Summary of Efficientvmamba: Atrous Selective Scan For Light Weight Visual Mamba, by Xiaohuan Pei et al.
EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba
by Xiaohuan Pei, Tao Huang, Chang Xu
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel efficient model variant, EfficientVMamba, to tackle the long-standing trade-off between accuracy and efficiency in light-weight model development. By integrating atrous-based selective scan approach and efficient skip sampling, EfficientVMamba harnesses both global and local representational features. The paper also investigates the integration of state space models (SSMs) with convolutions, introducing an efficient visual state space block combined with a convolution branch. Experimental results show that EfficientVMamba scales down computational complexity while achieving competitive performance across various vision tasks. For instance, EfficientVMamba-S outperforms Vim-Ti by 5.6% accuracy on ImageNet using 1.3G FLOPs, compared to 1.5G FLOPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new model that helps solve a problem in computer science called “light-weight model development.” Right now, some models are good at finding small details but bad at seeing the big picture, while others can see everything but use too much energy. The researchers created a new model that combines the strengths of both and makes it more efficient. They tested their model on different tasks like image recognition and found that it performed well and used less energy than other models. |
