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
Published: 2023-07-31 07:15:45

Abstract: 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.