Summary of Sauge: Taming Sam For Uncertainty-aligned Multi-granularity Edge Detection, by Xing Liufu et al.
SAUGE: Taming SAM for Uncertainty-Aligned Multi-Granularity Edge Detection
by Xing Liufu, Chaolei Tan, Xiaotong Lin, Yonggang Qi, Jinxuan Li, Jian-Fang Hu
First submitted to arxiv on: 17 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 paper proposes a novel approach to edge detection by leveraging the segment anything model (SAM) to handle uncertainty in per-pixel labels. Previous methods often rely on voting strategies or assume pre-defined label distributions, which can lead to limited performance. In contrast, SAM provides strong prior knowledge that inherently corresponds to object edges at various granularities, reflecting different edge options due to uncertainty. The proposed method regresses intermediate SAM features from different layers to object edges at multi-granularity levels, allowing for the exploration of diverse uncertainties in a data-driven fashion. A lightweight module is added to the frozen SAM to progressively fuse and adapt its intermediate features to estimate edges from coarse to fine. To normalize granularity levels, pseudo labels are created by linearly blending real edge labels with varying granularities. The resulting uncertainty-aligned edge detector can produce edges at any desired granularity, including an optimal one. Experimental results on BSDS500, Muticue, and NYUDv2 demonstrate the model’s superiority. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding edges in images. Edge detection is important because it helps machines understand what’s in a picture. Previous methods for edge detection didn’t work well when there was uncertainty in the labels. The authors of this paper found that by using a special model called SAM, they could handle this uncertainty better. They did this by looking at how the model works at different levels and adjusting it to make sure it can find edges at any level. This is important because it means machines can understand images better, even if the labels are uncertain. The authors tested their method on several image datasets and found that it worked much better than other methods. |
Keywords
» Artificial intelligence » Sam
