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title string | authors string | abstract string | pdf string | arXiv string | video string | bibtex string | url string | detail_url string | tags string | supp string | dataset string | string |
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Single Image Optical Flow Estimation With an Event Camera | Liyuan Pan, Miaomiao Liu, Richard Hartley | Event cameras are bio-inspired sensors that asynchronously report intensity changes in microsecond resolution. DAVIS can capture high dynamics of a scene and simultaneously output high temporal resolution events and low frame-rate intensity images. In this paper, we propose a single image (potentially blurred) and even... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pan_Single_Image_Optical_Flow_Estimation_With_an_Event_Camera_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00347 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Single_Image_Optical_Flow_Estimation_With_an_Event_Camera_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Single_Image_Optical_Flow_Estimation_With_an_Event_Camera_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention | Dat Huynh, Ehsan Elhamifar | We address the problem of fine-grained generalized zero-shot recognition of visually similar classes without training images for some classes. We propose a dense attribute-based attention mechanism that for each attribute focuses on the most relevant image regions, obtaining attribute-based features. Instead of alignin... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huynh_Fine-Grained_Generalized_Zero-Shot_Learning_via_Dense_Attribute-Based_Attention_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=Lq03ZdB6DXE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huynh_Fine-Grained_Generalized_Zero-Shot_Learning_via_Dense_Attribute-Based_Attention_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huynh_Fine-Grained_Generalized_Zero-Shot_Learning_via_Dense_Attribute-Based_Attention_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huynh_Fine-Grained_Generalized_Zero-Shot_CVPR_2020_supplemental.pdf | null | null |
Solving Jigsaw Puzzles With Eroded Boundaries | Dov Bridger, Dov Danon, Ayellet Tal | Jigsaw puzzle solving is an intriguing problem which has been explored in computer vision for decades. This paper focuses on a specific variant of the problem--solving puzzles with eroded boundaries. Such erosion makes the problem extremely difficult, since most existing solvers utilize solely the information at the bo... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bridger_Solving_Jigsaw_Puzzles_With_Eroded_Boundaries_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00755 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bridger_Solving_Jigsaw_Puzzles_With_Eroded_Boundaries_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bridger_Solving_Jigsaw_Puzzles_With_Eroded_Boundaries_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging | Qilin Sun, Ethan Tseng, Qiang Fu, Wolfgang Heidrich, Felix Heide | High-dynamic range (HDR) imaging is an essential imaging modality for a wide range of applications in uncontrolled environments, including autonomous driving, robotics, and mobile phone cameras. However, existing HDR techniques in commodity devices struggle with dynamic scenes due to multi-shot acquisition and post-pro... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sun_Learning_Rank-1_Diffractive_Optics_for_Single-Shot_High_Dynamic_Range_Imaging_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=35aZSQN27ck | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Learning_Rank-1_Diffractive_Optics_for_Single-Shot_High_Dynamic_Range_Imaging_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Learning_Rank-1_Diffractive_Optics_for_Single-Shot_High_Dynamic_Range_Imaging_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sun_Learning_Rank-1_Diffractive_CVPR_2020_supplemental.pdf | null | null |
Universal Source-Free Domain Adaptation | Jogendra Nath Kundu, Naveen Venkat, Rahul M V, R. Venkatesh Babu | There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift. Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of t... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kundu_Universal_Source-Free_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04393 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kundu_Universal_Source-Free_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kundu_Universal_Source-Free_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kundu_Universal_Source-Free_Domain_CVPR_2020_supplemental.pdf | null | null |
Meta-Transfer Learning for Zero-Shot Super-Resolution | Jae Woong Soh, Sunwoo Cho, Nam Ik Cho | Convolutional neural networks (CNNs) have shown dramatic improvements in single image super-resolution (SISR) by using large-scale external samples. Despite their remarkable performance based on the external dataset, they cannot exploit internal information within a specific image. Another problem is that they are appl... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Soh_Meta-Transfer_Learning_for_Zero-Shot_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12213 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Soh_Meta-Transfer_Learning_for_Zero-Shot_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Soh_Meta-Transfer_Learning_for_Zero-Shot_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Soh_Meta-Transfer_Learning_for_CVPR_2020_supplemental.pdf | null | null |
A Model-Driven Deep Neural Network for Single Image Rain Removal | Hong Wang, Qi Xie, Qian Zhao, Deyu Meng | Deep learning (DL) methods have achieved state-of-the-art performance in the task of single image rain removal. Most of current DL architectures, however, are still lack of sufficient interpretability and not fully integrated with physical structures inside general rain streaks. To this issue, in this paper, we propose... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_A_Model-Driven_Deep_Neural_Network_for_Single_Image_Rain_Removal_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.01333 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_A_Model-Driven_Deep_Neural_Network_for_Single_Image_Rain_Removal_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_A_Model-Driven_Deep_Neural_Network_for_Single_Image_Rain_Removal_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_A_Model-Driven_Deep_CVPR_2020_supplemental.pdf | null | null |
Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution | Yu Zhao, Fan Yang, Yuqi Fang, Hailing Liu, Niyun Zhou, Jun Zhang, Jiarui Sun, Sen Yang, Bjoern Menze, Xinjuan Fan, Jianhua Yao | Multiple instance learning (MIL) is a typical weakly-supervised learning method where the label is associated with a bag of instances instead of a single instance. Despite extensive research over past years, effectively deploying MIL remains an open and challenging problem, especially when the commonly assumed standard... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Predicting_Lymph_Node_Metastasis_Using_Histopathological_Images_Based_on_Multiple_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=sfGDvAqoJwg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Predicting_Lymph_Node_Metastasis_Using_Histopathological_Images_Based_on_Multiple_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Predicting_Lymph_Node_Metastasis_Using_Histopathological_Images_Based_on_Multiple_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy | Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang, Yujun Lin, Song Han | We present APQ, a novel design methodology for efficient deep learning deployment. Unlike previous methods that separately optimize the neural network architecture, pruning policy, and quantization policy, we design to optimize them in a joint manner. To deal with the larger design space it brings, we devise to train a... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.08509 | https://www.youtube.com/watch?v=fy1DUauWFaY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_APQ_Joint_Search_CVPR_2020_supplemental.pdf | null | null |
SSRNet: Scalable 3D Surface Reconstruction Network | Zhenxing Mi, Yiming Luo, Wenbing Tao | Existing learning-based surface reconstruction methods from point clouds are still facing challenges in terms of scalability and preservation of details on large-scale point clouds. In this paper, we propose the SSRNet, a novel scalable learning-based method for surface reconstruction. The proposed SSRNet constructs lo... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mi_SSRNet_Scalable_3D_Surface_Reconstruction_Network_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07401 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mi_SSRNet_Scalable_3D_Surface_Reconstruction_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mi_SSRNet_Scalable_3D_Surface_Reconstruction_Network_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mi_SSRNet_Scalable_3D_CVPR_2020_supplemental.zip | null | null |
Semantic Correspondence as an Optimal Transport Problem | Yanbin Liu, Linchao Zhu, Makoto Yamada, Yi Yang | Establishing dense correspondences across semantically similar images is a challenging task. Due to the large intra-class variation and background clutter, two common issues occur in current approaches. First, many pixels in a source image are assigned to one target pixel, i.e., many to one matching. Second, some objec... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Semantic_Correspondence_as_an_Optimal_Transport_Problem_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Semantic_Correspondence_as_an_Optimal_Transport_Problem_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Semantic_Correspondence_as_an_Optimal_Transport_Problem_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Semantic_Correspondence_as_CVPR_2020_supplemental.pdf | null | null |
Improving Confidence Estimates for Unfamiliar Examples | Zhizhong Li, Derek Hoiem | Intuitively, unfamiliarity should lead to lack of confidence. In reality, current algorithms often make highly confident yet wrong predictions when faced with relevant but unfamiliar examples. A classifier we trained to recognize gender is 12 times more likely to be wrong with a 99% confident prediction if presented wi... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Improving_Confidence_Estimates_for_Unfamiliar_Examples_CVPR_2020_paper.pdf | http://arxiv.org/abs/1804.03166 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Improving_Confidence_Estimates_for_Unfamiliar_Examples_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Improving_Confidence_Estimates_for_Unfamiliar_Examples_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Improving_Confidence_Estimates_CVPR_2020_supplemental.pdf | null | null |
