studied. And for semantic segmentation, we are only starting, I think, and there is only like two papers on prototype learning for semantic segmentation.” Can we imagine a specific use case for this work? Hugo thinks of one reuse case of how this would have worked, and he added it into the appendix of the paper by requirements of one of the reviewers during the WACV review process. It shows a basic example of why scale helps detect, confronting factors in the process. For the classification of a cow instead of a sheep, sometimes at lower scale, we look at texture pattern and the hair pattern looks like a sheep on a given cow. “But if you look at broader scale,” concludes Hugo, “then you will see like more focus on the shape of the part, of the different parts. And through this process, we can understand through our scale analysis why it was classified as sheep and not as a cow: because there was a too strong focus on the lower scale information and not the higher scale. This is a concrete example that we put in the appendix of the paper to help people understand in a real use case how this would work. And obviously this could be broadened to medical and other applications. As I mentioned quickly, we work on satellite data, so scale is very important!” Hugo works in the lab of Earth Observation and Computational Environmental Science at EPFL. 14 DAILY WACV Saturday Poster Presentation WACV Daily Editor: Ralph Anzarouth Publisher & Copyright: Computer Vision News All rights reserved. Unauthorized reproduction is strictly forbidden. Our editorial choices are fully independent from IEEE, WACV and the conference organizers.
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