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0 | Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence | [
"Dylan Campbell",
"Lars Petersson",
"Laurent Kneip",
"Hongdong Li"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Campbell_Globally-Optimal_Inlier_Set_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Campbell_Globally-Optimal_Inlier_Set_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Campbell_Globally-Optimal_Inlier_Set_ICCV_2017_supplemental.pdf | 1709.09384v1 | cvf | @InProceedings{Campbell_2017_ICCV,author = {Campbell, Dylan and Petersson, Lars and Kneip, Laurent and Li, Hongdong},title = {Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month =... | Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known befor... | [
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1 | Robust Pseudo Random Fields for Light-Field Stereo Matching | [
"Chao-Tsung Huang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Huang_Robust_Pseudo_Random_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Huang_Robust_Pseudo_Random_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Huang_2017_ICCV,author = {Huang, Chao-Tsung},title = {Robust Pseudo Random Fields for Light-Field Stereo Matching},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Markov Random Fields are widely used to model light-field stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to light-field statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumpti... | [
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2 | A Lightweight Approach for On-The-Fly Reflectance Estimation | [
"Kihwan Kim",
"Jinwei Gu",
"Stephen Tyree",
"Pavlo Molchanov",
"Matthias Niessner",
"Jan Kautz"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Kim_A_Lightweight_Approach_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Kim_A_Lightweight_Approach_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Kim_A_Lightweight_Approach_ICCV_2017_supplemental.pdf | 1705.07162v2 | cvf | @InProceedings{Kim_2017_ICCV,author = {Kim, Kihwan and Gu, Jinwei and Tyree, Stephen and Molchanov, Pavlo and Niessner, Matthias and Kautz, Jan},title = {A Lightweight Approach for On-The-Fly Reflectance Estimation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},y... | Estimating surface reflectance (BRDF) is one key component for complete 3D scene capture, with wide applications in virtual reality, augmented reality, and human computer interaction. Prior work is either limited to controlled environments (e.g., gonioreflectometers, light stages or multi-camera domes), or requires the... | [
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3 | Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus | [
"Runze Zhang",
"Siyu Zhu",
"Tian Fang",
"Long Quan"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhang_Distributed_Very_Large_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_Distributed_Very_Large_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Zhang_Distributed_Very_Large_ICCV_2017_supplemental.pdf | null | null | @InProceedings{Zhang_2017_ICCV,author = {Zhang, Runze and Zhu, Siyu and Fang, Tian and Quan, Long},title = {Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation. First, we d... | [
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4 | Practical Projective Structure From Motion (P2SfM) | [
"Ludovic Magerand",
"Alessio Del Bue"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Magerand_Practical_Projective_Structure_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Magerand_Practical_Projective_Structure_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Magerand_Practical_Projective_Structure_ICCV_2017_supplemental.zip | null | null | @InProceedings{Magerand_2017_ICCV,author = {Magerand, Ludovic and Del Bue, Alessio},title = {Practical Projective Structure From Motion (P2SfM)},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | This paper presents a solution to the Projective Structure from Motion (PSfM) problem able to deal efficiently with missing data, outliers and, for the first time, large scale 3D reconstruction scenarios. By embedding the projective depths into the projective parameters of the points and views, we decrease the number o... | [
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5 | Anticipating Daily Intention Using On-Wrist Motion Triggered Sensing | [
"Tz-Ying Wu",
"Ting-An Chien",
"Cheng-Sheng Chan",
"Chan-Wei Hu",
"Min Sun"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Wu_Anticipating_Daily_Intention_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Wu_Anticipating_Daily_Intention_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Wu_Anticipating_Daily_Intention_ICCV_2017_supplemental.pdf | 1710.07477v1 | cvf | @InProceedings{Wu_2017_ICCV,author = {Wu, Tz-Ying and Chien, Ting-An and Chan, Cheng-Sheng and Hu, Chan-Wei and Sun, Min},title = {Anticipating Daily Intention Using On-Wrist Motion Triggered Sensing},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-... | [
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6 | Rethinking Reprojection: Closing the Loop for Pose-Aware Shape Reconstruction From a Single Image | [
"Rui Zhu",
"Hamed Kiani Galoogahi",
"Chaoyang Wang",
"Simon Lucey"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Rethinking_Reprojection_Closing_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Rethinking_Reprojection_Closing_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Zhu_Rethinking_Reprojection_Closing_ICCV_2017_supplemental.pdf | 1707.04682 | title_judge | @InProceedings{Zhu_2017_ICCV,author = {Zhu, Rui and Kiani Galoogahi, Hamed and Wang, Chaoyang and Lucey, Simon},title = {Rethinking Reprojection: Closing the Loop for Pose-Aware Shape Reconstruction From a Single Image},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oc... | An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image. Hitherto, the problem has been addressed through the application of canonical deep learning methods to regress from the image directly to the 3D shape and pose labels. These approaches, however, are probl... | [
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7 | End-To-End Learning of Geometry and Context for Deep Stereo Regression | [
"Alex Kendall",
"Hayk Martirosyan",
"Saumitro Dasgupta",
"Peter Henry",
"Ryan Kennedy",
"Abraham Bachrach",
"Adam Bry"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Kendall_End-To-End_Learning_of_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Kendall_End-To-End_Learning_of_ICCV_2017_paper.pdf | null | 1703.04309v1 | cvf | @InProceedings{Kendall_2017_ICCV,author = {Kendall, Alex and Martirosyan, Hayk and Dasgupta, Saumitro and Henry, Peter and Kennedy, Ryan and Bachrach, Abraham and Bry, Adam},title = {End-To-End Learning of Geometry and Context for Deep Stereo Regression},booktitle = {Proceedings of the IEEE International Conference on ... | We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. We leverage knowledge of the problem's geometry to form a cost volume using deep feature representations. We learn to incorporate contextual information using 3-D convolutions over this volume. Disparity value... | [
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8 | Using Sparse Elimination for Solving Minimal Problems in Computer Vision | [
"Janne Heikkila"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Heikkila_Using_Sparse_Elimination_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Heikkila_Using_Sparse_Elimination_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Heikkila_2017_ICCV,author = {Heikkila, Janne},title = {Using Sparse Elimination for Solving Minimal Problems in Computer Vision},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Finding a closed form solution to a system of polynomial equations is a common problem in computer vision as well as in many other areas of engineering and science. Groebner basis techniques are often employed to provide the solution, but implementing an efficient Groebner basis solver to a given problem requires stron... | [
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9 | High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference | [
"Xiaoguang Han",
"Zhen Li",
"Haibin Huang",
"Evangelos Kalogerakis",
"Yizhou Yu"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Han_High-Resolution_Shape_Completion_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Han_High-Resolution_Shape_Completion_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Han_High-Resolution_Shape_Completion_ICCV_2017_supplemental.pdf | 1709.07599v1 | cvf | @InProceedings{Han_2017_ICCV,author = {Han, Xiaoguang and Li, Zhen and Huang, Haibin and Kalogerakis, Evangelos and Yu, Yizhou},title = {High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference},booktitle = {Proceedings of the IEEE International Conference on Compute... | We propose a data-driven method for recovering missing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized... | [
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10 | Temporal Tessellation: A Unified Approach for Video Analysis | [
"Dotan Kaufman",
"Gil Levi",
"Tal Hassner",
"Lior Wolf"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Kaufman_Temporal_Tessellation_A_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Kaufman_Temporal_Tessellation_A_ICCV_2017_paper.pdf | null | 1612.06950v2 | cvf | @InProceedings{Kaufman_2017_ICCV,author = {Kaufman, Dotan and Levi, Gil and Hassner, Tal and Wolf, Lior},title = {Temporal Tessellation: A Unified Approach for Video Analysis},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We present a general approach to video understanding, inspired by semantic transfer techniques that have been successfully used for 2D image analysis. Our method considers a video to be a 1D sequence of clips, each one associated with its own semantics. The nature of these semantics -- natural language captions or othe... | [
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11 | Learning Policies for Adaptive Tracking With Deep Feature Cascades | [
"Chen Huang",
"Simon Lucey",
"Deva Ramanan"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Huang_Learning_Policies_for_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Huang_Learning_Policies_for_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Huang_Learning_Policies_for_ICCV_2017_supplemental.zip | 1708.02973v2 | cvf | @InProceedings{Huang_2017_ICCV,author = {Huang, Chen and Lucey, Simon and Ramanan, Deva},title = {Learning Policies for Adaptive Tracking With Deep Feature Cascades},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Visual object tracking is a fundamental and time-critical vision task. Recent years have seen many shallow tracking methods based on real-time pixel-based correlation filters, as well as deep methods that have top performance but need a high-end GPU. In this paper, we learn to improve the speed of deep trackers without... | [
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12 | Temporal Shape Super-Resolution by Intra-Frame Motion Encoding Using High-Fps Structured Light | [
"Yuki Shiba",
"Satoshi Ono",
"Ryo Furukawa",
"Shinsaku Hiura",
