jingkuan Song | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qin_Forward_and_Backward_Information_Retention_for_Accurate_Binary_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.10788 | https://www.youtube.com/watch?v=EsbwQTDWeXA | https://openaccess.thecvf.com | 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/html/Qin_Forward_and_Backward_Information_Retention_for_Accurate_Binary_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Cooling-Shrinking Attack: Blinding the Tracker With Imperceptible Noises | Bin Yan, Dong Wang, Huchuan Lu, Xiaoyun Yang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yan_Cooling-Shrinking_Attack_Blinding_the_Tracker_With_Imperceptible_Noises_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09595 | null | https://openaccess.thecvf.com | 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/html/Yan_Cooling-Shrinking_Attack_Blinding_the_Tracker_With_Imperceptible_Noises_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
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 | 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... | 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 | null | https://www.youtube.com/watch?v=5NrIHdicyAo | https://openaccess.thecvf.com | 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/html/Xiang_Zooming_Slow-Mo_Fast_and_Accurate_One-Stage_Space-Time_Video_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xiang_Zooming_Slow-Mo_Fast_CVPR_2020_supplemental.pdf | null | null |
A Hierarchical Graph Network for 3D Object Detection on Point Clouds | Jintai Chen, Biwen Lei, Qingyu Song, Haochao Ying, Danny Z. Chen, Jian Wu | 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... | 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 | https://openaccess.thecvf.com | 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/html/Chen_A_Hierarchical_Graph_Network_for_3D_Object_Detection_on_Point_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Online Joint Multi-Metric Adaptation From Frequent Sharing-Subset Mining for Person Re-Identification | Jiahuan Zhou, Bing Su, Ying Wu | 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... | 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 | https://www.youtube.com/watch?v=Q38l8pKqNsc | https://openaccess.thecvf.com | 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/html/Zhou_Online_Joint_Multi-Metric_Adaptation_From_Frequent_Sharing-Subset_Mining_for_Person_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Discriminate Information for Online Action Detection | Hyunjun Eun, Jinyoung Moon, Jongyoul Park, Chanho Jung, Changick Kim | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.04461 | null | https://openaccess.thecvf.com | 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/html/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Video to Events: Recycling Video Datasets for Event Cameras | Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carrio, Davide Scaramuzza | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gehrig_Video_to_Events_Recycling_Video_Datasets_for_Event_Cameras_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Gehrig_Video_to_Events_Recycling_Video_Datasets_for_Event_Cameras_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gehrig_Video_to_Events_CVPR_2020_supplemental.zip | null | null |
Bundle Pooling for Polygonal Architecture Segmentation Problem | Huayi Zeng, Kevin Joseph, Adam Vest, Yasutaka Furukawa | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zeng_Bundle_Pooling_for_Polygonal_Architecture_Segmentation_Problem_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Zeng_Bundle_Pooling_for_Polygonal_Architecture_Segmentation_Problem_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects | Kiana Ehsani, Shubham Tulsiani, Saurabh Gupta, Ali Farhadi, Abhinav Gupta | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ehsani_Use_the_Force_Luke_Learning_to_Predict_Physical_Forces_by_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12045 | https://www.youtube.com/watch?v=dx3_nXcOqV0 | https://openaccess.thecvf.com | 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/html/Ehsani_Use_the_Force_Luke_Learning_to_Predict_Physical_Forces_by_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ehsani_Use_the_Force_CVPR_2020_supplemental.zip | null | null |
Articulation-Aware Canonical Surface Mapping | Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kulkarni_Articulation-Aware_Canonical_Surface_Mapping_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00614 | null | https://openaccess.thecvf.com | 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/html/Kulkarni_Articulation-Aware_Canonical_Surface_Mapping_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kulkarni_Articulation-Aware_Canonical_Surface_CVPR_2020_supplemental.pdf | null | null |
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks | Eugene Lee, Chen-Yi Lee | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.12813 | https://www.youtube.com/watch?v=Se0cf-uk_L8 | https://openaccess.thecvf.com | 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/html/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lee_NeuralScale_Efficient_Scaling_CVPR_2020_supplemental.pdf | null | null |
Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization | Yoonsik Kim, Jae Woong Soh, Gu Yong Park, Nam Ik Cho | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Transfer_Learning_From_Synthetic_to_Real-Noise_Denoising_With_Adaptive_Instance_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11244 | https://www.youtube.com/watch?v=qWnEkDE-oe8 | https://openaccess.thecvf.com | 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/html/Kim_Transfer_Learning_From_Synthetic_to_Real-Noise_Denoising_With_Adaptive_Instance_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kim_Transfer_Learning_From_CVPR_2020_supplemental.pdf | null | null |
Variational Context-Deformable ConvNets for Indoor Scene Parsing | Zhitong Xiong, Yuan Yuan, Nianhui Guo, Qi Wang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xiong_Variational_Context-Deformable_ConvNets_CVPR_2020_supplemental.pdf | null | null |
Augmenting Colonoscopy Using Extended and Directional CycleGAN for Lossy Image Translation | Shawn Mathew, Saad Nadeem, Sruti Kumari, Arie Kaufman | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mathew_Augmenting_Colonoscopy_Using_Extended_and_Directional_CycleGAN_for_Lossy_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12473 | https://www.youtube.com/watch?v=9JZdnwtsE6I | https://openaccess.thecvf.com | 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/html/Mathew_Augmenting_Colonoscopy_Using_Extended_and_Directional_CycleGAN_for_Lossy_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mathew_Augmenting_Colonoscopy_Using_CVPR_2020_supplemental.zip | null | null |
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 | 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,... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14031 | https://www.youtube.com/watch?v=UocwJjwjeII | https://openaccess.thecvf.com | 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/html/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_BANet_Bidirectional_Aggregation_CVPR_2020_supplemental.zip | null | null |
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 | 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... | 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 | http://arxiv.org/abs/1912.09628 | https://www.youtube.com/watch?v=fonR1Q5tvDU | https://openaccess.thecvf.com | 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/html/Yu_C2FNAS_Coarse-to-Fine_Neural_Architecture_Search_for_3D_Medical_Image_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Seeing the World in a Bag of Chips | Jeong Joon Park, Aleksander Holynski, Steven M. Seitz | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Park_Seeing_the_World_in_a_Bag_of_Chips_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.04642 | null | https://openaccess.thecvf.com | 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/html/Park_Seeing_the_World_in_a_Bag_of_Chips_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Park_Seeing_the_World_CVPR_2020_supplemental.pdf | null | null |
Cascaded Deep Video Deblurring Using Temporal Sharpness Prior | Jinshan Pan, Haoran Bai, Jinhui Tang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pan_Cascaded_Deep_Video_Deblurring_Using_Temporal_Sharpness_Prior_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02501 | null | https://openaccess.thecvf.com | 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/html/Pan_Cascaded_Deep_Video_Deblurring_Using_Temporal_Sharpness_Prior_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Pan_Cascaded_Deep_Video_CVPR_2020_supplemental.pdf | null | null |
Reflection Scene Separation From a Single Image | Renjie Wan, Boxin Shi, Haoliang Li, Ling-Yu Duan, Alex C. Kot | 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,... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wan_Reflection_Scene_Separation_From_a_Single_Image_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Wan_Reflection_Scene_Separation_From_a_Single_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SmallBigNet: Integrating Core and Contextual Views for Video Classification | Xianhang Li, Yali Wang, Zhipeng Zhou, Yu Qiao | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_SmallBigNet_Integrating_Core_and_Contextual_Views_for_Video_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.14582 | https://www.youtube.com/watch?v=JIj2VTzmgmM | https://openaccess.thecvf.com | 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/html/Li_SmallBigNet_Integrating_Core_and_Contextual_Views_for_Video_Classification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
From Two Rolling Shutters to One Global Shutter | Cenek Albl, Zuzana Kukelova, Viktor Larsson, Michal Polic, Tomas Pajdla, Konrad Schindler | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Albl_From_Two_Rolling_Shutters_to_One_Global_Shutter_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.01964 | null | https://openaccess.thecvf.com | 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/html/Albl_From_Two_Rolling_Shutters_to_One_Global_Shutter_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Albl_From_Two_Rolling_CVPR_2020_supplemental.pdf | null | null |
CvxNet: Learnable Convex Decomposition | Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey Hinton, Andrea Tagliasacchi | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.05736 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
