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

• URL: http://arxiv.org/abs/2405.05145v1
• Date: Tue, 16 Apr 2024 15:51:39 GMT
• Title: Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty
• Authors: Luca Mossina, Joseba Dalmau, Léo andéol,
• Abstract 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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