activity
20242026
most citedTowards Universal Soccer Video Understanding

1 citations · 1 across the 11 of their papers we have counts for

collaborators

12 papers

cs.AI2026

WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting

Zhaokai Wang, Tianlin Gui, Jiayuan Rao +3

Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear prediction before the answer is available…

cs.CV2026

Towards Comprehensive Basketball Understanding

Yirong Hu, Jiayuan Rao, Yu Zhang +2

Understanding a basketball game requires recognizing events, localizing actions, identifying players, and relating these to structured game knowledge. Existing benchmarks primarily…

cs.CV2026

TennisVAR: A Stroke-Evidence-Grounded Multimodal Large Language Model for Tactical Reasoning in Tennis Videos

Yifan Mei, Qingling Shi, Changli Wu +3

Sports-video understanding is moving beyond event recognition toward explaining how actions collectively shape match progression, however, existing tennis-video methods either perc…

cs.AI2026

BasketEvent: Understanding Who Did What and When in Basketball Videos

Yu Zhang, Jiayuan Rao, Haoning Wu +1

Comprehensive basketball video understanding requires resolving not only what event occurs, but also who is responsible and when the key evidence appears. However, exist- ing metho…

cs.CV2026

SoccerNet 2026 Challenges Results

Anthony Cioppa, Silvio Giancola, Håkan Ardö +102

The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video underst…

cs.AI2026

SoccerRef-Agents: Multi-Agent System for Automated Soccer Refereeing

Zi Meng, Wanli Song, Yi Hu +2

Refereeing is vital in sports, where fair, accurate, and explainable decisions are fundamental. While intelligent assistant technologies are being widely adopted in soccer refereei…

cs.AI2026

GenTac: Generative Modeling and Forecasting of Soccer Tactics

Jiayuan Rao, Tianlin Gui, Haoning Wu +2

Modeling open-play soccer tactics is a formidable challenge due to the stochastic, multi-agent nature of the game. Existing computational approaches typically produce single, deter…

cs.AI2025

STELLA: Guiding Large Language Models for Time Series Forecasting with Semantic Abstractions

Junjie Fan, Hongye Zhao, Linduo Wei +5

Recent adaptations of Large Language Models (LLMs) for time series forecasting often fail to effectively enhance information for raw series, leaving LLM reasoning capabilities unde…

cs.CV2025

SoccerMaster: A Vision Foundation Model for Soccer Understanding

Haolin Yang, Jiayuan Rao, Haoning Wu +1

Soccer understanding has recently garnered growing research interest due to its domain-specific complexity and unique challenges. Unlike prior works that typically rely on isolated…

cs.CV2025

Multi-Agent System for Comprehensive Soccer Understanding

Jiayuan Rao, Zifeng Li, Haoning Wu +3

Recent advances in soccer understanding have demonstrated rapid progress, yet existing research predominantly focuses on isolated or narrow tasks. To bridge this gap, we propose a…

cs.CV2024★ 1 cited

Towards Universal Soccer Video Understanding

Jiayuan Rao, Haoning Wu, Hao Jiang +3

As a globally celebrated sport, soccer has attracted widespread interest from fans all over the world. This paper aims to develop a comprehensive multi-modal framework for soccer v…

cs.CV2024

MatchTime: Towards Automatic Soccer Game Commentary Generation

Jiayuan Rao, Haoning Wu, Chang Liu +2

Soccer is a globally popular sport with a vast audience, in this paper, we consider constructing an automatic soccer game commentary model to improve the audiences' viewing experie…