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 | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Deitke_RoboTHOR_An_Open_Simulation-to-Real_Embodied_AI_Platform_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.06799 | null | https://openaccess.thecvf.com | 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/html/Deitke_RoboTHOR_An_Open_Simulation-to-Real_Embodied_AI_Platform_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Style Normalization and Restitution for Generalizable Person Re-Identification | Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen, Li Zhang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.11037 | https://www.youtube.com/watch?v=BDd2hxpgznk | https://openaccess.thecvf.com | 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/html/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jin_Style_Normalization_and_CVPR_2020_supplemental.pdf | null | null |
Training Noise-Robust Deep Neural Networks via Meta-Learning | Zhen Wang, Guosheng Hu, Qinghua Hu | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Training_Noise-Robust_Deep_Neural_Networks_via_Meta-Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Wang_Training_Noise-Robust_Deep_Neural_Networks_via_Meta-Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
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 | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_HUMBI_A_Large_Multiview_Dataset_of_Human_Body_Expressions_CVPR_2020_paper.pdf | http://arxiv.org/abs/1812.00281 | null | https://openaccess.thecvf.com | 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/html/Yu_HUMBI_A_Large_Multiview_Dataset_of_Human_Body_Expressions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yu_HUMBI_A_Large_CVPR_2020_supplemental.zip | null | null |
Towards Transferable Targeted Attack | Maosen Li, Cheng Deng, Tengjiao Li, Junchi Yan, Xinbo Gao, Heng Huang | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Towards_Transferable_Targeted_Attack_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Towards_Transferable_Targeted_Attack_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Towards_Transferable_Targeted_Attack_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Towards_Transferable_Targeted_CVPR_2020_supplemental.pdf | null | null |
Supervised Raw Video Denoising With a Benchmark Dataset on Dynamic Scenes | Huanjing Yue, Cong Cao, Lei Liao, Ronghe Chu, Jingyu Yang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14013 | null | https://openaccess.thecvf.com | 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/html/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yue_Supervised_Raw_Video_CVPR_2020_supplemental.pdf | null | null |
FDA: Fourier Domain Adaptation for Semantic Segmentation | Yanchao Yang, Stefano Soatto | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_FDA_Fourier_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05498 | null | https://openaccess.thecvf.com | 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/html/Yang_FDA_Fourier_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SGAS: Sequential Greedy Architecture Search | Guohao Li, Guocheng Qian, Itzel C. Delgadillo, Matthias Muller, Ali Thabet, Bernard Ghanem | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_SGAS_Sequential_Greedy_Architecture_Search_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00195 | null | https://openaccess.thecvf.com | 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/html/Li_SGAS_Sequential_Greedy_Architecture_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_SGAS_Sequential_Greedy_CVPR_2020_supplemental.pdf | null | null |
Instance Segmentation of Biological Images Using Harmonic Embeddings | Victor Kulikov, Victor Lempitsky | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kulikov_Instance_Segmentation_of_Biological_Images_Using_Harmonic_Embeddings_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.05257 | null | https://openaccess.thecvf.com | 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/html/Kulikov_Instance_Segmentation_of_Biological_Images_Using_Harmonic_Embeddings_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Rethinking Zero-Shot Video Classification: End-to-End Training for Realistic Applications | Biagio Brattoli, Joseph Tighe, Fedor Zhdanov, Pietro Perona, Krzysztof Chalupka | 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... | 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 | http://arxiv.org/abs/2003.01455 | https://www.youtube.com/watch?v=F5AB06sCJ90 | https://openaccess.thecvf.com | 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/html/Brattoli_Rethinking_Zero-Shot_Video_Classification_End-to-End_Training_for_Realistic_Applications_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Brattoli_Rethinking_Zero-Shot_Video_CVPR_2020_supplemental.pdf | null | null |
A Multigrid Method for Efficiently Training Video Models | Chao-Yuan Wu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, Philipp Krahenbuhl | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_A_Multigrid_Method_for_Efficiently_Training_Video_Models_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00998 | null | https://openaccess.thecvf.com | 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/html/Wu_A_Multigrid_Method_for_Efficiently_Training_Video_Models_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_A_Multigrid_Method_CVPR_2020_supplemental.pdf | null | null |
Attention-Aware Multi-View Stereo | Keyang Luo, Tao Guan, Lili Ju, Yuesong Wang, Zhuo Chen, Yawei Luo | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_Attention-Aware_Multi-View_Stereo_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Luo_Attention-Aware_Multi-View_Stereo_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