Learning Generative Models of Shape Handles | Matheus Gadelha, Giorgio Gori, Duygu Ceylan, Radomir Mech, Nathan Carr, Tamy Boubekeur, Rui Wang, Subhransu Maji | We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations. Our model can generate handle sets with varying cardinality and different types of ha... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gadelha_Learning_Generative_Models_of_Shape_Handles_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03028 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gadelha_Learning_Generative_Models_of_Shape_Handles_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gadelha_Learning_Generative_Models_of_Shape_Handles_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gadelha_Learning_Generative_Models_CVPR_2020_supplemental.pdf | null | null |
Toward a Universal Model for Shape From Texture | Dor Verbin, Todd Zickler | We consider the shape from texture problem, where the input is a single image of a curved, textured surface, and the texture and shape are both a priori unknown. We formulate this task as a three-player game between a shape process, a texture process, and a discriminator. The discriminator adapts a set of non-linear fi... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Verbin_Toward_a_Universal_Model_for_Shape_From_Texture_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Verbin_Toward_a_Universal_Model_for_Shape_From_Texture_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Verbin_Toward_a_Universal_Model_for_Shape_From_Texture_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Verbin_Toward_a_Universal_CVPR_2020_supplemental.pdf | null | null |
Stochastic Sparse Subspace Clustering | Ying Chen, Chun-Guang Li, Chong You | State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points. By enforcing such representation to be sparse, sparse subspace clustering is guaranteed to produce a subspace-preserving data affinity where two points are conn... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Stochastic_Sparse_Subspace_Clustering_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.01449 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Stochastic_Sparse_Subspace_Clustering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Stochastic_Sparse_Subspace_Clustering_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness | Ahmadreza Jeddi, Mohammad Javad Shafiee, Michelle Karg, Christian Scharfenberger, Alexander Wong | While deep neural networks have been achieving state-of-the-art performance across a wide variety of applications, their vulnerability to adversarial attacks limits their widespread deployment for safety-critical applications. Alongside other adversarial defense approaches being investigated, there has been a very rece... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jeddi_Learn2Perturb_An_End-to-End_Feature_Perturbation_Learning_to_Improve_Adversarial_Robustness_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.01090 | https://www.youtube.com/watch?v=-XYfnsmadDs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jeddi_Learn2Perturb_An_End-to-End_Feature_Perturbation_Learning_to_Improve_Adversarial_Robustness_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jeddi_Learn2Perturb_An_End-to-End_Feature_Perturbation_Learning_to_Improve_Adversarial_Robustness_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jeddi_Learn2Perturb_An_End-to-End_CVPR_2020_supplemental.pdf | null | null |
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes | Rajeev Yasarla, Vishwanath A. Sindagi, Vishal M. Patel | Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image derainin... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yasarla_Syn2Real_Transfer_Learning_for_Image_Deraining_Using_Gaussian_Processes_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.05580 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yasarla_Syn2Real_Transfer_Learning_for_Image_Deraining_Using_Gaussian_Processes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yasarla_Syn2Real_Transfer_Learning_for_Image_Deraining_Using_Gaussian_Processes_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yasarla_Syn2Real_Transfer_Learning_CVPR_2020_supplemental.pdf | null | null |
On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks | Yue Zhao, Yuwei Wu, Caihua Chen, Andrew Lim | While deep learning in 3D domain has achieved revolutionary performance in many tasks, the robustness of these models has not been sufficiently studied or explored. Regarding the 3D adversarial samples, most existing works focus on manipulation of local points, which may fail to invoke the global geometry properties, l... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12222 | https://www.youtube.com/watch?v=nY5fJxxzdRg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_On_Isometry_Robustness_CVPR_2020_supplemental.zip | null | null |
Self-Supervised Viewpoint Learning From Image Collections | Siva Karthik Mustikovela, Varun Jampani, Shalini De Mello, Sifei Liu, Umar Iqbal, Carsten Rother, Jan Kautz | Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabeled images of an object category from the internet, e.g., o... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01793 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mustikovela_Self-Supervised_Viewpoint_Learning_CVPR_2020_supplemental.zip | null | null |
On the Uncertainty of Self-Supervised Monocular Depth Estimation | Matteo Poggi, Filippo Aleotti, Fabio Tosi, Stefano Mattoccia | Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all. Despite the astonishing results yielded by such methodologies, learning to reason about the uncertainty of the estimated depth maps is of paramount importance for practical applications... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Poggi_On_the_Uncertainty_of_Self-Supervised_Monocular_Depth_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.06209 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Poggi_On_the_Uncertainty_of_Self-Supervised_Monocular_Depth_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Poggi_On_the_Uncertainty_of_Self-Supervised_Monocular_Depth_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Poggi_On_the_Uncertainty_CVPR_2020_supplemental.pdf | null | null |
StegaStamp: Invisible Hyperlinks in Physical Photographs | Matthew Tancik, Ben Mildenhall, Ren Ng | Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think about this is physical photographs that have unique QR codes invisibly embedded within them. This paper presents an architecture, algorithms, a... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tancik_StegaStamp_Invisible_Hyperlinks_in_Physical_Photographs_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.05343 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tancik_StegaStamp_Invisible_Hyperlinks_in_Physical_Photographs_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tancik_StegaStamp_Invisible_Hyperlinks_in_Physical_Photographs_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tancik_StegaStamp_Invisible_Hyperlinks_CVPR_2020_supplemental.pdf | null | null |
Anisotropic Convolutional Networks for 3D Semantic Scene Completion | Jie Li, Kai Han, Peng Wang, Yu Liu, Xia Yuan | As a voxel-wise labeling task, semantic scene completion (SSC) tries to simultaneously infer the occupancy and semantic labels for a scene from a single depth and/or RGB image. The key challenge for SSC is how to effectively take advantage of the 3D context to model various objects or stuffs with severe variations in s... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02122 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Have an Ear for Face Super-Resolution | Givi Meishvili, Simon Jenni, Paolo Favaro | We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size). When the resolution of the input image is very low (e.g., 8x8 pixels), the loss of information is so dire that important details of the original identity have been lost and... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Meishvili_Learning_to_Have_an_Ear_for_Face_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.12780 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Meishvili_Learning_to_Have_an_Ear_for_Face_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Meishvili_Learning_to_Have_an_Ear_for_Face_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Meishvili_Learning_to_Have_CVPR_2020_supplemental.pdf | null | null |
Cascaded Refinement Network for Point Cloud Completion | Xiaogang Wang, Marcelo H. Ang Jr., Gim Hee Lee | Point clouds are often sparse and incomplete. Existing shape completion methods are incapable of generating details of objects or learning the complex point distributions. To this end, we propose a cascaded refinement network together with a coarse-to-fine strategy to synthesize the detailed object shapes. Considering ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Cascaded_Refinement_Network_for_Point_Cloud_Completion_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03327v3 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cascaded_Refinement_Network_for_Point_Cloud_Completion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cascaded_Refinement_Network_for_Point_Cloud_Completion_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Cascaded_Refinement_Network_CVPR_2020_supplemental.pdf | null | null |
DOA-GAN: Dual-Order Attentive Generative Adversarial Network for Image Copy-Move Forgery Detection and Localization | Ashraful Islam, Chengjiang Long, Arslan Basharat, Anthony Hoogs | Images can be manipulated for nefarious purposes to hide content or to duplicate certain objects through copy-move operations. Discovering a well-crafted copy-move forgery in images can be very challenging for both humans and machines; for example, an object on a uniform background can be replaced by an image patch of ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Islam_DOA-GAN_Dual-Order_Attentive_Generative_Adversarial_Network_for_Image_Copy-Move_Forgery_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=ezmYkNblTP4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Islam_DOA-GAN_Dual-Order_Attentive_Generative_Adversarial_Network_for_Image_Copy-Move_Forgery_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Islam_DOA-GAN_Dual-Order_Attentive_Generative_Adversarial_Network_for_Image_Copy-Move_Forgery_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Islam_DOA-GAN_Dual-Order_Attentive_CVPR_2020_supplemental.pdf | null | null |
