6 papers · 1 filter
From Sports to Safety: Benchmarking Proactive Risk Inference in MLLMs
Jiawei Qiu, Yichen Xu, Jianzhe Ma +5
Timely anticipation of physical hazards is essential for real-world safety, yet existing MLLM evaluations focus on harmful content or general risks, leaving proactive physical haza…
EgoEsportsQA: An Egocentric Video Benchmark for Perception and Reasoning in Esports
Jianzhe Ma, Zhonghao Cao, Shangkui Chen +3
While video large language models (Video-LLMs) excel in understanding slow-paced, real-world egocentric videos, their capabilities in high-velocity, information-dense virtual envir…
RefereeBench: Are Video MLLMs Ready to be Multi-Sport Referees
Yichen Xu, Yuanhang Liu, Chuhan Wang +5
While Multimodal Large Language Models (MLLMs) excel at generic video understanding, their ability to support specialized, rule-grounded decision-making remains insufficiently expl…
UI-Ins: Enhancing GUI Grounding with Multi-Perspective Instruction-as-Reasoning
Liangyu Chen, Hanzhang Zhou, Chenglin Cai +10
GUI grounding, which maps natural-language instructions to actionable UI elements, is a core capability of GUI agents. Prior works largely treats instructions as a static proxy for…
ChartM: Benchmarking Chart Editing with Multimodal Instructions
Donglu Yang, Liang Zhang, Zihao Yue +4
Charts are a fundamental visualization format widely used in data analysis across research and industry. While enabling users to edit charts based on high-level intentions is of gr…
Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding
Ye Wang, Ziheng Wang, Boshen Xu +14
Temporal Video Grounding (TVG), the task of locating specific video segments based on language queries, is a core challenge in long-form video understanding. While recent Large Vis…