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 | 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... | 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 | http://arxiv.org/abs/1912.12898 | https://www.youtube.com/watch?v=NxR-vtRIHNQ | https://openaccess.thecvf.com | 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/html/Liao_PPDM_Parallel_Point_Detection_and_Matching_for_Real-Time_Human-Object_Interaction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models | Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Menon_PULSE_Self-Supervised_Photo_Upsampling_via_Latent_Space_Exploration_of_Generative_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03808 | https://www.youtube.com/watch?v=JCK-N4T_tMU | https://openaccess.thecvf.com | 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/html/Menon_PULSE_Self-Supervised_Photo_Upsampling_via_Latent_Space_Exploration_of_Generative_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Menon_PULSE_Self-Supervised_Photo_CVPR_2020_supplemental.pdf | null | null |
Discrete Model Compression With Resource Constraint for Deep Neural Networks | Shangqian Gao, Feihu Huang, Jian Pei, Heng Huang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_Discrete_Model_Compression_With_Resource_Constraint_for_Deep_Neural_Networks_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=2S2M3TJYSks | https://openaccess.thecvf.com | 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/html/Gao_Discrete_Model_Compression_With_Resource_Constraint_for_Deep_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gao_Discrete_Model_Compression_CVPR_2020_supplemental.pdf | null | null |
GhostNet: More Features From Cheap Operations | Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Han_GhostNet_More_Features_From_Cheap_Operations_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11907 | null | https://openaccess.thecvf.com | 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/html/Han_GhostNet_More_Features_From_Cheap_Operations_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization | Yue Jiang, Dantong Ji, Zhizhong Han, Matthias Zwicker | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_SDFDiff_Differentiable_Rendering_of_Signed_Distance_Fields_for_3D_Shape_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.07109 | https://www.youtube.com/watch?v=T7STQSQb_So | https://openaccess.thecvf.com | 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/html/Jiang_SDFDiff_Differentiable_Rendering_of_Signed_Distance_Fields_for_3D_Shape_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jiang_SDFDiff_Differentiable_Rendering_CVPR_2020_supplemental.zip | null | null |
Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image | Yuhui Quan, Mingqin Chen, Tongyao Pang, Hui Ji | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Quan_Self2Self_With_Dropout_Learning_Self-Supervised_Denoising_From_Single_Image_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=EzvaNiXrNAw | https://openaccess.thecvf.com | 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/html/Quan_Self2Self_With_Dropout_Learning_Self-Supervised_Denoising_From_Single_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Quan_Self2Self_With_Dropout_CVPR_2020_supplemental.pdf | null | null |
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image | Yuyu Guo, Lei Bi, Euijoon Ahn, Dagan Feng, Qian Wang, Jinman Kim | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_A_Spatiotemporal_Volumetric_Interpolation_Network_for_4D_Dynamic_Medical_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12680 | https://www.youtube.com/watch?v=CMcxisYox4U | https://openaccess.thecvf.com | 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/html/Guo_A_Spatiotemporal_Volumetric_Interpolation_Network_for_4D_Dynamic_Medical_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Where Am I Looking At? Joint Location and Orientation Estimation by Cross-View Matching | Yujiao Shi, Xin Yu, Dylan Campbell, Hongdong Li | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shi_Where_Am_I_Looking_At_Joint_Location_and_Orientation_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.03860 | https://www.youtube.com/watch?v=m1XIkhS1I54 | https://openaccess.thecvf.com | 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/html/Shi_Where_Am_I_Looking_At_Joint_Location_and_Orientation_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shi_Where_Am_I_CVPR_2020_supplemental.pdf | null | null |
Towards Large Yet Imperceptible Adversarial Image Perturbations With Perceptual Color Distance | Zhengyu Zhao, Zhuoran Liu, Martha Larson | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Towards_Large_Yet_Imperceptible_Adversarial_Image_Perturbations_With_Perceptual_Color_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.02466 | https://www.youtube.com/watch?v=2j74B_9VaJ8 | https://openaccess.thecvf.com | 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/html/Zhao_Towards_Large_Yet_Imperceptible_Adversarial_Image_Perturbations_With_Perceptual_Color_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_Towards_Large_Yet_CVPR_2020_supplemental.pdf | null | null |
Assessing Image Quality Issues for Real-World Problems | Tai-Yin Chiu, Yinan Zhao, Danna Gurari | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chiu_Assessing_Image_Quality_Issues_for_Real-World_Problems_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12511 | null | https://openaccess.thecvf.com | 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/html/Chiu_Assessing_Image_Quality_Issues_for_Real-World_Problems_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chiu_Assessing_Image_Quality_CVPR_2020_supplemental.pdf | null | null |
