Summary of Repurposing Stable Diffusion Attention For Training-free Unsupervised Interactive Segmentation, by Markus Karmann et al.
Repurposing Stable Diffusion Attention for Training-Free Unsupervised Interactive Segmentation
by Markus Karmann, Onay Urfalioglu
First submitted to arxiv on: 15 Nov 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 approach in this paper presents a novel, training-free unsupervised method for interactive point prompt based image segmentation. By interpreting the self-attention tensor as a Markov transition operator, the researchers iteratively construct a Markov chain to generate a Markov-map, which is shown to have less noise and sharper semantic boundaries compared to raw attention maps. This Markov-map is then integrated into a truncated nearest neighbor framework to achieve interactive point prompt based segmentation. The approach is evaluated on several datasets, achieving excellent results in terms of Number of Clicks (NoC) and outperforming state-of-the-art training-based unsupervised methods in most cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to segment images without needing to train a model. Instead, it uses the self-attention tensor from Stable Diffusion to create a Markov-map, which is then used for segmentation. This method is shown to be effective and efficient, producing better results than other methods that require training. |
Keywords
» Artificial intelligence » Attention » Diffusion » Image segmentation » Nearest neighbor » Prompt » Self attention » Unsupervised
