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0 | FaceForensics++: Learning to Detect Manipulated Facial Images | [
"Andreas Rossler",
"Davide Cozzolino",
"Luisa Verdoliva",
"Christian Riess",
"Justus Thies",
"Matthias Niessner"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Rossler_FaceForensics_Learning_to_Detect_Manipulated_Facial_Images_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Rossler_FaceForensics_Learning_to_Detect_Manipulated_Facial_Images_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Rossler_FaceForensics_Learning_to_ICCV_2019_supplemental.pdf | 1901.08971 | title_snapshot | @InProceedings{Rossler_2019_ICCV,author = {Rossler, Andreas and Cozzolino, Davide and Verdoliva, Luisa and Riess, Christian and Thies, Justus and Niessner, Matthias},title = {FaceForensics++: Learning to Detect Manipulated Facial Images},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper... | [
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1 | DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration | [
"Weixin Lu",
"Guowei Wan",
"Yao Zhou",
"Xiangyu Fu",
"Pengfei Yuan",
"Shiyu Song"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Lu_DeepVCP_An_End-to-End_Deep_Neural_Network_for_Point_Cloud_Registration_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Lu_DeepVCP_An_End-to-End_Deep_Neural_Network_for_Point_Cloud_Registration_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Lu_DeepVCP_An_End-to-End_ICCV_2019_supplemental.pdf | 1905.04153 | title_judge | @InProceedings{Lu_2019_ICCV,author = {Lu, Weixin and Wan, Guowei and Zhou, Yao and Fu, Xiangyu and Yuan, Pengfei and Song, Shiyu},title = {DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {Octobe... | We present DeepVCP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural net... | [
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2 | Shape Reconstruction Using Differentiable Projections and Deep Priors | [
"Matheus Gadelha",
"Rui Wang",
"Subhransu Maji"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Gadelha_Shape_Reconstruction_Using_Differentiable_Projections_and_Deep_Priors_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Gadelha_Shape_Reconstruction_Using_Differentiable_Projections_and_Deep_Priors_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Gadelha_2019_ICCV,author = {Gadelha, Matheus and Wang, Rui and Maji, Subhransu},title = {Shape Reconstruction Using Differentiable Projections and Deep Priors},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | We investigate the problem of reconstructing shapes from noisy and incomplete projections in the presence of viewpoint uncertainities. The problem is cast as an optimization over the shape given measurements obtained by a projection operator and a prior. We present differentiable projection operators for a number of re... | [
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3 | Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization | [
"Mans Larsson",
"Erik Stenborg",
"Carl Toft",
"Lars Hammarstrand",
"Torsten Sattler",
"Fredrik Kahl"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Larsson_Fine-Grained_Segmentation_Networks_Self-Supervised_Segmentation_for_Improved_Long-Term_Visual_Localization_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Larsson_Fine-Grained_Segmentation_Networks_Self-Supervised_Segmentation_for_Improved_Long-Term_Visual_Localization_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Larsson_Fine-Grained_Segmentation_Networks_ICCV_2019_supplemental.pdf | 1908.06387 | title_snapshot | @InProceedings{Larsson_2019_ICCV,author = {Larsson, Mans and Stenborg, Erik and Toft, Carl and Hammarstrand, Lars and Sattler, Torsten and Kahl, Fredrik},title = {Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization},booktitle = {Proceedings of the IEEE/CVF Interna... | Long-term visual localization is the problem of estimating the camera pose of a given query image in a scene whose appearance changes over time. It is an important problem in practice that is, for example, encountered in autonomous driving. In order to gain robustness to such changes, long-term localization approaches ... | [
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4 | SANet: Scene Agnostic Network for Camera Localization | [
"Luwei Yang",
"Ziqian Bai",
"Chengzhou Tang",
"Honghua Li",
"Yasutaka Furukawa",
"Ping Tan"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Yang_SANet_Scene_Agnostic_Network_for_Camera_Localization_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_SANet_Scene_Agnostic_Network_for_Camera_Localization_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Yang_SANet_Scene_Agnostic_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Yang_2019_ICCV,author = {Yang, Luwei and Bai, Ziqian and Tang, Chengzhou and Li, Honghua and Furukawa, Yasutaka and Tan, Ping},title = {SANet: Scene Agnostic Network for Camera Localization},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},yea... | This paper presents a scene agnostic neural architecture for camera localization, where model parameters and scenes are independent from each other.Despite recent advancement in learning based methods, most approaches require training for each scene one by one, not applicable for online applications such as SLAM and ro... | [
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5 | Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning | [
"Pedro Hermosilla",
"Tobias Ritschel",
"Timo Ropinski"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Hermosilla_Total_Denoising_Unsupervised_Learning_of_3D_Point_Cloud_Cleaning_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Hermosilla_Total_Denoising_Unsupervised_Learning_of_3D_Point_Cloud_Cleaning_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Hermosilla_Total_Denoising_Unsupervised_ICCV_2019_supplemental.pdf | 1904.07615 | title_snapshot | @InProceedings{Hermosilla_2019_ICCV,author = {Hermosilla, Pedro and Ritschel, Tobias and Ropinski, Timo},title = {Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel obse... | [
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6 | Hierarchical Self-Attention Network for Action Localization in Videos | [
"Rizard Renanda Adhi Pramono",
"Yie-Tarng Chen",
"Wen-Hsien Fang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Pramono_Hierarchical_Self-Attention_Network_for_Action_Localization_in_Videos_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Pramono_Hierarchical_Self-Attention_Network_for_Action_Localization_in_Videos_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Pramono_Hierarchical_Self-Attention_Network_ICCV_2019_supplemental.zip | null | null | @InProceedings{Pramono_2019_ICCV,author = {Pramono, Rizard Renanda Adhi and Chen, Yie-Tarng and Fang, Wen-Hsien},title = {Hierarchical Self-Attention Network for Action Localization in Videos},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | This paper presents a novel Hierarchical Self-Attention Network (HISAN) to generate spatial-temporal tubes for action localization in videos. The essence of HISAN is to combine the two-stream convolutional neural network (CNN) with hierarchical bidirectional self-attention mechanism, which comprises of two levels of bi... | [
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7 | Goal-Driven Sequential Data Abstraction | [
"Umar Riaz Muhammad",
"Yongxin Yang",
"Timothy M. Hospedales",
"Tao Xiang",
"Yi-Zhe Song"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Muhammad_Goal-Driven_Sequential_Data_Abstraction_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Muhammad_Goal-Driven_Sequential_Data_Abstraction_ICCV_2019_paper.pdf | null | 1907.12336 | title_snapshot | @InProceedings{Muhammad_2019_ICCV,author = {Muhammad, Umar Riaz and Yang, Yongxin and Hospedales, Timothy M. and Xiang, Tao and Song, Yi-Zhe},title = {Goal-Driven Sequential Data Abstraction},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Automatic data abstraction is an important capability for both benchmarking machine intelligence and supporting summarization applications. In the former one asks whether a machine can `understand' enough about the meaning of input data to produce a meaningful but more compact abstraction. In the latter this capability... | [
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8 | Jointly Aligning Millions of Images With Deep Penalised Reconstruction Congealing | [
"Roberto Annunziata",
"Christos Sagonas",
"Jacques Cali"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Annunziata_Jointly_Aligning_Millions_of_Images_With_Deep_Penalised_Reconstruction_Congealing_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Annunziata_Jointly_Aligning_Millions_of_Images_With_Deep_Penalised_Reconstruction_Congealing_ICCV_2019_paper.pdf | null | 1908.04130 | title_snapshot | @InProceedings{Annunziata_2019_ICCV,author = {Annunziata, Roberto and Sagonas, Christos and Cali, Jacques},title = {Jointly Aligning Millions of Images With Deep Penalised Reconstruction Congealing},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {20... | Extrapolating fine-grained pixel-level correspondences in a fully unsupervised manner from a large set of misaligned images can benefit several computer vision and graphics problems, e.g. co-segmentation, super-resolution, image edit propagation, structure-from-motion, and 3D reconstruction. Several joint image alignme... | [
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9 | Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation | [
"Seungmin Lee",
"Dongwan Kim",
"Namil Kim",
"Seong-Gyun Jeong"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Lee_Drop_to_Adapt_Learning_Discriminative_Features_for_Unsupervised_Domain_Adaptation_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Drop_to_Adapt_Learning_Discriminative_Features_for_Unsupervised_Domain_Adaptation_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Lee_Drop_to_Adapt_ICCV_2019_supplemental.pdf | 1910.05562 | title_snapshot | @InProceedings{Lee_2019_ICCV,author = {Lee, Seungmin and Kim, Dongwan and Kim, Namil and Jeong, Seong-Gyun},title = {Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {... | Recent works on domain adaptation exploit adversarial training to obtain domain-invariant feature representations from the joint learning of feature extractor and domain discriminator networks. However, domain adversarial methods render suboptimal performances since they attempt to match the distributions among the dom... | [
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10 | NLNL: Negative Learning for Noisy Labels | [
"Youngdong Kim",
"Junho Yim",
"Juseung Yun",
"Junmo Kim"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Kim_NLNL_Negative_Learning_for_Noisy_Labels_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Kim_NLNL_Negative_Learning_for_Noisy_Labels_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Kim_NLNL_Negative_Learning_ICCV_2019_supplemental.pdf | 1908.07387 | title_snapshot | @InProceedings{Kim_2019_ICCV,author = {Kim, Youngdong and Yim, Junho and Yun, Juseung and Kim, Junmo},title = {NLNL: Negative Learning for Noisy Labels},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Convolutional Neural Networks (CNNs) provide excellent performance when used for image classification. The classical method of training CNNs is by labeling images in a supervised manner as in "input image belongs to this label" (Positive Learning; PL), which is a fast and accurate method if the labels are assigned corr... | [
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11 | Adversarial Robustness vs. Model Compression, or Both? | [
"Shaokai Ye",
"Kaidi Xu",
"Sijia Liu",
"Hao Cheng",
"Jan-Henrik Lambrechts",
"Huan Zhang",
"Aojun Zhou",
"Kaisheng Ma",
"Yanzhi Wang",
"Xue Lin"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Ye_Adversarial_Robustness_vs._Model_Compression_or_Both_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Ye_Adversarial_Robustness_vs._Model_Compression_or_Both_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Ye_Adversarial_Robustness_vs._ICCV_2019_supplemental.pdf | 1903.12561 | title_snapshot | @InProceedings{Ye_2019_ICCV,author = {Ye, Shaokai and Xu, Kaidi and Liu, Sijia and Cheng, Hao and Lambrechts, Jan-Henrik and Zhang, Huan and Zhou, Aojun and Ma, Kaisheng and Wang, Yanzhi and Lin, Xue},title = {Adversarial Robustness vs. Model Compression, or Both?},booktitle = {Proceedings of the IEEE/CVF International... | It is well known that deep neural networks (DNNs) are vulnerable to adversarial attacks, which are implemented by adding crafted perturbations onto benign examples. Min-max robust optimization based adversarial training can provide a notion of security against adversarial attacks. However, adversarial robustness requir... | [
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12 | On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method | [
"Pu Zhao",
"Sijia Liu",
"Pin-Yu Chen",
"Nghia Hoang",
"Kaidi Xu",
"Bhavya Kailkhura",
"Xue Lin"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhao_On_the_Design_of_Black-Box_Adversarial_Examples_by_Leveraging_Gradient-Free_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhao_On_the_Design_of_Black-Box_Adversarial_Examples_by_Leveraging_Gradient-Free_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zhao_On_the_Design_ICCV_2019_supplemental.pdf | 1907.11684 | title_snapshot | @InProceedings{Zhao_2019_ICCV,author = {Zhao, Pu and Liu, Sijia and Chen, Pin-Yu and Hoang, Nghia and Xu, Kaidi and Kailkhura, Bhavya and Lin, Xue},title = {On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method},booktitle = {Proceedings of the IEEE/CVF In... | Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations. Despite the long-term vision, however, existing studies on black-box adversarial attacks... | [
