Selective Prediction for Semantic Segmentation using Post-Hoc Confidence Estimation and Its Performance under Distribution Shift
<|reference_start|>Selective Prediction for Semantic Segmentation using Post-Hoc Confidence Estimation and Its Performance under Distribution Shift: Semantic segmentation plays a crucial role in various computer vision applications, yet its efficacy is often hindered by the lack of high-quality labeled data. To address this challenge, a common strategy is to leverage models trained on data from different populations, such as publicly available datasets. This approach, however, leads to the distribution shift problem, presenting a reduced performance on the population of interest. In scenarios where model errors can have significant consequences, selective prediction methods offer a means to mitigate risks and reduce reliance on expert supervision. This paper investigates selective prediction for semantic segmentation in low-resource settings, thus focusing on post-hoc confidence estimators applied to pre-trained models operating under distribution shift. We propose a novel image-level confidence measure tailored for semantic segmentation and demonstrate its effectiveness through experiments on three medical imaging tasks. Our findings show that post-hoc confidence estimators offer a cost-effective approach to reducing the impacts of distribution shift.<|reference_end|>
@article{borges2024selective,
    title={Selective Prediction for Semantic Segmentation using Post-Hoc Confidence
  Estimation and Its Performance under Distribution Shift},
    author={Bruno Laboissiere Camargos Borges, Bruno Machado Pacheco, Danilo Silva},
    journal={arXiv preprint arXiv:2402.10665},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10665},
    primaryClass={cs.LG cs.CV}
}