# MSUE: Multi-Modal Soccer Understanding Expert

Source: [MSUE: Multi-Modal Soccer Understanding Expert](<https://www.sota2.com/research/paper/6a2a4147d33dfb23e78394b9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-12T05:00:14.585Z

Authors: Litao Li, Yibo Yu, Yufeng Hu, Zhuo Yang, Jiali Wen, Yixin Chen, Yixi Zhou

Published: 2026-06-10T14:00:55.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Visual Question Answering on SoccerNet VQA (test)](<https://www.sota2.com/research/sota/visual-question-answering-on-soccernet-vqa-test.md>): Visual Question Answering; SoccerNet VQA (test); Accuracy: 91.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2606.12106>)

[PDF](<https://arxiv.org/pdf/2606.12106.pdf>)