Adaptive Dilated Network With Self-Correction Supervision for Counting | Shuai Bai, Zhiqun He, Yu Qiao, Hanzhe Hu, Wei Wu, Junjie Yan | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bai_Adaptive_Dilated_Network_With_Self-Correction_Supervision_for_Counting_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Bai_Adaptive_Dilated_Network_With_Self-Correction_Supervision_for_Counting_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bai_Adaptive_Dilated_Network_CVPR_2020_supplemental.pdf | null | null |
Camouflaged Object Detection | Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, Ling Shao | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fan_Camouflaged_Object_Detection_CVPR_2020_supplemental.zip | null | null |
Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve | Thomas Schops, Viktor Larsson, Marc Pollefeys, Torsten Sattler | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Schops_Why_Having_10000_Parameters_in_Your_Camera_Model_Is_Better_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Schops_Why_Having_10000_Parameters_in_Your_Camera_Model_Is_Better_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Schops_Why_Having_10000_CVPR_2020_supplemental.pdf | null | null |
BiDet: An Efficient Binarized Object Detector | Ziwei Wang, Ziyi Wu, Jiwen Lu, Jie Zhou | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_BiDet_An_Efficient_Binarized_Object_Detector_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03961 | null | https://openaccess.thecvf.com | 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/html/Wang_BiDet_An_Efficient_Binarized_Object_Detector_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Searching for Actions on the Hyperbole | Teng Long, Pascal Mettes, Heng Tao Shen, Cees G. M. Snoek | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Long_Searching_for_Actions_on_the_Hyperbole_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Long_Searching_for_Actions_on_the_Hyperbole_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Long_Searching_for_Actions_CVPR_2020_supplemental.pdf | null | null |
SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans | Angela Dai, Christian Diller, Matthias Niessner | 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... | 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 | null | https://www.youtube.com/watch?v=gADedihdK8c | https://openaccess.thecvf.com | 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/html/Dai_SG-NN_Sparse_Generative_Neural_Networks_for_Self-Supervised_Scene_Completion_of_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Dai_SG-NN_Sparse_Generative_CVPR_2020_supplemental.pdf | null | null |
Stereoscopic Flash and No-Flash Photography for Shape and Albedo Recovery | Xu Cao, Michael Waechter, Boxin Shi, Ye Gao, Bo Zheng, Yasuyuki Matsushita | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cao_Stereoscopic_Flash_and_No-Flash_Photography_for_Shape_and_Albedo_Recovery_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Cao_Stereoscopic_Flash_and_No-Flash_Photography_for_Shape_and_Albedo_Recovery_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cao_Stereoscopic_Flash_and_CVPR_2020_supplemental.pdf | null | null |
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation | Jiahua Dong, Yang Cong, Gan Sun, Bineng Zhong, Xiaowei Xu | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dong_What_Can_Be_Transferred_Unsupervised_Domain_Adaptation_for_Endoscopic_Lesions_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.11500 | https://www.youtube.com/watch?v=DDV8X_z6Aac | https://openaccess.thecvf.com | 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/html/Dong_What_Can_Be_Transferred_Unsupervised_Domain_Adaptation_for_Endoscopic_Lesions_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Generate 3D Training Data Through Hybrid Gradient | Dawei Yang, Jia Deng | 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 ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Learning_to_Generate_3D_Training_Data_Through_Hybrid_Gradient_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Yang_Learning_to_Generate_3D_Training_Data_Through_Hybrid_Gradient_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Learning_to_Generate_CVPR_2020_supplemental.pdf | null | null |
On Joint Estimation of Pose, Geometry and svBRDF From a Handheld Scanner | Carolin Schmitt, Simon Donne, Gernot Riegler, Vladlen Koltun, Andreas Geiger | 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... | 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 | https://openaccess.thecvf.com | 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/html/Schmitt_On_Joint_Estimation_of_Pose_Geometry_and_svBRDF_From_a_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Synchronizing Probability Measures on Rotations via Optimal Transport | Tolga Birdal, Michael Arbel, Umut Simsekli, Leonidas J. Guibas | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Birdal_Synchronizing_Probability_Measures_on_Rotations_via_Optimal_Transport_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00663 | null | https://openaccess.thecvf.com | 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/html/Birdal_Synchronizing_Probability_Measures_on_Rotations_via_Optimal_Transport_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Birdal_Synchronizing_Probability_Measures_CVPR_2020_supplemental.pdf | null | null |