Rotation Equivariant Graph Convolutional Network for Spherical Image Classification | Qin Yang, Chenglin Li, Wenrui Dai, Junni Zou, Guo-Jun Qi, Hongkai Xiong | Convolutional neural networks (CNNs) designed for low-dimensional regular grids will unfortunately lead to non-optimal solutions for analyzing spherical images, due to their different geometrical properties from planar images. In this paper, we generalize the grid-based CNNs to a non-Euclidean space by taking into acco... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Rotation_Equivariant_Graph_Convolutional_Network_for_Spherical_Image_Classification_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=E9KhudotR54 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Rotation_Equivariant_Graph_Convolutional_Network_for_Spherical_Image_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Rotation_Equivariant_Graph_Convolutional_Network_for_Spherical_Image_Classification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Composing Good Shots by Exploiting Mutual Relations | Debang Li, Junge Zhang, Kaiqi Huang, Ming-Hsuan Yang | Finding views with a good composition from an input image is a common but challenging problem. There are usually at least dozens of candidates (regions) in an image, and how to evaluate these candidates is subjective. Most existing methods only use the feature corresponding to each candidate to evaluate the quality. Ho... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Composing_Good_Shots_by_Exploiting_Mutual_Relations_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Composing_Good_Shots_by_Exploiting_Mutual_Relations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Composing_Good_Shots_by_Exploiting_Mutual_Relations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Composing_Good_Shots_CVPR_2020_supplemental.pdf | null | null |
DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing | Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, Zhaopeng Cui | We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function. Due to the nature of the implicit function, the rendering process requires tremendous function queries, which is particularly problemat... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_DIST_Rendering_Deep_Implicit_Signed_Distance_Function_With_Differentiable_Sphere_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.13225 | https://www.youtube.com/watch?v=_TAlKknAqyI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_DIST_Rendering_Deep_Implicit_Signed_Distance_Function_With_Differentiable_Sphere_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_DIST_Rendering_Deep_Implicit_Signed_Distance_Function_With_Differentiable_Sphere_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_DIST_Rendering_Deep_CVPR_2020_supplemental.zip | null | null |
Progressive Relation Learning for Group Activity Recognition | Guyue Hu, Bo Cui, Yuan He, Shan Yu | Group activities usually involve spatio-temporal dynamics among many interactive individuals, while only a few participants at several key frames essentially define the activity. Therefore, effectively modeling the group-relevant and suppressing the irrelevant actions (and interactions) are vital for group activity rec... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Progressive_Relation_Learning_for_Group_Activity_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.02948 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Progressive_Relation_Learning_for_Group_Activity_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Progressive_Relation_Learning_for_Group_Activity_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hu_Progressive_Relation_Learning_CVPR_2020_supplemental.pdf | null | null |
Boundary-Aware 3D Building Reconstruction From a Single Overhead Image | Jisan Mahmud, True Price, Akash Bapat, Jan-Michael Frahm | We propose a boundary-aware multi-task deep-learning-based framework for fast 3D building modeling from a single overhead image. Unlike most existing techniques which rely on multiple images for 3D scene modeling, we seek to model the buildings in the scene from a single overhead image by jointly learning a modified si... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mahmud_Boundary-Aware_3D_Building_Reconstruction_From_a_Single_Overhead_Image_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mahmud_Boundary-Aware_3D_Building_Reconstruction_From_a_Single_Overhead_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mahmud_Boundary-Aware_3D_Building_Reconstruction_From_a_Single_Overhead_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mahmud_Boundary-Aware_3D_Building_CVPR_2020_supplemental.pdf | null | null |
Learning Formation of Physically-Based Face Attributes | Ruilong Li, Karl Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, Hao Li | Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level resolution, coupled with material attributes for use in physically-based rendering. We aim to maximize the variety of the participant's face id... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Learning_Formation_of_Physically-Based_Face_Attributes_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03458 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Learning_Formation_of_Physically-Based_Face_Attributes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Learning_Formation_of_Physically-Based_Face_Attributes_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Learning_Formation_of_CVPR_2020_supplemental.zip | null | null |
Deep Metric Learning via Adaptive Learnable Assessment | Wenzhao Zheng, Jiwen Lu, Jie Zhou | In this paper, we propose a deep metric learning via adaptive learnable assessment (DML-ALA) method for image retrieval and clustering, which aims to learn a sample assessment strategy to maximize the generalization of the trained metric. Unlike existing deep metric learning methods that usually utilize a fixed samplin... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Deep_Metric_Learning_via_Adaptive_Learnable_Assessment_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Deep_Metric_Learning_via_Adaptive_Learnable_Assessment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Deep_Metric_Learning_via_Adaptive_Learnable_Assessment_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Rethinking Computer-Aided Tuberculosis Diagnosis | Yun Liu, Yu-Huan Wu, Yunfeng Ban, Huifang Wang, Ming-Ming Cheng | As a serious infectious disease, tuberculosis (TB) is one of the major threats to human health worldwide, leading to millions of death every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. Computer-aided tubercul... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Rethinking_Computer-Aided_Tuberculosis_Diagnosis_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Rethinking_Computer-Aided_Tuberculosis_Diagnosis_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Rethinking_Computer-Aided_Tuberculosis_Diagnosis_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Creating Something From Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing | Hengtong Hu, Lingxi Xie, Richang Hong, Qi Tian | In recent years, cross-modal hashing (CMH) has attracted increasing attentions, mainly because its potential ability of mapping contents from different modalities, especially in vision and language, into the same space, so that it becomes efficient in cross-modal data retrieval. There are two main frameworks for CMH, d... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Creating_Something_From_Nothing_Unsupervised_Knowledge_Distillation_for_Cross-Modal_Hashing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00280 | https://www.youtube.com/watch?v=Io1uloVOEJk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Creating_Something_From_Nothing_Unsupervised_Knowledge_Distillation_for_Cross-Modal_Hashing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Creating_Something_From_Nothing_Unsupervised_Knowledge_Distillation_for_Cross-Modal_Hashing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Adversarial Examples Improve Image Recognition | Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, Quoc V. Le | Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional ex... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xie_Adversarial_Examples_Improve_Image_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.09665 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_Adversarial_Examples_Improve_Image_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_Adversarial_Examples_Improve_Image_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution | Yapeng Tian, Yulun Zhang, Yun Fu, Chenliang Xu | Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames). Due to varying motion of cameras or objects, the reference frame and each support frame are not ali... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tian_TDAN_Temporally-Deformable_Alignment_Network_for_Video_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/1812.02898 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tian_TDAN_Temporally-Deformable_Alignment_Network_for_Video_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tian_TDAN_Temporally-Deformable_Alignment_Network_for_Video_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Retina-Like Visual Image Reconstruction via Spiking Neural Model | Lin Zhu, Siwei Dong, Jianing Li, Tiejun Huang, Yonghong Tian | The high-sensitivity vision of primates, including humans, is mediated by a small retinal region called the fovea. As a novel bio-inspired vision sensor, spike camera mimics the fovea to record the nature scenes by continuous-time spikes instead of frame-based manner. However, reconstructing visual images from the spik... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_Retina-Like_Visual_Image_Reconstruction_via_Spiking_Neural_Model_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Retina-Like_Visual_Image_Reconstruction_via_Spiking_Neural_Model_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Retina-Like_Visual_Image_Reconstruction_via_Spiking_Neural_Model_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhu_Retina-Like_Visual_Image_CVPR_2020_supplemental.zip | null | null |
