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Preprints, Working Papers, ... Year : 2024

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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Dates and versions

hal-04565173 , version 1 (01-05-2024)

Identifiers

  • HAL Id : hal-04565173 , version 1

Cite

Luca Mossina, Joseba Dalmau, Léo Andéol. Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty. 2024. ⟨hal-04565173⟩
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