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13 | DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks | [
"Sagnik Das",
"Ke Ma",
"Zhixin Shu",
"Dimitris Samaras",
"Roy Shilkrot"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Das_DewarpNet_Single-Image_Document_Unwarping_With_Stacked_3D_and_2D_Regression_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Das_DewarpNet_Single-Image_Document_Unwarping_With_Stacked_3D_and_2D_Regression_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Das_2019_ICCV,author = {Das, Sagnik and Ma, Ke and Shu, Zhixin and Samaras, Dimitris and Shilkrot, Roy},title = {DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {Oct... | Capturing document images with hand-held devices in unstructured environments is a common practice nowadays. However, "casual" photos of documents are usually unsuitable for automatic information extraction, mainly due to physical distortion of the document paper, as well as various camera positions and illumination co... | [
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14 | Learning Robust Facial Landmark Detection via Hierarchical Structured Ensemble | [
"Xu Zou",
"Sheng Zhong",
"Luxin Yan",
"Xiangyun Zhao",
"Jiahuan Zhou",
"Ying Wu"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zou_Learning_Robust_Facial_Landmark_Detection_via_Hierarchical_Structured_Ensemble_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zou_Learning_Robust_Facial_Landmark_Detection_via_Hierarchical_Structured_Ensemble_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Zou_2019_ICCV,author = {Zou, Xu and Zhong, Sheng and Yan, Luxin and Zhao, Xiangyun and Zhou, Jiahuan and Wu, Ying},title = {Learning Robust Facial Landmark Detection via Hierarchical Structured Ensemble},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = ... | Heatmap regression-based models have significantly advanced the progress of facial landmark detection. However, the lack of structural constraints always generates inaccurate heatmaps resulting in poor landmark detection performance. While hierarchical structure modeling methods have been proposed to tackle this issue,... | [
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15 | Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement | [
"Zitong Yu",
"Wei Peng",
"Xiaobai Li",
"Xiaopeng Hong",
"Guoying Zhao"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Yu_Remote_Heart_Rate_Measurement_From_Highly_Compressed_Facial_Videos_An_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Yu_Remote_Heart_Rate_Measurement_From_Highly_Compressed_Facial_Videos_An_ICCV_2019_paper.pdf | null | 1907.11921 | title_snapshot | @InProceedings{Yu_2019_ICCV,author = {Yu, Zitong and Peng, Wei and Li, Xiaobai and Hong, Xiaopeng and Zhao, Guoying},title = {Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement},booktitle = {Proceedings of the IEEE/CVF International Conference... | Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing rPPG approaches rely on analyzing very fine details of facial videos, which are prone to be affected by video compression. Here we propose a two-... | [
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16 | Face-to-Parameter Translation for Game Character Auto-Creation | [
"Tianyang Shi",
"Yi Yuan",
"Changjie Fan",
"Zhengxia Zou",
"Zhenwei Shi",
"Yong Liu"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Shi_Face-to-Parameter_Translation_for_Game_Character_Auto-Creation_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Shi_Face-to-Parameter_Translation_for_Game_Character_Auto-Creation_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Shi_Face-to-Parameter_Translation_for_ICCV_2019_supplemental.pdf | 1909.01064 | title_snapshot | @InProceedings{Shi_2019_ICCV,author = {Shi, Tianyang and Yuan, Yi and Fan, Changjie and Zou, Zhengxia and Shi, Zhenwei and Liu, Yong},title = {Face-to-Parameter Translation for Game Character Auto-Creation},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},ye... | Character customization system is an important component in Role-Playing Games (RPGs), where players are allowed to edit the facial appearance of their in-game characters with their own preferences rather than using default templates. This paper proposes a method for automatically creating in-game characters of players... | [
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17 | Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions | [
"Guha Balakrishnan",
"Adrian V. Dalca",
"Amy Zhao",
"John V. Guttag",
"Fredo Durand",
"William T. Freeman"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Balakrishnan_Visual_Deprojection_Probabilistic_Recovery_of_Collapsed_Dimensions_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Balakrishnan_Visual_Deprojection_Probabilistic_Recovery_of_Collapsed_Dimensions_ICCV_2019_paper.pdf | null | 1909.00475 | title_snapshot | @InProceedings{Balakrishnan_2019_ICCV,author = {Balakrishnan, Guha and Dalca, Adrian V. and Zhao, Amy and Guttag, John V. and Durand, Fredo and Freeman, William T.},title = {Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer... | We introduce visual deprojection: the task of recovering an image or video that has been collapsed along a dimension. Projections arise in various contexts, such as long-exposure photography, where a dynamic scene is collapsed in time to produce a motion-blurred image, and corner cameras, where reflected light from a s... | [
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18 | StructureFlow: Image Inpainting via Structure-Aware Appearance Flow | [
"Yurui Ren",
"Xiaoming Yu",
"Ruonan Zhang",
"Thomas H. Li",
"Shan Liu",
"Ge Li"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Ren_StructureFlow_Image_Inpainting_via_Structure-Aware_Appearance_Flow_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Ren_StructureFlow_Image_Inpainting_via_Structure-Aware_Appearance_Flow_ICCV_2019_paper.pdf | null | 1908.03852 | title_snapshot | @InProceedings{Ren_2019_ICCV,author = {Ren, Yurui and Yu, Xiaoming and Zhang, Ruonan and Li, Thomas H. and Liu, Shan and Li, Ge},title = {StructureFlow: Image Inpainting via Structure-Aware Appearance Flow},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},ye... | Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting tas... | [
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19 | Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization | [
"Md Mahfuzur Rahman Siddiquee",
"Zongwei Zhou",
"Nima Tajbakhsh",
"Ruibin Feng",
"Michael B. Gotway",
"Yoshua Bengio",
"Jianming Liang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Siddiquee_Learning_Fixed_Points_in_Generative_Adversarial_Networks_From_Image-to-Image_Translation_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Siddiquee_Learning_Fixed_Points_in_Generative_Adversarial_Networks_From_Image-to-Image_Translation_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Siddiquee_Learning_Fixed_Points_ICCV_2019_supplemental.pdf | 1908.06965 | title_snapshot | @InProceedings{Siddiquee_2019_ICCV,author = {Siddiquee, Md Mahfuzur Rahman and Zhou, Zongwei and Tajbakhsh, Nima and Feng, Ruibin and Gotway, Michael B. and Bengio, Yoshua and Liang, Jianming},title = {Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Loc... | Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN "virtually heal" an... | [
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20 | Generative Adversarial Training for Weakly Supervised Cloud Matting | [
"Zhengxia Zou",
"Wenyuan Li",
"Tianyang Shi",
"Zhenwei Shi",
"Jieping Ye"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zou_Generative_Adversarial_Training_for_Weakly_Supervised_Cloud_Matting_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zou_Generative_Adversarial_Training_for_Weakly_Supervised_Cloud_Matting_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zou_Generative_Adversarial_Training_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Zou_2019_ICCV,author = {Zou, Zhengxia and Li, Wenyuan and Shi, Tianyang and Shi, Zhenwei and Ye, Jieping},title = {Generative Adversarial Training for Weakly Supervised Cloud Matting},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {20... | The detection and removal of cloud in remote sensing images are essential for earth observation applications. Most previous methods consider cloud detection as a pixel-wise semantic segmentation process (cloud v.s. background), which inevitably leads to a category-ambiguity problem when dealing with semi-transparent cl... | [
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21 | PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data | [
"Zheng Tang",
"Milind Naphade",
"Stan Birchfield",
"Jonathan Tremblay",
"William Hodge",
"Ratnesh Kumar",
"Shuo Wang",
"Xiaodong Yang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Tang_PAMTRI_Pose-Aware_Multi-Task_Learning_for_Vehicle_Re-Identification_Using_Highly_Randomized_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Tang_PAMTRI_Pose-Aware_Multi-Task_Learning_for_Vehicle_Re-Identification_Using_Highly_Randomized_ICCV_2019_paper.pdf | null | 2005.00673 | title_snapshot | @InProceedings{Tang_2019_ICCV,author = {Tang, Zheng and Naphade, Milind and Birchfield, Stan and Tremblay, Jonathan and Hodge, William and Kumar, Ratnesh and Wang, Shuo and Yang, Xiaodong},title = {PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data},booktitle = {... | In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention. Vehicle ReID is challenging due to 1) high intra-class variability (caused by the dependency of shape and appearance on viewpoint), and 2) small inter-class variability ... | [
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22 | Generative Adversarial Networks for Extreme Learned Image Compression | [
"Eirikur Agustsson",
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"Fabian Mentzer",
"Radu Timofte",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Agustsson_Generative_Adversarial_Networks_for_Extreme_Learned_Image_Compression_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Agustsson_Generative_Adversarial_Networks_for_Extreme_Learned_Image_Compression_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Agustsson_Generative_Adversarial_Networks_ICCV_2019_supplemental.pdf | 1804.02958 | title_snapshot | @InProceedings{Agustsson_2019_ICCV,author = {Agustsson, Eirikur and Tschannen, Michael and Mentzer, Fabian and Timofte, Radu and Gool, Luc Van},title = {Generative Adversarial Networks for Extreme Learned Image Compression},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},mon... | We present a learned image compression system based on GANs, operating at extremely low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi-scale discriminator, which we train jointly for a generative learned compression objective. The model synthesizes details it cannot afford to store,... | [
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23 | Instance-Guided Context Rendering for Cross-Domain Person Re-Identification | [
"Yanbei Chen",
"Xiatian Zhu",
"Shaogang Gong"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Instance-Guided_Context_Rendering_for_Cross-Domain_Person_Re-Identification_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Instance-Guided_Context_Rendering_for_Cross-Domain_Person_Re-Identification_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Chen_Instance-Guided_Context_Rendering_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Chen_2019_ICCV,author = {Chen, Yanbei and Zhu, Xiatian and Gong, Shaogang},title = {Instance-Guided Context Rendering for Cross-Domain Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Existing person re-identification (re-id) methods mostly assume the availability of large-scale identity labels for model learning in any target domain deployment. This greatly limits their scalability in practice. To tackle this limitation, we propose a novel Instance-Guided Context Rendering scheme, which transfers t... | [
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24 | What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance | [
"Mahmoud Afifi",
"Michael S. Brown"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Afifi_What_Else_Can_Fool_Deep_Learning_Addressing_Color_Constancy_Errors_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Afifi_What_Else_Can_Fool_Deep_Learning_Addressing_Color_Constancy_Errors_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Afifi_What_Else_Can_ICCV_2019_supplemental.pdf | 1912.06960 | title_snapshot | @InProceedings{Afifi_2019_ICCV,author = {Afifi, Mahmoud and Brown, Michael S.},title = {What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | There is active research targeting local image manipulations that can fool deep neural networks (DNNs) into producing incorrect results. This paper examines a type of global image manipulation that can produce similar adverse effects. Specifically, we explore how strong color casts caused by incorrectly applied computa... | [
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25 | Beyond Cartesian Representations for Local Descriptors | [
"Patrick Ebel",
"Anastasiia Mishchuk",
"Kwang Moo Yi",
"Pascal Fua",
