Understanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and Transformers. The newly proposed architecture of state space model, e.g., Mamba, shows promising traits to extend its success in long sequence modeling to video modeling. To assess whether Mamba can be a viable alternative to Transformers in the video understanding domain, in this work, we conduct a comprehensive set of studies, probing different roles Mamba can play in modeling videos, while investigating diverse tasks where Mamba could exhibit superiority. We categorize Mamba into four roles for modeling videos, deriving a Video Mamba Suite composed of 14 models/modules, and evaluating them on 12 video understanding tasks. Our extensive experiments reveal the strong potential of Mamba on both video-only and video-language tasks while showing promising efficiency-performance trade-offs. We hope this work could provide valuable data points and insights for future research on video understanding. Code is public: https://github.com/OpenGVLab/video-mamba-suite.
@article{arxiv.2403.09626,
title = {Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding},
author = {Guo Chen and Yifei Huang and Jilan Xu and Baoqi Pei and Zhe Chen and Zhiqi Li and Jiahao Wang and Kunchang Li and Tong Lu and Limin Wang},
journal= {arXiv preprint arXiv:2403.09626},
year = {2024}
}