Summary of Semi-supervised Semantic Segmentation Based on Pseudo-labels: a Survey, by Lingyan Ran et al.
Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
by Lingyan Ran, Yali Li, Guoqiang Liang, Yanning Zhang
First submitted to arxiv on: 4 Mar 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 Semantic segmentation is a crucial computer vision task that classifies pixels based on their meaning. However, training models requires vast amounts of labeled data, which is time-consuming and laborious to obtain. This review presents a comprehensive overview of pseudo-label methods for semi-supervised semantic segmentation, categorized by perspective and application area. Pseudo-labels can be used in medical and remote-sensing image segmentation tasks. The paper also suggests potential future research directions to address existing challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making computer vision easier! It talks about a big problem in a field called semantic segmentation. Right now, we need tons of labeled data to train models, but that’s super hard and takes forever. So, this review looks at special tricks called pseudo-labels that can help us train better models with less data. They show how these tricks work in different areas like medicine and satellite imaging. And finally, they give some ideas for what we could do next. |
Keywords
» Artificial intelligence » Image segmentation » Semantic segmentation » Semi supervised
