Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty

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 confid...

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Bibliographic Details
Published in2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp. 3574 - 3584
Main Authors Mossina, Luca, Dalmau, Joseba, Andeol, Leo
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.06.2024
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Summary: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.
ISSN:2160-7516
DOI:10.1109/CVPRW63382.2024.00361