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

Enxin Song1,∗, Wenhao Chai2,∗,†, Guanhong Wang1,∗, Yucheng Zhang1,‡,
Haoyang Zhou1,‡, Feiyang Wu1,‡, Haozhe Chi1, Xun Guo3,✉,
Tian Ye4, Yanting Zhang5, Yan Lu3, Jenq-Neng Hwang2, Gaoang Wang1,✉

1 Zhejiang Unversity   2 University of Washington   3 Microsoft Research Asia
4 Hong Kong University of Science and Technology (GZ)  5 Donghua University

Equal contribution    Project lead    Data collection    Corresponding author



Recently, integrating video foundation models and large language models to build a video understanding system can overcome 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 impose additional challenges. Taking advantage of the AtkinsonShiffrin memory model, with tokens in Transformers being employed as the carriers of memory in combination with our specially designed memory mechanism, we propose the MovieChat to overcome these challenges. MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1K benchmark with 1K long video and 14K manual annotations for validation of the effectiveness of our method.


Demo Video

Comparison Case

Question and answer about a clip from YouTube, which is a tutorial on how to cook steak. The entire instructional process begins with marinating the steak, followed by pan-searing it, preparing side dishes, and ultimately plating the meal. Green ( Red ) highlights the correct (wrong) answer and yellow indicates that the model is hallucinating.

Breakpoint Mode

Global Mode

Benchmark: MovieChat-1K

To better evaluate the performance of MovieChat, we collect a new benchmark for long video understanding tasks, MovieChat-1K, which contains 1K high quality video clips sourced from various movies and TV series with 14K manual annotations.

To the best of our knowledge, a long video understanding dataset has not yet been established. Our work represents the initial step in creating and making it publicly available.We create MovieChat1K, containing 1k long videos and corresponding 1k dense captions, and 13k visual question-answer pairs.For each video, we manually set and provide 1 dense caption for the whole video, 3 question-answering pairs for global mode and 10 question-answering pairs with timestamps for breakpoint mode.

We collect videos from 15 popular categories with varying distribution, including documentary film, detective film, animation film, and so on. Among these, each video comprises multiple alternating scenes, contributing to a diverse and dynamic visual narrative within the context of the collection. Over 90% of the videos exhibit a duration ranging from 10K to 12K frames, while 14.6% of videos extending beyond 12K frames. Only 8.6% of videos have duration less than 10k frames.

Question-answering Pairs

Word Distribution

Note that MovieChat-1K is specifically designed for long video comprehension tasks, the majority of questions are open-ended, with only a quarter classified as multiple-choice questions, marked by initiators such as ‘Do,’ ‘Does,’ ‘Is,’ or ‘Are.’ We also compute the word distributions of our provided question-answer pairs, which includes common objects (people, clothes, etc.), time (day, night, etc.), scenes (indoor, outdoor, etc.), and so on.

Sentence length distribution

MovieChat1K exhibits diverse lengths of question-answer pairs in the segmented clip level. Despite the distribution of questionanswer pairs varies between the global mode and breakpoint mode, the majority of questions tends to concentrate between 5-15 words in length, while the length of answers generally have fewer than 10 words.

Dense Captions

To facilitate a more detailed understanding of long videos, we provide a dense caption for each video. MovieChat-1K exhibits diverse caption lengths in the segmented clip level. Approximately two-thirds of the clips have captions with 100-149 words, while one-fifth of the clip captions have fewer than 100 words. About 11% of clips have long captions with more than 150 words.

To analyze the word distribution of our generated captions, we compute their distributions. The resulting word distribution of the captions is presented in Fig. B6, which includes common objects (man, woman, people, girl, etc.), attributes (detective, various, small, white, etc.), locations (inside, behind, south, next, etc.), scenes (room, house, building, office, etc.), actions/events (talk, enter, leave, take, etc.), and more.

In terms of actionness, MovieChat-1K captions contains nearly the same number of verbs as with the WebVid10M dataset. To evaluate this, we use the NLTK toolkit to analyze the number of verbs in captions, focusing on extracting and tagging all unique verbs. We find a total of 109,485 verbs in the WebVid10M caption dataset, while the MovieChat-1K captions contain 102,988 unique instances of verbs. While these counts may not be entirely accurate due to our simple counting method, we believe they provide a rough indication of the actionness of the two datasets.


title={MovieChat: From Dense Token to Sparse Memory for Long Video Understanding},
author={Song, Enxin and Chai, Wenhao and Wang, Guanhong and Zhang, Yucheng and Zhou, Haoyang and Wu, Feiyang and Guo, Xun and Ye, Tian and Lu, Yan and Hwang, Jenq-Neng and others},
journal={arXiv preprint arXiv:2307.16449},