activity
20242026
most citedLanguage and Multimodal Models in Sports: A Survey of Datasets and Applications

3 citations · 6 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI2026

SportD: How do VLMs physically strategize?

Jasin Cekinmez, Addison J. Wu, Haotian Xia +6

Vision-language models (VLMs) can describe a scene, but can they act well within one? We study whether VLMs can make sound strategic decisions, using soccer as an objective testbed…

cs.CV2025

DeepSport: A Multimodal Large Language Model for Comprehensive Sports Video Reasoning via Agentic Reinforcement Learning

Junbo Zou, Haotian Xia, Zhen Ye +5

Sports video understanding requires perceiving high-speed dynamics, complex rules, and long temporal contexts. Yet, current Multimodal Large Language Models (MLLMs) remain narrowly…

cs.CV2025

SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports

Haotian Xia, Haonan Ge, Junbo Zou +16

Deeply understanding sports requires an intricate blend of fine-grained visual perception and rule-based reasoning - a challenge that pushes the limits of current multimodal models…

cs.CE2025

VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding

Zhaowei Liu, Xin Guo, Haotian Xia +11

Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the…

cs.CV2024★ 2 cited

SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models

Haotian Xia, Zhengbang Yang, Junbo Zou +10

Multimodal Large Language Models (MLLMs) are advancing the ability to reason about complex sports scenarios by integrating textual and visual information. To comprehensively evalua…

cs.CL2024

Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video

Zhengbang Yang, Haotian Xia, Jingxi Li +3

Understanding sports is crucial for the advancement of Natural Language Processing (NLP) due to its intricate and dynamic nature. Reasoning over complex sports scenarios has posed…

cs.CL2024★ 3 cited

Language and Multimodal Models in Sports: A Survey of Datasets and Applications

Haotian Xia, Zhengbang Yang, Yun Zhao +7

Recent integration of Natural Language Processing (NLP) and multimodal models has advanced the field of sports analytics. This survey presents a comprehensive review of the dataset…

cs.CL2024★ 1 cited

SportQA: A Benchmark for Sports Understanding in Large Language Models

Haotian Xia, Zhengbang Yang, Yuqing Wang +7

A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP). This holds particular significance in the…