Summary of Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model, by Lianghui Zhu et al.
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
by Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, Xinggang Wang
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Mamba deep learning model has shown promise in long sequence modeling using state space models (SSMs). However, applying SSMs to visual data is challenging due to its position-sensitivity and need for global context. This paper proposes a new generic vision backbone called Vim, which uses bidirectional Mamba blocks to mark image sequences with position embeddings and compress visual representations. Vim outperforms well-established vision transformers like DeiT on ImageNet classification, COCO object detection, and ADE20k semantic segmentation tasks while achieving significant computation and memory efficiency improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vim is a new way to do computer vision that’s better than other methods called Transformers. It uses something called bidirectional Mamba blocks to understand images in a more efficient way. This means it can process big images quickly, which is important for things like recognizing objects or understanding what’s happening in a scene. Vim does this without needing as much computer power or memory, making it useful for real-world applications. |
Keywords
* Artificial intelligence * Classification * Deep learning * Object detection * Semantic segmentation