"Hiroshi Kawasaki"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Shiba_Temporal_Shape_Super-Resolution_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Shiba_Temporal_Shape_Super-Resolution_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Shiba_Temporal_Shape_Super-Resolution_ICCV_2017_supplemental.zip | 1710.00517v1 | cvf | @InProceedings{Shiba_2017_ICCV,author = {Shiba, Yuki and Ono, Satoshi and Furukawa, Ryo and Hiura, Shinsaku and Kawasaki, Hiroshi},title = {Temporal Shape Super-Resolution by Intra-Frame Motion Encoding Using High-Fps Structured Light},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (IC... | One of the solutions of depth imaging of moving scene is to project a static pattern on the object and use just a single image for reconstruction. However, if the motion of the object is too fast with respect to the exposure time of the image sensor, patterns on the captured image are blurred and reconstruction fails. ... | [
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13 | Real-Time Monocular Pose Estimation of 3D Objects Using Temporally Consistent Local Color Histograms | [
"Henning Tjaden",
"Ulrich Schwanecke",
"Elmar Schomer"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Tjaden_Real-Time_Monocular_Pose_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Tjaden_Real-Time_Monocular_Pose_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Tjaden_Real-Time_Monocular_Pose_ICCV_2017_supplemental.zip | null | null | @InProceedings{Tjaden_2017_ICCV,author = {Tjaden, Henning and Schwanecke, Ulrich and Schomer, Elmar},title = {Real-Time Monocular Pose Estimation of 3D Objects Using Temporally Consistent Local Color Histograms},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year ... | We present a novel approach to 6DOF pose estimation and segmentation of rigid 3D objects using a single monocular RGB camera based on temporally consistent, local color histograms. We show that this approach outperforms previous methods in cases of cluttered backgrounds, heterogenous objects, and occlusions. The propos... | [
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14 | CAD Priors for Accurate and Flexible Instance Reconstruction | [
"Tolga Birdal",
"Slobodan Ilic"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Birdal_CAD_Priors_for_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Birdal_CAD_Priors_for_ICCV_2017_paper.pdf | null | 1705.03111v2 | cvf | @InProceedings{Birdal_2017_ICCV,author = {Birdal, Tolga and Ilic, Slobodan},title = {CAD Priors for Accurate and Flexible Instance Reconstruction},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We present an efficient and automatic approach for accurate reconstruction of instances of big 3D objects from multiple, unorganized and unstructured point clouds, in presence of dynamic clutter and occlusions. In contrast to conventional scanning, where the background is assumed to be rather static, we aim at handling... | [
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15 | Colored Point Cloud Registration Revisited | [
"Jaesik Park",
"Qian-Yi Zhou",
"Vladlen Koltun"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Park_Colored_Point_Cloud_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Park_Colored_Point_Cloud_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Park_2017_ICCV,author = {Park, Jaesik and Zhou, Qian-Yi and Koltun, Vladlen},title = {Colored Point Cloud Registration Revisited},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We present an algorithm for tightly aligning two colored point clouds. The key idea is to optimize a joint photometric and geometric objective that locks the alignment along both the normal direction and the tangent plane. We extend a photometric objective for aligning RGB-D images to point clouds, by locally parameter... | [
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16 | Learning Compact Geometric Features | [
"Marc Khoury",
"Qian-Yi Zhou",
"Vladlen Koltun"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Khoury_Learning_Compact_Geometric_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Khoury_Learning_Compact_Geometric_ICCV_2017_paper.pdf | null | 1709.05056v1 | cvf | @InProceedings{Khoury_2017_ICCV,author = {Khoury, Marc and Zhou, Qian-Yi and Koltun, Vladlen},title = {Learning Compact Geometric Features},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We present an approach to learning features that represent the local geometry around a point in an unstructured point cloud. Such features play a central role in geometric registration, which supports diverse applications in robotics and 3D vision. Current state-of-the-art local features for unstructured point clouds h... | [
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17 | Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction | [
"Jeong-Kyun Lee",
"Jaewon Yea",
"Min-Gyu Park",
"Kuk-Jin Yoon"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Lee_Joint_Layout_Estimation_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Lee_Joint_Layout_Estimation_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Lee_Joint_Layout_Estimation_ICCV_2017_supplemental.pdf | 1704.07632v2 | cvf | @InProceedings{Lee_2017_ICCV,author = {Lee, Jeong-Kyun and Yea, Jaewon and Park, Min-Gyu and Yoon, Kuk-Jin},title = {Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | In this paper, we propose an approach to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, we alternate layout e... | [
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18 | A Geometric Framework for Statistical Analysis of Trajectories With Distinct Temporal Spans | [
"Rudrasis Chakraborty",
"Vikas Singh",
"Nagesh Adluru",
"Baba C. Vemuri"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Chakraborty_A_Geometric_Framework_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Chakraborty_A_Geometric_Framework_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Chakraborty_2017_ICCV,author = {Chakraborty, Rudrasis and Singh, Vikas and Adluru, Nagesh and Vemuri, Baba C.},title = {A Geometric Framework for Statistical Analysis of Trajectories With Distinct Temporal Spans},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},mon... | Analyzing data representing multifarious trajectories is central to the many fields in Science and Engineering; for example, trajectories representing a tennis serve, a gymnast's parallel bar routine, progression/remission of disease and so on. We present a novel geometric algorithm for performing statistical analysis ... | [
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19 | An Optimal Transportation Based Univariate Neuroimaging Index | [
"Liang Mi",
"Wen Zhang",
"Junwei Zhang",
"Yonghui Fan",
"Dhruman Goradia",
"Kewei Chen",
"Eric M. Reiman",
"Xianfeng Gu",
"Yalin Wang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Mi_An_Optimal_Transportation_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Mi_An_Optimal_Transportation_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Mi_2017_ICCV,author = {Mi, Liang and Zhang, Wen and Zhang, Junwei and Fan, Yonghui and Goradia, Dhruman and Chen, Kewei and Reiman, Eric M. and Gu, Xianfeng and Wang, Yalin},title = {An Optimal Transportation Based Univariate Neuroimaging Index},booktitle = {Proceedings of the IEEE International Conferen... | The alterations of brain structures and functions have been considered closely correlated to the change of cognitive performance due to neurodegenerative diseases such as Alzheimer's disease. In this paper, we introduce a variational framework to compute the optimal transformation (OT) in 3D space and propose a univari... | [
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20 | S3FD: Single Shot Scale-Invariant Face Detector | [
"Shifeng Zhang",
"Xiangyu Zhu",
"Zhen Lei",
"Hailin Shi",
"Xiaobo Wang",
"Stan Z. Li"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhang_S3FD_Single_Shot_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_S3FD_Single_Shot_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Zhang_S3FD_Single_Shot_ICCV_2017_supplemental.pdf | 1708.05237 | title_judge | @InProceedings{Zhang_2017_ICCV,author = {Zhang, Shifeng and Zhu, Xiangyu and Lei, Zhen and Shi, Hailin and Wang, Xiaobo and Li, Stan Z.},title = {S3FD: Single Shot Scale-Invariant Face Detector},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically... | [
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21 | Amulet: Aggregating Multi-Level Convolutional Features for Salient Object Detection | [
"Pingping Zhang",
"Dong Wang",
"Huchuan Lu",
"Hongyu Wang",
"Xiang Ruan"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhang_Amulet_Aggregating_Multi-Level_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_Amulet_Aggregating_Multi-Level_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Zhang_Amulet_Aggregating_Multi-Level_ICCV_2017_supplemental.pdf | 1708.02001v1 | cvf | @InProceedings{Zhang_2017_ICCV,author = {Zhang, Pingping and Wang, Dong and Lu, Huchuan and Wang, Hongyu and Ruan, Xiang},title = {Amulet: Aggregating Multi-Level Convolutional Features for Salient Object Detection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},y... | Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is u... | [
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22 | Learning Uncertain Convolutional Features for Accurate Saliency Detection | [
"Pingping Zhang",
"Dong Wang",
"Huchuan Lu",
"Hongyu Wang",
"Baocai Yin"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhang_Learning_Uncertain_Convolutional_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_Learning_Uncertain_Convolutional_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Zhang_Learning_Uncertain_Convolutional_ICCV_2017_supplemental.pdf | 1708.02031v1 | cvf | @InProceedings{Zhang_2017_ICCV,author = {Zhang, Pingping and Wang, Dong and Lu, Huchuan and Wang, Hongyu and Yin, Baocai},title = {Learning Uncertain Convolutional Features for Accurate Saliency Detection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {201... | Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection. The key contribution of this work is to learn deep uncertain convolutional features (UCF), which en... | [
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23 | Zero-Order Reverse Filtering | [
"Xin Tao",
"Chao Zhou",
"Xiaoyong Shen",
"Jue Wang",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Tao_Zero-Order_Reverse_Filtering_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Tao_Zero-Order_Reverse_Filtering_ICCV_2017_paper.pdf | null | 1704.04037v1 | cvf | @InProceedings{Tao_2017_ICCV,author = {Tao, Xin and Zhou, Chao and Shen, Xiaoyong and Wang, Jue and Jia, Jiaya},title = {Zero-Order Reverse Filtering},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | In this paper, we study an unconventional but practically meaningful reversibility problem of commonly used image filters. We broadly define filters as operations to smooth images or to produce layers via global or local algorithms. And we raise the intriguingly problem if they are reservable to the status before filte... | [
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24 | Learning Blind Motion Deblurring | [
"Patrick Wieschollek",
"Michael Hirsch",
"Bernhard Scholkopf",
"Hendrik P. A. Lensch"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Wieschollek_Learning_Blind_Motion_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Wieschollek_Learning_Blind_Motion_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Wieschollek_Learning_Blind_Motion_ICCV_2017_supplemental.pdf | 1708.04208v1 | cvf | @InProceedings{Wieschollek_2017_ICCV,author = {Wieschollek, Patrick and Hirsch, Michael and Scholkopf, Bernhard and Lensch, Hendrik P. A.},title = {Learning Blind Motion Deblurring},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | As handheld video cameras are now commonplace and available in every smartphone images and videos can be recorded almost everywhere at any time. However, taking a quick shot frequently ends up in a blurry result due to unwanted camera shake during recording or moving objects in the scene. Removing these artifacts from ... | [
