collaborators

5 papers

cs.CV2025

Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models

Meng Cao, Pengfei Hu, Yingyao Wang +12

Recent advancements in Large Video Language Models (LVLMs) have highlighted their potential for multi-modal understanding, yet evaluating their factual grounding in videos remains…

cs.CV2025

ScaleLong: A Multi-Timescale Benchmark for Long Video Understanding

David Ma, Huaqing Yuan, Xingjian Wang +16

Although long-video understanding demands that models capture hierarchical temporal information -- from clip (seconds) and shot (tens of seconds) to event (minutes) and story (hour…

cs.CV2025

IV-Bench: A Benchmark for Image-Grounded Video Perception and Reasoning in Multimodal LLMs

David Ma, Yuanxing Zhang, Jincheng Ren +17

Existing evaluation frameworks for Multimodal Large Language Models (MLLMs) primarily focus on image reasoning or general video understanding tasks, largely overlooking the signifi…

cs.CV2024

PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos

Meng Cao, Haoran Tang, Haoze Zhao +7

Recent advancements in video-based large language models (Video LLMs) have witnessed the emergence of diverse capabilities to reason and interpret dynamic visual content. Among the…

cs.CL2024

ING-VP: MLLMs cannot Play Easy Vision-based Games Yet

Haoran Zhang, Hangyu Guo, Shuyue Guo +4

As multimodal large language models (MLLMs) continue to demonstrate increasingly competitive performance across a broad spectrum of tasks, more intricate and comprehensive benchmar…