Recent advances in soccer understanding have demonstrated rapid progress, yet existing research predominantly focuses on isolated or narrow tasks. To bridge this gap, we propose a comprehensive framework for holistic soccer understanding. Concretely, we make the following contributions in this paper: (i) we construct SoccerWiki, the first large-scale multimodal soccer knowledge base, integrating rich domain knowledge about players, teams, referees, and venues to enable knowledge-driven reasoning; (ii) we present SoccerBench, the largest and most comprehensive soccer-specific benchmark, featuring around 10K multimodal (text, image, video) multi-choice QA pairs across 13 distinct tasks; (iii) we introduce SoccerAgent, a novel multi-agent system that decomposes complex soccer questions via collaborative reasoning, leveraging domain expertise from SoccerWiki and achieving robust performance; (iv) extensive evaluations and comparisons with representative MLLMs on SoccerBench highlight the superiority of our agentic system.
@article{arxiv.2505.03735,
title = {Multi-Agent System for Comprehensive Soccer Understanding},
author = {Jiayuan Rao and Zifeng Li and Haoning Wu and Ya Zhang and Yanfeng Wang and Weidi Xie},
journal= {arXiv preprint arXiv:2505.03735},
year = {2025}
}
Comments
Accepted by ACM MM 2025; Project Page: https://jyrao.github.io/SoccerAgent/