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25 | Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising | [
"Bihan Wen",
"Yanjun Li",
"Luke Pfister",
"Yoram Bresler"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Wen_Joint_Adaptive_Sparsity_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Wen_Joint_Adaptive_Sparsity_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Wen_Joint_Adaptive_Sparsity_ICCV_2017_supplemental.zip | null | null | @InProceedings{Wen_2017_ICCV,author = {Wen, Bihan and Li, Yanjun and Pfister, Luke and Bresler, Yoram},title = {Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month ... | Recent works on adaptive sparse and low-rank signal modeling have demonstrated their usefulness, especially in image/video processing applications. While a patch-based sparse model imposes local structure, low-rankness of the grouped patches exploits non-local correlation. Applying either approach alone usually limits ... | [
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26 | Learning to Super-Resolve Blurry Face and Text Images | [
"Xiangyu Xu",
"Deqing Sun",
"Jinshan Pan",
"Yujin Zhang",
"Hanspeter Pfister",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Xu_Learning_to_Super-Resolve_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Xu_Learning_to_Super-Resolve_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Xu_2017_ICCV,author = {Xu, Xiangyu and Sun, Deqing and Pan, Jinshan and Zhang, Yujin and Pfister, Hanspeter and Yang, Ming-Hsuan},title = {Learning to Super-Resolve Blurry Face and Text Images},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {... | We present an algorithm to directly restore a clear high-resolution image from a blurry low-resolution input. This problem is highly ill-posed and the basic assumptions for existing super-resolution methods (requiring clear input) and deblurring methods (requiring high-resolution input) no longer hold. We focus on face... | [
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27 | Video Frame Interpolation via Adaptive Separable Convolution | [
"Simon Niklaus",
"Long Mai",
"Feng Liu"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Niklaus_Video_Frame_Interpolation_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Niklaus_Video_Frame_Interpolation_ICCV_2017_paper.pdf | null | 1708.01692v1 | cvf | @InProceedings{Niklaus_2017_ICCV,author = {Niklaus, Simon and Mai, Long and Liu, Feng},title = {Video Frame Interpolation via Adaptive Separable Convolution},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-samp... | [
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28 | Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection | [
"Pierre Baque",
"Francois Fleuret",
"Pascal Fua"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Baque_Deep_Occlusion_Reasoning_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Baque_Deep_Occlusion_Reasoning_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Baque_Deep_Occlusion_Reasoning_ICCV_2017_supplemental.pdf | 1704.05775v2 | cvf | @InProceedings{Baque_2017_ICCV,author = {Baque, Pierre and Fleuret, Francois and Fua, Pascal},title = {Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | People detection in 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking algorithms, whose performance still degrades severely when scenes become very crowded. In this work, we introduce a new architecture that combines Con... | [
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29 | Encouraging LSTMs to Anticipate Actions Very Early | [
"Mohammad Sadegh Aliakbarian",
"Fatemeh Sadat Saleh",
"Mathieu Salzmann",
"Basura Fernando",
"Lars Petersson",
"Lars Andersson"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Aliakbarian_Encouraging_LSTMs_to_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Aliakbarian_Encouraging_LSTMs_to_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Aliakbarian_Encouraging_LSTMs_to_ICCV_2017_supplemental.pdf | 1703.07023v3 | cvf | @InProceedings{Aliakbarian_2017_ICCV,author = {Sadegh Aliakbarian, Mohammad and Sadat Saleh, Fatemeh and Salzmann, Mathieu and Fernando, Basura and Petersson, Lars and Andersson, Lars},title = {Encouraging LSTMs to Anticipate Actions Very Early},booktitle = {Proceedings of the IEEE International Conference on Computer ... | In contrast to the widely studied problem of recognizing an action given a complete sequence, action anticipation aims to identify the action from only partially available videos. As such, it is therefore key to the success of computer vision applications requiring to react as early as possible, such as autonomous navi... | [
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30 | PathTrack: Fast Trajectory Annotation With Path Supervision | [
"Santiago Manen",
"Michael Gygli",
"Dengxin Dai",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Manen_PathTrack_Fast_Trajectory_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Manen_PathTrack_Fast_Trajectory_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Manen_PathTrack_Fast_Trajectory_ICCV_2017_supplemental.zip | 1703.02437v2 | cvf | @InProceedings{Manen_2017_ICCV,author = {Manen, Santiago and Gygli, Michael and Dai, Dengxin and Van Gool, Luc},title = {PathTrack: Fast Trajectory Annotation With Path Supervision},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Progress in Multiple Object Tracking (MOT) has been limited by the size of the available datasets. We present an efficient framework to annotate trajectories and use it to produce a MOT dataset of unprecedented size. A novel path supervision paradigm lets the annotator loosely track the object with a cursor while watch... | [
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31 | Tracking the Untrackable: Learning to Track Multiple Cues With Long-Term Dependencies | [
"Amir Sadeghian",
"Alexandre Alahi",
"Silvio Savarese"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Sadeghian_Tracking_the_Untrackable_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Sadeghian_Tracking_the_Untrackable_ICCV_2017_paper.pdf | null | 1701.01909v2 | cvf | @InProceedings{Sadeghian_2017_ICCV,author = {Sadeghian, Amir and Alahi, Alexandre and Savarese, Silvio},title = {Tracking the Untrackable: Learning to Track Multiple Cues With Long-Term Dependencies},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues over a long period of time in a coherent fashion. In this paper, we present an online method that encodes long-term temporal dependencies across multiple cues. One key challenge of tracking methods is to accurately track o... | [
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32 | MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation | [
"Junhwa Hur",
"Stefan Roth"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Hur_MirrorFlow_Exploiting_Symmetries_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Hur_MirrorFlow_Exploiting_Symmetries_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Hur_MirrorFlow_Exploiting_Symmetries_ICCV_2017_supplemental.pdf | 1708.05355v1 | cvf | @InProceedings{Hur_2017_ICCV,author = {Hur, Junhwa and Roth, Stefan},title = {MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Optical flow estimation is one of the most studied problems in computer vision, yet recent benchmark datasets continue to reveal problem areas of today's approaches. Occlusions have remained one of the key challenges. In this paper, we propose a symmetric optical flow method to address the well-known chicken-and-egg re... | [
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33 | Tracking as Online Decision-Making: Learning a Policy From Streaming Videos With Reinforcement Learning | [
"James Supancic,III",
"Deva Ramanan"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Supancic_Tracking_as_Online_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Supancic_Tracking_as_Online_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Supancic_Tracking_as_Online_ICCV_2017_supplemental.pdf | 1707.04991 | title_snapshot | @InProceedings{Supancic_2017_ICCV,author = {Supancic,III, James and Ramanan, Deva},title = {Tracking as Online Decision-Making: Learning a Policy From Streaming Videos With Reinforcement Learning},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We formulate tracking as an online decision-making process, where a tracking agent must follow an object despite ambiguous image frames and a limited computational budget. Crucially, the agent must decide where to look in the upcoming frames, when to reinitialize because it believes the target has been lost, and when t... | [
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34 | Non-Convex Rank/Sparsity Regularization and Local Minima | [
"Carl Olsson",
"Marcus Carlsson",
"Fredrik Andersson",
"Viktor Larsson"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Olsson_Non-Convex_RankSparsity_Regularization_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Olsson_Non-Convex_RankSparsity_Regularization_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Olsson_Non-Convex_RankSparsity_Regularization_ICCV_2017_supplemental.pdf | 1703.07171v1 | cvf | @InProceedings{Olsson_2017_ICCV,author = {Olsson, Carl and Carlsson, Marcus and Andersson, Fredrik and Larsson, Viktor},title = {Non-Convex Rank/Sparsity Regularization and Local Minima},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | This paper considers the problem of recovering either a low rank matrix or a sparse vector from observations of linear combinations of the vector or matrix elements. Recent methods replace the non-convex regularization with l1 or nuclear norm relaxations. It is well known that this approach recovers near optimal soluti... | [
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35 | A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework | [
"Weixin Luo",
"Wen Liu",
"Shenghua Gao"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Luo_A_Revisit_of_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Luo_A_Revisit_of_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Luo_2017_ICCV,author = {Luo, Weixin and Liu, Wen and Gao, Shenghua},title = {A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Motivated by the capability of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames be encoded with similar reconstruction coefficients. Then we map the TSC with a special type of stacked Recurrent Neural Network (sRNN). By taking advan... | [
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36 | HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis | [
"Xihui Liu",
"Haiyu Zhao",
"Maoqing Tian",
"Lu Sheng",
"Jing Shao",
"Shuai Yi",
"Junjie Yan",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Liu_HydraPlus-Net_Attentive_Deep_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_HydraPlus-Net_Attentive_Deep_ICCV_2017_paper.pdf | null | 1709.09930 | title_snapshot | @InProceedings{Liu_2017_ICCV,author = {Liu, Xihui and Zhao, Haiyu and Tian, Maoqing and Sheng, Lu and Shao, Jing and Yi, Shuai and Yan, Junjie and Wang, Xiaogang},title = {HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (IC... | Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems. Despite that the convolutional neural networks are remarkable in learning discriminative features from images, the learning of comprehensive features of pedestrians for fine-grai... | [
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37 | No Fuss Distance Metric Learning Using Proxies | [
