VideoMamba: Spatio-Temporal Selective State Space Model

• URL: http://arxiv.org/abs/2407.08476v1
• Date: Thu, 11 Jul 2024 13:11:21 GMT
• Title: VideoMamba: Spatio-Temporal Selective State Space Model
• Authors: Jinyoung Park, Hee-Seon Kim, Kangwook Ko, Minbeom Kim, Changick Kim,
• Abstract summary: VideoMamba is a novel adaptation of the pure Mamba architecture, specifically designed for video recognition. VideoMamba is not only resource-efficient but also effective in capturing long-range dependency in videos. 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.
• Score: 18.310796559944347
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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