ICCV
Collection
Accepted papers for ICCV (IEEE/CVF International Conference on Computer Vision), one dataset per year. • 7 items • Updated
paper_id uint32 0 1.07k | title stringlengths 10 156 | authors listlengths 1 17 | cvf_url stringlengths 93 188 | pdf_url stringlengths 94 189 | supp_url stringlengths 103 141 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values | bibtex large_stringlengths 235 583 | abstract large_stringlengths 541 3.46k | embedding listlengths 768 768 |
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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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