"Yair Movshovitz-Attias",
"Alexander Toshev",
"Thomas K. Leung",
"Sergey Ioffe",
"Saurabh Singh"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Movshovitz-Attias_No_Fuss_Distance_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Movshovitz-Attias_No_Fuss_Distance_ICCV_2017_paper.pdf | null | 1703.07464 | cvf | @InProceedings{Movshovitz-Attias_2017_ICCV,author = {Movshovitz-Attias, Yair and Toshev, Alexander and Leung, Thomas K. and Ioffe, Sergey and Singh, Saurabh},title = {No Fuss Distance Metric Learning Using Proxies},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},ye... | We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point x is similar to a set of positive points... | [
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38 | Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation | [
"Matteo Ruggero Ronchi",
"Pietro Perona"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Ronchi_Benchmarking_and_Error_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Ronchi_Benchmarking_and_Error_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Ronchi_Benchmarking_and_Error_ICCV_2017_supplemental.zip | 1707.05388v2 | cvf | @InProceedings{Ronchi_2017_ICCV,author = {Ruggero Ronchi, Matteo and Perona, Pietro},title = {Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We propose a new method to analyze the impact of errors in algorithms for multi-instance pose estimation and a principled benchmark that can be used to compare them. We define and characterize three classes of errors - localization, scoring, and background - study how they are influenced by instance attributes and thei... | [
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39 | Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification | [
"Zhongdao Wang",
"Luming Tang",
"Xihui Liu",
"Zhuliang Yao",
"Shuai Yi",
"Jing Shao",
"Junjie Yan",
"Shengjin Wang",
"Hongsheng Li",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Wang_Orientation_Invariant_Feature_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Wang_Orientation_Invariant_Feature_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Wang_2017_ICCV,author = {Wang, Zhongdao and Tang, Luming and Liu, Xihui and Yao, Zhuliang and Yi, Shuai and Shao, Jing and Yan, Junjie and Wang, Shengjin and Li, Hongsheng and Wang, Xiaogang},title = {Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identificatio... | In this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation... | [
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40 | Fashion Forward: Forecasting Visual Style in Fashion | [
"Ziad Al-Halah",
"Rainer Stiefelhagen",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Al-Halah_Fashion_Forward_Forecasting_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Al-Halah_Fashion_Forward_Forecasting_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Al-Halah_Fashion_Forward_Forecasting_ICCV_2017_supplemental.pdf | 1705.06394 | cvf | @InProceedings{Al-Halah_2017_ICCV,author = {Al-Halah, Ziad and Stiefelhagen, Rainer and Grauman, Kristen},title = {Fashion Forward: Forecasting Visual Style in Fashion},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | What is the future of fashion? Tackling this question from a data-driven vision perspective, we propose to forecast visual style trends before they occur. We introduce the first approach to predict the future popularity of styles discovered from fashion images in an unsupervised manner. Using these styles as a basis, w... | [
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41 | Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach | [
"Xingyi Zhou",
"Qixing Huang",
"Xiao Sun",
"Xiangyang Xue",
"Yichen Wei"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhou_Towards_3D_Human_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhou_Towards_3D_Human_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Zhou_Towards_3D_Human_ICCV_2017_supplemental.pdf | 1704.02447v2 | cvf | @InProceedings{Zhou_2017_ICCV,author = {Zhou, Xingyi and Huang, Qixing and Sun, Xiao and Xue, Xiangyang and Wei, Yichen},title = {Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {201... | In this paper, we study the task of 3D human pose estimation in the wild. This task is challenging due to lack of training data, as existing datasets are either in the wild images with 2D pose or in the lab images with 3D pose. We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in ... | [
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42 | Flow-Guided Feature Aggregation for Video Object Detection | [
"Xizhou Zhu",
"Yujie Wang",
"Jifeng Dai",
"Lu Yuan",
"Yichen Wei"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Flow-Guided_Feature_Aggregation_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Flow-Guided_Feature_Aggregation_ICCV_2017_paper.pdf | null | 1703.10025v2 | cvf | @InProceedings{Zhu_2017_ICCV,author = {Zhu, Xizhou and Wang, Yujie and Dai, Jifeng and Yuan, Lu and Wei, Yichen},title = {Flow-Guided Feature Aggregation for Video Object Detection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to... | [
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43 | Reasoning About Fine-Grained Attribute Phrases Using Reference Games | [
"Jong-Chyi Su",
"Chenyun Wu",
"Huaizu Jiang",
"Subhransu Maji"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Su_Reasoning_About_Fine-Grained_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Su_Reasoning_About_Fine-Grained_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Su_Reasoning_About_Fine-Grained_ICCV_2017_supplemental.pdf | 1708.08874v1 | cvf | @InProceedings{Su_2017_ICCV,author = {Su, Jong-Chyi and Wu, Chenyun and Jiang, Huaizu and Maji, Subhransu},title = {Reasoning About Fine-Grained Attribute Phrases Using Reference Games},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We present a framework for learning to describe fine-grained visual differences between instances using attribute phrases. Attribute phrases capture distinguishing aspects of an object (e.g., "propeller on the nose" or "door near the wing" for airplanes) in a compositional manner. Instances within a category can be des... | [
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44 | DeNet: Scalable Real-Time Object Detection With Directed Sparse Sampling | [
"Lachlan Tychsen-Smith",
"Lars Petersson"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Tychsen-Smith_DeNet_Scalable_Real-Time_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Tychsen-Smith_DeNet_Scalable_Real-Time_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Tychsen-Smith_DeNet_Scalable_Real-Time_ICCV_2017_supplemental.pdf | 1703.10295 | cvf | @InProceedings{Tychsen-Smith_2017_ICCV,author = {Tychsen-Smith, Lachlan and Petersson, Lars},title = {DeNet: Scalable Real-Time Object Detection With Directed Sparse Sampling},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We define the object detection from imagery problem as estimating a very large but extremely sparse bounding box dependent probability distribution. Subsequently we identify a sparse distribution estimation scheme, Directed Sparse Sampling, and employ it in a single end-to-end CNN based detection model. This methodolog... | [
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45 | MIHash: Online Hashing With Mutual Information | [
"Fatih Cakir",
"Kun He",
"Sarah Adel Bargal",
"Stan Sclaroff"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Cakir_MIHash_Online_Hashing_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Cakir_MIHash_Online_Hashing_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Cakir_MIHash_Online_Hashing_ICCV_2017_supplemental.pdf | 1703.08919v2 | cvf | @InProceedings{Cakir_2017_ICCV,author = {Cakir, Fatih and He, Kun and Adel Bargal, Sarah and Sclaroff, Stan},title = {MIHash: Online Hashing With Mutual Information},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data. In this paper, we first address a key challenge for online hashing: the binary codes for indexed da... | [
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46 | SafetyNet: Detecting and Rejecting Adversarial Examples Robustly | [
"Jiajun Lu",
"Theerasit Issaranon",
"David Forsyth"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Lu_SafetyNet_Detecting_and_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Lu_SafetyNet_Detecting_and_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Lu_SafetyNet_Detecting_and_ICCV_2017_supplemental.pdf | 1704.00103v2 | cvf | @InProceedings{Lu_2017_ICCV,author = {Lu, Jiajun and Issaranon, Theerasit and Forsyth, David},title = {SafetyNet: Detecting and Rejecting Adversarial Examples Robustly},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples. Our construction suggests some insights into how deep networks work. We provide a reasonable analyses that our construction is difficult to defeat, and show experimentally that our metho... | [
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47 | Recurrent Models for Situation Recognition | [
"Arun Mallya",
"Svetlana Lazebnik"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Mallya_Recurrent_Models_for_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Mallya_Recurrent_Models_for_ICCV_2017_paper.pdf | null | 1703.06233v2 | cvf | @InProceedings{Mallya_2017_ICCV,author = {Mallya, Arun and Lazebnik, Svetlana},title = {Recurrent Models for Situation Recognition},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | This work proposes Recurrent Neural Network (RNN) models to predict structured 'image situations' -- actions and noun entities fulfilling semantic roles related to the action. In contrast to prior work relying on Conditional Random Fields (CRFs), we use a specialized action prediction network followed by an RNN for nou... | [
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48 | Multi-Label Image Recognition by Recurrently Discovering Attentional Regions | [
"Zhouxia Wang",
"Tianshui Chen",
"Guanbin Li",
"Ruijia Xu",
"Liang Lin"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Wang_Multi-Label_Image_Recognition_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Wang_Multi-Label_Image_Recognition_ICCV_2017_paper.pdf | null | 1711.02816v1 | cvf | @InProceedings{Wang_2017_ICCV,author = {Wang, Zhouxia and Chen, Tianshui and Li, Guanbin and Xu, Ruijia and Lin, Liang},title = {Multi-Label Image Recognition by Recurrently Discovering Attentional Regions},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {20... | This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding. Current solutions for this task usually rely on an extra step of extracting hypothesis regions (i.e., region proposals), resulting in redundant computation and s... | [
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49 | Deep Determinantal Point Process for Large-Scale Multi-Label Classification | [
"Pengtao Xie",
"Ruslan Salakhutdinov",
"Luntian Mou",
"Eric P. Xing"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Xie_Deep_Determinantal_Point_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Xie_Deep_Determinantal_Point_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Xie_2017_ICCV,author = {Xie, Pengtao and Salakhutdinov, Ruslan and Mou, Luntian and Xing, Eric P.},title = {Deep Determinantal Point Process for Large-Scale Multi-Label Classification},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We study large-scale multi-label classification (MLC) on two recently released datasets: Youtube-8M and Open Images that contain millions of data instances and thousands of classes. The unprecedented problem scale poses great challenges for MLC. First, finding out the correct label subset out of exponentially many choi... | [