Resolution Adaptive Networks for Efficient Inference | Le Yang, Yizeng Han, Xi Chen, Shiji Song, Jifeng Dai, Gao Huang | Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks. Existing works mainly exploit architecture redundancy in network depth or width. In this paper, we focus on spatial redundancy of input samples and propose a novel Resolution Adaptive Net... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Resolution_Adaptive_Networks_for_Efficient_Inference_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07326 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Resolution_Adaptive_Networks_for_Efficient_Inference_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Resolution_Adaptive_Networks_for_Efficient_Inference_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Resolution_Adaptive_Networks_CVPR_2020_supplemental.pdf | null | null |
Normal Assisted Stereo Depth Estimation | Uday Kusupati, Shuo Cheng, Rui Chen, Hao Su | Accurate stereo depth estimation plays a critical role in various 3D tasks in both indoor and outdoor environments. Recently, learning-based multi-view stereo methods have demonstrated competitive performance with limited number of views. However, in challenging scenarios, especially when building cross-view correspond... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kusupati_Normal_Assisted_Stereo_Depth_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.10444 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kusupati_Normal_Assisted_Stereo_Depth_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kusupati_Normal_Assisted_Stereo_Depth_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kusupati_Normal_Assisted_Stereo_CVPR_2020_supplemental.pdf | null | null |
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image | Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, Manmohan Chandraker | We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we obtain a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. Our novel inverse rendering network incorporates phys... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Inverse_Rendering_for_Complex_Indoor_Scenes_Shape_Spatially-Varying_Lighting_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.02722 | https://www.youtube.com/watch?v=RvWlDWtTozw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Inverse_Rendering_for_Complex_Indoor_Scenes_Shape_Spatially-Varying_Lighting_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Inverse_Rendering_for_Complex_Indoor_Scenes_Shape_Spatially-Varying_Lighting_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Inverse_Rendering_for_CVPR_2020_supplemental.zip | null | null |
Explorable Super Resolution | Yuval Bahat, Tomer Michaeli | Single image super resolution (SR) has seen major performance leaps in recent years. However, existing methods do not allow exploring the infinitely many plausible reconstructions that might have given rise to the observed low-resolution (LR) image. These different explanations to the LR image may dramatically vary in ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bahat_Explorable_Super_Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01839 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bahat_Explorable_Super_Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bahat_Explorable_Super_Resolution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction With Deep T1 Prior | Bo Zhou, S. Kevin Zhou | MRI with multiple protocols is commonly used for diagnosis, but it suffers from a long acquisition time, which yields the image quality vulnerable to say motion artifacts. To accelerate, various methods have been proposed to reconstruct full images from under-sampled k-space data. However, these algorithms are inadequa... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_DuDoRNet_Learning_a_Dual-Domain_Recurrent_Network_for_Fast_MRI_Reconstruction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.03799 | https://www.youtube.com/watch?v=0fbHXV-ZmKY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_DuDoRNet_Learning_a_Dual-Domain_Recurrent_Network_for_Fast_MRI_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_DuDoRNet_Learning_a_Dual-Domain_Recurrent_Network_for_Fast_MRI_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_DuDoRNet_Learning_a_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting | Dongnan Liu, Donghao Zhang, Yang Song, Fan Zhang, Lauren O'Donnell, Heng Huang, Mei Chen, Weidong Cai | Unsupervised domain adaptation (UDA) for nuclei instance segmentation is important for digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift across datasets. In this work, we propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Unsupervised_Instance_Segmentation_in_Microscopy_Images_via_Panoptic_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.02066 | https://www.youtube.com/watch?v=6llwr2PWF7M | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Unsupervised_Instance_Segmentation_in_Microscopy_Images_via_Panoptic_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Unsupervised_Instance_Segmentation_in_Microscopy_Images_via_Panoptic_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Unsupervised_Instance_Segmentation_CVPR_2020_supplemental.pdf | null | null |
Heterogeneous Knowledge Distillation Using Information Flow Modeling | Nikolaos Passalis, Maria Tzelepi, Anastasios Tefas | Knowledge Distillation (KD) methods are capable of transferring the knowledge encoded in a large and complex teacher into a smaller and faster student. Early methods were usually limited to transferring the knowledge only between the last layers of the networks, while latter approaches were capable of performing multi... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Passalis_Heterogeneous_Knowledge_Distillation_Using_Information_Flow_Modeling_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.00727 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Passalis_Heterogeneous_Knowledge_Distillation_Using_Information_Flow_Modeling_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Passalis_Heterogeneous_Knowledge_Distillation_Using_Information_Flow_Modeling_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Passalis_Heterogeneous_Knowledge_Distillation_CVPR_2020_supplemental.pdf | null | null |
CARS: Continuous Evolution for Efficient Neural Architecture Search | Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, Chang Xu | Searching techniques in most of existing neural architecture search (NAS) algorithms are mainly dominated by differentiable methods for the efficiency reason. In contrast, we develop an efficient continuous evolutionary approach for searching neural networks. Architectures in the population that share parameters within... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_CARS_Continuous_Evolution_for_Efficient_Neural_Architecture_Search_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.04977 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_CARS_Continuous_Evolution_for_Efficient_Neural_Architecture_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_CARS_Continuous_Evolution_for_Efficient_Neural_Architecture_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_CARS_Continuous_Evolution_CVPR_2020_supplemental.pdf | null | null |
Bi-Directional Interaction Network for Person Search | Wenkai Dong, Zhaoxiang Zhang, Chunfeng Song, Tieniu Tan | Existing works have designed end-to-end frameworks based on Faster-RCNN for person search. Due to the large receptive fields in deep networks, the feature maps of each proposal, cropped from the stem feature maps, involve redundant context information outside the bounding boxes. However, person search is a fine-grained... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ZSTAD: Zero-Shot Temporal Activity Detection | Lingling Zhang, Xiaojun Chang, Jun Liu, Minnan Luo, Sen Wang, Zongyuan Ge, Alexander Hauptmann | An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large s... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_ZSTAD_Zero-Shot_Temporal_Activity_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.05583 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_ZSTAD_Zero-Shot_Temporal_Activity_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_ZSTAD_Zero-Shot_Temporal_Activity_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Unpaired Image Super-Resolution Using Pseudo-Supervision | Shunta Maeda | In most studies on learning-based image super-resolution (SR), the paired training dataset is created by downscaling high-resolution (HR) images with a predetermined operation (e.g., bicubic). However, these methods fail to super-resolve real-world low-resolution (LR) images, for which the degradation process is much m... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Maeda_Unpaired_Image_Super-Resolution_Using_Pseudo-Supervision_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11397 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Maeda_Unpaired_Image_Super-Resolution_Using_Pseudo-Supervision_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Maeda_Unpaired_Image_Super-Resolution_Using_Pseudo-Supervision_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Maeda_Unpaired_Image_Super-Resolution_CVPR_2020_supplemental.pdf | null | null |
Training Quantized Neural Networks With a Full-Precision Auxiliary Module | Bohan Zhuang, Lingqiao Liu, Mingkui Tan, Chunhua Shen, Ian Reid | In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function. We propose a solution by training the low-precision network with a full-precision auxiliary module. Speci... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhuang_Training_Quantized_Neural_Networks_With_a_Full-Precision_Auxiliary_Module_CVPR_2020_paper.pdf | http://arxiv.org/abs/1903.11236 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhuang_Training_Quantized_Neural_Networks_With_a_Full-Precision_Auxiliary_Module_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhuang_Training_Quantized_Neural_Networks_With_a_Full-Precision_Auxiliary_Module_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ReSprop: Reuse Sparsified Backpropagation | Negar Goli, Tor M. Aamodt | The success of Convolutional Neural Networks (CNNs) in various applications is accompanied by a significant increase in computation and training time. In this work, we focus on accelerating training by observing that about 90% of gradients are reusable during training. Leveraging this observation, we propose a new algo... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Goli_ReSprop_Reuse_Sparsified_Backpropagation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Goli_ReSprop_Reuse_Sparsified_Backpropagation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Goli_ReSprop_Reuse_Sparsified_Backpropagation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network | Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, Yanning Zhang | Blind image quality assessment (BIQA) for authentically distorted images has always been a challenging problem, since images captured in the wild include varies contents and diverse types of distortions. The vast majority of prior BIQA methods focus on how to predict synthetic image quality, but fail when applied to re... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=qksOm2bHRyg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Su_Blindly_Assess_Image_CVPR_2020_supplemental.pdf | null | null |