"Eduard Trulls"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Ebel_Beyond_Cartesian_Representations_for_Local_Descriptors_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Ebel_Beyond_Cartesian_Representations_for_Local_Descriptors_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Ebel_Beyond_Cartesian_Representations_ICCV_2019_supplemental.pdf | 1908.05547 | title_snapshot | @InProceedings{Ebel_2019_ICCV,author = {Ebel, Patrick and Mishchuk, Anastasiia and Yi, Kwang Moo and Fua, Pascal and Trulls, Eduard},title = {Beyond Cartesian Representations for Local Descriptors},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {201... | The dominant approach for learning local patch descriptors relies on small image regions whose scale must be properly estimated a priori by a keypoint detector. In other words, if two patches are not in correspondence, their descriptors will not match. A strategy often used to alleviate this problem is to "pool" the pi... | [
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26 | Distilling Knowledge From a Deep Pose Regressor Network | [
"Muhamad Risqi U. Saputra",
"Pedro P. B. de Gusmao",
"Yasin Almalioglu",
"Andrew Markham",
"Niki Trigoni"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Saputra_Distilling_Knowledge_From_a_Deep_Pose_Regressor_Network_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Saputra_Distilling_Knowledge_From_a_Deep_Pose_Regressor_Network_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Saputra_Distilling_Knowledge_From_ICCV_2019_supplemental.pdf | 1908.00858 | title_snapshot | @InProceedings{Saputra_2019_ICCV,author = {Saputra, Muhamad Risqi U. and Gusmao, Pedro P. B. de and Almalioglu, Yasin and Markham, Andrew and Trigoni, Niki},title = {Distilling Knowledge From a Deep Pose Regressor Network},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},mont... | This paper presents a novel method to distill knowledge from a deep pose regressor network for efficient Visual Odometry (VO). Standard distillation relies on "dark knowledge" for successful knowledge transfer. As this knowledge is not available in pose regression and the teacher prediction is not always accurate, we p... | [
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27 | Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression | [
"Kyung-Rae Kim",
"Whan Choi",
"Yeong Jun Koh",
"Seong-Gyun Jeong",
"Chang-Su Kim"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Kim_Instance-Level_Future_Motion_Estimation_in_a_Single_Image_Based_on_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Kim_Instance-Level_Future_Motion_Estimation_in_a_Single_Image_Based_on_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Kim_Instance-Level_Future_Motion_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Kim_2019_ICCV,author = {Kim, Kyung-Rae and Choi, Whan and Koh, Yeong Jun and Jeong, Seong-Gyun and Kim, Chang-Su},title = {Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},mo... | A novel algorithm to estimate instance-level future motion in a single image is proposed in this paper. We first represent the future motion of an instance with its direction, speed, and action classes. Then, we develop a deep neural network that exploits different levels of semantic information to perform the future m... | [
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28 | Vision-Infused Deep Audio Inpainting | [
"Hang Zhou",
"Ziwei Liu",
"Xudong Xu",
"Ping Luo",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Vision-Infused_Deep_Audio_Inpainting_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Vision-Infused_Deep_Audio_Inpainting_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zhou_Vision-Infused_Deep_Audio_ICCV_2019_supplemental.pdf | 1910.10997 | title_snapshot | @InProceedings{Zhou_2019_ICCV,author = {Zhou, Hang and Liu, Ziwei and Xu, Xudong and Luo, Ping and Wang, Xiaogang},title = {Vision-Infused Deep Audio Inpainting},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Multi-modality perception is essential to develop interactive intelligence. In this work, we consider a new task of visual information-infused audio inpainting, i.e., synthesizing missing audio segments that correspond to their accompanying videos. We identify two key aspects for a successful inpainter: (1) It is desir... | [
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29 | HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision | [
"Zhen Dong",
"Zhewei Yao",
"Amir Gholami",
"Michael W. Mahoney",
"Kurt Keutzer"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Dong_HAWQ_Hessian_AWare_Quantization_of_Neural_Networks_With_Mixed-Precision_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Dong_HAWQ_Hessian_AWare_Quantization_of_Neural_Networks_With_Mixed-Precision_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Dong_HAWQ_Hessian_AWare_ICCV_2019_supplemental.pdf | 1905.03696 | title_snapshot | @InProceedings{Dong_2019_ICCV,author = {Dong, Zhen and Yao, Zhewei and Gholami, Amir and Mahoney, Michael W. and Keutzer, Kurt},title = {HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October... | Model size and inference speed/power have become a major challenge in the deployment of neural networks for many applications. A promising approach to address these problems is quantization. However, uniformly quantizing a model to ultra-low precision leads to significant accuracy degradation. A novel solution for this... | [
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30 | Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks | [
"Jun-Ho Choi",
"Huan Zhang",
"Jun-Hyuk Kim",
"Cho-Jui Hsieh",
"Jong-Seok Lee"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Evaluating_Robustness_of_Deep_Image_Super-Resolution_Against_Adversarial_Attacks_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Choi_Evaluating_Robustness_of_Deep_Image_Super-Resolution_Against_Adversarial_Attacks_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Choi_Evaluating_Robustness_of_ICCV_2019_supplemental.pdf | 1904.06097 | title_snapshot | @InProceedings{Choi_2019_ICCV,author = {Choi, Jun-Ho and Zhang, Huan and Kim, Jun-Hyuk and Hsieh, Cho-Jui and Lee, Jong-Seok},title = {Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {O... | Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many image processing applications. This paper investigates the robustness of deep learning-based super-resolution methods against adversarial attacks, which can significantly de... | [
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31 | Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild | [
"Kibok Lee",
"Kimin Lee",
"Jinwoo Shin",
"Honglak Lee"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Lee_Overcoming_Catastrophic_Forgetting_With_Unlabeled_Data_in_the_Wild_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Overcoming_Catastrophic_Forgetting_With_Unlabeled_Data_in_the_Wild_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Lee_Overcoming_Catastrophic_Forgetting_ICCV_2019_supplemental.pdf | 1903.12648 | title_snapshot | @InProceedings{Lee_2019_ICCV,author = {Lee, Kibok and Lee, Kimin and Shin, Jinwoo and Lee, Honglak},title = {Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Lifelong learning with deep neural networks is well-known to suffer from catastrophic forgetting: the performance on previous tasks drastically degrades when learning a new task. To alleviate this effect, we propose to leverage a large stream of unlabeled data easily obtainable in the wild. In particular, we design a n... | [
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32 | Symmetric Cross Entropy for Robust Learning With Noisy Labels | [
"Yisen Wang",
"Xingjun Ma",
"Zaiyi Chen",
"Yuan Luo",
"Jinfeng Yi",
"James Bailey"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Symmetric_Cross_Entropy_for_Robust_Learning_With_Noisy_Labels_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Symmetric_Cross_Entropy_for_Robust_Learning_With_Noisy_Labels_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Wang_Symmetric_Cross_Entropy_ICCV_2019_supplemental.pdf | 1908.06112 | title_snapshot | @InProceedings{Wang_2019_ICCV,author = {Wang, Yisen and Ma, Xingjun and Chen, Zaiyi and Luo, Yuan and Yi, Jinfeng and Bailey, James},title = {Symmetric Cross Entropy for Robust Learning With Noisy Labels},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year... | Training accurate deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Though a number of approaches have been proposed for learning with noisy labels, many open issues remain. In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy lab... | [
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33 | Few-Shot Learning With Embedded Class Models and Shot-Free Meta Training | [
"Avinash Ravichandran",
"Rahul Bhotika",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Ravichandran_Few-Shot_Learning_With_Embedded_Class_Models_and_Shot-Free_Meta_Training_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Ravichandran_Few-Shot_Learning_With_Embedded_Class_Models_and_Shot-Free_Meta_Training_ICCV_2019_paper.pdf | null | 1905.04398 | title_snapshot | @InProceedings{Ravichandran_2019_ICCV,author = {Ravichandran, Avinash and Bhotika, Rahul and Soatto, Stefano},title = {Few-Shot Learning With Embedded Class Models and Shot-Free Meta Training},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | We propose a method for learning embeddings for few-shot learning that is suitable for use with any number of shots (shot-free). Rather than fixing the class prototypes to be the Euclidean average of sample embeddings, we allow them to live in a higher-dimensional space (embedded class models) and learn the prototypes ... | [
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34 | Dual Directed Capsule Network for Very Low Resolution Image Recognition | [
"Maneet Singh",
"Shruti Nagpal",
"Richa Singh",
"Mayank Vatsa"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Singh_Dual_Directed_Capsule_Network_for_Very_Low_Resolution_Image_Recognition_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Singh_Dual_Directed_Capsule_Network_for_Very_Low_Resolution_Image_Recognition_ICCV_2019_paper.pdf | null | 1908.10027 | title_snapshot | @InProceedings{Singh_2019_ICCV,author = {Singh, Maneet and Nagpal, Shruti and Singh, Richa and Vatsa, Mayank},title = {Dual Directed Capsule Network for Very Low Resolution Image Recognition},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Very low resolution (VLR) image recognition corresponds to classifying images with resolution 16x16 or less. Though it has widespread applicability when objects are captured at a very large stand-off distance (e.g. surveillance scenario) or from wide angle mobile cameras, it has received limited attention. This researc... | [
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35 | Recognizing Part Attributes With Insufficient Data | [
"Xiangyun Zhao",
"Yi Yang",
"Feng Zhou",
"Xiao Tan",
"Yuchen Yuan",
"Yingze Bao",
"Ying Wu"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhao_Recognizing_Part_Attributes_With_Insufficient_Data_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhao_Recognizing_Part_Attributes_With_Insufficient_Data_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zhao_Recognizing_Part_Attributes_ICCV_2019_supplemental.pdf | 1908.03335 | title_snapshot | @InProceedings{Zhao_2019_ICCV,author = {Zhao, Xiangyun and Yang, Yi and Zhou, Feng and Tan, Xiao and Yuan, Yuchen and Bao, Yingze and Wu, Ying},title = {Recognizing Part Attributes With Insufficient Data},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year... | Recognizing the attributes of objects and their parts is central to many computer vision applications. Although great progress has been made to apply object-level recognition, recognizing the attributes of parts remains less applicable since the training data for part attributes recognition is usually scarce especially... | [
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36 | USIP: Unsupervised Stable Interest Point Detection From 3D Point Clouds | [
"Jiaxin Li",
"Gim Hee Lee"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Li_USIP_Unsupervised_Stable_Interest_Point_Detection_From_3D_Point_Clouds_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Li_USIP_Unsupervised_Stable_Interest_Point_Detection_From_3D_Point_Clouds_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Li_USIP_Unsupervised_Stable_ICCV_2019_supplemental.pdf | 1904.00229 | title_snapshot | @InProceedings{Li_2019_ICCV,author = {Li, Jiaxin and Lee, Gim Hee},title = {USIP: Unsupervised Stable Interest Point Detection From 3D Point Clouds},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | In this paper, we propose the USIP detector: an Unsupervised Stable Interest Point detector that can detect highly repeatable and accurately localized keypoints from 3D point clouds under arbitrary transformations without the need for any ground truth training data. Our USIP detector consists of a feature proposal netw... | [
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37 | Mixed High-Order Attention Network for Person Re-Identification | [
"Binghui Chen",
"Weihong Deng",
"Jiani Hu"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Mixed_High-Order_Attention_Network_for_Person_Re-Identification_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Mixed_High-Order_Attention_Network_for_Person_Re-Identification_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Chen_Mixed_High-Order_Attention_ICCV_2019_supplemental.pdf | 1908.05819 | title_snapshot | @InProceedings{Chen_2019_ICCV,author = {Chen, Binghui and Deng, Weihong and Hu, Jiani},title = {Mixed High-Order Attention Network for Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Attention has become more attractive in person re-identification (ReID) as it is capable of biasing the allocation of available resources towards the most informative parts of an input signal. However, state-of-the-art works concentrate only on coarse or first-order attention design, e.g. spatial and channels attention... | [
