Summary of Vision Mamba: a Comprehensive Survey and Taxonomy, by Xiao Liu et al.
Vision Mamba: A Comprehensive Survey and Taxonomy
by Xiao Liu, Chenxu Zhang, Lei Zhang
First submitted to arxiv on: 7 May 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 A recent paper introduces Mamba, a novel AI architecture that leverages State Space Models (SSMs) to analyze dynamic systems. By mapping sequence data to SSMs, Mamba can capture long-term dependencies in the data, outperforming traditional models like Transformers. The paper demonstrates Mamba’s efficiency and strong representational capabilities in natural language processing (NLP), while maintaining linear time complexity. Researchers have extended Mamba to various visual domains, including computer vision, multi-modal analysis, medical image analysis, and remote sensing image analysis. To better understand Mamba’s applications in these domains, this paper presents a comprehensive survey and taxonomy study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a new AI architecture that uses State Space Models (SSMs) to analyze dynamic systems. This model can capture long-term dependencies in sequence data, making it more efficient than traditional models like Transformers. Researchers have been testing Mamba in different areas, such as natural language processing and computer vision. In this paper, we look at how Mamba is being used in these fields and what its strengths and weaknesses are. |
Keywords
» Artificial intelligence » Multi modal » Natural language processing » Nlp
