[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84979-en":3,"doc-seo-84979-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84979,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","SoccerNet 2026 Challenges Results","The SoccerNet 2026 Challenges mark the sixth annual open benchmarking effort for advancing computer vision research in sports video understanding. The edition covers five vision tasks: Ball Action Anticipation, Player-Centric Ball Action Spotting, Novel View Synthesis, Spiideo SoccerNet Synloc, and Visual Question Answering across text, image, and video. For each task, participants receive annotated data, a unified evaluation protocol, and a public baseline, with leaderboards and summarized top submissions based on held-out challenge results. In total, 427 teams submitted 1,129 entries, and 28 teams provided reviewed technical reports.","1  \nSoccerNet 2026 Challenges Results  \n.CV] 8  \nNguyen27 ,32 , Hoang-Phuc Nguyen27 , Trong-Thuan Nguyen27 ,32 , Christian Orduz37 , Kwanyong Park24 , Fabian Perez37 , Parthsarthi Rawat38 , SuHyun Rim21 , Hoover Rueda-Chacón37 , Atom Scott36 ,39 , Minori Sugimura34 ,35 , Yuyang Sun40 , Shengeng Tang20 , Minh-Triet Tran27 ,32 , Ikuma Uchida34 ,35 , Juan Vanegas37 , Thanh-Nhan Vo27 ,32 , Jiangtao Wang41 , Yaxiong Wang20 ,  \nRuifeng Wang41 , Rio Watanabe33 , Jiali Wen 19 , Yongliang Xu Yang40 , Zhuo Yang43 , Xinyu Ye44 , Yibo Yu45 , Zihan  \n26 Nanjing University, Nanjing, China · 27 University of Science, Ho Chi Minh City,  \nVietnam · 28 Nanyang Technological University, Singapore, Singapore · 29 National University of Singapore, Singapore, Singapore · 30 Chung-Ang University, Seoul, Republic of Korea · 31 Artificial Intelligence Society Golem, Warsaw University of Technology, Warsaw, Poland · 32 Vietnam National University, Ho Chi Minh City, Vietnam · 33 MIXI Inc, Tokyo, Japan · 34 AllClip, Inc. , Tokyo, Japan · 35 University of Tsukuba, Tsukuba, Japan · 36 Playbox Inc. , Tokyo, Japan · 37 Universidad Industrial de Santander, Bucaramanga, Colombia · 38 GameChanger by Dick’s Sporting Goods, MA, USA · 39 Nagoya University, Nagoya, Japan · 40 Southeast University, Nanjing, China · 41 University of Science and Technology of China, Hefei, China · 42 Southwest University, Chongqing, China · 43 Peking University, Beijing, China · 44 University of North Carolina at Chapel Hill, Chapel Hill, USA · 45 Johns Hopkins University,  \nBaltimore, USA · 46 Beijing Freedo Technology Co. , Ltd. , Beijing, China ·  \n47 University of California, Berkeley, Berkeley, USA  \nAbstract. The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video understanding. This year’s challenges span five vision-based tasks: (1) Ball Action Anticipation, predicting the timing and class of ball-related actions within a short future window from a preceding observation window; (2) Player-Centric Ball Action Spotting, temporally localizing and classifying ball-related actions while assigning each action to the acting player through team affiliation and jersey number; (3) Novel View Synthesis, rendering images unobserved from camera poses in multi-view football scenes; (4) Spiideo SoccerNet Synloc, localizing athletes in real-world pitch coordinates from a single calibrated static-camera image; and (5) Visual Question Answering, answering multiple-choice questions about football broadcasts across text, image, and video inputs. For each task, participants were provided with annotated data, a unified evaluation protocol, and a public baseline. This edition saw broad participation, with 427 teams submitting  \n1 , 129 entries across the five tasks and 28 teams contributing reviewed technical reports. This paper describes each task and its evaluation protocol, presents the challenge leaderboards, and summarizes the leading submissions, with the aim of documenting the current state of each task as measured on held-out challenge data.  \nKeywords: Sports · Video understanding · Challenges · Spotting · Localization · Novel view synthesis · Visual question answering  \n1 Introduction  \nSports video understanding has grown into a well-established area of computer vision research [28, 71 , 105], drawing on athlete segmentation [17, 18], detection [107], and tracking [66, 68 , 89 , 100], re-identification [8, 68 , 99], pose estimation [54, 63], action recognition [26, 42–45, 56] and spotting [11, 15 , 16 , 34 , 35 , 50 ,  \n0 ⋆ Equal contribution.† Task leader. ‡ Task supervisor.  \nSN2026 3  \nBAA PCBAS NVS SSS VQA  \nFig. 1: Overview of the challenges. In 2026, the SoccerNet challenges encompass five vision tasks: (1) Ball Action Anticipation (BAA), focusing on predicting the timing and class of ball-related actions occurring within a five-second window from a preceding 30-secon","cbCaifi3OQseWxiA","https://ap.wps.com/l/cbCaifi3OQseWxiA","pdf",3521248,1,40,"English","en",105,"# Introduction\n## Challenge Overview\n## Evaluation Setup and Leaderboards\n## Task Descriptions","[{\"question\":\"What are the five tasks included in the SoccerNet 2026 challenges?\",\"answer\":\"The challenges include Ball Action Anticipation, Player-Centric Ball Action Spotting, Novel View Synthesis, Spiideo SoccerNet Synloc, and Visual Question Answering across text, image, and video.\"},{\"question\":\"How are results evaluated and made comparable across different methods?\",\"answer\":\"Each task provides annotated data, a unified evaluation protocol, and a public baseline, with rankings determined on a private challenge split using standardized metrics.\"},{\"question\":\"What participation level did the SoccerNet 2026 edition see?\",\"answer\":\"The edition involved 427 teams submitting 1,129 entries across the five tasks, and 28 teams contributed reviewed technical 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