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About this paper
Guo Chen, Yifei Huang, Jilan Xu, Baoqi Pei, Zhe Chen, Zhiqi Li, Jiahao Wang, Kunchang Li and 2 more
This work comprehensively evaluates the State Space Model (SSM) architecture, specifically Mamba, as a versatile alternative to Transformers for various video understanding tasks. It systematically demonstrates that Mamba-based models often achieve competitive or superior performance while offering significant efficiency gains, particularly for long video sequences.
Key insights
- Mamba-based models consistently achieve competitive or superior performance compared to Transformer baselines across a wide array of video understanding tasks, including action localization, segmentation, and long-form video question answering.
- The inherent linear computational complexity of Mamba architectures translates to significant efficiency advantages and superior scalability for processing long video sequences, directly addressing a primary bottleneck of Transformers.
- The proposed Decomposed Bidirectionally Mamba (DBM) block proved particularly effective in specific temporal modeling contexts and on smaller-scale datasets, demonstrating Mamba's adaptability to different task requirements.
Summary by alphaXiv.
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