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50 | Visual Semantic Planning Using Deep Successor Representations | [
"Yuke Zhu",
"Daniel Gordon",
"Eric Kolve",
"Dieter Fox",
"Li Fei-Fei",
"Abhinav Gupta",
"Roozbeh Mottaghi",
"Ali Farhadi"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Visual_Semantic_Planning_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Visual_Semantic_Planning_ICCV_2017_paper.pdf | null | 1705.08080v2 | cvf | @InProceedings{Zhu_2017_ICCV,author = {Zhu, Yuke and Gordon, Daniel and Kolve, Eric and Fox, Dieter and Fei-Fei, Li and Gupta, Abhinav and Mottaghi, Roozbeh and Farhadi, Ali},title = {Visual Semantic Planning Using Deep Successor Representations},booktitle = {Proceedings of the IEEE International Conference on Computer... | A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world. In this work, we address the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a dynamic environment fr... | [
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51 | Neural Person Search Machines | [
"Hao Liu",
"Jiashi Feng",
"Zequn Jie",
"Karlekar Jayashree",
"Bo Zhao",
"Meibin Qi",
"Jianguo Jiang",
"Shuicheng Yan"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Liu_Neural_Person_Search_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Neural_Person_Search_ICCV_2017_paper.pdf | null | 1707.06777v1 | cvf | @InProceedings{Liu_2017_ICCV,author = {Liu, Hao and Feng, Jiashi and Jie, Zequn and Jayashree, Karlekar and Zhao, Bo and Qi, Meibin and Jiang, Jianguo and Yan, Shuicheng},title = {Neural Person Search Machines},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year =... | We investigate the problem of person search in the wild in this work. Instead of comparing the query against all candidate regions generated in a query-blind manner, we propose to recursively shrink the search area from the whole image till achieving precise localization of the target person, by fully exploiting inform... | [
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52 | DualNet: Learn Complementary Features for Image Recognition | [
"Saihui Hou",
"Xu Liu",
"Zilei Wang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Hou_DualNet_Learn_Complementary_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_DualNet_Learn_Complementary_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Hou_DualNet_Learn_Complementary_ICCV_2017_supplemental.pdf | null | null | @InProceedings{Hou_2017_ICCV,author = {Hou, Saihui and Liu, Xu and Wang, Zilei},title = {DualNet: Learn Complementary Features for Image Recognition},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | In this work we propose a novel framework named DualNet aiming at learning more accurate representation for image recognition. Here two parallel neural networks are coordinated to learn complementary features and thus a wider network is constructed. Specifically, we logically divide an end-to-end deep convolutional neu... | [
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53 | Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization | [
"Sijia Cai",
"Wangmeng Zuo",
"Lei Zhang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Cai_Higher-Order_Integration_of_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Cai_Higher-Order_Integration_of_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Cai_2017_ICCV,author = {Cai, Sijia and Zuo, Wangmeng and Zhang, Lei},title = {Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | The success of fine-grained visual categorization (FGVC) extremely relies on the modeling of appearance and interactions of various semantic parts. This makes FGVC very challenging because: (i) part annotation and detection require expert guidance and are very expensive; (ii) parts are of different sizes; and (iii) the... | [
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54 | Show, Adapt and Tell: Adversarial Training of Cross-Domain Image Captioner | [
"Tseng-Hung Chen",
"Yuan-Hong Liao",
"Ching-Yao Chuang",
"Wan-Ting Hsu",
"Jianlong Fu",
"Min Sun"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Chen_Show_Adapt_and_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Show_Adapt_and_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Chen_Show_Adapt_and_ICCV_2017_supplemental.pdf | 1705.00930v2 | cvf | @InProceedings{Chen_2017_ICCV,author = {Chen, Tseng-Hung and Liao, Yuan-Hong and Chuang, Ching-Yao and Hsu, Wan-Ting and Fu, Jianlong and Sun, Min},title = {Show, Adapt and Tell: Adversarial Training of Cross-Domain Image Captioner},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)... | Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel a... | [
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55 | Attribute Recognition by Joint Recurrent Learning of Context and Correlation | [
"Jingya Wang",
"Xiatian Zhu",
"Shaogang Gong",
"Wei Li"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Wang_Attribute_Recognition_by_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Wang_Attribute_Recognition_by_ICCV_2017_paper.pdf | null | 1709.08553v1 | cvf | @InProceedings{Wang_2017_ICCV,author = {Wang, Jingya and Zhu, Xiatian and Gong, Shaogang and Li, Wei},title = {Attribute Recognition by Joint Recurrent Learning of Context and Correlation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Recognising semantic pedestrian attributes in surveillance images is a challenging task for computer vision, particularly when the imaging quality is poor with complex background clutter and uncontrolled viewing conditions, and the number of labelled training data is small. In this work, we formulate a Joint Recurrent ... | [
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56 | VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization | [
"Saihui Hou",
"Yushan Feng",
"Zilei Wang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Hou_VegFru_A_Domain-Specific_ICCV_2017_supplemental.pdf | null | null | @InProceedings{Hou_2017_ICCV,author = {Hou, Saihui and Feng, Yushan and Wang, Zilei},title = {VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | VegFru: A Domain-Specific Dataset for Fine-grained Visual Categorization In this paper, we propose a novel domain-specific dataset named VegFru for fine-grained visual categorization (FGVC). While the existing datasets for FGVC are mainly focused on animal breeds or man-made objects with limited labelled data, VegFru i... | [
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57 | Increasing CNN Robustness to Occlusions by Reducing Filter Support | [
"Elad Osherov",
"Michael Lindenbaum"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Osherov_Increasing_CNN_Robustness_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Osherov_Increasing_CNN_Robustness_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Osherov_2017_ICCV,author = {Osherov, Elad and Lindenbaum, Michael},title = {Increasing CNN Robustness to Occlusions by Reducing Filter Support},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Convolutional neural networks (CNNs) provide the current state of the art in visual object classification, but they are far less accurate when classifying partially occluded objects. A straightforward way to improve classification under occlusion conditions is to train the classifier using partially occluded object exa... | [
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58 | Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-Similar Vehicles | [
"Ke Yan",
"Yonghong Tian",
"Yaowei Wang",
"Wei Zeng",
"Tiejun Huang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Yan_Exploiting_Multi-Grain_Ranking_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Yan_Exploiting_Multi-Grain_Ranking_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Yan_2017_ICCV,author = {Yan, Ke and Tian, Yonghong and Wang, Yaowei and Zeng, Wei and Huang, Tiejun},title = {Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-Similar Vehicles},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct... | Precise search of visually-similar vehicles poses a great challenge in computer vision, which needs to find exactly the same vehicle among a massive vehicles with visually similar appearances for a given query image. In this paper, we model the relationship of vehicle images as multiple grains. Following this, we propo... | [
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59 | Recurrent Scale Approximation for Object Detection in CNN | [
"Yu Liu",
"Hongyang Li",
"Junjie Yan",
"Fangyin Wei",
"Xiaogang Wang",
"Xiaoou Tang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Liu_Recurrent_Scale_Approximation_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Recurrent_Scale_Approximation_ICCV_2017_paper.pdf | null | 1707.09531v2 | cvf | @InProceedings{Liu_2017_ICCV,author = {Liu, Yu and Li, Hongyang and Yan, Junjie and Wei, Fangyin and Wang, Xiaogang and Tang, Xiaoou},title = {Recurrent Scale Approximation for Object Detection in CNN},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Since convolutional neural network (CNN) lacks an inherent mechanism to handle large scale variations, we always need to compute feature maps multiple times for multi-scale object detection, which has the bottleneck of computational cost in practice. To address this, we devise a recurrent scale approximation (RSA) to c... | [
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60 | Embedding 3D Geometric Features for Rigid Object Part Segmentation | [
"Yafei Song",
"Xiaowu Chen",
"Jia Li",
"Qinping Zhao"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Song_Embedding_3D_Geometric_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Song_Embedding_3D_Geometric_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Song_2017_ICCV,author = {Song, Yafei and Chen, Xiaowu and Li, Jia and Zhao, Qinping},title = {Embedding 3D Geometric Features for Rigid Object Part Segmentation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Object part segmentation is a challenging and fundamental problem in computer vision. Its difficulties may be caused by the varying viewpoints, poses, and topological structures, which can be attributed to an essential reason, i.e., a specific object is a 3D model rather than a 2D figure. Therefore, we conjecture that ... | [
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61 | Towards Context-Aware Interaction Recognition for Visual Relationship Detection | [
"Bohan Zhuang",
"Lingqiao Liu",
"Chunhua Shen",
"Ian Reid"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhuang_Towards_Context-Aware_Interaction_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhuang_Towards_Context-Aware_Interaction_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Zhuang_2017_ICCV,author = {Zhuang, Bohan and Liu, Lingqiao and Shen, Chunhua and Reid, Ian},title = {Towards Context-Aware Interaction Recognition for Visual Relationship Detection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Recognizing how objects interact with each other is a crucial task in visual recognition. If we define the context of the interaction to be the objects involved, then most current methods can be categorized as either: (i) training a single classifier on the combination of the interaction and its context; or (ii) aiming... | [
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62 | When Unsupervised Domain Adaptation Meets Tensor Representations | [
"Hao Lu",
"Lei Zhang",
"Zhiguo Cao",
"Wei Wei",
"Ke Xian",
"Chunhua Shen",
