MovieChat: From Dense Token to Sparse Memory for Long Video
  Understanding
Authors: name: Enxin Song, name: Wenhao Chai, name: Guanhong Wang, name: Yucheng Zhang, name: Haoyang Zhou, name: Feiyang Wu, name: Xun Guo, name: Tian Ye, name: Yan Lu, name: Jenq-Neng Hwang, name: Gaoang Wang

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.