Camera Trace Erasing | Chang Chen, Zhiwei Xiong, Xiaoming Liu, Feng Wu | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Camera_Trace_Erasing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06951 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Camera_Trace_Erasing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Camera_Trace_Erasing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Camera_Trace_Erasing_CVPR_2020_supplemental.pdf | null | null |
Robust 3D Self-Portraits in Seconds | Zhe Li, Tao Yu, Chuanyu Pan, Zerong Zheng, Yebin Liu | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Robust_3D_Self-Portraits_in_Seconds_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02460 | null | https://openaccess.thecvf.com | 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/html/Li_Robust_3D_Self-Portraits_in_Seconds_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Instance Shadow Detection | Tianyu Wang, Xiaowei Hu, Qiong Wang, Pheng-Ann Heng, Chi-Wing Fu | 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, ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Instance_Shadow_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07034 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Instance_Shadow_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Instance_Shadow_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
MemNAS: Memory-Efficient Neural Architecture Search With Grow-Trim Learning | Peiye Liu, Bo Wu, Huadong Ma, Mingoo Seok | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=YmE6cWK9rpk | https://openaccess.thecvf.com | 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/html/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
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 | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03383 | null | https://openaccess.thecvf.com | 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/html/Wang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Deep_Distance_Transform_CVPR_2020_supplemental.pdf | null | null |
FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding | Dian Shao, Yue Zhao, Bo Dai, Dahua Lin | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.06704 | https://www.youtube.com/watch?v=ChvW59jM4O0 | https://openaccess.thecvf.com | 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/html/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shao_FineGym_A_Hierarchical_CVPR_2020_supplemental.zip | null | null |
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 | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
One Man's Trash Is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples | Chang Xiao, Changxi Zheng | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xiao_One_Mans_Trash_Is_Another_Mans_Treasure_Resisting_Adversarial_Examples_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=4Gpvnpt8oRA | https://openaccess.thecvf.com | 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/html/Xiao_One_Mans_Trash_Is_Another_Mans_Treasure_Resisting_Adversarial_Examples_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xiao_One_Mans_Trash_CVPR_2020_supplemental.pdf | null | null |
Image Processing Using Multi-Code GAN Prior | Jinjin Gu, Yujun Shen, Bolei Zhou | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.07116 | null | https://openaccess.thecvf.com | 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/html/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ColorFool: Semantic Adversarial Colorization | Ali Shahin Shamsabadi, Ricardo Sanchez-Matilla, Andrea Cavallaro | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shamsabadi_ColorFool_Semantic_Adversarial_Colorization_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shamsabadi_ColorFool_Semantic_Adversarial_Colorization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shamsabadi_ColorFool_Semantic_Adversarial_Colorization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shamsabadi_ColorFool_Semantic_Adversarial_CVPR_2020_supplemental.zip | null | null |
Bi3D: Stereo Depth Estimation via Binary Classifications | Abhishek Badki, Alejandro Troccoli, Kihwan Kim, Jan Kautz, Pradeep Sen, Orazio Gallo | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.07274 | null | https://openaccess.thecvf.com | 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/html/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Badki_Bi3D_Stereo_Depth_CVPR_2020_supplemental.zip | null | null |
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry | Nan Yang, Lukas von Stumberg, Rui Wang, Daniel Cremers | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.01060 | https://www.youtube.com/watch?v=bS9u28-2p7w | https://openaccess.thecvf.com | 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/html/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_D3VO_Deep_Depth_CVPR_2020_supplemental.pdf | null | null |
Fantastic Answers and Where to Find Them: Immersive Question-Directed Visual Attention | Ming Jiang, Shi Chen, Jinhui Yang, Qi Zhao | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_Fantastic_Answers_and_Where_to_Find_Them_Immersive_Question-Directed_Visual_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=N2-7j7uS0qo | https://openaccess.thecvf.com | 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/html/Jiang_Fantastic_Answers_and_Where_to_Find_Them_Immersive_Question-Directed_Visual_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jiang_Fantastic_Answers_and_CVPR_2020_supplemental.pdf | null | null |