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38 | Budget-Aware Adapters for Multi-Domain Learning | [
"Rodrigo Berriel",
"Stephane Lathuillere",
"Moin Nabi",
"Tassilo Klein",
"Thiago Oliveira-Santos",
"Nicu Sebe",
"Elisa Ricci"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Berriel_Budget-Aware_Adapters_for_Multi-Domain_Learning_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Berriel_Budget-Aware_Adapters_for_Multi-Domain_Learning_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Berriel_Budget-Aware_Adapters_for_ICCV_2019_supplemental.pdf | 1905.06242 | title_snapshot | @InProceedings{Berriel_2019_ICCV,author = {Berriel, Rodrigo and Lathuillere, Stephane and Nabi, Moin and Klein, Tassilo and Oliveira-Santos, Thiago and Sebe, Nicu and Ricci, Elisa},title = {Budget-Aware Adapters for Multi-Domain Learning},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vis... | Multi-Domain Learning (MDL) refers to the problem of learning a set of models derived from a common deep architecture, each one specialized to perform a task in a certain domain (e.g., photos, sketches, paintings). This paper tackles MDL with a particular interest in obtaining domain-specific models with an adjustable ... | [
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39 | Compact Trilinear Interaction for Visual Question Answering | [
"Tuong Do",
"Thanh-Toan Do",
"Huy Tran",
"Erman Tjiputra",
"Quang D. Tran"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Do_Compact_Trilinear_Interaction_for_Visual_Question_Answering_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Do_Compact_Trilinear_Interaction_for_Visual_Question_Answering_ICCV_2019_paper.pdf | null | 1909.11874 | title_snapshot | @InProceedings{Do_2019_ICCV,author = {Do, Tuong and Do, Thanh-Toan and Tran, Huy and Tjiputra, Erman and Tran, Quang D.},title = {Compact Trilinear Interaction for Visual Question Answering},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | In Visual Question Answering (VQA), answers have a great correlation with question meaning and visual contents. Thus, to selectively utilize image, question and answer information, we propose a novel trilinear interaction model which simultaneously learns high level associations between these three inputs. In addition,... | [
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40 | Towards Latent Attribute Discovery From Triplet Similarities | [
"Ishan Nigam",
"Pavel Tokmakov",
"Deva Ramanan"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Nigam_Towards_Latent_Attribute_Discovery_From_Triplet_Similarities_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Nigam_Towards_Latent_Attribute_Discovery_From_Triplet_Similarities_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Nigam_Towards_Latent_Attribute_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Nigam_2019_ICCV,author = {Nigam, Ishan and Tokmakov, Pavel and Ramanan, Deva},title = {Towards Latent Attribute Discovery From Triplet Similarities},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | This paper addresses the task of learning latent attributes from triplet similarity comparisons. Consider, for instance, the three shoes in Fig. 1(a). They can be compared according to color, comfort, size, or shape resulting in different rankings. Most approaches for embedding learning either make a simplifying assump... | [
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41 | GeoStyle: Discovering Fashion Trends and Events | [
"Utkarsh Mall",
"Kevin Matzen",
"Bharath Hariharan",
"Noah Snavely",
"Kavita Bala"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Mall_GeoStyle_Discovering_Fashion_Trends_and_Events_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Mall_GeoStyle_Discovering_Fashion_Trends_and_Events_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Mall_GeoStyle_Discovering_Fashion_ICCV_2019_supplemental.pdf | 1908.11412 | title_snapshot | @InProceedings{Mall_2019_ICCV,author = {Mall, Utkarsh and Matzen, Kevin and Hariharan, Bharath and Snavely, Noah and Bala, Kavita},title = {GeoStyle: Discovering Fashion Trends and Events},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Understanding fashion styles and trends is of great potential interest to retailers and consumers alike. The photos people upload to social media are a historical and public data source of how people dress across the world and at different times. While we now have tools to automatically recognize the clothing and style... | [
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42 | Towards Adversarially Robust Object Detection | [
"Haichao Zhang",
"Jianyu Wang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Towards_Adversarially_Robust_Object_Detection_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Towards_Adversarially_Robust_Object_Detection_ICCV_2019_paper.pdf | null | 1907.10310 | title_snapshot | @InProceedings{Zhang_2019_ICCV,author = {Zhang, Haichao and Wang, Jianyu},title = {Towards Adversarially Robust Object Detection},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Object detection is an important vision task and has emerged as an indispensable component in many vision system, rendering its robustness as an increasingly important performance factor for practical applications. While object detection models have been demonstrated to be vulnerable against adversarial attacks by many... | [
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43 | Automatic and Robust Skull Registration Based on Discrete Uniformization | [
"Junli Zhao",
"Xin Qi",
"Chengfeng Wen",
"Na Lei",
"Xianfeng Gu"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhao_Automatic_and_Robust_Skull_Registration_Based_on_Discrete_Uniformization_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhao_Automatic_and_Robust_Skull_Registration_Based_on_Discrete_Uniformization_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zhao_Automatic_and_Robust_ICCV_2019_supplemental.zip | null | null | @InProceedings{Zhao_2019_ICCV,author = {Zhao, Junli and Qi, Xin and Wen, Chengfeng and Lei, Na and Gu, Xianfeng},title = {Automatic and Robust Skull Registration Based on Discrete Uniformization},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}... | Skull registration plays a fundamental role in forensic science and is crucial for craniofacial reconstruction. The complicated topology, lack of anatomical features, and low quality reconstructed mesh make skull registration challenging. In this work, we propose an automatic skull registration method based on the disc... | [
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44 | Few-Shot Image Recognition With Knowledge Transfer | [
"Zhimao Peng",
"Zechao Li",
"Junge Zhang",
"Yan Li",
"Guo-Jun Qi",
"Jinhui Tang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Peng_Few-Shot_Image_Recognition_With_Knowledge_Transfer_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Peng_Few-Shot_Image_Recognition_With_Knowledge_Transfer_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Peng_2019_ICCV,author = {Peng, Zhimao and Li, Zechao and Zhang, Junge and Li, Yan and Qi, Guo-Jun and Tang, Jinhui},title = {Few-Shot Image Recognition With Knowledge Transfer},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Human can well recognize images of novel categories just after browsing few examples of these categories. One possible reason is that they have some external discriminative visual information about these categories from their prior knowledge. Inspired from this, we propose a novel Knowledge Transfer Network architectur... | [
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45 | Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings | [
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"Dima Damen"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Wray_Fine-Grained_Action_Retrieval_Through_Multiple_Parts-of-Speech_Embeddings_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Wray_Fine-Grained_Action_Retrieval_Through_Multiple_Parts-of-Speech_Embeddings_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Wray_Fine-Grained_Action_Retrieval_ICCV_2019_supplemental.zip | 1908.03477 | title_snapshot | @InProceedings{Wray_2019_ICCV,author = {Wray, Michael and Larlus, Diane and Csurka, Gabriela and Damen, Dima},title = {Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | We address the problem of cross-modal fine-grained action retrieval between text and video. Cross-modal retrieval is commonly achieved through learning a shared embedding space, that can indifferently embed modalities. In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accom... | [
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46 | Vehicle Re-Identification in Aerial Imagery: Dataset and Approach | [
"Peng Wang",
"Bingliang Jiao",
"Lu Yang",
"Yifei Yang",
"Shizhou Zhang",
"Wei Wei",
"Yanning Zhang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Vehicle_Re-Identification_in_Aerial_Imagery_Dataset_and_Approach_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Vehicle_Re-Identification_in_Aerial_Imagery_Dataset_and_Approach_ICCV_2019_paper.pdf | null | 1904.01400 | title_snapshot | @InProceedings{Wang_2019_ICCV,author = {Wang, Peng and Jiao, Bingliang and Yang, Lu and Yang, Yifei and Zhang, Shizhou and Wei, Wei and Zhang, Yanning},title = {Vehicle Re-Identification in Aerial Imagery: Dataset and Approach},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}... | In this work, we construct a large-scale dataset for vehicle re-identification (ReID), which contains 137k images of 13k vehicle instances captured by UAV-mounted cameras. To our knowledge, it is the largest UAV-based vehicle ReID dataset. To increase intra-class variation, each vehicle is captured by at least two UAVs... | [
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47 | Bridging the Domain Gap for Ground-to-Aerial Image Matching | [
"Krishna Regmi",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Regmi_Bridging_the_Domain_Gap_for_Ground-to-Aerial_Image_Matching_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Regmi_Bridging_the_Domain_Gap_for_Ground-to-Aerial_Image_Matching_ICCV_2019_paper.pdf | null | 1904.11045 | title_snapshot | @InProceedings{Regmi_2019_ICCV,author = {Regmi, Krishna and Shah, Mubarak},title = {Bridging the Domain Gap for Ground-to-Aerial Image Matching},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | The visual entities in cross-view (e.g. ground and aerial) images exhibit drastic domain changes due to the differences in viewpoints each set of images is captured from. Existing state-of-the-art methods address the problem by learning view-invariant images descriptors. We propose a novel method for solving this task ... | [
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48 | A Robust Learning Approach to Domain Adaptive Object Detection | [
"Mehran Khodabandeh",
"Arash Vahdat",
"Mani Ranjbar",
"William G. Macready"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Khodabandeh_A_Robust_Learning_Approach_to_Domain_Adaptive_Object_Detection_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Khodabandeh_A_Robust_Learning_Approach_to_Domain_Adaptive_Object_Detection_ICCV_2019_paper.pdf | null | 1904.02361 | title_snapshot | @InProceedings{Khodabandeh_2019_ICCV,author = {Khodabandeh, Mehran and Vahdat, Arash and Ranjbar, Mani and Macready, William G.},title = {A Robust Learning Approach to Domain Adaptive Object Detection},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = ... | Domain shift is unavoidable in real-world applications of object detection. For example, in self-driving cars, the target domain consists of unconstrained road environments which cannot all possibly be observed in training data. Similarly, in surveillance applications sufficiently representative training data may be la... | [
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49 | Graph-Based Object Classification for Neuromorphic Vision Sensing | [
"Yin Bi",
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"Eirina Bourtsoulatze",
"Yiannis Andreopoulos"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Bi_Graph-Based_Object_Classification_for_Neuromorphic_Vision_Sensing_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Bi_Graph-Based_Object_Classification_for_Neuromorphic_Vision_Sensing_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Bi_Graph-Based_Object_Classification_ICCV_2019_supplemental.pdf | 1908.06648 | title_snapshot | @InProceedings{Bi_2019_ICCV,author = {Bi, Yin and Chadha, Aaron and Abbas, Alhabib and Bourtsoulatze, Eirina and Andreopoulos, Yiannis},title = {Graph-Based Object Classification for Neuromorphic Vision Sensing},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {Octobe... | Neuromorphic vision sensing (NVS) devices represent visual information as sequences of asynchronous discrete events (a.k.a., "spikes'") in response to changes in scene reflectance. Unlike conventional active pixel sensing (APS), NVS allows for significantly higher event sampling rates at substantially increased energy ... | [
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50 | Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving | [
"Jiwoong Choi",