"Anton van den Hengel"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Lu_When_Unsupervised_Domain_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Lu_When_Unsupervised_Domain_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Lu_When_Unsupervised_Domain_ICCV_2017_supplemental.pdf | 1707.05956v1 | cvf | @InProceedings{Lu_2017_ICCV,author = {Lu, Hao and Zhang, Lei and Cao, Zhiguo and Wei, Wei and Xian, Ke and Shen, Chunhua and van den Hengel, Anton},title = {When Unsupervised Domain Adaptation Meets Tensor Representations},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = ... | Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another. It is thus of great practical importance to the application of such methods. Despite the fact that tensor representations are widely used in Computer Vision to capture multi-lin... | [
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63 | Look, Listen and Learn | [
"Relja Arandjelovic",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Arandjelovic_Look_Listen_and_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Arandjelovic_Look_Listen_and_ICCV_2017_paper.pdf | null | 1705.08168v2 | cvf | @InProceedings{Arandjelovic_2017_ICCV,author = {Arandjelovic, Relja and Zisserman, Andrew},title = {Look, Listen and Learn},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos? There is a valuable, but so far untapped, source of information contained in the video itself -- the correspondence between the visual and the audio streams, and we introduce a novel "Audio-Visual Correspond... | [
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64 | Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization | [
"Ramprasaath R. Selvaraju",
"Michael Cogswell",
"Abhishek Das",
"Ramakrishna Vedantam",
"Devi Parikh",
"Dhruv Batra"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_supplemental.zip | 1610.02391 | title_snapshot | @InProceedings{Selvaraju_2017_ICCV,author = {Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},title = {Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization},booktitle = {Proceedings of the IEEE International Confer... | We propose a technique for producing 'visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say logits for 'dog' or even a cap... | [
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65 | Image-Based Localization Using LSTMs for Structured Feature Correlation | [
"Florian Walch",
"Caner Hazirbas",
"Laura Leal-Taixe",
"Torsten Sattler",
"Sebastian Hilsenbeck",
"Daniel Cremers"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Walch_Image-Based_Localization_Using_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Walch_Image-Based_Localization_Using_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Walch_Image-Based_Localization_Using_ICCV_2017_supplemental.pdf | 1611.07890 | cvf | @InProceedings{Walch_2017_ICCV,author = {Walch, Florian and Hazirbas, Caner and Leal-Taixe, Laura and Sattler, Torsten and Hilsenbeck, Sebastian and Cremers, Daniel},title = {Image-Based Localization Using LSTMs for Structured Feature Correlation},booktitle = {Proceedings of the IEEE International Conference on Compute... | In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output, which play the role of a struc... | [
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66 | Personalized Image Aesthetics | [
"Jian Ren",
"Xiaohui Shen",
"Zhe Lin",
"Radomir Mech",
"David J. Foran"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Ren_Personalized_Image_Aesthetics_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Ren_Personalized_Image_Aesthetics_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Ren_Personalized_Image_Aesthetics_ICCV_2017_supplemental.pdf | null | null | @InProceedings{Ren_2017_ICCV,author = {Ren, Jian and Shen, Xiaohui and Lin, Zhe and Mech, Radomir and Foran, David J.},title = {Personalized Image Aesthetics},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Automatic image aesthetics rating has received a growing interest with the recent breakthrough in deep learning. Although many studies exist for learning a generic or universal aesthetics model, investigation of aesthetics models incorporating individual user's preference is quite limited. We address this personalized ... | [
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67 | Predicting Deeper Into the Future of Semantic Segmentation | [
"Pauline Luc",
"Natalia Neverova",
"Camille Couprie",
"Jakob Verbeek",
"Yann LeCun"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Luc_Predicting_Deeper_Into_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Luc_Predicting_Deeper_Into_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Luc_Predicting_Deeper_Into_ICCV_2017_supplemental.pdf | 1703.07684v3 | cvf | @InProceedings{Luc_2017_ICCV,author = {Luc, Pauline and Neverova, Natalia and Couprie, Camille and Verbeek, Jakob and LeCun, Yann},title = {Predicting Deeper Into the Future of Semantic Segmentation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g . in robotics or autonomous driving, which depend on visual scene understanding for decision making. While prediction of the raw RGB pixel values in future v... | [
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68 | Coordinating Filters for Faster Deep Neural Networks | [
"Wei Wen",
"Cong Xu",
"Chunpeng Wu",
"Yandan Wang",
"Yiran Chen",
"Hai Li"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Wen_Coordinating_Filters_for_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Wen_Coordinating_Filters_for_ICCV_2017_paper.pdf | null | 1703.09746v3 | cvf | @InProceedings{Wen_2017_ICCV,author = {Wen, Wei and Xu, Cong and Wu, Chunpeng and Wang, Yandan and Chen, Yiran and Li, Hai},title = {Coordinating Filters for Faster Deep Neural Networks},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Very large-scale Deep Neural Networks (DNNs) have achieved remarkable successes in a large variety of computer vision tasks. However, the high computation intensity of DNNs makes it challenging to deploy these models on resource-limited systems. Some studies used low-rank approaches that approximate the filters by low-... | [
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69 | Unsupervised Representation Learning by Sorting Sequences | [
"Hsin-Ying Lee",
"Jia-Bin Huang",
"Maneesh Singh",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Lee_Unsupervised_Representation_Learning_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Lee_Unsupervised_Representation_Learning_ICCV_2017_paper.pdf | null | 1708.01246v1 | cvf | @InProceedings{Lee_2017_ICCV,author = {Lee, Hsin-Ying and Huang, Jia-Bin and Singh, Maneesh and Yang, Ming-Hsuan},title = {Unsupervised Representation Learning by Sorting Sequences},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e. in non-chronological order) as inputs and train a convo... | [
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70 | A Read-Write Memory Network for Movie Story Understanding | [
"Seil Na",
"Sangho Lee",
"Jisung Kim",
"Gunhee Kim"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Na_A_Read-Write_Memory_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Na_A_Read-Write_Memory_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Na_A_Read-Write_Memory_ICCV_2017_supplemental.pdf | 1709.09345v4 | cvf | @InProceedings{Na_2017_ICCV,author = {Na, Seil and Lee, Sangho and Kim, Jisung and Kim, Gunhee},title = {A Read-Write Memory Network for Movie Story Understanding},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We propose a novel memory network model named Read-Write Memory Network (RWMN) to perform question and answering tasks for large-scale, multimodal movie story understanding. The key focus of our RWMN model is to design the read network and the write network that consist of multiple convolutional layers, which enable me... | [
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71 | SegFlow: Joint Learning for Video Object Segmentation and Optical Flow | [
"Jingchun Cheng",
"Yi-Hsuan Tsai",
"Shengjin Wang",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Cheng_SegFlow_Joint_Learning_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_SegFlow_Joint_Learning_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Cheng_SegFlow_Joint_Learning_ICCV_2017_supplemental.pdf | 1709.06750v1 | cvf | @InProceedings{Cheng_2017_ICCV,author = {Cheng, Jingchun and Tsai, Yi-Hsuan and Wang, Shengjin and Yang, Ming-Hsuan},title = {SegFlow: Joint Learning for Video Object Segmentation and Optical Flow},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos. The proposed SegFlow has two branches where useful information of object segmentation and optical flow is propagated bidirectionally in a unified framework. The segmenta... | [
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72 | Unsupervised Action Discovery and Localization in Videos | [
"Khurram Soomro",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Soomro_Unsupervised_Action_Discovery_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Soomro_Unsupervised_Action_Discovery_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Soomro_2017_ICCV,author = {Soomro, Khurram and Shah, Mubarak},title = {Unsupervised Action Discovery and Localization in Videos},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | This paper is the first to address the problem of unsupervised action localization in videos. Given unlabeled data without bounding box annotations, we propose a novel approach that: 1) Discovers action class labels and 2) Spatio-temporally localizes actions in videos. It begins by computing local video features to app... | [
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73 | Dense-Captioning Events in Videos | [
"Ranjay Krishna",
"Kenji Hata",
"Frederic Ren",
"Li Fei-Fei",
"Juan Carlos Niebles"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Krishna_Dense-Captioning_Events_in_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Krishna_Dense-Captioning_Events_in_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Krishna_Dense-Captioning_Events_in_ICCV_2017_supplemental.pdf | 1705.00754v1 | cvf | @InProceedings{Krishna_2017_ICCV,author = {Krishna, Ranjay and Hata, Kenji and Ren, Frederic and Fei-Fei, Li and Carlos Niebles, Juan},title = {Dense-Captioning Events in Videos},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Most natural videos contain numerous events. For example, in a video of a "man playing a piano", the video might also contain "another man dancing" or "a crowd clapping". We introduce the task of dense-captioning events, which involves both detecting and describing events in a video. We propose a new model that is able... | [
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74 | Learning Long-Term Dependencies for Action Recognition With a Biologically-Inspired Deep Network | [
"Yemin Shi",
"Yonghong Tian",
"Yaowei Wang",
"Wei Zeng",
"Tiejun Huang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Shi_Learning_Long-Term_Dependencies_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Shi_Learning_Long-Term_Dependencies_ICCV_2017_paper.pdf | null | 1611.05216v3 | cvf | @InProceedings{Shi_2017_ICCV,author = {Shi, Yemin and Tian, Yonghong and Wang, Yaowei and Zeng, Wei and Huang, Tiejun},title = {Learning Long-Term Dependencies for Action Recognition With a Biologically-Inspired Deep Network},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month... | Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task. As one of the key models for sequence learning, recurrent neural network (RNN) and its variants such as long short term memory (LSTM) and gated recurrent uni... | [
