title: Towards Universal Soccer Video Understanding
summary: As a globally celebrated sport, soccer has attracted widespread interest from
  fans all over the world. This paper aims to develop a comprehensive multi-modal
  framework for soccer video understanding. Specifically, we make the following
  contributions in this paper: (i) we introduce SoccerReplay-1988, the largest
  multi-modal soccer dataset to date, featuring videos and detailed annotations
  from 1,988 complete matches, with an automated annotation pipeline; (ii) we
  present the first visual-language foundation model in the soccer domain,
  MatchVision, which leverages spatiotemporal information across soccer videos
  and excels in various downstream tasks; (iii) we conduct extensive experiments
  and ablation studies on event classification, commentary generation, and
  multi-view foul recognition. MatchVision demonstrates state-of-the-art
  performance on all of them, substantially outperforming existing models, which
  highlights the superiority of our proposed data and model. We believe that this
  work will offer a standard paradigm for sports understanding research.
paper_authors._id: 6752678b4ca8baad001c64db
paper_authors.name: Jiayuan Rao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T09:09:14.974Z
paper_authors._id: 6625ce8074ae2df4e3effa92
paper_authors.avatarUrl: /avatars/686cd85cf8d3d8f0beb0737811144294.svg
paper_authors.fullname: Jiayuan Rao 饶珈源
paper_authors.type: user
paper_authors.user: Homie0609
paper_authors._id: 6752678b4ca8baad001c64dc
paper_authors.name: Haoning Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T09:09:16.901Z
paper_authors._id: 632c7a0d1d303f5f9acf01b8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
paper_authors.fullname: Haoning Wu
paper_authors.type: user
paper_authors.user: haoningwu
paper_authors._id: 6752678b4ca8baad001c64dd
paper_authors.name: Hao Jiang
paper_authors._id: 6752678b4ca8baad001c64de
paper_authors.name: Ya Zhang
paper_authors._id: 6752678b4ca8baad001c64df
paper_authors.name: Yanfeng Wang
paper_authors._id: 6752678b4ca8baad001c64e0
paper_authors.name: Weidi Xie
paper_title: Towards Universal Soccer Video Understanding
paper_summary: As a globally celebrated sport, soccer has attracted widespread interest from
  fans all over the world. This paper aims to develop a comprehensive multi-modal
  framework for soccer video understanding. Specifically, we make the following
  contributions in this paper: (i) we introduce SoccerReplay-1988, the largest
  multi-modal soccer dataset to date, featuring videos and detailed annotations
  from 1,988 complete matches, with an automated annotation pipeline; (ii) we
  present the first visual-language foundation model in the soccer domain,
  MatchVision, which leverages spatiotemporal information across soccer videos
  and excels in various downstream tasks; (iii) we conduct extensive experiments
  and ablation studies on event classification, commentary generation, and
  multi-view foul recognition. MatchVision demonstrates state-of-the-art
  performance on all of them, substantially outperforming existing models, which
  highlights the superiority of our proposed data and model. We believe that this
  work will offer a standard paradigm for sports understanding research.
paper_ai_summary: A comprehensive multi-modal framework, including SoccerReplay-1988 dataset and MatchVision model, achieves state-of-the-art performance in soccer video understanding tasks.
paper_ai_keywords: SoccerReplay-1988 MatchVision multi-modal framework spatiotemporal information event classification commentary generation multi-view foul recognition