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.
Cite
Text
Mossina et al. "Conformal Semantic Image Segmentation: Post-Hoc Quantification of Predictive Uncertainty." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00361Markdown
[Mossina et al. "Conformal Semantic Image Segmentation: Post-Hoc Quantification of Predictive Uncertainty." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/mossina2024cvprw-conformal/) doi:10.1109/CVPRW63382.2024.00361BibTeX
@inproceedings{mossina2024cvprw-conformal,
title = {{Conformal Semantic Image Segmentation: Post-Hoc Quantification of Predictive Uncertainty}},
author = {Mossina, Luca and Dalmau, Joseba and Andéol, Léo},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {3574-3584},
doi = {10.1109/CVPRW63382.2024.00361},
url = {https://mlanthology.org/cvprw/2024/mossina2024cvprw-conformal/}
}