Summary of Nubbledrop: a Simple Way to Improve Matching Strategy For Prompted One-shot Segmentation, by Zhiyu Xu et al.
NubbleDrop: A Simple Way to Improve Matching Strategy for Prompted One-Shot Segmentation
by Zhiyu Xu, Qingliang Chen
First submitted to arxiv on: 19 May 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 This paper presents advancements in one-shot segmentation models that leverage large data trained segmentation models like SAM. Recent contributions, such as PerSAM and MATCHER, utilize SAM with a few reference images to generate high-quality segmentation masks for target images. The proposed method, NubbleDrop, enhances the validity and robustness of the matching strategy without additional computational cost by randomly dropping feature channels containing deceptive information. This technique can be applied to other similarity computing scenarios. The paper demonstrates the effectiveness of NubbleDrop through a comprehensive set of experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary One-shot segmentation models are really good at creating masks for new images based on a few examples. Some recent papers have improved this by using large data trained models like SAM, PerSAM, and MATCHER. But these methods can be biased and not very robust. To fix this, the authors propose a simple way to make them better without adding extra computational cost. They call it NubbleDrop. It works by randomly ignoring some features that might give bad information. This helps the model create more accurate masks. The paper shows how well this method works in different scenarios. |
Keywords
» Artificial intelligence » One shot » Sam