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75 | Compressive Quantization for Fast Object Instance Search in Videos | [
"Tan Yu",
"Zhenzhen Wang",
"Junsong Yuan"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Yu_Compressive_Quantization_for_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Yu_Compressive_Quantization_for_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Yu_2017_ICCV,author = {Yu, Tan and Wang, Zhenzhen and Yuan, Junsong},title = {Compressive Quantization for Fast Object Instance Search in Videos},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Most of current visual search systems focus on image-to-image (point-to-point) search such as image and object retrieval. Nevertheless, fast image-to-video (point-to-set) search is much less exploited. This paper tackles object instance search in videos, where efficient point-to-set matching is essential. Through joint... | [
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76 | Complex Event Detection by Identifying Reliable Shots From Untrimmed Videos | [
"Hehe Fan",
"Xiaojun Chang",
"De Cheng",
"Yi Yang",
"Dong Xu",
"Alexander G. Hauptmann"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Fan_Complex_Event_Detection_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Fan_Complex_Event_Detection_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Fan_2017_ICCV,author = {Fan, Hehe and Chang, Xiaojun and Cheng, De and Yang, Yi and Xu, Dong and Hauptmann, Alexander G.},title = {Complex Event Detection by Identifying Reliable Shots From Untrimmed Videos},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = ... | The goal of complex event detection is to automatically detect whether an event of interest happens in temporally untrimmed long videos which usually consist of multiple video shots. Observing some video shots in positive (resp. negative) videos are irrelevant (resp. relevant) to the given event class, we formulate thi... | [
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77 | Deep Direct Regression for Multi-Oriented Scene Text Detection | [
"Wenhao He",
"Xu-Yao Zhang",
"Fei Yin",
"Cheng-Lin Liu"
] | https://openaccess.thecvf.com/content_iccv_2017/html/He_Deep_Direct_Regression_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/He_Deep_Direct_Regression_ICCV_2017_paper.pdf | null | 1703.08289v1 | cvf | @InProceedings{He_2017_ICCV,author = {He, Wenhao and Zhang, Xu-Yao and Yin, Fei and Liu, Cheng-Lin},title = {Deep Direct Regression for Multi-Oriented Scene Text Detection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some bounding box proposal... | [
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78 | Open Set Domain Adaptation | [
"Pau Panareda Busto",
"Juergen Gall"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Busto_Open_Set_Domain_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Busto_Open_Set_Domain_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Busto_Open_Set_Domain_ICCV_2017_supplemental.pdf | 1907.12865v1 | cvf | @InProceedings{Busto_2017_ICCV,author = {Panareda Busto, Pau and Gall, Juergen},title = {Open Set Domain Adaptation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | When the training and the test data belong to different domains, the accuracy of an object classifier is significantly reduced. Therefore, several algorithms have been proposed in the last years to diminish the so called domain shift between datasets. However, all available evaluation protocols for domain adaptation de... | [
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79 | Deformable Convolutional Networks | [
"Jifeng Dai",
"Haozhi Qi",
"Yuwen Xiong",
"Yi Li",
"Guodong Zhang",
"Han Hu",
"Yichen Wei"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Dai_Deformable_Convolutional_Networks_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Dai_Deformable_Convolutional_Networks_ICCV_2017_paper.pdf | null | 1703.06211v3 | cvf | @InProceedings{Dai_2017_ICCV,author = {Dai, Jifeng and Qi, Haozhi and Xiong, Yuwen and Li, Yi and Zhang, Guodong and Hu, Han and Wei, Yichen},title = {Deformable Convolutional Networks},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Bot... | [
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80 | Ensemble Diffusion for Retrieval | [
"Song Bai",
"Zhichao Zhou",
"Jingdong Wang",
"Xiang Bai",
"Longin Jan Latecki",
"Qi Tian"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Bai_Ensemble_Diffusion_for_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Bai_Ensemble_Diffusion_for_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Bai_Ensemble_Diffusion_for_ICCV_2017_supplemental.pdf | null | null | @InProceedings{Bai_2017_ICCV,author = {Bai, Song and Zhou, Zhichao and Wang, Jingdong and Bai, Xiang and Jan Latecki, Longin and Tian, Qi},title = {Ensemble Diffusion for Retrieval},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | As a postprocessing procedure, diffusion process has demonstrated its ability of substantially improving the performance of various visual retrieval systems. Whereas, great efforts are also devoted to similarity (or metric) fusion, seeing that only one individual type of similarity cannot fully reveal the intrinsic rel... | [
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81 | FoveaNet: Perspective-Aware Urban Scene Parsing | [
"Xin Li",
"Zequn Jie",
"Wei Wang",
"Changsong Liu",
"Jimei Yang",
"Xiaohui Shen",
"Zhe Lin",
"Qiang Chen",
"Shuicheng Yan",
"Jiashi Feng"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Li_FoveaNet_Perspective-Aware_Urban_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Li_FoveaNet_Perspective-Aware_Urban_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Li_FoveaNet_Perspective-Aware_Urban_ICCV_2017_supplemental.pdf | 1708.02421v1 | cvf | @InProceedings{Li_2017_ICCV,author = {Li, Xin and Jie, Zequn and Wang, Wei and Liu, Changsong and Yang, Jimei and Shen, Xiaohui and Lin, Zhe and Chen, Qiang and Yan, Shuicheng and Feng, Jiashi},title = {FoveaNet: Perspective-Aware Urban Scene Parsing},booktitle = {Proceedings of the IEEE International Conference on Com... | Parsing urban scene images is critical for self-driving. Most of current solutions employ generic image parsing models that treat all scales and locations in the images equally and do not consider the geometry property of car-captured urban scene images. Thus, they suffer from heterogeneous object scales caused by pers... | [
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82 | Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild | [
"Christopher Funk",
"Yanxi Liu"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Funk_Beyond_Planar_Symmetry_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Funk_Beyond_Planar_Symmetry_ICCV_2017_paper.pdf | null | 1704.03568v2 | cvf | @InProceedings{Funk_2017_ICCV,author = {Funk, Christopher and Liu, Yanxi},title = {Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive. Motivated by a new study demonstrating the extremely high inter-person accuracy of human perceived symmetries in the wild, we have constructed the first ... | [
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83 | Learning to Reason: End-To-End Module Networks for Visual Question Answering | [
"Ronghang Hu",
"Jacob Andreas",
"Marcus Rohrbach",
"Trevor Darrell",
"Kate Saenko"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Hu_Learning_to_Reason_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Hu_Learning_to_Reason_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Hu_Learning_to_Reason_ICCV_2017_supplemental.zip | 1704.05526v3 | cvf | @InProceedings{Hu_2017_ICCV,author = {Hu, Ronghang and Andreas, Jacob and Rohrbach, Marcus and Darrell, Trevor and Saenko, Kate},title = {Learning to Reason: End-To-End Module Networks for Visual Question Answering},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},y... | Natural language questions are inherently compositional, and many are most easily answered by reasoning about their decomposition into modular sub-problems. For example, to answer "is there an equal number of balls and boxes?" we can look for balls, look for boxes, count them, and compare the results. The recently prop... | [
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84 | Hard-Aware Deeply Cascaded Embedding | [
"Yuhui Yuan",
"Kuiyuan Yang",
"Chao Zhang"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Yuan_Hard-Aware_Deeply_Cascaded_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Yuan_Hard-Aware_Deeply_Cascaded_ICCV_2017_paper.pdf | null | 1611.05720v2 | cvf | @InProceedings{Yuan_2017_ICCV,author = {Yuan, Yuhui and Yang, Kuiyuan and Zhang, Chao},title = {Hard-Aware Deeply Cascaded Embedding},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Riding on the waves of deep neural networks, deep metric learning has achieved promising results in various tasks by using triplet network or Siamese network. Though the basic goal of making images from the same category closer than the ones from different categories is intuitive, it is hard to optimize the objective d... | [
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85 | Query-Guided Regression Network With Context Policy for Phrase Grounding | [
"Kan Chen",
"Rama Kovvuri",
"Ram Nevatia"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Chen_Query-Guided_Regression_Network_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Query-Guided_Regression_Network_ICCV_2017_paper.pdf | null | 1708.01676v1 | cvf | @InProceedings{Chen_2017_ICCV,author = {Chen, Kan and Kovvuri, Rama and Nevatia, Ram},title = {Query-Guided Regression Network With Context Policy for Phrase Grounding},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Given a textual description of an image, phrase grounding localizes objects in the image referred by query phrases in the description. State-of-the-art methods address the problem by ranking a set of proposals based on the relevance to each query, which are limited by the performance of independent proposal generation ... | [
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86 | SUBIC: A Supervised, Structured Binary Code for Image Search | [
"Himalaya Jain",
"Joaquin Zepeda",
"Patrick Perez",
"Remi Gribonval"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Jain_SUBIC_A_Supervised_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Jain_SUBIC_A_Supervised_ICCV_2017_paper.pdf | null | 1708.02932v1 | cvf | @InProceedings{Jain_2017_ICCV,author = {Jain, Himalaya and Zepeda, Joaquin and Perez, Patrick and Gribonval, Remi},title = {SUBIC: A Supervised, Structured Binary Code for Image Search},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants are usually employed to achieve such compression while minimizing the loss of accuracy. Yet, unlike binary hashing schemes, these unsupervi... | [
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87 | Revisiting Unreasonable Effectiveness of Data in Deep Learning Era | [
"Chen Sun",
"Abhinav Shrivastava",
"Saurabh Singh",
"Abhinav Gupta"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Sun_Revisiting_Unreasonable_Effectiveness_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Sun_Revisiting_Unreasonable_Effectiveness_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Sun_Revisiting_Unreasonable_Effectiveness_ICCV_2017_supplemental.pdf | 1707.02968v2 | cvf | @InProceedings{Sun_2017_ICCV,author = {Sun, Chen and Shrivastava, Abhinav and Singh, Saurabh and Gupta, Abhinav},title = {Revisiting Unreasonable Effectiveness of Data in Deep Learning Era},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the s... | [
