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arXiv · 2606.12106

MSUE: Multi-Modal Soccer Understanding Expert

Litao Li·Yibo Yu·Yufeng Hu·Zhuo Yang·Jiali Wen·Yixin Chen·Yixi Zhou
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Abstract

This paper presents our solution to the 2026 SoccerNet VQA Challenge. We first develop a cost-effective data synthesis pipeline driven by a Vision-Language Model (VLM), which systematically restructures raw domain data into diverse VQA samples, including concise answers and long-form responses. Second, we propose MSUE, a multi-expert question answering architecture that employs a Large Language Model (LLM) to dynamically dispatch questions to text, image, and video experts. These experts are instantiated as a strong text baseline Gemini3-Flash, a fine-tuned Qwen3-VL, and an external knowledge base, respectively, working collaboratively to enhance VQA performance. MSUE achieves an accuracy of \textbf{0.95} on the challenge benchmark, securing third place in the leaderboard.

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Litao Li, Yibo Yu, Yufeng Hu, Zhuo Yang, Jiali Wen, Yixin Chen, Yixi Zhou. 2026-06-10. MSUE: Multi-Modal Soccer Understanding Expert. https://arxiv.org/abs/2606.12106

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