  We introduce VideoMamba, a novel adaptation of the pure Mamba architecture,
specifically designed for video recognition. Unlike transformers that rely on
self-attention mechanisms leading to high computational costs by quadratic
complexity, VideoMamba leverages Mamba's linear complexity and selective SSM
mechanism for more efficient processing. The proposed Spatio-Temporal Forward
and Backward SSM allows the model to effectively capture the complex
relationship between non-sequential spatial and sequential temporal information
in video. Consequently, VideoMamba is not only resource-efficient but also
effective in capturing long-range dependency in videos, demonstrated by
competitive performance and outstanding efficiency on a variety of video
understanding benchmarks. Our work highlights the potential of VideoMamba as a
powerful tool for video understanding, offering a simple yet effective baseline
for future research in video analysis.
