MovieChat: From Dense Token to Sparse Memory for Long Video Understanding

EnxinSong, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Xun Guo, Owen, Yan Lu, Jenq-Neng Hwang, Gaoang Wang

MovieChat: From Dense Token to Sparse Memory for Long Video Understanding: 17 upvotes on Hugging Face Daily Papers, #7 of 13 papers on 2023-08-01. Day-by-day upvote history.

Recently, integrating video foundation models and large language models to build a video understanding system overcoming the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory cost, and long-term temporal connection are the remaining challenges. Inspired by Atkinson-Shiffrin memory model, we develop an memory mechanism including a rapidly updated short-term memory and a compact thus sustained long-term memory. We employ tokens in Transformers as the carriers of memory. MovieChat achieves state-of-the-art performace in long video understanding.

Paper page on Hugging Face · arXiv

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