SoccerNet-FoulRet: Retrieving Semantically Similar Soccer Foul Videos
We cast this as a retrieval problem and introduce SoccerNet-FoulRet, the first benchmark for semantic foul retrieval.
ProofPaper ↗
Key points
- Refereeing decisions in professional soccer remain inconsistent because referees cannot easily compare a contentious foul against similar past cases.
- Given a query foul, the task is to retrieve past fouls judged to be relevant precedents, regardless of camera angle, teams, or appearance.
- This differs from prior video-to-video retrieval, which matches clips by visual similarity or a shared event.
- We build the benchmark from the SoccerNet-MVFoul dataset and evaluate retrieval ability of zero-shot video and vision-language embedders together with a task-specific fine-tuned baseline on 693 human-verified queries and category-relevance labels.
Sources (1)
- [1]SoccerNet-FoulRet: Retrieving Semantically Similar Soccer Foul VideosarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 09:31 AM
We cast this as a retrieval problem and introduce SoccerNet-FoulRet, the first benchmark for semantic foul retrieval.
Refereeing decisions in professional soccer remain inconsistent because referees cannot easily compare a contentious foul against similar past cases.
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