"Dayoung Chun",
"Hyun Kim",
"Hyuk-Jae Lee"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Gaussian_YOLOv3_An_Accurate_and_Fast_Object_Detector_Using_Localization_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Choi_Gaussian_YOLOv3_An_Accurate_and_Fast_Object_Detector_Using_Localization_ICCV_2019_paper.pdf | null | 1904.04620 | title_snapshot | @InProceedings{Choi_2019_ICCV,author = {Choi, Jiwoong and Chun, Dayoung and Kim, Hyun and Lee, Hyuk-Jae},title = {Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},mont... | The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder sa... | [
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51 | Sharpen Focus: Learning With Attention Separability and Consistency | [
"Lezi Wang",
"Ziyan Wu",
"Srikrishna Karanam",
"Kuan-Chuan Peng",
"Rajat Vikram Singh",
"Bo Liu",
"Dimitris N. Metaxas"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Sharpen_Focus_Learning_With_Attention_Separability_and_Consistency_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Sharpen_Focus_Learning_With_Attention_Separability_and_Consistency_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Wang_Sharpen_Focus_Learning_ICCV_2019_supplemental.pdf | 1811.07484 | title_snapshot | @InProceedings{Wang_2019_ICCV,author = {Wang, Lezi and Wu, Ziyan and Karanam, Srikrishna and Peng, Kuan-Chuan and Singh, Rajat Vikram and Liu, Bo and Metaxas, Dimitris N.},title = {Sharpen Focus: Learning With Attention Separability and Consistency},booktitle = {Proceedings of the IEEE/CVF International Conference on C... | Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks. Despite good localization for an individual class of interest, these techniques produce attention maps with substantially overlapping responses among different clas... | [
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52 | Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition | [
"Tianshui Chen",
"Muxin Xu",
"Xiaolu Hui",
"Hefeng Wu",
"Liang Lin"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Learning_Semantic-Specific_Graph_Representation_for_Multi-Label_Image_Recognition_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Learning_Semantic-Specific_Graph_Representation_for_Multi-Label_Image_Recognition_ICCV_2019_paper.pdf | null | 1908.07325 | title_snapshot | @InProceedings{Chen_2019_ICCV,author = {Chen, Tianshui and Xu, Muxin and Hui, Xiaolu and Wu, Hefeng and Lin, Liang},title = {Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},y... | Recognizing multiple labels of images is a practical and challenging task, and significant progress has been made by searching semantic-aware regions and modeling label dependency. However, current methods cannot locate the semantic regions accurately due to the lack of part-level supervision or semantic guidance. More... | [
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53 | DeceptionNet: Network-Driven Domain Randomization | [
"Sergey Zakharov",
"Wadim Kehl",
"Slobodan Ilic"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zakharov_DeceptionNet_Network-Driven_Domain_Randomization_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zakharov_DeceptionNet_Network-Driven_Domain_Randomization_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zakharov_DeceptionNet_Network-Driven_Domain_ICCV_2019_supplemental.pdf | 1904.02750 | title_snapshot | @InProceedings{Zakharov_2019_ICCV,author = {Zakharov, Sergey and Kehl, Wadim and Ilic, Slobodan},title = {DeceptionNet: Network-Driven Domain Randomization},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | We present a novel approach to tackle domain adaptation between synthetic and real data. Instead, of employing "blind" domain randomization, i.e., augmenting synthetic renderings with random backgrounds or changing illumination and colorization, we leverage the task network as its own adversarial guide toward useful au... | [
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54 | Pose-Guided Feature Alignment for Occluded Person Re-Identification | [
"Jiaxu Miao",
"Yu Wu",
"Ping Liu",
"Yuhang Ding",
"Yi Yang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Miao_Pose-Guided_Feature_Alignment_for_Occluded_Person_Re-Identification_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Miao_Pose-Guided_Feature_Alignment_for_Occluded_Person_Re-Identification_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Miao_2019_ICCV,author = {Miao, Jiaxu and Wu, Yu and Liu, Ping and Ding, Yuhang and Yang, Yi},title = {Pose-Guided Feature Alignment for Occluded Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Persons are often occluded by various obstacles in person retrieval scenarios. Previous person re-identification (re-id) methods, either overlook this issue or resolve it based on an extreme assumption. To alleviate the occlusion problem, we propose to detect the occluded regions, and explicitly exclude those regions d... | [
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55 | Robust Person Re-Identification by Modelling Feature Uncertainty | [
"Tianyuan Yu",
"Da Li",
"Yongxin Yang",
"Timothy M. Hospedales",
"Tao Xiang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Yu_Robust_Person_Re-Identification_by_Modelling_Feature_Uncertainty_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Yu_Robust_Person_Re-Identification_by_Modelling_Feature_Uncertainty_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Yu_2019_ICCV,author = {Yu, Tianyuan and Li, Da and Yang, Yongxin and Hospedales, Timothy M. and Xiang, Tao},title = {Robust Person Re-Identification by Modelling Feature Uncertainty},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {201... | We aim to learn deep person re-identification (ReID) models that are robust against noisy training data. Two types of noise are prevalent in practice: (1) label noise caused by human annotator errors and (2) data outliers caused by person detector errors or occlusion. Both types of noise pose serious problems for train... | [
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56 | Co-Segmentation Inspired Attention Networks for Video-Based Person Re-Identification | [
"Arulkumar Subramaniam",
"Athira Nambiar",
"Anurag Mittal"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Subramaniam_Co-Segmentation_Inspired_Attention_Networks_for_Video-Based_Person_Re-Identification_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Subramaniam_Co-Segmentation_Inspired_Attention_Networks_for_Video-Based_Person_Re-Identification_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Subramaniam_Co-Segmentation_Inspired_Attention_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Subramaniam_2019_ICCV,author = {Subramaniam, Arulkumar and Nambiar, Athira and Mittal, Anurag},title = {Co-Segmentation Inspired Attention Networks for Video-Based Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year... | Person re-identification (Re-ID) is an important real-world surveillance problem that entails associating a person's identity over a network of cameras. Video-based Re-ID approaches have gained significant attention recently since a video, and not just an image, is often available. In this work, we propose a novel Co-s... | [
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57 | A Delay Metric for Video Object Detection: What Average Precision Fails to Tell | [
"Huizi Mao",
"Xiaodong Yang",
"William J. Dally"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Mao_A_Delay_Metric_for_Video_Object_Detection_What_Average_Precision_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Mao_A_Delay_Metric_for_Video_Object_Detection_What_Average_Precision_ICCV_2019_paper.pdf | null | 1908.06368 | title_snapshot | @InProceedings{Mao_2019_ICCV,author = {Mao, Huizi and Yang, Xiaodong and Dally, William J.},title = {A Delay Metric for Video Object Detection: What Average Precision Fails to Tell},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Average precision (AP) is a widely used metric to evaluate detection accuracy of image and video object detectors. In this paper, we analyze the object detection from video and point out that mAP alone is not sufficient to capture the temporal nature of video object detection. To tackle this problem, we propose a compr... | [
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58 | IL2M: Class Incremental Learning With Dual Memory | [
"Eden Belouadah",
"Adrian Popescu"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Belouadah_IL2M_Class_Incremental_Learning_With_Dual_Memory_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Belouadah_IL2M_Class_Incremental_Learning_With_Dual_Memory_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Belouadah_IL2M_Class_Incremental_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Belouadah_2019_ICCV,author = {Belouadah, Eden and Popescu, Adrian},title = {IL2M: Class Incremental Learning With Dual Memory},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | This paper presents a class incremental learning (IL) method which exploits fine tuning and a dual memory to reduce the negative effect of catastrophic forgetting in image recognition. First, we simplify the current fine tuning based approaches which use a combination of classification and distillation losses to compen... | [
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59 | Asymmetric Non-Local Neural Networks for Semantic Segmentation | [
"Zhen Zhu",
"Mengde Xu",
"Song Bai",
"Tengteng Huang",
"Xiang Bai"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhu_Asymmetric_Non-Local_Neural_Networks_for_Semantic_Segmentation_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhu_Asymmetric_Non-Local_Neural_Networks_for_Semantic_Segmentation_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zhu_Asymmetric_Non-Local_Neural_ICCV_2019_supplemental.pdf | 1908.07678 | title_snapshot | @InProceedings{Zhu_2019_ICCV,author = {Zhu, Zhen and Xu, Mengde and Bai, Song and Huang, Tengteng and Bai, Xiang},title = {Asymmetric Non-Local Neural Networks for Semantic Segmentation},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation, which has two prominent components: Asymmetric Pyramid Non-local Blo... | [
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60 | CCNet: Criss-Cross Attention for Semantic Segmentation | [
"Zilong Huang",
"Xinggang Wang",
"Lichao Huang",
"Chang Huang",
"Yunchao Wei",
"Wenyu Liu"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Huang_CCNet_Criss-Cross_Attention_for_Semantic_Segmentation_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_CCNet_Criss-Cross_Attention_for_Semantic_Segmentation_ICCV_2019_paper.pdf | null | 1811.11721 | title_snapshot | @InProceedings{Huang_2019_ICCV,author = {Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu},title = {CCNet: Criss-Cross Attention for Semantic Segmentation},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},ye... | Full-image dependencies provide useful contextual information to benefit visual understanding problems. In this work, we propose a Criss-Cross Network (CCNet) for obtaining such contextual information in a more effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module in CCNet harves... | [
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61 | Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function | [
"Shousheng Luo",
"Xue-Cheng Tai",
"Limei Huo",
"Yang Wang",
"Roland Glowinski"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Luo_Convex_Shape_Prior_for_Multi-Object_Segmentation_Using_a_Single_Level_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Luo_Convex_Shape_Prior_for_Multi-Object_Segmentation_Using_a_Single_Level_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Luo_2019_ICCV,author = {Luo, Shousheng and Tai, Xue-Cheng and Huo, Limei and Wang, Yang and Glowinski, Roland},title = {Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = ... | Many objects in real world have convex shapes. It is a difficult task to have representations for convex shapes with good and fast numerical solutions. This paper proposes a method to incorporate convex shape prior for multi-object segmentation using level set method. The relationship between the convexity of the segme... | [
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62 | Surface Networks via General Covers | [
"Niv Haim",
"Nimrod Segol",
"Heli Ben-Hamu",
"Haggai Maron",
"Yaron Lipman"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Haim_Surface_Networks_via_General_Covers_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Haim_Surface_Networks_via_General_Covers_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Haim_Surface_Networks_via_ICCV_2019_supplemental.pdf | 1812.10705 | title_snapshot | @InProceedings{Haim_2019_ICCV,author = {Haim, Niv and Segol, Nimrod and Ben-Hamu, Heli and Maron, Haggai and Lipman, Yaron},title = {Surface Networks via General Covers},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Developing deep learning techniques for geometric data is an active and fruitful research area. This paper tackles the problem of sphere-type surface learning by developing a novel surface-to-image representation. Using this representation we are able to quickly adapt successful CNN models to the surface setting. The s... | [
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63 | SSAP: Single-Shot Instance Segmentation With Affinity Pyramid | [
"Naiyu Gao",
"Yanhu Shan",
"Yupei Wang",
"Xin Zhao",
"Yinan Yu",
"Ming Yang",
