Summary of Selective Visual Prompting in Vision Mamba, by Yifeng Yao et al.
Selective Visual Prompting in Vision Mamba
by Yifeng Yao, Zichen Liu, Zhenyu Cui, Yuxin Peng, Jiahuan Zhou
First submitted to arxiv on: 12 Dec 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 The proposed Selective Visual Prompting (SVP) method efficiently fine-tunes Vim models for diverse downstream vision tasks. Building upon the unique design of selective state space models, SVP addresses limitations in existing visual prompting methods by leveraging lightweight selective prompters and a dual-path structure. This innovative approach enables adaptive activation of update and forget gates within Mamba blocks, promoting discriminative information propagation. The experimental results demonstrate that SVP outperforms state-of-the-art methods on various large-scale benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vim models are super smart at computer vision tasks because they’re designed to be efficient and work well together. To make them even better, scientists came up with a new way to fine-tune Vim models using something called visual prompting. However, this method doesn’t work very well for Vim models because it’s based on an old idea that isn’t perfect for Vims. So, the scientists created a new and improved method called Selective Visual Prompting (SVP) that helps Vim models learn better by giving them special instructions at each step. |
Keywords
» Artificial intelligence » Prompting
