arXiv:2606.12106v1 Announce Type: cross 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,
The development of advanced multi-modal VQA systems like MSUE reflects the increasing sophistication of AI in understanding complex, real-world data, driven by competitive challenges and the maturation of VLM and LLM technologies.
This development indicates a significant leap in AI's ability to interpret and synthesize information from diverse media, moving towards more human-like comprehension and reasoning, which has broad implications for automation and decision support.
AI systems are becoming more adept at processing and integrating visual, temporal, and textual data simultaneously, suggesting a future where AI can perform complex analytical tasks previously limited to human experts.
- · AI-powered analytics platforms
- · Sports analytics industry
- · AI research and development (VLM/LLM)
- · Content creators and media
- · Monolithic single-modal AI systems
- · Manual data annotation services
- · Entry-level data analysts
More accurate and automated analysis of complex event streams becomes possible through multi-modal AI.
This improved understanding could lead to fully autonomous AI agents capable of specialized domain expertise and decision-making.
The integration of such expert systems into critical infrastructure could redefine human-AI collaboration and oversight in various sectors.
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Read at arXiv cs.AI