Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty
<|reference_start|>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 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.<|reference_end|>
@article{mossina2024conformal,
    title={Conformal Semantic Image Segmentation: Post-hoc Quantification of
  Predictive Uncertainty},
    author={Luca Mossina, Joseba Dalmau, L'eo and'eol},
    journal={arXiv preprint arXiv:2405.05145},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05145},
    primaryClass={cs.CV cs.LG}
}