VTimeLLM: Empower LLM to Grasp Video Moments
Bin Huang, Xin Wang, Hong Chen, Zihan Song, Wenwu Zhu
Abstract
Large language models (LLMs) have shown remarkable text understanding capabilities, which have been ex-tended as Video LLMs to handle video data for compre-hending visual details. However, existing Video LLMs can only provide a coarse description of the entire video, failing to capture the precise start and end time bound-ary of specific events. In this paper, we solve this issue via proposing VTimeLLM, a novel Video LLM designed for fine-grained video moment understanding and reasoning with respect to time boundary. Specifically, our VTimeLLM adopts a boundary-aware three-stage training strategy, which respectively utilizes image-text pairs for feature alignment, multiple-event videos to increase temporal-boundary awareness, and high-quality video-instruction tuning to further improve temporal understanding ability as well as align with human intents. Extensive experiments demonstrate that in fine-grained time-related comprehension tasks for videos such as Temporal Video Grounding and Dense Video Captioning, VTimeLLM significantly outperforms existing Video LLMs. Besides, benefits from the fine-grained temporal understanding of the videos further enable VTimeLLM to beat existing Video LLMs in video di-alogue benchmark, showing its superior cross-modal understanding and reasoning abilities. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Our project page is at https://github.com/huangb23/VTimeLLM