Summary of Plainmamba: Improving Non-hierarchical Mamba in Visual Recognition, by Chenhongyi Yang et al.
PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition
by Chenhongyi Yang, Zehui Chen, Miguel Espinosa, Linus Ericsson, Zhenyu Wang, Jiaming Liu, Elliot J. Crowley
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The PlainMamba model is a non-hierarchical state space model designed for general visual recognition. It builds upon the Mamba model, which has shown competitive results on sequential data. The authors adapt the selective scanning process of Mamba to the visual domain by introducing continuous 2D scanning and direction-aware updating. This allows the model to learn features from two-dimensional images while preserving spatial continuity and directional information. The PlainMamba architecture is designed to be easy to use, easy to scale, and simplified by removing special tokens. The authors evaluate it on various visual recognition tasks, achieving performance gains over previous non-hierarchical models and competitiveness with hierarchical alternatives. Notably, PlainMamba requires less computing while maintaining high performance for tasks requiring high-resolution inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PlainMamba is a new way to recognize images using a simple model that doesn’t get too complicated. It’s like a simplified version of another model called Mamba, which works well with sequences of data. The new model makes it easier to learn from 2D images by moving in a continuous pattern and paying attention to directions. This helps the model understand spatial relationships between objects. The authors tested PlainMamba on different image recognition tasks and found that it performs better than previous models without complex structures. |
Keywords
* Artificial intelligence * Attention
