Summary of Competing For Pixels: a Self-play Algorithm For Weakly-supervised Segmentation, by Shaheer U. Saeed et al.
Competing for pixels: a self-play algorithm for weakly-supervised segmentation
by Shaheer U. Saeed, Shiqi Huang, João Ramalhinho, Iani J.M.B. Gayo, Nina Montaña-Brown, Ester Bonmati, Stephen P. Pereira, Brian Davidson, Dean C. Barratt, Matthew J. Clarkson, Yipeng Hu
First submitted to arxiv on: 26 May 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 This paper proposes a novel weakly-supervised segmentation (WSS) method that leverages reinforcement learning self-play to gamify image segmentation of regions of interest (ROIs). The approach, which relies on image-level labels indicating object presence, is designed to minimize over- or under-segmentation, a common issue with WSS methods. The authors formulate segmentation as a competition between two agents that compete to select ROI-containing patches until exhaustion of all such patches. A reward function is used to compute the score at each time-step, which represents the likelihood of object presence within the selection. The game terminates when either agent exhausts all ROI-containing patches or finds one. This setup ensures minimization of over- or under-segmentation. Experimental results on four datasets demonstrate significant performance improvements over recent state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes image segmentation easier and better by using a new way to train models with only simple labels. The old way of training segmentation models requires lots of detailed labels, but this new approach uses a game-like setup where two teams compete to find the right parts of an image. This helps the model learn what’s important and what’s not, which makes it more accurate. The team tested their idea on four different datasets and showed that it works much better than other current methods. |
Keywords
» Artificial intelligence » Image segmentation » Likelihood » Reinforcement learning » Supervised
