3 papers
cs.CV2025
In the Eye of MLLM: Benchmarking Egocentric Video Intent Understanding with Gaze-Guided Prompting
Taiying Peng, Jiacheng Hua, Miao Liu +1
The emergence of advanced multimodal large language models (MLLMs) has significantly enhanced AI assistants' ability to process complex information across modalities. Recently, ego…
cs.CV2025
ST-Think: How Multimodal Large Language Models Reason About 4D Worlds from Ego-Centric Videos
Peiran Wu, Yunze Liu, Miao Liu +1
Humans excel at spatial-temporal reasoning, effortlessly interpreting dynamic visual events from an egocentric viewpoint. However, whether multimodal large language models (MLLMs)…
cs.CV2025
X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding
Wenqi Zhou, Kai Cao, Hao Zheng +8
Long-form egocentric video understanding provides rich contextual information and unique insights into long-term human behaviors, holding significant potential for applications in…