Summary of Mamba in Vision: a Comprehensive Survey Of Techniques and Applications, by Md Maklachur Rahman et al.
Mamba in Vision: A Comprehensive Survey of Techniques and Applications
by Md Maklachur Rahman, Abdullah Aman Tutul, Ankur Nath, Lamyanba Laishram, Soon Ki Jung, Tracy Hammond
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 This paper introduces Mamba, a novel approach to overcome the limitations of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision. While CNNs excel at extracting local features, they struggle to capture long-range dependencies without complex modifications. ViTs, on the other hand, model global relationships but suffer from high computational costs due to quadratic self-attention mechanisms. Mamba addresses these limitations by leveraging Selective Structured State Space Models with linear complexity. The paper provides a survey of Mamba’s unique contributions, computational benefits, and applications in computer vision, while also identifying challenges and potential future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a new way to improve computer vision models. It helps solve problems that Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have when trying to understand images. CNNs are good at finding small details, but struggle to see the big picture. ViTs can see the big picture, but take too long to process. Mamba combines the best of both worlds by using a special type of model that’s fast and accurate. The paper explains how Mamba works and what it can do, as well as some challenges and ideas for future research. |
Keywords
» Artificial intelligence » Self attention
