New MSUE system achieves 0.95 accuracy in SoccerNet VQA Challenge

Researchers have developed MSUE, a multi-expert system designed for the 2026 SoccerNet VQA Challenge. This system utilizes a Vision-Language Model to synthesize training data and employs a Large Language Model to route questions to specialized text, image, or video experts. By integrating Gemini3-Flash, a fine-tuned Qwen3-VL, and an external knowledge base, MSUE achieved a 0.95 accuracy on the challenge benchmark, securing third place. AI

IMPACT Demonstrates a novel multi-expert architecture for multimodal understanding, potentially influencing future VQA systems.

RANK_REASON The cluster contains an academic paper detailing a new model architecture and its performance on a specific benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Gemini3-Flash
• Large Language Model
• Qwen3-VL
• SoccerNet VQA Challenge
• Vision-Language Model

• paper
• model release

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