TimeChat: A Time-Sensitive Multimodal Large Language Model for Long Video Understanding

Abstract

This work proposes TimeChat a time-sensitive multimodal large language model specifically designed for long video understanding. Our model incorporates two key architectural contributions: (1) a timestamp-aware frame encoder that binds visual content with the timestamp of each frame and (2) a sliding video Q-Former that produces a video token sequence of varying lengths to accommodate videos of various durations. Additionally we construct an instruction-tuning dataset encompassing 6 tasks and a total of 125K instances to further enhance TimeChat's instruction-following performance. Experiment results across various video understanding tasks such as dense captioning temporal grounding and highlight detection demonstrate TimeChat's strong zero-shot temporal localization and reasoning capabilities. For example it achieves +9.2 F1 score and +2.8 CIDEr on YouCook2 +5.8 HIT@1 on QVHighlights and +27.5 R@1 (IoU=0.5) on Charades-STA compared to state-of-the-art video large language models holding the potential to serve as a versatile video assistant for long-form video comprehension tasks and satisfy realistic user requirements.