3 citations · 6 across the 11 of their papers we have counts for
11 papers
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…
Perception Before Reasoning: Dynamic Latent Reasoning for Video Understanding and Question Answering
Haotian Xia, Zilin Xiao, Junbo Zou +2
Video question answering requires models to ground language queries in visual evidence and, when necessary, reason over that evidence across time. Existing methods typically rely o…
StoryAlign: Evaluating and Training Reward Models for Story Generation
Haotian Xia, Hao Peng, Yunjia Qi +4
Story generation aims to automatically produce coherent, structured, and engaging narratives. Although large language models (LLMs) have significantly advanced text generation, sto…
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…
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…
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…
FACTS: Fine-Grained Action Classification for Tactical Sports
Christopher Lai, Jason Mo, Haotian Xia +1
Classifying fine-grained actions in fast-paced, close-combat sports such as fencing and boxing presents unique challenges due to the complexity, speed, and nuance of movements. Tra…
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…
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…
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…
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…