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Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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Unsupervised Motion Classification by means of Efficient Feature Selection and Tracking |
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2004 |
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IEEE Int. Symp. on 3D Data Processing, Visualization and Transmission |
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Thessaloniki (Greece) |
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ADAS @ adas @ SAM2004a |
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456 |
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Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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3D Gait Estimation from Monoscopic Video |
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2004 |
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IEEE International Conference on Image Processing |
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Singapore |
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ADAS @ adas @ SAM2004c |
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495 |
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Anna Salvatella; Maria Vanrell; Ramon Baldrich |
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Subtexture Components for Texture Description |
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2003 |
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Lecture Notes in Computer Science, vol 2652, pp 884–892 |
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CAT @ cat @ SVR2003 |
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421 |
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Anthony Cioppa; Silvio Giancola; Vladimir Somers; Floriane Magera; Xin Zhou; Hassan Mkhallati; Adrien Deliège; Jan Held; Carlos Hinojosa; Amir M. Mansourian; Pierre Miralles; Olivier Barnich; Christophe De Vleeschouwer; Alexandre Alahi; Bernard Ghanem; Marc Van Droogenbroeck; Abdullah Kamal; Adrien Maglo; Albert Clapes; Amr Abdelaziz; Artur Xarles; Astrid Orcesi; Atom Scott; Bin Liu; Byoungkwon Lim; Chen Chen; Fabian Deuser; Feng Yan; Fufu Yu; Gal Shitrit; Guanshuo Wang; Gyusik Choi; Hankyul Kim; Hao Guo; Hasby Fahrudin; Hidenari Koguchi; Håkan Ardo; Ibrahim Salah; Ido Yerushalmy; Iftikar Muhammad; Ikuma Uchida; Ishay Beery; Jaonary Rabarisoa; Jeongae Lee; Jiajun Fu; Jianqin Yin; Jinghang Xu; Jongho Nang; Julien Denize; Junjie Li; Junpei Zhang; Juntae Kim; Kamil Synowiec; Kenji Kobayashi; Kexin Zhang; Konrad Habel; Kota Nakajima; Licheng Jiao; Lin Ma; Lizhi Wang; Luping Wang; Menglong Li; Mengying Zhou; Mohamed Nasr; Mohamed Abdelwahed; Mykola Liashuha; Nikolay Falaleev; Norbert Oswald; Qiong Jia; Quoc-Cuong Pham; Ran Song; Romain Herault; Rui Peng; Ruilong Chen; Ruixuan Liu; Ruslan Baikulov; Ryuto Fukushima; Sergio Escalera; Seungcheon Lee; Shimin Chen; Shouhong Ding; Taiga Someya; Thomas B. Moeslund; Tianjiao Li; Wei Shen; Wei Zhang; Wei Li; Wei Dai; Weixin Luo; Wending Zhao; Wenjie Zhang; Xinquan Yang; Yanbiao Ma; Yeeun Joo; Yingsen Zeng; Yiyang Gan; Yongqiang Zhu; Yujie Zhong; Zheng Ruan; Zhiheng Li; Zhijian Huang; Ziyu Meng |
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Title |
SoccerNet 2023 Challenges Results |
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2023 |
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Arxiv |
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The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on this https URL. Baselines and development kits can be found on this https URL. |
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HUPBA |
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Admin @ si @ CGS2023 |
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3991 |
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Antonio Carta; Andrea Cossu; Vincenzo Lomonaco; Davide Bacciu; Joost Van de Weijer |
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Title |
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning |
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2023 |
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Arxiv |
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Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs. How do we achieve forward transfer at zero cost for the single SCDs? We formalize this problem as a Distributed Continual Learning scenario, where SCD adapt to local tasks and a CL model consolidates the knowledge from the resulting stream of models without looking at the SCD's private data. Unfortunately, current CL methods are not directly applicable to this scenario. We propose Data-Agnostic Consolidation (DAC), a novel double knowledge distillation method that consolidates the stream of SC models without using the original data. DAC performs distillation in the latent space via a novel Projected Latent Distillation loss. Experimental results show that DAC enables forward transfer between SCDs and reaches state-of-the-art accuracy on Split CIFAR100, CORe50 and Split TinyImageNet, both in reharsal-free and distributed CL scenarios. Somewhat surprisingly, even a single out-of-distribution image is sufficient as the only source of data during consolidation. |
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Admin @ si @ CCL2023 |
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3871 |
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Author |
Antonio Lopez |
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Ridge/Valley-like structures: Creases, separatrices and drainage patterns |
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1997 |
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Computer vision on–line |
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CVC |
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ADAS @ adas @ Lop1997 |
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488 |
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Antonio Lopez; Cristina Cañero; Joan Serrat; J. Saludes; Felipe Lumbreras; T. Graf |
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Detection of lane markings based on ridgeness and RANSAC |
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2005 |
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Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 733–738 |
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lane markings |
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Vienna (Austria) |
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ADAS @ adas @ LCS2005 |
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588 |
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Antonio Lopez; David Lloret; Joan Serrat |
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Creaseness measures for CT and MR image registration. |
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1998 |
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CVPR’98 , IEEE Computer Society, pgs.694–699 |
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Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy |
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Santa Barbara, USA. |
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ADAS @ adas @ LLS1998a |
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11 |
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Antonio Lopez; David Lloret; Joan Serrat; Juan J. Villanueva |
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Multilocal Creaseness Based on the Level-Set Extrinsic Curvarture. |
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2000 |
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Computer Vision and Image Understanding, Academic Press, 77(2):111–144. |
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ADAS @ adas @ LLS 2000 b |
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172 |
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Antonio Lopez; Felipe Lumbreras; A. Martinez; Joan Serrat; Xavier Roca; Javier Varona; Jordi Vitria |
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Aplicaciones de la vision por computador a la industria. |
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1997 |
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CVC |
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ADAS @ adas @ LLM1997b |
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Antonio Lopez; Felipe Lumbreras; Joan Serrat |
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Creaseness form level set extrinsec curvature. |
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1998 |
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5th European Conference on Computer Vision (ECCV’98), Lecture Notes in Computer Science,vol 1407, pgs. 156–169 |
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Freiburg, Germany. |
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ADAS @ adas @ LLS1998b |
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12 |
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Antonio Lopez; Felipe Lumbreras; Joan Serrat; Juan J. Villanueva |
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Evaluation of Methods for Ridge and Valley Detection |
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1999 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 21:327–335 |
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ADAS @ adas @ LLS1999b |
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483 |
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Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
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Temporal Coherence Analysis for Intelligent Headlight Control |
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2008 |
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2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles |
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Intelligent Headlights |
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ADAS @ adas @ LHB2008b |
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1112 |
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Antonio Lopez; Joan Serrat |
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Tracing crease curves by solving a system of differential equations. |
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1996 |
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ADAS @ adas @ LoS1996 |
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84 |
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Antonio Lopez; Joan Serrat |
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Image Analysis through Surface Geometric Descriptors |
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1995 |
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VI National Simposium on Pattern Recognition and image Analysis. |
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Cordoba |
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ADAS |
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no |
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ADAS @ adas @ LoS1995 |
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133 |
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| Permanent link to this record |
