From the 2 of 20 linked papers with an AI index.
20 papers
PercepCap: Video Captioner with Structured Spatio-Temporal Perception
Yifan Xu, Zihao Wang, Zhixiao Wang +6
Video captioning requires fine-grained spatio-temporal understanding of videos, including spatial perception of where objects are located and temporal perception of when events occ…
TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs
Yuhan Zhu, Changlian Ma, Xiangyu Zeng +12
Video multimodal large language models (MLLMs) can describe what happens in a video, but rarely identify when the supporting evidence occurs. We study generalist video temporal gro…
VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding
Xinhao Li, Yuhan Zhu, Xiangyu Zeng +24
VideoChat3 is a fully open, 4B-parameter video-centric multimodal large language model that combines an efficient Inflated 3D Vision Transformer and adaptive frame resolution with…
VIABench: A Comprehensive Video Benchmark Collected from Blind Individuals for Visual Impairment Assistance
Yunfeng Liu, Yuandong Yang, Jiarui Han +5
The paper introduces VIABench, a video benchmark built from first‑person recordings by visually impaired users to evaluate multimodal large language models on tasks like proactive…
StreamOV: Streaming Omni-Video Understanding via Evidence-Guided Memory and Response Triggering
Ming Xie, Zizheng Huang, Xudong Tan +6
While streaming omni-video understanding demands continuous perception and proactive, real-time interaction, this crucial area remains largely under-explored. Current omni-modal me…
FreeRet: MLLMs as Training-Free Retrievers
Yuhan Zhu, Xiangyu Zeng, Chenting Wang +6
Multimodal large language models (MLLMs) are emerging as versatile foundations for mixed-modality retrieval. Yet, they often require heavy post-hoc training to convert them into co…