Summary of Segment Anything Without Supervision, by Xudong Wang and Jingfeng Yang and Trevor Darrell
Segment Anything without Supervision
by XuDong Wang, Jingfeng Yang, Trevor Darrell
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The Segmentation Anything Model (SAM) is an unsupervised learning method for whole-image segmentation that eliminates the need for human annotations. Researchers introduce Unsupervised SAM (UnSAM), a divide-and-conquer strategy to uncover visual scenes’ hierarchical structure. Initially, top-down clustering methods partition unlabeled images into instance/semantic level segments. Then, bottom-up clustering iteratively merges pixels within each segment into larger groups, forming a hierarchical structure. These unsupervised masks are used to supervise model training. Compared across seven datasets, UnSAM’s performance is competitive with the supervised SAM and outperforms the previous state-of-the-art in unsupervised segmentation by 11%. Additionally, integrating unsupervised pseudo masks with SA-1B’s ground-truth masks allows for lightly semi-supervised UnSAM to segment entities overlooked by supervised SAM, achieving an AR of over 6.7% and AP of 3.9%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have created a new way to automatically divide images into smaller parts without needing human help. This method is called Unsupervised Segmentation Anything Model (UnSAM). It works by breaking down the image into small pieces, then grouping those pieces together based on what they look like. The computer can learn from these groups and use them to teach itself how to do better segmentation tasks. In tests, this new method performed almost as well as a more complicated version that needed human help, and even did better in some cases. |
Keywords
» Artificial intelligence » Clustering » Image segmentation » Sam » Semi supervised » Supervised » Unsupervised