"Kaiqi Huang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Gao_SSAP_Single-Shot_Instance_Segmentation_With_Affinity_Pyramid_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Gao_SSAP_Single-Shot_Instance_Segmentation_With_Affinity_Pyramid_ICCV_2019_paper.pdf | null | 1909.01616 | title_snapshot | @InProceedings{Gao_2019_ICCV,author = {Gao, Naiyu and Shan, Yanhu and Wang, Yupei and Zhao, Xin and Yu, Yinan and Yang, Ming and Huang, Kaiqi},title = {SSAP: Single-Shot Instance Segmentation With Affinity Pyramid},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {Oct... | Recently, proposal-free instance segmentation has received increasing attention due to its concise and efficient pipeline. Generally, proposal-free methods generate instance-agnostic semantic segmentation labels and instance-aware features to group pixels into different object instances. However, previous methods mostl... | [
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64 | Learning Propagation for Arbitrarily-Structured Data | [
"Sifei Liu",
"Xueting Li",
"Varun Jampani",
"Shalini De Mello",
"Jan Kautz"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Liu_Learning_Propagation_for_Arbitrarily-Structured_Data_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Learning_Propagation_for_Arbitrarily-Structured_Data_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Liu_Learning_Propagation_for_ICCV_2019_supplemental.pdf | 1909.11237 | title_snapshot | @InProceedings{Liu_2019_ICCV,author = {Liu, Sifei and Li, Xueting and Jampani, Varun and Mello, Shalini De and Kautz, Jan},title = {Learning Propagation for Arbitrarily-Structured Data},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Processing an input signal that contains arbitrary structures, e.g., superpixels and point clouds, remains a big challenge in computer vision. Linear diffusion, an effective model for image processing, has been recently integrated with deep learning algorithms. In this paper, we propose to learn pairwise relations amon... | [
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65 | MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input | [
"Jun Hao Liew",
"Scott Cohen",
"Brian Price",
"Long Mai",
"Sim-Heng Ong",
"Jiashi Feng"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Liew_MultiSeg_Semantically_Meaningful_Scale-Diverse_Segmentations_From_Minimal_User_Input_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Liew_MultiSeg_Semantically_Meaningful_Scale-Diverse_Segmentations_From_Minimal_User_Input_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Liew_MultiSeg_Semantically_Meaningful_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Liew_2019_ICCV,author = {Liew, Jun Hao and Cohen, Scott and Price, Brian and Mai, Long and Ong, Sim-Heng and Feng, Jiashi},title = {MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision ... | Existing deep learning-based interactive image segmentation approaches typically assume the target-of-interest is always a single object and fail to account for the potential diversity in user expectations, thus requiring excessive user input when it comes to segmenting an object part or a group of objects instead. Mot... | [
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66 | Robust Motion Segmentation From Pairwise Matches | [
"Federica Arrigoni",
"Tomas Pajdla"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Arrigoni_Robust_Motion_Segmentation_From_Pairwise_Matches_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Arrigoni_Robust_Motion_Segmentation_From_Pairwise_Matches_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Arrigoni_Robust_Motion_Segmentation_ICCV_2019_supplemental.pdf | 1905.09043 | title_snapshot | @InProceedings{Arrigoni_2019_ICCV,author = {Arrigoni, Federica and Pajdla, Tomas},title = {Robust Motion Segmentation From Pairwise Matches},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | In this paper we consider the problem of motion segmentation, where only pairwise correspondences are assumed as input without prior knowledge about tracks. The problem is formulated as a two-step process. First, motion segmentation is performed on image pairs independently. Secondly, we combine independent pairwise se... | [
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67 | InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting | [
"Hao-Shu Fang",
"Jianhua Sun",
"Runzhong Wang",
"Minghao Gou",
"Yong-Lu Li",
"Cewu Lu"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Fang_InstaBoost_Boosting_Instance_Segmentation_via_Probability_Map_Guided_Copy-Pasting_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Fang_InstaBoost_Boosting_Instance_Segmentation_via_Probability_Map_Guided_Copy-Pasting_ICCV_2019_paper.pdf | null | 1908.07801 | title_snapshot | @InProceedings{Fang_2019_ICCV,author = {Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu},title = {InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV... | Instance segmentation requires a large number of training samples to achieve satisfactory performance and benefits from proper data augmentation. To enlarge the training set and increase the diversity, previous methods have investigated using data annotation from other domain (e.g. bbox, point) in a weakly supervised m... | [
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68 | Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network | [
"Mei Wang",
"Weihong Deng",
"Jiani Hu",
"Xunqiang Tao",
"Yaohai Huang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Racial_Faces_in_the_Wild_Reducing_Racial_Bias_by_Information_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Racial_Faces_in_the_Wild_Reducing_Racial_Bias_by_Information_ICCV_2019_paper.pdf | null | 1812.00194 | title_snapshot | @InProceedings{Wang_2019_ICCV,author = {Wang, Mei and Deng, Weihong and Hu, Jiani and Tao, Xunqiang and Huang, Yaohai},title = {Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},mont... | Racial bias is an important issue in biometric, but has not been thoroughly studied in deep face recognition. In this paper, we first contribute a dedicated dataset called Racial Faces in-the-Wild (RFW) database, on which we firmly validated the racial bias of four commercial APIs and four state-of-the-art (SOTA) algor... | [
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69 | Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition | [
"Jingxiao Zheng",
"Ruichi Yu",
"Jun-Cheng Chen",
"Boyu Lu",
"Carlos D. Castillo",
"Rama Chellappa"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zheng_Uncertainty_Modeling_of_Contextual-Connections_Between_Tracklets_for_Unconstrained_Video-Based_Face_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zheng_Uncertainty_Modeling_of_Contextual-Connections_Between_Tracklets_for_Unconstrained_Video-Based_Face_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zheng_Uncertainty_Modeling_of_ICCV_2019_supplemental.pdf | 1905.02756 | title_snapshot | @InProceedings{Zheng_2019_ICCV,author = {Zheng, Jingxiao and Yu, Ruichi and Chen, Jun-Cheng and Lu, Boyu and Castillo, Carlos D. and Chellappa, Rama},title = {Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition},booktitle = {Proceedings of the IEEE/CVF Interna... | Unconstrained video-based face recognition is a challenging problem due to significant within-video variations caused by pose, occlusion and blur. To tackle this problem, an effective idea is to propagate the identity from high-quality faces to low-quality ones through contextual connections, which are constructed base... | [
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70 | Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading | [
"Xingxuan Zhang",
"Feng Cheng",
"Shilin Wang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Spatio-Temporal_Fusion_Based_Convolutional_Sequence_Learning_for_Lip_Reading_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Spatio-Temporal_Fusion_Based_Convolutional_Sequence_Learning_for_Lip_Reading_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Zhang_2019_ICCV,author = {Zhang, Xingxuan and Cheng, Feng and Wang, Shilin},title = {Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Current state-of-the-art approaches for lip reading are based on sequence-to-sequence architectures that are designed for natural machine translation and audio speech recognition. Hence, these methods do not fully exploit the characteristics of the lip dynamics, causing two main drawbacks. First, the short-range tempor... | [
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71 | Occlusion-Aware Networks for 3D Human Pose Estimation in Video | [
"Yu Cheng",
"Bo Yang",
"Bo Wang",
"Wending Yan",
"Robby T. Tan"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Cheng_Occlusion-Aware_Networks_for_3D_Human_Pose_Estimation_in_Video_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Cheng_Occlusion-Aware_Networks_for_3D_Human_Pose_Estimation_in_Video_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Cheng_Occlusion-Aware_Networks_for_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Cheng_2019_ICCV,author = {Cheng, Yu and Yang, Bo and Wang, Bo and Yan, Wending and Tan, Robby T.},title = {Occlusion-Aware Networks for 3D Human Pose Estimation in Video},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Occlusion is a key problem in 3D human pose estimation from a monocular video. To address this problem, we introduce an occlusion-aware deep-learning framework. By employing estimated 2D confidence heatmaps of keypoints and an optical-flow consistency constraint, we filter out the unreliable estimations of occluded key... | [
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72 | Context-Aware Feature and Label Fusion for Facial Action Unit Intensity Estimation With Partially Labeled Data | [
"Yong Zhang",
"Haiyong Jiang",
"Baoyuan Wu",
"Yanbo Fan",
"Qiang Ji"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Context-Aware_Feature_and_Label_Fusion_for_Facial_Action_Unit_Intensity_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Context-Aware_Feature_and_Label_Fusion_for_Facial_Action_Unit_Intensity_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zhang_Context-Aware_Feature_and_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Zhang_2019_ICCV,author = {Zhang, Yong and Jiang, Haiyong and Wu, Baoyuan and Fan, Yanbo and Ji, Qiang},title = {Context-Aware Feature and Label Fusion for Facial Action Unit Intensity Estimation With Partially Labeled Data},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vis... | Facial action unit (AU) intensity estimation is a fundamental task for facial behaviour analysis. Most previous methods use a whole face image as input for intensity prediction. Considering that AUs are defined according to their corresponding local appearance, a few patch-based methods utilize image features of local ... | [
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73 | Distill Knowledge From NRSfM for Weakly Supervised 3D Pose Learning | [
"Chaoyang Wang",
"Chen Kong",
"Simon Lucey"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Distill_Knowledge_From_NRSfM_for_Weakly_Supervised_3D_Pose_Learning_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Distill_Knowledge_From_NRSfM_for_Weakly_Supervised_3D_Pose_Learning_ICCV_2019_paper.pdf | null | 1908.06377 | title_snapshot | @InProceedings{Wang_2019_ICCV,author = {Wang, Chaoyang and Kong, Chen and Lucey, Simon},title = {Distill Knowledge From NRSfM for Weakly Supervised 3D Pose Learning},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | We propose to learn a 3D pose estimator by distilling knowledge from Non-Rigid Structure from Motion (NRSfM). Our method uses solely 2D landmark annotations. No 3D data, multi-view/temporal footage, or object specific prior is required. This alleviates the data bottleneck, which is one of the major concern for supervis... | [
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74 | MONET: Multiview Semi-Supervised Keypoint Detection via Epipolar Divergence | [
"Yuan Yao",
"Yasamin Jafarian",
"Hyun Soo Park"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Yao_MONET_Multiview_Semi-Supervised_Keypoint_Detection_via_Epipolar_Divergence_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Yao_MONET_Multiview_Semi-Supervised_Keypoint_Detection_via_Epipolar_Divergence_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Yao_MONET_Multiview_Semi-Supervised_ICCV_2019_supplemental.pdf | 1806.00104 | title_snapshot | @InProceedings{Yao_2019_ICCV,author = {Yao, Yuan and Jafarian, Yasamin and Park, Hyun Soo},title = {MONET: Multiview Semi-Supervised Keypoint Detection via Epipolar Divergence},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | This paper presents MONET---an end-to-end semi-supervised learning framework for a keypoint detector using multiview image streams. In particular, we consider general subjects such as non-human species where attaining a large scale annotated dataset is challenging. While multiview geometry can be used to self-supervise... | [
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75 | Talking With Hands 16.2M: A Large-Scale Dataset of Synchronized Body-Finger Motion and Audio for Conversational Motion Analysis and Synthesis | [
"Gilwoo Lee",
"Zhiwei Deng",
"Shugao Ma",
"Takaaki Shiratori",
"Siddhartha S. Srinivasa",
