Summary of Sam-sp: Self-prompting Makes Sam Great Again, by Chunpeng Zhou et al.
SAM-SP: Self-Prompting Makes SAM Great Again
by Chunpeng Zhou, Kangjie Ning, Qianqian Shen, Sheng Zhou, Zhi Yu, Haishuai Wang
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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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 Segment Anything Model (SAM), a Visual Foundation Model (VFM), has shown impressive results in zero-shot segmentation tasks on various natural image datasets. However, SAM’s performance degrades significantly when applied to specific domains like medical images. To improve its generalizability, researchers have explored fine-tuning strategies, but these approaches still rely heavily on domain-specific expert-level prompts during evaluation, limiting the model’s practicality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The recently developed Segment Anything Model (SAM) is a type of Visual Foundation Model (VFM) that does very well at separating things in pictures. It works great for normal photos, but when it tries to do this with medical images, its performance gets much worse. To make SAM better at working on different types of images, people are trying to tweak the model, but they still need expert help to test it. |
Keywords
» Artificial intelligence » Fine tuning » Sam » Zero shot
