Summary of The Impact Of Semi-supervised Learning on Line Segment Detection, by Johanna Engman et al.
The Impact of Semi-Supervised Learning on Line Segment Detection
by Johanna Engman, Karl Åström, Magnus Oskarsson
First submitted to arxiv on: 7 Nov 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 presents a novel approach to detecting line segments in images, leveraging a semi-supervised framework that combines consistency loss with differently augmented and perturbed unlabeled images. The method achieves comparable results to fully supervised approaches, making it suitable for applications where annotation is challenging or expensive. This is particularly relevant for real-time and online applications, such as forestry domain-specific scenarios. The authors investigate the use of small and efficient learning backbones, which is a crucial aspect of this method’s applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find lines in pictures using some new computer learning techniques. It shows that even with only a little bit of labeled information, we can get good results by looking at many different versions of the same image. This is useful when it’s hard or expensive to label all the images. The method is fast and works well for things like finding tree lines in forests. |
Keywords
» Artificial intelligence » Semi supervised » Supervised