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88 | A Generative Model of People in Clothing | [
"Christoph Lassner",
"Gerard Pons-Moll",
"Peter V. Gehler"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Lassner_A_Generative_Model_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Lassner_A_Generative_Model_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Lassner_A_Generative_Model_ICCV_2017_supplemental.pdf | 1705.04098 | cvf | @InProceedings{Lassner_2017_ICCV,author = {Lassner, Christoph and Pons-Moll, Gerard and Gehler, Peter V.},title = {A Generative Model of People in Clothing},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We present the first image-based generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the h... | [
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89 | Escape From Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models | [
"Roman Klokov",
"Victor Lempitsky"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Klokov_Escape_From_Cells_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Klokov_Escape_From_Cells_ICCV_2017_paper.pdf | null | 1704.01222v2 | cvf | @InProceedings{Klokov_2017_ICCV,author = {Klokov, Roman and Lempitsky, Victor},title = {Escape From Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds. The new architecture performs multiplicative transformations and shares parameters of these transformations according to the subdivisions of the point clouds imposed ... | [
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90 | Improved Image Captioning via Policy Gradient Optimization of SPIDEr | [
"Siqi Liu",
"Zhenhai Zhu",
"Ning Ye",
"Sergio Guadarrama",
"Kevin Murphy"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Liu_Improved_Image_Captioning_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Improved_Image_Captioning_ICCV_2017_paper.pdf | null | 1612.00370v4 | cvf | @InProceedings{Liu_2017_ICCV,author = {Liu, Siqi and Zhu, Zhenhai and Ye, Ning and Guadarrama, Sergio and Murphy, Kevin},title = {Improved Image Captioning via Policy Gradient Optimization of SPIDEr},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Current image captioning methods are usually trained via maximum likelihood estimation. However, the log-likelihood score of a caption does not correlate well with human assessments of quality. Standard syntactic evaluation metrics, such as BLEU, METEOR and ROUGE, are also not well correlated. The newer SPICE and CIDEr... | [
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91 | Rolling Shutter Correction in Manhattan World | [
"Pulak Purkait",
"Christopher Zach",
"Ales Leonardis"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Purkait_Rolling_Shutter_Correction_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Purkait_Rolling_Shutter_Correction_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Purkait_Rolling_Shutter_Correction_ICCV_2017_supplemental.pdf | null | null | @InProceedings{Purkait_2017_ICCV,author = {Purkait, Pulak and Zach, Christopher and Leonardis, Ales},title = {Rolling Shutter Correction in Manhattan World},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | A vast majority of consumer cameras operate the rolling shutter mechanism, which often produces distorted images due to inter-row delay while capturing an image. Recent methods for monocular rolling shutter compensation utilize blur kernel, straightness of line segments, as well as angle and length preservation. Howeve... | [
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92 | Local-To-Global Point Cloud Registration Using a Dictionary of Viewpoint Descriptors | [
"David Avidar",
"David Malah",
"Meir Barzohar"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Avidar_Local-To-Global_Point_Cloud_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Avidar_Local-To-Global_Point_Cloud_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Avidar_2017_ICCV,author = {Avidar, David and Malah, David and Barzohar, Meir},title = {Local-To-Global Point Cloud Registration Using a Dictionary of Viewpoint Descriptors},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Local-to global point cloud registration is a challenging task due to the substantial differences between these two types of data, and the different techniques used to acquire them. Global clouds cover large-scale environments and are usually acquired aerially, e.g., 3D modeling of a city using Airborne Laser Scanning ... | [
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93 | 3D-PRNN: Generating Shape Primitives With Recurrent Neural Networks | [
"Chuhang Zou",
"Ersin Yumer",
"Jimei Yang",
"Duygu Ceylan",
"Derek Hoiem"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zou_3D-PRNN_Generating_Shape_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zou_3D-PRNN_Generating_Shape_ICCV_2017_paper.pdf | null | 1708.01648 | title_snapshot | @InProceedings{Zou_2017_ICCV,author = {Zou, Chuhang and Yumer, Ersin and Yang, Jimei and Ceylan, Duygu and Hoiem, Derek},title = {3D-PRNN: Generating Shape Primitives With Recurrent Neural Networks},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data. Inspired by the nature of human perception of 3D shapes as a collection of simple parts, we explore such an abstract shape represe... | [
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94 | BodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera | [
"Tao Yu",
"Kaiwen Guo",
"Feng Xu",
"Yuan Dong",
"Zhaoqi Su",
"Jianhui Zhao",
"Jianguo Li",
"Qionghai Dai",
"Yebin Liu"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Yu_BodyFusion_Real-Time_Capture_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Yu_BodyFusion_Real-Time_Capture_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Yu_2017_ICCV,author = {Yu, Tao and Guo, Kaiwen and Xu, Feng and Dong, Yuan and Su, Zhaoqi and Zhao, Jianhui and Li, Jianguo and Dai, Qionghai and Liu, Yebin},title = {BodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera},booktitle = {Proceedings of the IEEE Inter... | We propose BodyFusion, a novel real-time geometry fusion method that can track and reconstruct non-rigid surface motion of a human performance using a single consumer-grade depth camera. To reduce the ambiguities of the non-rigid deformation parameterization on the surface graph nodes, we take advantage of the internal... | [
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95 | Quasiconvex Plane Sweep for Triangulation With Outliers | [
"Qianggong Zhang",
"Tat-Jun Chin",
"David Suter"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhang_Quasiconvex_Plane_Sweep_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_Quasiconvex_Plane_Sweep_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Zhang_Quasiconvex_Plane_Sweep_ICCV_2017_supplemental.zip | null | null | @InProceedings{Zhang_2017_ICCV,author = {Zhang, Qianggong and Chin, Tat-Jun and Suter, David},title = {Quasiconvex Plane Sweep for Triangulation With Outliers},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | Triangulation is a fundamental task in 3D computer vision. Unsurprisingly, it is a well-investigated problem with many mature algorithms. However, algorithms for robust triangulation, which are necessary to produce correct results in the presence of egregiously incorrect measurements (i.e., outliers), have received muc... | [
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96 | "Maximizing Rigidity" Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction From Multiple Perspective Views | [
"Pan Ji",
"Hongdong Li",
"Yuchao Dai",
"Ian Reid"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Ji_Maximizing_Rigidity_Revisited_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Ji_Maximizing_Rigidity_Revisited_ICCV_2017_paper.pdf | null | 1707.05009v1 | cvf | @InProceedings{Ji_2017_ICCV,author = {Ji, Pan and Li, Hongdong and Dai, Yuchao and Reid, Ian},title = {"Maximizing Rigidity" Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction From Multiple Perspective Views},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICC... | Rigid structure-from-motion (RSfM) and non-rigid structure-from-motion (NRSfM) have long been treated in the literature as separate (different) problems. Inspired by a previous work which solved directly for 3D scene structure by factoring the relative camera poses out, we revisit the principle of "maximizing rigidity"... | [
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97 | Surface Registration via Foliation | [
"Xiaopeng Zheng",
"Chengfeng Wen",
"Na Lei",
"Ming Ma",
"Xianfeng Gu"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zheng_Surface_Registration_via_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zheng_Surface_Registration_via_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Zheng_2017_ICCV,author = {Zheng, Xiaopeng and Wen, Chengfeng and Lei, Na and Ma, Ming and Gu, Xianfeng},title = {Surface Registration via Foliation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | This work introduces a novel surface registration method based on foliation. A foliation decomposes the surface into a family of closed loops, such that the decomposition has local tensor product structure. By projecting each loop to a point, the surface is collapsed into a graph. Two homeomorphic surfaces with consist... | [
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98 | Rolling-Shutter-Aware Differential SfM and Image Rectification | [
"Bingbing Zhuang",
"Loong-Fah Cheong",
"Gim Hee Lee"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Zhuang_Rolling-Shutter-Aware_Differential_SfM_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Zhuang_Rolling-Shutter-Aware_Differential_SfM_ICCV_2017_paper.pdf | https://openaccess.thecvf.com/content_ICCV_2017/supplemental/Zhuang_Rolling-Shutter-Aware_Differential_SfM_ICCV_2017_supplemental.pdf | 1903.03943v2 | cvf | @InProceedings{Zhuang_2017_ICCV,author = {Zhuang, Bingbing and Cheong, Loong-Fah and Hee Lee, Gim},title = {Rolling-Shutter-Aware Differential SfM and Image Rectification},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {Oct},year = {2017}} | In this paper, we develop a modified differential Structure from Motion (SfM) algorithm that can estimate relative pose from two frames despite of Rolling Shutter (RS) artifacts. In particular, we show that under constant velocity assumption, the errors induced by the rolling shutter effect can be easily rectified by a... | [
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99 | Corner-Based Geometric Calibration of Multi-Focus Plenoptic Cameras | [
"Sotiris Nousias",
"Francois Chadebecq",
"Jonas Pichat",
"Pearse Keane",
"Sebastien Ourselin",
"Christos Bergeles"
] | https://openaccess.thecvf.com/content_iccv_2017/html/Nousias_Corner-Based_Geometric_Calibration_ICCV_2017_paper.html | https://openaccess.thecvf.com/content_ICCV_2017/papers/Nousias_Corner-Based_Geometric_Calibration_ICCV_2017_paper.pdf | null | null | null | @InProceedings{Nousias_2017_ICCV,author = {Nousias, Sotiris and Chadebecq, Francois and Pichat, Jonas and Keane, Pearse and Ourselin, Sebastien and Bergeles, Christos},title = {Corner-Based Geometric Calibration of Multi-Focus Plenoptic Cameras},booktitle = {Proceedings of the IEEE International Conference on Computer ... | We propose a method for geometric calibration of multi-focus plenoptic cameras using raw images. Multi-focus plenoptic cameras feature several types of micro-lenses spatially aligned in front of the camera sensor to generate micro-images at different magnifications. This multi-lens arrangement provides computational-ph... | [
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