Summary of Localmamba: Visual State Space Model with Windowed Selective Scan, by Tao Huang et al.
LocalMamba: Visual State Space Model with Windowed Selective Scan
by Tao Huang, Xiaohuan Pei, Shan You, Fei Wang, Chen Qian, Chang Xu
First submitted to arxiv on: 14 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 paper aims to improve the performance of Vision Mamba (ViM) in image understanding tasks by optimizing scan directions for sequence modeling. Current ViM approaches overlook local 2D dependencies, resulting in elongated distances between adjacent tokens. The authors introduce a novel local scanning strategy that divides images into windows, capturing local dependencies while maintaining a global perspective. They also propose a dynamic method to independently search for optimal scan choices for each layer, leading to significant performance improvements. The approach outperforms Vim-Ti by 3.1% on ImageNet with the same FLOPs, demonstrating its effectiveness in capturing image representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper wants to make Vision Mamba better at understanding pictures. Right now, it’s not doing as well as other ways of looking at images. The authors came up with a new way to look at the pictures that takes into account what’s next to each other, rather than just looking at the whole picture. This helps them understand the images better. They also found a way to make sure each part of the model is using the best way to look at the image. This made their results even better! For example, they did 3.1% better on ImageNet. |
