MSUE：多模态足球理解专家
MSUE: Multi-Modal Soccer Understanding Expert
AI总结：
提出MSUE多专家问答架构，结合VLM数据合成管道与LLM动态调度文本、图像、视频专家，在SoccerNet VQA挑战中达到0.95准确率，获第三名。
摘要：
本文介绍了我们对2026年SoccerNet VQA挑战赛的解决方案。我们首先开发了一个由视觉语言模型（VLM）驱动的低成本数据合成管道，该系统将原始领域数据系统地重构为多样化的VQA样本，包括简洁答案和长文本回复。其次，我们提出了MSUE，一种多专家问答架构，采用大语言模型（LLM）将问题动态分发给文本、图像和视频专家。这些专家分别实例化为强大的文本基线Gemini3-Flash、微调的Qwen3-VL和外部知识库，协同工作以提升VQA性能。MSUE在挑战基准上达到了\textbf{0.95}的准确率，在排行榜上获得第三名。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
