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[bibtex]@InProceedings{Mossina_2024_CVPR, author = {Mossina, Luca and Dalmau, Joseba and And\'eol, L\'eo}, title = {Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3574-3584} }
Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty
Abstract
We propose a post-hoc computationally lightweight method to quantify predictive uncertainty in semantic image segmentation. Our approach uses conformal prediction to generate statistically valid prediction sets that are guaranteed to include the ground-truth segmentation mask at a predefined confidence level. We introduce a novel visualization technique of conformalized predictions based on heatmaps and provide metrics to assess their empirical validity. We demonstrate the effectiveness of our approach on well-known benchmark datasets and image segmentation prediction models and conclude with practical insights.
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