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction | Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, Qi Tian | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Dynamic_Multiscale_Graph_Neural_Networks_for_3D_Skeleton_Based_Human_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08802 | https://www.youtube.com/watch?v=nNWA-EOBwWw | https://openaccess.thecvf.com | 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/html/Li_Dynamic_Multiscale_Graph_Neural_Networks_for_3D_Skeleton_Based_Human_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Dynamic_Multiscale_Graph_CVPR_2020_supplemental.zip | null | null |
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 | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Nie_Total3DUnderstanding_Joint_Layout_Object_Pose_and_Mesh_Reconstruction_for_Indoor_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12212 | https://www.youtube.com/watch?v=jUIGpWFybJs | https://openaccess.thecvf.com | 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/html/Nie_Total3DUnderstanding_Joint_Layout_Object_Pose_and_Mesh_Reconstruction_for_Indoor_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Nie_Total3DUnderstanding_Joint_Layout_CVPR_2020_supplemental.pdf | null | null |
GPS-Net: Graph Property Sensing Network for Scene Graph Generation | Xin Lin, Changxing Ding, Jinquan Zeng, Dacheng Tao | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_GPS-Net_Graph_Property_Sensing_Network_for_Scene_Graph_Generation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Lin_GPS-Net_Graph_Property_Sensing_Network_for_Scene_Graph_Generation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_GPS-Net_Graph_Property_CVPR_2020_supplemental.pdf | null | null |
Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes | Zhengqin Li, Yu-Ying Yeh, Manmohan Chandraker | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Through_the_Looking_Glass_Neural_3D_Reconstruction_of_Transparent_Shapes_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.10904 | null | https://openaccess.thecvf.com | 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/html/Li_Through_the_Looking_Glass_Neural_3D_Reconstruction_of_Transparent_Shapes_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Through_the_Looking_CVPR_2020_supplemental.zip | null | null |
Recursive Social Behavior Graph for Trajectory Prediction | Jianhua Sun, Qinhong Jiang, Cewu Lu | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sun_Recursive_Social_Behavior_Graph_for_Trajectory_Prediction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.10402 | null | https://openaccess.thecvf.com | 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/html/Sun_Recursive_Social_Behavior_Graph_for_Trajectory_Prediction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Attention Scaling for Crowd Counting | Xiaoheng Jiang, Li Zhang, Mingliang Xu, Tianzhu Zhang, Pei Lv, Bing Zhou, Xin Yang, Yanwei Pang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_Attention_Scaling_for_Crowd_Counting_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Jiang_Attention_Scaling_for_Crowd_Counting_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
FocalMix: Semi-Supervised Learning for 3D Medical Image Detection | Dong Wang, Yuan Zhang, Kexin Zhang, Liwei Wang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_FocalMix_Semi-Supervised_Learning_for_3D_Medical_Image_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09108 | null | https://openaccess.thecvf.com | 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/html/Wang_FocalMix_Semi-Supervised_Learning_for_3D_Medical_Image_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Bi-Directional Relationship Inferring Network for Referring Image Segmentation | Zhiwei Hu, Guang Feng, Jiayu Sun, Lihe Zhang, Huchuan Lu | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=0wh5XXdKUBI | https://openaccess.thecvf.com | 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/html/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation | Matias Tassano, Julie Delon, Thomas Veit | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.01361 | null | https://openaccess.thecvf.com | 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/html/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tassano_FastDVDnet_Towards_Real-Time_CVPR_2020_supplemental.pdf | null | null |
Composed Query Image Retrieval Using Locally Bounded Features | Mehrdad Hosseinzadeh, Yang Wang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hosseinzadeh_Composed_Query_Image_Retrieval_Using_Locally_Bounded_Features_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Hosseinzadeh_Composed_Query_Image_Retrieval_Using_Locally_Bounded_Features_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring | Yuesong Nan, Yuhui Quan, Hui Ji | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Nan_Variational-EM-Based_Deep_Learning_for_Noise-Blind_Image_Deblurring_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Nan_Variational-EM-Based_Deep_Learning_for_Noise-Blind_Image_Deblurring_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Nan_Variational-EM-Based_Deep_Learning_CVPR_2020_supplemental.pdf | null | null |
Central Similarity Quantization for Efficient Image and Video Retrieval | Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Feng | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yuan_Central_Similarity_Quantization_for_Efficient_Image_and_Video_Retrieval_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.00347 | null | https://openaccess.thecvf.com | 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/html/Yuan_Central_Similarity_Quantization_for_Efficient_Image_and_Video_Retrieval_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yuan_Central_Similarity_Quantization_CVPR_2020_supplemental.pdf | null | null |
