A Survey of Large Models in Sports
Sports have witnessed growing global enthusiasm in recent years, serving as a vital force for physical health, cultural exchange, social connection, and economic growth. The rapid advancement of large models, particularly (multimodal) large language models (M)LLMs, has demonstrated transformative potential to reshape sports understanding, analysis, and interaction across diverse domains. This paper presents a comprehensive survey of large models in sports, including (i) an overview of tasks and applications across different participant groups; (ii) a detailed analysis of sports-related dataset
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- PossiblePossibly related (embedding) · 46%Co-pilot, Not Autopilot: A Practical Method for Using Large Language Models in Interventional Cardiology - EMJ →
- PossiblePossibly related (embedding) · 45%Identifying Interactions at Scale for LLMs →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%HKUDS/LightRAG →
“Shared author/contributor keys: jin”
- FuzzyOverlapping authors or contributors · 62%keras-team/keras →
“Shared author/contributor keys: jin”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Yichen Xu →
“A Survey of Large Models in Sports”
- LinkedLinked via arxiv author · 85%Jianzhe Ma →
“A Survey of Large Models in Sports”