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New benchmark tackles soccer foul inconsistency with semantic video retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SoccerNet-FoulRet, a new benchmark designed to address the inconsistency in refereeing decisions in professional soccer by enabling the retrieval of semantically similar foul videos. This system aims to help referees compare contentious fouls against relevant past cases, irrespective of visual differences. Initial evaluations show that even the strongest zero-shot models struggle with this task, achieving less than 5% HitRate@10 on human-verified precedents, indicating that semantic foul retrieval remains a significant challenge. AI

IMPACT This benchmark could improve fairness in sports officiating by providing referees with relevant precedents for foul calls.

RANK_REASON The cluster describes a new academic paper introducing a novel benchmark and dataset for a specific research problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark tackles soccer foul inconsistency with semantic video retrieval

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The cluster describes a new academic paper introducing a novel benchmark and dataset for a specific research problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Silvio Giancola ·

    SoccerNet-FoulRet: Retrieving Semantically Similar Soccer Foul Videos

    Refereeing decisions in professional soccer remain inconsistent because referees cannot easily compare a contentious foul against similar past cases. We cast this as a retrieval problem and introduce SoccerNet-FoulRet, the first benchmark for semantic foul retrieval. Given a quer…

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