VideoMamba: State Space Model for Efficient Video Understanding
Authors: name: Kunchang Li, name: Xinhao Li, name: Yi Wang, name: Yinan He, name: Yali Wang, name: Limin Wang, name: Yu Qiao

Addressing the dual challenges of local redundancy and global dependencies in
video understanding, this work innovatively adapts the Mamba to the video
domain. The proposed VideoMamba overcomes the limitations of existing 3D
convolution neural networks and video transformers. Its linear-complexity
operator enables efficient long-term modeling, which is crucial for
high-resolution long video understanding. Extensive evaluations reveal
VideoMamba's four core abilities: (1) Scalability in the visual domain without
extensive dataset pretraining, thanks to a novel self-distillation technique;
(2) Sensitivity for recognizing short-term actions even with fine-grained
motion differences; (3) Superiority in long-term video understanding,
showcasing significant advancements over traditional feature-based models; and
(4) Compatibility with other modalities, demonstrating robustness in
multi-modal contexts. Through these distinct advantages, VideoMamba sets a new
benchmark for video understanding, offering a scalable and efficient solution
for comprehensive video understanding. All the code and models are available at
https://github.com/OpenGVLab/VideoMamba.