Bundle Adjustment on a Graph Processor | Joseph Ortiz, Mark Pupilli, Stefan Leutenegger, Andrew J. Davison | Graph processors such as Graphcore's Intelligence Processing Unit (IPU) are part of the major new wave of novel computer architecture for AI, and have a general design with massively parallel computation, distributed on-chip memory and very high inter-core communication bandwidth which allows breakthrough performance f... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ortiz_Bundle_Adjustment_on_a_Graph_Processor_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03134 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ortiz_Bundle_Adjustment_on_a_Graph_Processor_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ortiz_Bundle_Adjustment_on_a_Graph_Processor_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Multi-View Neural Human Rendering | Minye Wu, Yuehao Wang, Qiang Hu, Jingyi Yu | We present an end-to-end Neural Human Renderer (NHR) for dynamic human captures under the multi-view setting. NHR adopts PointNet++ for feature extraction (FE) to enable robust 3D correspondence matching on low quality, dynamic 3D reconstructions. To render new views, we map 3D features onto the target camera as a 2D f... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Multi-View_Neural_Human_Rendering_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Multi-View_Neural_Human_Rendering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Multi-View_Neural_Human_Rendering_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Self-Supervised Monocular Trained Depth Estimation Using Self-Attention and Discrete Disparity Volume | Adrian Johnston, Gustavo Carneiro | Monocular depth estimation has become one of the most studied applications in computer vision, where the most accurate approaches are based on fully supervised learning models. However, the acquisition of accurate and large ground truth data sets to model these fully supervised methods is a major challenge for the furt... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Johnston_Self-Supervised_Monocular_Trained_Depth_Estimation_Using_Self-Attention_and_Discrete_Disparity_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13951 | https://www.youtube.com/watch?v=_CLIht3A5pw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Johnston_Self-Supervised_Monocular_Trained_Depth_Estimation_Using_Self-Attention_and_Discrete_Disparity_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Johnston_Self-Supervised_Monocular_Trained_Depth_Estimation_Using_Self-Attention_and_Discrete_Disparity_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Johnston_Self-Supervised_Monocular_Trained_CVPR_2020_supplemental.pdf | null | null |
Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging | Zihao W. Wang, Peiqi Duan, Oliver Cossairt, Aggelos Katsaggelos, Tiejun Huang, Boxin Shi | We present a novel computational imaging system with high resolution and low noise. Our system consists of a traditional video camera which captures high-resolution intensity images, and an event camera which encodes high-speed motion as a stream of asynchronous binary events. To process the hybrid input, we propose a ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Joint_Filtering_of_Intensity_Images_and_Neuromorphic_Events_for_High-Resolution_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=hEkIgJ1eJ5g | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Joint_Filtering_of_Intensity_Images_and_Neuromorphic_Events_for_High-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Joint_Filtering_of_Intensity_Images_and_Neuromorphic_Events_for_High-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Joint_Filtering_of_CVPR_2020_supplemental.pdf | null | null |
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-Based Approach | Haichuan Yang, Shupeng Gui, Yuhao Zhu, Ji Liu | Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory and computation units. Recently, network compression through a variety of techniques such as pruning and quantization have been proposed to r... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Automatic_Neural_Network_Compression_by_Sparsity-Quantization_Joint_Learning_A_Constrained_CVPR_2020_paper.pdf | http://arxiv.org/abs/1910.05897 | https://www.youtube.com/watch?v=maeD0qvzBec | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Automatic_Neural_Network_Compression_by_Sparsity-Quantization_Joint_Learning_A_Constrained_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Automatic_Neural_Network_Compression_by_Sparsity-Quantization_Joint_Learning_A_Constrained_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
When2com: Multi-Agent Perception via Communication Graph Grouping | Yen-Cheng Liu, Junjiao Tian, Nathaniel Glaser, Zsolt Kira | While significant advances have been made for single-agent perception, many applications require multiple sensing agents and cross-agent communication due to benefits such as coverage and robustness. It is therefore critical to develop frameworks which support multi-agent collaborative perception in a distributed and b... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_When2com_Multi-Agent_Perception_via_Communication_Graph_Grouping_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.00176 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_When2com_Multi-Agent_Perception_via_Communication_Graph_Grouping_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_When2com_Multi-Agent_Perception_via_Communication_Graph_Grouping_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
MAGSAC++, a Fast, Reliable and Accurate Robust Estimator | Daniel Barath, Jana Noskova, Maksym Ivashechkin, Jiri Matas | We propose MAGSAC++ and Progressive NAPSAC sampler, P-NAPSAC in short. In MAGSAC++, we replace the model quality and polishing functions of the original method by an iteratively re-weighted least-squares fitting with weights determined via marginalizing over the noise scale. MAGSAC++ is fast -- often an order of magnit... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Barath_MAGSAC_a_Fast_Reliable_and_Accurate_Robust_Estimator_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Barath_MAGSAC_a_Fast_Reliable_and_Accurate_Robust_Estimator_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Barath_MAGSAC_a_Fast_Reliable_and_Accurate_Robust_Estimator_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Barath_MAGSAC_a_Fast_CVPR_2020_supplemental.zip | null | null |
Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search | Dazhou Guo, Dakai Jin, Zhuotun Zhu, Tsung-Ying Ho, Adam P. Harrison, Chun-Hung Chao, Jing Xiao, Le Lu | OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where inconsistencies across radiation oncologists and prohibitive labor costs motivate automated approaches. However, leading methods using standard fully convolutional network workflows that are challenged when the number of OARs becom... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Organ_at_Risk_Segmentation_for_Head_and_Neck_Cancer_Using_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.08426 | https://www.youtube.com/watch?v=MnZ366oy10Q | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Organ_at_Risk_Segmentation_for_Head_and_Neck_Cancer_Using_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Organ_at_Risk_Segmentation_for_Head_and_Neck_Cancer_Using_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guo_Organ_at_Risk_CVPR_2020_supplemental.pdf | null | null |
Learning Human-Object Interaction Detection Using Interaction Points | Tiancai Wang, Tong Yang, Martin Danelljan, Fahad Shahbaz Khan, Xiangyu Zhang, Jian Sun | Understanding interactions between humans and objects is one of the fundamental problems in visual classification and an essential step towards detailed scene understanding. Human-object interaction (HOI) detection strives to localize both the human and an object as well as the identification of complex interactions be... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Learning_Human-Object_Interaction_Detection_Using_Interaction_Points_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14023 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_Human-Object_Interaction_Detection_Using_Interaction_Points_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_Human-Object_Interaction_Detection_Using_Interaction_Points_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Kinematics Analysis for Monocular 3D Human Pose Estimation | Jingwei Xu, Zhenbo Yu, Bingbing Ni, Jiancheng Yang, Xiaokang Yang, Wenjun Zhang | For monocular 3D pose estimation conditioned on 2D detection, noisy/unreliable input is a key obstacle in this task. Simple structure constraints attempting to tackle this problem, e.g., symmetry loss and joint angle limit, could only provide marginal improvements and are commonly treated as auxiliary losses in previou... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation | Yunhan Zhao, Shu Kong, Daeyun Shin, Charless Fowlkes | Leveraging synthetically rendered data offers great potential to improve monocular depth estimation and other geometric estimation tasks, but closing the synthetic-real domain gap is a non-trivial and important task. While much recent work has focused on unsupervised domain adaptation, we consider a more realistic scen... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Domain_Decluttering_Simplifying_Images_to_Mitigate_Synthetic-Real_Domain_Shift_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12114 | https://www.youtube.com/watch?v=bQMxtAVYFrg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Domain_Decluttering_Simplifying_Images_to_Mitigate_Synthetic-Real_Domain_Shift_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Domain_Decluttering_Simplifying_Images_to_Mitigate_Synthetic-Real_Domain_Shift_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_Domain_Decluttering_Simplifying_CVPR_2020_supplemental.pdf | null | null |
