Summary of Visual Prompt Selection For In-context Learning Segmentation, by Wei Suo et al.
Visual Prompt Selection for In-Context Learning Segmentation
by Wei Suo, Lanqing Lai, Mengyang Sun, Hanwang Zhang, Peng Wang, Yanning Zhang
First submitted to arxiv on: 14 Jul 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 new stepwise context search method to improve image segmentation models inspired by In-Context Learning (ICL). It rethinks the example selection strategy, demonstrating ICL-based segmentation models are sensitive to different contexts. The proposed method constructs a small yet rich candidate pool and adaptively searches for well-matched contexts, reducing annotation costs while enhancing segmentation performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to identify different objects in an image, like people, animals, or buildings. Computer scientists want to develop better ways to do this task called “image segmentation.” Recently, they’ve been inspired by a new idea called In-Context Learning (ICL). This paper focuses on finding the best way to choose examples for ICL-based segmentation models. |
Keywords
» Artificial intelligence » Image segmentation
