MSUE: Multi-Modal Soccer Understanding Expert System

MSUE: Multi-Modal Soccer Understanding Expert System

Original Author/Team: Paper author team (submitted to arXiv) Source Platform: arXiv Original Title: MSUE: Multi-Modal Soccer Understanding Expert Original Link: http://arxiv.org/abs/2606.12106v1 Publication Date: June 10, 2026

Key Points

• Core Innovation: Uses a VLM-driven data synthesis pipeline to generate diverse VQA samples, and a multi-expert architecture to dynamically assign questions to text, image, and video experts
• Challenge Performance: Won third place with an accuracy of 0.95 in the 2026 SoccerNet VQA Challenge

This thread will introduce MSUE's background, technical innovations, experimental results, and application prospects in detail across different floors.

Overview of the SoccerNet VQA Challenge

The SoccerNet VQA Challenge is a key event in the intersection of computer vision and natural language processing, focusing on automatic understanding and question answering for soccer videos. This task is highly challenging, requiring simultaneous understanding of:

• Video Dynamics: Continuous frames and tactical changes in soccer matches
• Image Content: Player positions, actions, and scenes in key frames
• Text Information: Match rules, team information, historical data, etc.
• Question Intent: Diverse questions from users (ranging from simple facts to complex reasoning)

The 2026 challenge sets higher requirements for participating systems, which need to handle more complex scenarios and fine-grained question types.

Data Synthesis Solution Addresses Domain Data Bottleneck

Problem Background

Domain-specific high-quality annotated data is a key bottleneck for visual question answering system performance. Obtaining large-scale VQA annotated data in the soccer domain is costly and time-consuming.

Solution

The research team developed a cost-effective data synthesis pipeline, with the core being a Visual Language Model (VLM):

1. Systematic Reconstruction: Reconstruct raw match data (videos, commentary text, statistical data) into diverse VQA samples
2. Diverse Output: Generate concise answers and long-form responses, covering scenarios of varying complexity
3. Cost-Effectiveness: Significantly reduces data preparation costs while maintaining data quality

Workflow

• Content Extraction: Extract key events, player actions, and tactical changes from raw match data
• Question Generation: Automatically generate natural language questions based on extracted content
• Answer Construction: Generate standard answers (including short answers and detailed explanations) for each question
• Quality Control: Ensure sample accuracy and diversity through VLM reasoning capabilities

MSUE's Multi-Expert Architecture Design

The core of MSUE is a multi-expert collaborative architecture, with a Large Language Model (LLM) as the central scheduler to dynamically assign queries to suitable expert modules.

Three Expert Modules

1. Text Expert: Gemini3-Flash

   • Responsibility: Handle text-based questions (e.g., match rules, historical records, statistical queries)
   • Application Scenarios: "Which team won the 2022 World Cup?", "What is the definition of offside?"

2. Image Expert: Fine-tuned Qwen3-VL

   • Responsibility: Handle questions related to static image content
   • Application Scenarios: "Who is the player in the red jersey in the image?", "What happened at this moment?"

3. Video/External Knowledge Expert

   • Responsibility: Integrate external knowledge resources to provide supplementary information
   • Application Scenarios: Questions requiring integration of historical data or rule explanations

Dynamic Distribution Logic

The LLM scheduler understands the question intent and selects the optimal expert combination:

• Pure text questions → Activate only the text expert
• Image-related questions → Activate the image expert, and request supplementary information from the text expert if necessary
• Complex reasoning questions → Coordinate multiple experts to collaborate and produce a comprehensive output

MSUE's Challenge Performance and Success Factors

Challenge Results

MSUE achieved an accuracy of 0.95 in the 2026 SoccerNet VQA challenge benchmark and ranked third on the leaderboard.

Analysis of Success Factors

1. Data Advantage: VLM-driven data synthesis provides high-quality, diverse training data
2. Architecture Advantage: The multi-expert design selects the optimal processing strategy for different question types
3. Synergistic Effect: The collaborative capability of the three experts outperforms that of a single model

MSUE's Contributions to Sports AI and Extended Applications

Contributions to Sports AI

• Data Efficiency: Demonstrates how to use VLM to reduce domain-specific data annotation costs
• Architectural Innovation: The multi-expert collaborative architecture provides a scalable solution for complex multi-modal tasks
• Domain Adaptation: Proves that general models can be adapted to professional domain needs through fine-tuning

Extended Application Potential

• Other Sports: Basketball, tennis, baseball, and other sports involving complex dynamics and rules
• Video Surveillance: Scenarios requiring understanding of continuous frames and question answering
• Education: Understanding and question answering for educational videos
• Media Analysis: Automatic commentary and content generation for sports events

MSUE's Current Limitations and Future Plans

Current Limitations

• Domain Specificity: Optimized mainly for soccer scenarios; additional work is needed to migrate to other sports
• Real-Time Performance: Computational overhead of video processing and multi-expert coordination may affect real-time applications
• Knowledge Update: External knowledge bases need regular updates to reflect the latest information

Future Research Directions

1. Cross-Domain Migration: Explore the applicability of the architecture to other sports and video understanding tasks
2. Efficiency Optimization: Research lightweight expert models and efficient coordination mechanisms
3. Knowledge Fusion: Improve the integration of external knowledge bases to support complex reasoning
4. Real-Time System: Develop real-time question answering systems suitable for live broadcast scenarios

MSUE's Value and Future Impact

MSUE represents an important advancement in the field of multi-modal sports video understanding, achieving excellent results in the SoccerNet VQA Challenge through VLM-driven data synthesis and a multi-expert collaborative architecture.

Its core value lies in demonstrating a new approach to addressing complex multi-modal tasks: using foundation models to reduce data preparation costs and improving system performance through specialized division of labor. This combination of "data synthesis + multi-expert" provides a reference for fields such as visual question answering and video understanding.

As the digitalization of the sports industry accelerates, MSUE-like technologies will play an important role in scenarios such as event analysis, intelligent commentary, and fan interaction.