End-to-End Illuminant Estimation Based on Deep Metric Learning | Bolei Xu, Jingxin Liu, Xianxu Hou, Bozhi Liu, Guoping Qiu | Previous deep learning approaches to color constancy usually directly estimate illuminant value from input image. Such approaches might suffer heavily from being sensitive to the variation of image content. To overcome this problem, we introduce a deep metric learning approach named Illuminant-Guided Triplet Network (I... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PatchVAE: Learning Local Latent Codes for Recognition | Kamal Gupta, Saurabh Singh, Abhinav Shrivastava | Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations. A promising technique for unsupervised learning is the framework of Variational Auto-encoders (VAEs). However, unsupervised representations learned by VAEs are significantly outperforme... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gupta_PatchVAE_Learning_Local_Latent_Codes_for_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03623 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gupta_PatchVAE_Learning_Local_Latent_Codes_for_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gupta_PatchVAE_Learning_Local_Latent_Codes_for_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gupta_PatchVAE_Learning_Local_CVPR_2020_supplemental.pdf | null | null |
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation | Xiang Li, Tianhan Wei, Yau Pun Chen, Yu-Wing Tai, Chi-Keung Tang | Over the past few years, we have witnessed the success of deep learning in image recognition thanks to the availability of large-scale human-annotated datasets such as PASCAL VOC, ImageNet, and COCO. Although these datasets have covered a wide range of object categories, there are still a significant number of objects ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_FSS-1000_A_1000-Class_Dataset_for_Few-Shot_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_FSS-1000_A_1000-Class_Dataset_for_Few-Shot_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_FSS-1000_A_1000-Class_Dataset_for_Few-Shot_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_FSS-1000_A_1000-Class_CVPR_2020_supplemental.zip | https://cove.thecvf.com/datasets/338 | null |
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers | Shady Abu Hussein, Tom Tirer, Raja Giryes | The single image super-resolution task is one of the most examined inverse problems in the past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior performance over alternative methods when the acquisition process uses a fixed known downscaling kernel---typically a bicubic kernel. However, seve... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Abu_Hussein_Correction_Filter_for_Single_Image_Super-Resolution_Robustifying_Off-the-Shelf_Deep_Super-Resolvers_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=r0CjykCZnlk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Abu_Hussein_Correction_Filter_for_Single_Image_Super-Resolution_Robustifying_Off-the-Shelf_Deep_Super-Resolvers_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Abu_Hussein_Correction_Filter_for_Single_Image_Super-Resolution_Robustifying_Off-the-Shelf_Deep_Super-Resolvers_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Abu_Hussein_Correction_Filter_for_CVPR_2020_supplemental.pdf | null | null |
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning | Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, Zhangyang Wang | Pretrained models from self-supervision are prevalently used in fine-tuning downstream tasks faster or for better accuracy. However, gaining robustness from pretraining is left unexplored. We introduce adversarial training into self-supervision, to provide general-purpose robust pretrained models for the first time. We... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Adversarial_Robustness_From_Self-Supervised_Pre-Training_to_Fine-Tuning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12862 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Adversarial_Robustness_From_Self-Supervised_Pre-Training_to_Fine-Tuning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Adversarial_Robustness_From_Self-Supervised_Pre-Training_to_Fine-Tuning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Adversarial_Robustness_From_CVPR_2020_supplemental.pdf | null | null |
Efficient Adversarial Training With Transferable Adversarial Examples | Haizhong Zheng, Ziqi Zhang, Juncheng Gu, Honglak Lee, Atul Prakash | Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training. In this paper, we fir... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Efficient_Adversarial_Training_With_Transferable_Adversarial_Examples_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.11969 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Efficient_Adversarial_Training_With_Transferable_Adversarial_Examples_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Efficient_Adversarial_Training_With_Transferable_Adversarial_Examples_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zheng_Efficient_Adversarial_Training_CVPR_2020_supplemental.pdf | null | null |
Adversarial Texture Optimization From RGB-D Scans | Jingwei Huang, Justus Thies, Angela Dai, Abhijit Kundu, Chiyu "Max" Jiang, Leonidas J. Guibas, Matthias Niessner, Thomas Funkhouser | Realistic color texture generation is an important step in RGB-D surface reconstruction, but remains challenging in practice due to inaccuracies in reconstructed geometry, misaligned camera poses, and view-dependent imaging artifacts. In this work, we present a novel approach for color texture generation using a condit... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Adversarial_Texture_Optimization_From_RGB-D_Scans_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Adversarial_Texture_Optimization_From_RGB-D_Scans_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Adversarial_Texture_Optimization_From_RGB-D_Scans_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huang_Adversarial_Texture_Optimization_CVPR_2020_supplemental.zip | null | null |
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization | Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo | Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Saito_PIFuHD_Multi-Level_Pixel-Aligned_Implicit_Function_for_High-Resolution_3D_Human_Digitization_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00452 | https://www.youtube.com/watch?v=ufwjC_MtF9M | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Saito_PIFuHD_Multi-Level_Pixel-Aligned_Implicit_Function_for_High-Resolution_3D_Human_Digitization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Saito_PIFuHD_Multi-Level_Pixel-Aligned_Implicit_Function_for_High-Resolution_3D_Human_Digitization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
TextureFusion: High-Quality Texture Acquisition for Real-Time RGB-D Scanning | Joo Ho Lee, Hyunho Ha, Yue Dong, Xin Tong, Min H. Kim | Real-time RGB-D scanning technique has become widely used to progressively scan objects with a hand-held sensor. Existing online methods restore color information per voxel, and thus their quality is often limited by the tradeoff between spatial resolution and time performance. Also, such methods often suffer from blur... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_TextureFusion_High-Quality_Texture_Acquisition_for_Real-Time_RGB-D_Scanning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=VnqaqkSgAQg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_TextureFusion_High-Quality_Texture_Acquisition_for_Real-Time_RGB-D_Scanning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_TextureFusion_High-Quality_Texture_Acquisition_for_Real-Time_RGB-D_Scanning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lee_TextureFusion_High-Quality_Texture_CVPR_2020_supplemental.zip | null | null |
TomoFluid: Reconstructing Dynamic Fluid From Sparse View Videos | Guangming Zang, Ramzi Idoughi, Congli Wang, Anthony Bennett, Jianguo Du, Scott Skeen, William L. Roberts, Peter Wonka, Wolfgang Heidrich | Visible light tomography is a promising and increasingly popular technique for fluid imaging. However, the use of a sparse number of viewpoints in the capturing setups makes the reconstruction of fluid flows very challenging. In this paper, we present a state-of-the-art 4D tomographic reconstruction framework that inte... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zang_TomoFluid_Reconstructing_Dynamic_Fluid_From_Sparse_View_Videos_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zang_TomoFluid_Reconstructing_Dynamic_Fluid_From_Sparse_View_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zang_TomoFluid_Reconstructing_Dynamic_Fluid_From_Sparse_View_Videos_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Point Cloud Completion by Skip-Attention Network With Hierarchical Folding | Xin Wen, Tianyang Li, Zhizhong Han, Yu-Shen Liu | Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, the global representation often suffers from the informati... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.03871 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wen_Point_Cloud_Completion_CVPR_2020_supplemental.pdf | null | null |
Revisiting Knowledge Distillation via Label Smoothing Regularization | Li Yuan, Francis EH Tay, Guilin Li, Tao Wang, Jiashi Feng | Knowledge Distillation (KD) aims to distill the knowledge of a cumbersome teacher model into a lightweight student model. Its success is generally attributed to the privileged information on similarities among categories provided by the teacher model, and in this sense, only strong teacher models are deployed to teach ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yuan_Revisiting_Knowledge_Distillation_via_Label_Smoothing_Regularization_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Revisiting_Knowledge_Distillation_via_Label_Smoothing_Regularization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Revisiting_Knowledge_Distillation_via_Label_Smoothing_Regularization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yuan_Revisiting_Knowledge_Distillation_CVPR_2020_supplemental.pdf | null | null |