"Yaser Sheikh"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Lee_Talking_With_Hands_16.2M_A_Large-Scale_Dataset_of_Synchronized_Body-Finger_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Talking_With_Hands_16.2M_A_Large-Scale_Dataset_of_Synchronized_Body-Finger_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Lee_Talking_With_Hands_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Lee_2019_ICCV,author = {Lee, Gilwoo and Deng, Zhiwei and Ma, Shugao and Shiratori, Takaaki and Srinivasa, Siddhartha S. and Sheikh, Yaser},title = {Talking With Hands 16.2M: A Large-Scale Dataset of Synchronized Body-Finger Motion and Audio for Conversational Motion Analysis and Synthesis},booktitle = {P... | We present a 16.2-million frame (50-hour) multimodal dataset of two-person face-to-face spontaneous conversations. Our dataset features synchronized body and finger motion as well as audio data. To the best of our knowledge, it represents the largest motion capture and audio dataset of natural conversations to date. Th... | [
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76 | Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network | [
"Lingxue Song",
"Dihong Gong",
"Zhifeng Li",
"Changsong Liu",
"Wei Liu"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Song_Occlusion_Robust_Face_Recognition_Based_on_Mask_Learning_With_Pairwise_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Song_Occlusion_Robust_Face_Recognition_Based_on_Mask_Learning_With_Pairwise_ICCV_2019_paper.pdf | null | 1908.06290 | title_judge | @InProceedings{Song_2019_ICCV,author = {Song, Lingxue and Gong, Dihong and Li, Zhifeng and Liu, Changsong and Liu, Wei},title = {Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV... | Deep Convolutional Neural Networks (CNNs) have been pushing the frontier of face recognition over past years. However, existing CNN models are far less accurate when handling partially occluded faces. These general face models generalize poorly for occlusions on variable facial areas. Inspired by the fact that human vi... | [
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77 | Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection | [
"Xuanyi Dong",
"Yi Yang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Dong_Teacher_Supervises_Students_How_to_Learn_From_Partially_Labeled_Images_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Dong_Teacher_Supervises_Students_How_to_Learn_From_Partially_Labeled_Images_ICCV_2019_paper.pdf | null | 1908.02116 | title_snapshot | @InProceedings{Dong_2019_ICCV,author = {Dong, Xuanyi and Yang, Yi},title = {Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Facial landmark detection aims to localize the anatomically defined points of human faces. In this paper, we study facial landmark detection from partially labeled facial images. A typical approach is to (1) train a detector on the labeled images; (2) generate new training samples using this detector's prediction as ps... | [
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78 | A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation From a Single Depth Image | [
"Fu Xiong",
"Boshen Zhang",
"Yang Xiao",
"Zhiguo Cao",
"Taidong Yu",
"Joey Tianyi Zhou",
"Junsong Yuan"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Xiong_A2J_Anchor-to-Joint_Regression_Network_for_3D_Articulated_Pose_Estimation_From_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Xiong_A2J_Anchor-to-Joint_Regression_Network_for_3D_Articulated_Pose_Estimation_From_ICCV_2019_paper.pdf | null | 1908.09999 | title_snapshot | @InProceedings{Xiong_2019_ICCV,author = {Xiong, Fu and Zhang, Boshen and Xiao, Yang and Cao, Zhiguo and Yu, Taidong and Zhou, Joey Tianyi and Yuan, Junsong},title = {A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation From a Single Depth Image},booktitle = {Proceedings of the IEEE/CVF Internation... | For 3D hand and body pose estimation task in depth image, a novel anchor-based approach termed Anchor-to-Joint regression network (A2J) with the end-to-end learning ability is proposed. Within A2J, anchor points able to capture global-local spatial context information are densely set on depth image as local regressors ... | [
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79 | TexturePose: Supervising Human Mesh Estimation With Texture Consistency | [
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"Kostas Daniilidis"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Pavlakos_TexturePose_Supervising_Human_Mesh_Estimation_With_Texture_Consistency_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Pavlakos_TexturePose_Supervising_Human_Mesh_Estimation_With_Texture_Consistency_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Pavlakos_TexturePose_Supervising_Human_ICCV_2019_supplemental.pdf | 1910.11322 | title_snapshot | @InProceedings{Pavlakos_2019_ICCV,author = {Pavlakos, Georgios and Kolotouros, Nikos and Daniilidis, Kostas},title = {TexturePose: Supervising Human Mesh Estimation With Texture Consistency},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | This work addresses the problem of model-based human pose estimation. Recent approaches have made significant progress towards regressing the parameters of parametric human body models directly from images. Because of the absence of images with 3D shape ground truth, relevant approaches rely on 2D annotations or sophis... | [
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80 | FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape From Single RGB Images | [
"Christian Zimmermann",
"Duygu Ceylan",
"Jimei Yang",
"Bryan Russell",
"Max Argus",
"Thomas Brox"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zimmermann_FreiHAND_A_Dataset_for_Markerless_Capture_of_Hand_Pose_and_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zimmermann_FreiHAND_A_Dataset_for_Markerless_Capture_of_Hand_Pose_and_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zimmermann_FreiHAND_A_Dataset_ICCV_2019_supplemental.pdf | 1909.04349 | title_snapshot | @InProceedings{Zimmermann_2019_ICCV,author = {Zimmermann, Christian and Ceylan, Duygu and Yang, Jimei and Russell, Bryan and Argus, Max and Brox, Thomas},title = {FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape From Single RGB Images},booktitle = {Proceedings of the IEEE/CVF International Conference o... | Estimating 3D hand pose from single RGB images is a highly ambiguous problem that relies on an unbiased training dataset. In this paper, we analyze cross-dataset generalization when training on existing datasets. We find that approaches perform well on the datasets they are trained on, but do not generalize to other da... | [
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81 | Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles | [
"Nitin Saini",
"Eric Price",
"Rahul Tallamraju",
"Raffi Enficiaud",
"Roman Ludwig",
"Igor Martinovic",
"Aamir Ahmad",
"Michael J. Black"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Saini_Markerless_Outdoor_Human_Motion_Capture_Using_Multiple_Autonomous_Micro_Aerial_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Saini_Markerless_Outdoor_Human_Motion_Capture_Using_Multiple_Autonomous_Micro_Aerial_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Saini_Markerless_Outdoor_Human_ICCV_2019_supplemental.zip | null | null | @InProceedings{Saini_2019_ICCV,author = {Saini, Nitin and Price, Eric and Tallamraju, Rahul and Enficiaud, Raffi and Ludwig, Roman and Martinovic, Igor and Ahmad, Aamir and Black, Michael J.},title = {Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles},booktitle = {Proceedings of th... | Capturing human motion in natural scenarios means moving motion capture out of the lab and into the wild. Typical approaches rely on fixed, calibrated, cameras and reflective markers on the body, significantly limiting the motions that can be captured. To make motion capture truly unconstrained, we describe the first f... | [
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82 | Toyota Smarthome: Real-World Activities of Daily Living | [
"Srijan Das",
"Rui Dai",
"Michal Koperski",
"Luca Minciullo",
"Lorenzo Garattoni",
"Francois Bremond",
"Gianpiero Francesca"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Das_Toyota_Smarthome_Real-World_Activities_of_Daily_Living_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Das_Toyota_Smarthome_Real-World_Activities_of_Daily_Living_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Das_Toyota_Smarthome_Real-World_ICCV_2019_supplemental.pdf | null | null | @InProceedings{Das_2019_ICCV,author = {Das, Srijan and Dai, Rui and Koperski, Michal and Minciullo, Luca and Garattoni, Lorenzo and Bremond, Francois and Francesca, Gianpiero},title = {Toyota Smarthome: Real-World Activities of Daily Living},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer ... | The performance of deep neural networks is strongly influenced by the quantity and quality of annotated data. Most of the large activity recognition datasets consist of data sourced from the web, which does not reflect challenges that exist in activities of daily living. In this paper, we introduce a large real-world v... | [
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83 | Relation Parsing Neural Network for Human-Object Interaction Detection | [
"Penghao Zhou",
"Mingmin Chi"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Relation_Parsing_Neural_Network_for_Human-Object_Interaction_Detection_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Relation_Parsing_Neural_Network_for_Human-Object_Interaction_Detection_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Zhou_2019_ICCV,author = {Zhou, Penghao and Chi, Mingmin},title = {Relation Parsing Neural Network for Human-Object Interaction Detection},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Human-Object Interaction Detection devotes to infer a triplet < human, verb, object > between human and objects. In this paper, we propose a novel model, i.e., Relation Parsing Neural Network (RPNN), to detect human-object interactions. Specifically, the network is represented by two graphs, i.e., Object-Bodypart Graph... | [
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84 | DistInit: Learning Video Representations Without a Single Labeled Video | [
"Rohit Girdhar",
"Du Tran",
"Lorenzo Torresani",
"Deva Ramanan"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Girdhar_DistInit_Learning_Video_Representations_Without_a_Single_Labeled_Video_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Girdhar_DistInit_Learning_Video_Representations_Without_a_Single_Labeled_Video_ICCV_2019_paper.pdf | null | 1901.09244 | title_snapshot | @InProceedings{Girdhar_2019_ICCV,author = {Girdhar, Rohit and Tran, Du and Torresani, Lorenzo and Ramanan, Deva},title = {DistInit: Learning Video Representations Without a Single Labeled Video},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models has not been able to keep up with the ever increasing depth and sophistication of ... | [
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85 | Zero-Shot Anticipation for Instructional Activities | [
"Fadime Sener",
"Angela Yao"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Sener_Zero-Shot_Anticipation_for_Instructional_Activities_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Sener_Zero-Shot_Anticipation_for_Instructional_Activities_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Sener_Zero-Shot_Anticipation_for_ICCV_2019_supplemental.pdf | 1812.02501 | title_snapshot | @InProceedings{Sener_2019_ICCV,author = {Sener, Fadime and Yao, Angela},title = {Zero-Shot Anticipation for Instructional Activities},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | How can we teach a robot to predict what will happen next for an activity it has never seen before? We address the problem of zero-shot anticipation by presenting a hierarchical model that generalizes instructional knowledge from large-scale text-corpora and transfers the knowledge to the visual domain. Given a portion... | [
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86 | Making the Invisible Visible: Action Recognition Through Walls and Occlusions | [
"Tianhong Li",
"Lijie Fan",
"Mingmin Zhao",
"Yingcheng Liu",
"Dina Katabi"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Li_Making_the_Invisible_Visible_Action_Recognition_Through_Walls_and_Occlusions_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Making_the_Invisible_Visible_Action_Recognition_Through_Walls_and_Occlusions_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Li_Making_the_Invisible_ICCV_2019_supplemental.zip | 1909.09300 | title_snapshot | @InProceedings{Li_2019_ICCV,author = {Li, Tianhong and Fan, Lijie and Zhao, Mingmin and Liu, Yingcheng and Katabi, Dina},title = {Making the Invisible Visible: Action Recognition Through Walls and Occlusions},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},... | Understanding people's actions and interactions typically depends on seeing them. Automating the process of action recognition from visual data has been the topic of much research in the computer vision community. But what if it is too dark, or if the person is occluded or behind a wall? In this paper, we introduce a n... | [
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87 | Recursive Visual Sound Separation Using Minus-Plus Net | [
"Xudong Xu",
"Bo Dai",
"Dahua Lin"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Xu_Recursive_Visual_Sound_Separation_Using_Minus-Plus_Net_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Recursive_Visual_Sound_Separation_Using_Minus-Plus_Net_ICCV_2019_paper.pdf | null | 1908.11602 | title_snapshot | @InProceedings{Xu_2019_ICCV,author = {Xu, Xudong and Dai, Bo and Lin, Dahua},title = {Recursive Visual Sound Separation Using Minus-Plus Net},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Sounds provide rich semantics, complementary to visual data, for many tasks. However, in practice, sounds from multiple sources are often mixed together. In this paper we propose a novel framework, referred to as MinusPlus Network (MP-Net), for the task of visual sound separation. MP-Net separates sounds recursively in... | [
