His research focuses on interpretability and wildfire prediction. This is one theoretical paper into a more broadened thesis where we want to see if we can match through the interpretable model what we know from science on the wildfire pattern, focusing on the boreal ecosystem, in Canada and other countries. Hugo is building a huge benchmark on this wildfire prediction task for Canada, going back to more theoretical work on interpretability to see how can we apply those methods to non-RGB image data. To ask questions about Hugo’s work on interpretable semantic segmentation, visit Poster Session 1 today (Saturday) from 11:15 to 13:00. 15 DAILY WACV Saturday Multi-Scale Grouped Prototypes …
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