Modeling Biological Immunity to Adversarial Examples | Edward Kim, Jocelyn Rego, Yijing Watkins, Garrett T. Kenyon | While deep learning continues to permeate through all fields of signal processing and machine learning, a critical exploit in these frameworks exists and remains unsolved. These exploits, or adversarial examples, are a type of signal attack that can change the output class of a classifier by perturbing the stimulus sig... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Modeling_Biological_Immunity_to_Adversarial_Examples_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Modeling_Biological_Immunity_to_Adversarial_Examples_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Modeling_Biological_Immunity_to_Adversarial_Examples_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Rethinking Differentiable Search for Mixed-Precision Neural Networks | Zhaowei Cai, Nuno Vasconcelos | Low-precision networks, with weights and activations quantized to low bit-width, are widely used to accelerate inference on edge devices. However, current solutions are uniform, using identical bit-width for all filters. This fails to account for the different sensitivities of different filters and is suboptimal. Mixed... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cai_Rethinking_Differentiable_Search_for_Mixed-Precision_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05795 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Rethinking_Differentiable_Search_for_Mixed-Precision_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Rethinking_Differentiable_Search_for_Mixed-Precision_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Wavelet Synthesis Net for Disparity Estimation to Synthesize DSLR Calibre Bokeh Effect on Smartphones | Chenchi Luo, Yingmao Li, Kaimo Lin, George Chen, Seok-Jun Lee, Jihwan Choi, Youngjun Francis Yoo, Michael O. Polley | Modern smartphone cameras can match traditional DSLR cameras in many areas thanks to the introduction of camera arrays and multi-frame processing. Among all types of DSLR effects, the narrow depth of field (DoF) or so called bokeh probably arouses most interest. Today's smartphones try to overcome the physical lens and... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_Wavelet_Synthesis_Net_for_Disparity_Estimation_to_Synthesize_DSLR_Calibre_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=tkDazJlKGlU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Wavelet_Synthesis_Net_for_Disparity_Estimation_to_Synthesize_DSLR_Calibre_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Wavelet_Synthesis_Net_for_Disparity_Estimation_to_Synthesize_DSLR_Calibre_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Luo_Wavelet_Synthesis_Net_CVPR_2020_supplemental.pdf | null | null |
Structure-Guided Ranking Loss for Single Image Depth Prediction | Ke Xian, Jianming Zhang, Oliver Wang, Long Mai, Zhe Lin, Zhiguo Cao | Single image depth prediction is a challenging task due to its ill-posed nature and challenges with capturing ground truth for supervision. Large-scale disparity data generated from stereo photos and 3D videos is a promising source of supervision, however, such disparity data can only approximate the inverse ground tru... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xian_Structure-Guided_Ranking_Loss_for_Single_Image_Depth_Prediction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xian_Structure-Guided_Ranking_Loss_for_Single_Image_Depth_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xian_Structure-Guided_Ranking_Loss_for_Single_Image_Depth_Prediction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xian_Structure-Guided_Ranking_Loss_CVPR_2020_supplemental.pdf | null | null |
Perspective Plane Program Induction From a Single Image | Yikai Li, Jiayuan Mao, Xiuming Zhang, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu | We study the inverse graphics problem of inferring a holistic representation for natural images. Given an input image, our goal is to induce a neuro-symbolic, program-like representation that jointly models camera poses, object locations, and global scene structures. Such high-level, holistic scene representations furt... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Perspective_Plane_Program_Induction_From_a_Single_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.14708 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Perspective_Plane_Program_Induction_From_a_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Perspective_Plane_Program_Induction_From_a_Single_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Perspective_Plane_Program_CVPR_2020_supplemental.pdf | null | null |
ActionBytes: Learning From Trimmed Videos to Localize Actions | Mihir Jain, Amir Ghodrati, Cees G. M. Snoek | This paper tackles the problem of localizing actions in long untrimmed videos. Different from existing works, which all use annotated untrimmed videos during training, we learn only from short trimmed videos. This enables learning from large-scale datasets originally designed for action classification. We propose a met... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jain_ActionBytes_Learning_From_Trimmed_Videos_to_Localize_Actions_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jain_ActionBytes_Learning_From_Trimmed_Videos_to_Localize_Actions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jain_ActionBytes_Learning_From_Trimmed_Videos_to_Localize_Actions_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction | Fuyang Zhang, Nelson Nauata, Yasutaka Furukawa | This paper proposes a novel message passing neural (MPN) architecture Conv-MPN, which reconstructs an outdoor building as a planar graph from a single RGB image. Conv-MPN is specifically designed for cases where nodes of a graph have explicit spatial embedding. In our problem, nodes correspond to building edges in an i... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Conv-MPN_Convolutional_Message_Passing_Neural_Network_for_Structured_Outdoor_Architecture_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=zUX3IL_t7jI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Conv-MPN_Convolutional_Message_Passing_Neural_Network_for_Structured_Outdoor_Architecture_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Conv-MPN_Convolutional_Message_Passing_Neural_Network_for_Structured_Outdoor_Architecture_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Conv-MPN_Convolutional_Message_CVPR_2020_supplemental.pdf | null | null |
Novel Object Viewpoint Estimation Through Reconstruction Alignment | Mohamed El Banani, Jason J. Corso, David F. Fouhey | The goal of this paper is to estimate the viewpoint for a novel object. Standard viewpoint estimation approaches generally fail on this task due to their reliance on a 3D model for alignment or large amounts of class-specific training data and their corresponding canonical pose. We overcome those limitations by learnin... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Banani_Novel_Object_Viewpoint_Estimation_Through_Reconstruction_Alignment_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.03586 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Banani_Novel_Object_Viewpoint_Estimation_Through_Reconstruction_Alignment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Banani_Novel_Object_Viewpoint_Estimation_Through_Reconstruction_Alignment_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Banani_Novel_Object_Viewpoint_CVPR_2020_supplemental.pdf | null | null |
PaStaNet: Toward Human Activity Knowledge Engine | Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma, Mingyang Chen, Cewu Lu | Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics. H... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_PaStaNet_Toward_Human_Activity_Knowledge_Engine_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00945 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_PaStaNet_Toward_Human_Activity_Knowledge_Engine_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_PaStaNet_Toward_Human_Activity_Knowledge_Engine_CVPR_2020_paper.html | CVPR 2020 | null | https://cove.thecvf.com/datasets/319 | null |
Dynamic Fluid Surface Reconstruction Using Deep Neural Network | Simron Thapa, Nianyi Li, Jinwei Ye | Recovering the dynamic fluid surface is a long-standing challenging problem in computer vision. Most existing image-based methods require multiple views or a dedicated imaging system. Here we present a learning-based single-image approach for 3D fluid surface reconstruction. Specifically, we design a deep neural networ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Thapa_Dynamic_Fluid_Surface_Reconstruction_Using_Deep_Neural_Network_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Thapa_Dynamic_Fluid_Surface_Reconstruction_Using_Deep_Neural_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Thapa_Dynamic_Fluid_Surface_Reconstruction_Using_Deep_Neural_Network_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Thapa_Dynamic_Fluid_Surface_CVPR_2020_supplemental.pdf | null | null |
MPM: Joint Representation of Motion and Position Map for Cell Tracking | Junya Hayashida, Kazuya Nishimura, Ryoma Bise | Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association). Most cell tracking methods perform the association task independently from the detection task. However, there is no guarantee of preserving coherence betwe... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hayashida_MPM_Joint_Representation_of_Motion_and_Position_Map_for_Cell_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10749 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hayashida_MPM_Joint_Representation_of_Motion_and_Position_Map_for_Cell_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hayashida_MPM_Joint_Representation_of_Motion_and_Position_Map_for_Cell_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
AdderNet: Do We Really Need Multiplications in Deep Learning? | Hanting Chen, Yunhe Wang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu | Compared with cheap addition operation, multiplication operation is of much higher computation complexity. The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float valu... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_AdderNet_Do_We_Really_Need_Multiplications_in_Deep_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.13200 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_AdderNet_Do_We_Really_Need_Multiplications_in_Deep_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_AdderNet_Do_We_Really_Need_Multiplications_in_Deep_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_AdderNet_Do_We_CVPR_2020_supplemental.pdf | null | null |