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88 | Unsupervised Video Interpolation Using Cycle Consistency | [
"Fitsum A. Reda",
"Deqing Sun",
"Aysegul Dundar",
"Mohammad Shoeybi",
"Guilin Liu",
"Kevin J. Shih",
"Andrew Tao",
"Jan Kautz",
"Bryan Catanzaro"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Reda_Unsupervised_Video_Interpolation_Using_Cycle_Consistency_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Reda_Unsupervised_Video_Interpolation_Using_Cycle_Consistency_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Reda_Unsupervised_Video_Interpolation_ICCV_2019_supplemental.pdf | 1906.05928 | title_snapshot | @InProceedings{Reda_2019_ICCV,author = {Reda, Fitsum A. and Sun, Deqing and Dundar, Aysegul and Shoeybi, Mohammad and Liu, Guilin and Shih, Kevin J. and Tao, Andrew and Kautz, Jan and Catanzaro, Bryan},title = {Unsupervised Video Interpolation Using Cycle Consistency},booktitle = {Proceedings of the IEEE/CVF Internatio... | Learning to synthesize high frame rate videos via interpolation requires large quantities of high frame rate training videos, which, however, are scarce, especially at high resolutions. Here, we propose unsupervised techniques to synthesize high frame rate videos directly from low frame rate videos using cycle consiste... | [
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89 | Deformable Surface Tracking by Graph Matching | [
"Tao Wang",
"Haibin Ling",
"Congyan Lang",
"Songhe Feng",
"Xiaohui Hou"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Deformable_Surface_Tracking_by_Graph_Matching_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Deformable_Surface_Tracking_by_Graph_Matching_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Wang_2019_ICCV,author = {Wang, Tao and Ling, Haibin and Lang, Congyan and Feng, Songhe and Hou, Xiaohui},title = {Deformable Surface Tracking by Graph Matching},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | This paper addresses the problem of deformable surface tracking from monocular images. Specifically, we propose a graph-based approach that effectively explores the structure information of the surface to enhance tracking performance. Our approach solves simultaneously for feature correspondence, outlier rejection and ... | [
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90 | Deep Meta Learning for Real-Time Target-Aware Visual Tracking | [
"Janghoon Choi",
"Junseok Kwon",
"Kyoung Mu Lee"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Deep_Meta_Learning_for_Real-Time_Target-Aware_Visual_Tracking_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Choi_Deep_Meta_Learning_for_Real-Time_Target-Aware_Visual_Tracking_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Choi_Deep_Meta_Learning_ICCV_2019_supplemental.zip | 1712.09153 | title_snapshot | @InProceedings{Choi_2019_ICCV,author = {Choi, Janghoon and Kwon, Junseok and Lee, Kyoung Mu},title = {Deep Meta Learning for Real-Time Target-Aware Visual Tracking},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | In this paper, we propose a novel on-line visual tracking framework based on the Siamese matching network and meta-learner network, which run at real-time speeds. Conventional deep convolutional feature-based discriminative visual tracking algorithms require continuous re-training of classifiers or correlation filters,... | [
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91 | Looking to Relations for Future Trajectory Forecast | [
"Chiho Choi",
"Behzad Dariush"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Looking_to_Relations_for_Future_Trajectory_Forecast_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Choi_Looking_to_Relations_for_Future_Trajectory_Forecast_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Choi_Looking_to_Relations_ICCV_2019_supplemental.pdf | 1905.08855 | title_snapshot | @InProceedings{Choi_2019_ICCV,author = {Choi, Chiho and Dariush, Behzad},title = {Looking to Relations for Future Trajectory Forecast},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Inferring relational behavior between road users as well as road users and their surrounding physical space is an important step toward effective modeling and prediction of navigation strategies adopted by participants in road scenes. To this end, we propose a relation-aware framework for future trajectory forecast. Ou... | [
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92 | Anchor Diffusion for Unsupervised Video Object Segmentation | [
"Zhao Yang",
"Qiang Wang",
"Luca Bertinetto",
"Weiming Hu",
"Song Bai",
"Philip H. S. Torr"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Yang_Anchor_Diffusion_for_Unsupervised_Video_Object_Segmentation_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Anchor_Diffusion_for_Unsupervised_Video_Object_Segmentation_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Yang_Anchor_Diffusion_for_ICCV_2019_supplemental.pdf | 1910.10895 | title_snapshot | @InProceedings{Yang_2019_ICCV,author = {Yang, Zhao and Wang, Qiang and Bertinetto, Luca and Hu, Weiming and Bai, Song and Torr, Philip H. S.},title = {Anchor Diffusion for Unsupervised Video Object Segmentation},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {Octobe... | Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. Despite their complexity, these kinds of approach tend to favour short-term temporal dependencies and are thus prone to accumulating inaccuracies, which cause drift over time. Moreover, simple (... | [
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93 | Tracking Without Bells and Whistles | [
"Philipp Bergmann",
"Tim Meinhardt",
"Laura Leal-Taixe"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Bergmann_Tracking_Without_Bells_and_Whistles_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Bergmann_Tracking_Without_Bells_and_Whistles_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Bergmann_Tracking_Without_Bells_ICCV_2019_supplemental.pdf | 1903.05625 | title_snapshot | @InProceedings{Bergmann_2019_ICCV,author = {Bergmann, Philipp and Meinhardt, Tim and Leal-Taixe, Laura},title = {Tracking Without Bells and Whistles},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | The problem of tracking multiple objects in a video sequence poses several challenging tasks. For tracking-by-detection, these include object re-identification, motion prediction and dealing with occlusions. We present a tracker (without bells and whistles) that accomplishes tracking without specifically targeting any ... | [
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94 | Perspective-Guided Convolution Networks for Crowd Counting | [
"Zhaoyi Yan",
"Yuchen Yuan",
"Wangmeng Zuo",
"Xiao Tan",
"Yezhen Wang",
"Shilei Wen",
"Errui Ding"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Yan_Perspective-Guided_Convolution_Networks_for_Crowd_Counting_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Yan_Perspective-Guided_Convolution_Networks_for_Crowd_Counting_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Yan_Perspective-Guided_Convolution_Networks_ICCV_2019_supplemental.pdf | 1909.06966 | title_snapshot | @InProceedings{Yan_2019_ICCV,author = {Yan, Zhaoyi and Yuan, Yuchen and Zuo, Wangmeng and Tan, Xiao and Wang, Yezhen and Wen, Shilei and Ding, Errui},title = {Perspective-Guided Convolution Networks for Crowd Counting},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = ... | In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i.e. PGCNet), which aims to overcome the dramatic intra-scene scale variations of people due to the perspective effect. While most state-of-the-arts adopt multi-scale or multi-column archi... | [
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95 | End-to-End Wireframe Parsing | [
"Yichao Zhou",
"Haozhi Qi",
"Yi Ma"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_End-to-End_Wireframe_Parsing_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_End-to-End_Wireframe_Parsing_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Zhou_End-to-End_Wireframe_Parsing_ICCV_2019_supplemental.pdf | 1905.03246 | title_snapshot | @InProceedings{Zhou_2019_ICCV,author = {Zhou, Yichao and Qi, Haozhi and Ma, Yi},title = {End-to-End Wireframe Parsing},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | We present a conceptually simple yet effective algorithm to detect wireframes in a given image. Compared to the previous methods which first predict an intermediate heat map and then extract straight lines with heuristic algorithms, our method is end-to-end trainable and can directly output a vectorized wireframe that ... | [
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96 | Incremental Class Discovery for Semantic Segmentation With RGBD Sensing | [
"Yoshikatsu Nakajima",
"Byeongkeun Kang",
"Hideo Saito",
"Kris Kitani"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Nakajima_Incremental_Class_Discovery_for_Semantic_Segmentation_With_RGBD_Sensing_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Nakajima_Incremental_Class_Discovery_for_Semantic_Segmentation_With_RGBD_Sensing_ICCV_2019_paper.pdf | null | 1907.10008 | title_snapshot | @InProceedings{Nakajima_2019_ICCV,author = {Nakajima, Yoshikatsu and Kang, Byeongkeun and Saito, Hideo and Kitani, Kris},title = {Incremental Class Discovery for Semantic Segmentation With RGBD Sensing},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year =... | This work addresses the task of open world semantic segmentation using RGBD sensing to discover new semantic classes over time. Although there are many types of objects in the real-word, current semantic segmentation methods make a closed world assumption and are trained only to segment a limited number of object class... | [
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97 | SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation | [
"Liang Du",
"Jingang Tan",
"Hongye Yang",
"Jianfeng Feng",
"Xiangyang Xue",
"Qibao Zheng",
"Xiaoqing Ye",
"Xiaolin Zhang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Du_SSF-DAN_Separated_Semantic_Feature_Based_Domain_Adaptation_Network_for_Semantic_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Du_SSF-DAN_Separated_Semantic_Feature_Based_Domain_Adaptation_Network_for_Semantic_ICCV_2019_paper.pdf | null | null | null | @InProceedings{Du_2019_ICCV,author = {Du, Liang and Tan, Jingang and Yang, Hongye and Feng, Jianfeng and Xue, Xiangyang and Zheng, Qibao and Ye, Xiaoqing and Zhang, Xiaolin},title = {SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation},booktitle = {Proceedings of the IEEE/CVF I... | Despite the great success achieved by supervised fully convolutional models in semantic segmentation, training the models requires a large amount of labor-intensive work to generate pixel-level annotations. Recent works exploit synthetic data to train the model for semantic segmentation, but the domain adaptation betwe... | [
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98 | SpaceNet MVOI: A Multi-View Overhead Imagery Dataset | [
"Nicholas Weir",
"David Lindenbaum",
"Alexei Bastidas",
"Adam Van Etten",
"Sean McPherson",
"Jacob Shermeyer",
"Varun Kumar",
"Hanlin Tang"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Weir_SpaceNet_MVOI_A_Multi-View_Overhead_Imagery_Dataset_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Weir_SpaceNet_MVOI_A_Multi-View_Overhead_Imagery_Dataset_ICCV_2019_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2019/supplemental/Weir_SpaceNet_MVOI_A_ICCV_2019_supplemental.pdf | 1903.12239 | title_snapshot | @InProceedings{Weir_2019_ICCV,author = {Weir, Nicholas and Lindenbaum, David and Bastidas, Alexei and Etten, Adam Van and McPherson, Sean and Shermeyer, Jacob and Kumar, Varun and Tang, Hanlin},title = {SpaceNet MVOI: A Multi-View Overhead Imagery Dataset},booktitle = {Proceedings of the IEEE/CVF International Conferen... | Detection and segmentation of objects in overheard imagery is a challenging task. The variable density, random orientation, small size, and instance-to-instance heterogeneity of objects in overhead imagery calls for approaches distinct from existing models designed for natural scene datasets. Though new overhead imager... | [
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99 | Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting | [
"Vishwanath A. Sindagi",
"Vishal M. Patel"
] | https://openaccess.thecvf.com/content_ICCV_2019/html/Sindagi_Multi-Level_Bottom-Top_and_Top-Bottom_Feature_Fusion_for_Crowd_Counting_ICCV_2019_paper.html | https://openaccess.thecvf.com/content_ICCV_2019/papers/Sindagi_Multi-Level_Bottom-Top_and_Top-Bottom_Feature_Fusion_for_Crowd_Counting_ICCV_2019_paper.pdf | null | 1908.10937 | title_snapshot | @InProceedings{Sindagi_2019_ICCV,author = {Sindagi, Vishwanath A. and Patel, Vishal M.},title = {Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting},booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month = {October},year = {2019}} | Crowd counting presents enormous challenges in the form of large variation in scales within images and across the dataset. These issues are further exacerbated in highly congested scenes. Approaches based on straightforward fusion of multi-scale features from a deep network seem to be obvious solutions to this problem.... | [
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