Taking a Deeper Look at Co-Salient Object Detection | Deng-Ping Fan, Zheng Lin, Ge-Peng Ji, Dingwen Zhang, Huazhu Fu, Ming-Ming Cheng | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fan_Taking_a_Deeper_Look_at_Co-Salient_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Fan_Taking_a_Deeper_Look_at_Co-Salient_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fan_Taking_a_Deeper_CVPR_2020_supplemental.zip | null | null |
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics | Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, Siwei Lyu | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Celeb-DF_A_Large-Scale_Challenging_Dataset_for_DeepFake_Forensics_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Li_Celeb-DF_A_Large-Scale_Challenging_Dataset_for_DeepFake_Forensics_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
TEA: Temporal Excitation and Aggregation for Action Recognition | Yan Li, Bin Ji, Xintian Shi, Jianguo Zhang, Bin Kang, Limin Wang | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_TEA_Temporal_Excitation_and_Aggregation_for_Action_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01398 | null | https://openaccess.thecvf.com | 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/html/Li_TEA_Temporal_Excitation_and_Aggregation_for_Action_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_TEA_Temporal_Excitation_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Person Re-Identification via Softened Similarity Learning | Yutian Lin, Lingxi Xie, Yu Wu, Chenggang Yan, Qi Tian | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03547 | null | https://openaccess.thecvf.com | 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/html/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
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 | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Frequency_Domain_Compact_3D_Convolutional_Neural_Networks_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | 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/html/Chen_Frequency_Domain_Compact_3D_Convolutional_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve | Sen Jia, Neil D. B. Bruce | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jia_Revisiting_Saliency_Metrics_Farthest-Neighbor_Area_Under_Curve_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10540 | null | https://openaccess.thecvf.com | 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/html/Jia_Revisiting_Saliency_Metrics_Farthest-Neighbor_Area_Under_Curve_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jia_Revisiting_Saliency_Metrics_CVPR_2020_supplemental.pdf | null | null |
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization | Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, Gu-Yeon Wei | 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... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kwon_Structured_Compression_by_Weight_Encryption_for_Unstructured_Pruning_and_Quantization_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.10138 | https://www.youtube.com/watch?v=MOsCX_xV474 | https://openaccess.thecvf.com | 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/html/Kwon_Structured_Compression_by_Weight_Encryption_for_Unstructured_Pruning_and_Quantization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
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CVPR 2020 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2020 Open Access website (https://openaccess.thecvf.com/CVPR2020) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2020). For researchers who are interested in doing analysis of CVPR 2020 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2020 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "Dual Super-Resolution Learning for Semantic Segmentation",
"authors": "Li Wang, Dong Li, Yousong Zhu, Lu Tian, Yi Shan",
"abstract": "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 Learning (DSRL) to effectively improve the segmentation accuracy without introducing extra computation costs. Specifically, the proposed method consists of three parts: Semantic Segmentation Super-Resolution (SSSR), Single Image Super-Resolution (SISR) and Feature Affinity (FA) module, which can keep high-resolution representations with low-resolution input while simultaneously reducing the model computation complexity. Moreover, it can be easily generalized to other tasks, e.g., human pose estimation. This simple yet effective method leads to strong representations and is evidenced by promising performance on both semantic segmentation and human pose estimation. Specifically, for semantic segmentation on CityScapes, we can achieve \\geq2% higher mIoU with similar FLOPs, and keep the performance with 70% FLOPs. For human pose estimation, we can gain \\geq2% mAP with the same FLOPs and maintain mAP with 30% fewer FLOPs. Code and models are available at https://github.com/wanglixilinx/DSRL.",
"pdf": "https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.pdf",
"bibtex": "https://openaccess.thecvf.com",
"url": "https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html",
"detail_url": "https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html",
"tags": "CVPR 2020"
}
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