Summary of Weakly-supervised Semantic Segmentation Of Circular-scan, Synthetic-aperture-sonar Imagery, by Isaac J. Sledge et al.
Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery
by Isaac J. Sledge, Dominic M. Byrne, Jonathan L. King, Steven H. Ostertag, Denton L. Woods, James L. Prater, Jermaine L. Kennedy, Timothy M. Marston, Jose C. Principe
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 framework for semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The approach consists of two parts: the first part is trained in a supervised manner on image-level labels to identify semi-sparse, spatially-discriminative regions, while the second part uses these regions as weakly labeled segmentation seeds. The framework leverages information-theoretic loss and structured-prediction regularizers to progressively resize the seed extents and delineate class-specific transitions in local image content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to look at special kinds of images called CSAS. It helps computers understand what’s in these images by finding important parts and growing them into useful shapes. The computer uses previous images to get better at recognizing patterns and making the right choices. This is important because it can help us make sense of a lot of data. |
Keywords
* Artificial intelligence * Semantic segmentation * Supervised
