Summary of Multi-scale Grouped Prototypes For Interpretable Semantic Segmentation, by Hugo Porta et al.
Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation
by Hugo Porta, Emanuele Dalsasso, Diego Marcos, Devis Tuia
First submitted to arxiv on: 14 Sep 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 proposed method for interpretable semantic segmentation leverages multi-scale image representation to learn diverse prototypical parts at various scales, enhancing model sparsity and interpretability. This approach constructs a dense prediction map based on the similarity between parts of the test image and the prototypes, allowing users to inspect the link between predicted outputs and patterns learned by the model. The prototype layer explicitly learns scale-specific prototypical parts, while a sparse grouping mechanism produces multi-scale sparse groups. Experimental results demonstrate improved interpretability and narrowed performance gap with non-interpretable counterparts on Pascal VOC, Cityscapes, and ADE20K datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to make semantic segmentation more understandable by using different scales of information from images. The method learns specific parts or “prototypes” at different sizes, which helps the model make better predictions. This also makes it easier for people to see how the model is working and what it’s learned. The results show that this approach can improve the model’s performance and make it more interpretable. |
Keywords
» Artificial intelligence » Semantic segmentation