Adaptive Interaction Modeling via Graph Operations Search | Haoxin Li, Wei-Shi Zheng, Yu Tao, Haifeng Hu, Jian-Huang Lai | Interaction modeling is important for video action analysis. Recently, several works design specific structures to model interactions in videos. However, their structures are manually designed and non-adaptive, which require structures design efforts and more importantly could not model interactions adaptively. In this... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Adaptive_Interaction_Modeling_via_Graph_Operations_Search_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.02113 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Adaptive_Interaction_Modeling_via_Graph_Operations_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Adaptive_Interaction_Modeling_via_Graph_Operations_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Adaptive_Interaction_Modeling_CVPR_2020_supplemental.zip | null | null |
Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision | Michael Niemeyer, Lars Mescheder, Michael Oechsle, Andreas Geiger | Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these appro... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Niemeyer_Differentiable_Volumetric_Rendering_Learning_Implicit_3D_Representations_Without_3D_Supervision_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.07372 | https://www.youtube.com/watch?v=hbo7f76rTYs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Niemeyer_Differentiable_Volumetric_Rendering_Learning_Implicit_3D_Representations_Without_3D_Supervision_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Niemeyer_Differentiable_Volumetric_Rendering_Learning_Implicit_3D_Representations_Without_3D_Supervision_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising | Haokui Zhang, Ying Li, Hao Chen, Chunhua Shen | Recently, neural architecture search (NAS) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks. In this paper, we propose HiNAS (Hierarchical NAS), an effort towards employing NAS to automatically design effective neural network architectures for image... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.08228 | https://www.youtube.com/watch?v=fRzOtwBl7Eg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Memory-Efficient_Hierarchical_Neural_CVPR_2020_supplemental.pdf | null | null |
Embodied Language Grounding With 3D Visual Feature Representations | Mihir Prabhudesai, Hsiao-Yu Fish Tung, Syed Ashar Javed, Maximilian Sieb, Adam W. Harley, Katerina Fragkiadaki | We propose associating language utterances to 3D visual abstractions of the scene they describe. The 3D visual abstractions are encoded as 3-dimensional visual feature maps. We infer these 3D visual scene feature maps from RGB images of the scene via view prediction: when the generated 3D scene feature map is neurally ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Prabhudesai_Embodied_Language_Grounding_With_3D_Visual_Feature_Representations_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Prabhudesai_Embodied_Language_Grounding_With_3D_Visual_Feature_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Prabhudesai_Embodied_Language_Grounding_With_3D_Visual_Feature_Representations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Prabhudesai_Embodied_Language_Grounding_CVPR_2020_supplemental.pdf | null | null |
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching | Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, Ping Tan | The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity. These methods are limited when high-resolution outputs are needed since the memory and time costs grow cubically as the volume resolution increases. In this paper,... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gu_Cascade_Cost_Volume_for_High-Resolution_Multi-View_Stereo_and_Stereo_Matching_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.06378 | https://www.youtube.com/watch?v=rcJiRQqDKbo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Cascade_Cost_Volume_for_High-Resolution_Multi-View_Stereo_and_Stereo_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Cascade_Cost_Volume_for_High-Resolution_Multi-View_Stereo_and_Stereo_Matching_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
All in One Bad Weather Removal Using Architectural Search | Ruoteng Li, Robby T. Tan, Loong-Fah Cheong | Many methods have set state-of-the-art performance on restoring images degraded by bad weather such as rain, haze, fog, and snow, however they are designed specifically to handle one type of degradation. In this paper, we propose a method that can handle multiple bad weather degradations: rain, fog, snow and adherent r... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement | Zehao Yu, Shenghua Gao | Almost all previous deep learning-based multi-view stereo (MVS) approaches focus on improving reconstruction quality. Besides quality, efficiency is also a desirable feature for MVS in real scenarios. Towards this end, this paper presents a Fast-MVSNet, a novel sparse-to-dense coarse-to-fine framework, for fast and acc... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_Fast-MVSNet_Sparse-to-Dense_Multi-View_Stereo_With_Learned_Propagation_and_Gauss-Newton_Refinement_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=34c3Vf9g7kM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Fast-MVSNet_Sparse-to-Dense_Multi-View_Stereo_With_Learned_Propagation_and_Gauss-Newton_Refinement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Fast-MVSNet_Sparse-to-Dense_Multi-View_Stereo_With_Learned_Propagation_and_Gauss-Newton_Refinement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yu_Fast-MVSNet_Sparse-to-Dense_Multi-View_CVPR_2020_supplemental.pdf | null | null |
Auxiliary Training: Towards Accurate and Robust Models | Linfeng Zhang, Muzhou Yu, Tong Chen, Zuoqiang Shi, Chenglong Bao, Kaisheng Ma | Training process is crucial for the deployment of the network in applications which have two strict requirements on both accuracy and robustness. However, most existing approaches are in a dilemma, i.e. model accuracy and robustness form an embarrassing tradeoff - the improvement of one leads to the drop of the other. ... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Auxiliary_Training_Towards_Accurate_and_Robust_Models_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Auxiliary_Training_Towards_Accurate_and_Robust_Models_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Auxiliary_Training_Towards_Accurate_and_Robust_Models_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Cascaded Human-Object Interaction Recognition | Tianfei Zhou, Wenguan Wang, Siyuan Qi, Haibin Ling, Jianbing Shen | Rapid progress has been witnessed for human-object interaction (HOI) recognition, but most existing models are confined to single-stage reasoning pipelines. Considering the intrinsic complexity of the task, we introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instan... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Cascaded_Human-Object_Interaction_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.04262 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Cascaded_Human-Object_Interaction_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Cascaded_Human-Object_Interaction_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Holistically-Attracted Wireframe Parsing | Nan Xue, Tianfu Wu, Song Bai, Fudong Wang, Gui-Song Xia, Liangpei Zhang, Philip H.S. Torr | This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xue_Holistically-Attracted_Wireframe_Parsing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.01663 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Holistically-Attracted_Wireframe_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Holistically-Attracted_Wireframe_Parsing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing | Qibin Hou, Li Zhang, Ming-Ming Cheng, Jiashi Feng | Spatial pooling has been proven highly effective to capture long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling str... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13328 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
OASIS: A Large-Scale Dataset for Single Image 3D in the Wild | Weifeng Chen, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, Jia Deng | Single-view 3D is the task of recovering 3D properties such as depth and surface normals from a single image. We hypothesize that a major obstacle to single-image 3D is data. We address this issue by presenting Open Annotations of Single Image Surfaces (OASIS), a dataset for single-image 3D in the wild consisting of an... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_OASIS_A_Large-Scale_Dataset_for_Single_Image_3D_in_the_CVPR_2020_paper.pdf | http://arxiv.org/abs/2007.13215 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_OASIS_A_Large-Scale_Dataset_for_Single_Image_3D_in_the_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_OASIS_A_Large-Scale_Dataset_for_Single_Image_3D_in_the_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_OASIS_A_Large-Scale_CVPR_2020_supplemental.pdf | null | null |
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus | Florian Kluger, Eric Brachmann, Hanno Ackermann, Carsten Rother, Michael Ying Yang, Bodo Rosenhahn | We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scenes, fitting planes to architectural imagery, or estimating multiple rigid motions within the same sequence. In contrast to previous works, w... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.02643 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kluger_CONSAC_Robust_Multi-Model_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/315 | null |
Deep Global Registration | Christopher Choy, Wei Dong, Vladlen Koltun | We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensional convolutional network for correspondence confidence prediction, a differentiable Weighted Procrustes algorithm for closed-form pose estima... | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Choy_Deep_Global_Registration_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.11540 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Choy_Deep_Global_Registration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Choy_Deep_Global_Registration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Choy_Deep_Global_Registration_CVPR_2020_supplemental.pdf | null | null |
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