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.

翻译：语义分割在众多计算机视觉应用中扮演着关键角色，然而其有效性常受限于高质量标注数据的匮乏。为应对该挑战，常见策略是采用基于不同群体数据（如公开数据集）训练的模型。然而，这种方法会引发分布偏移问题，导致在目标群体上的性能下降。在模型错误可能造成严重后果的场景中，选择性预测方法提供了一种降低风险并减少对专家监督依赖的手段。本文研究了低资源条件下语义分割的选择性预测问题，重点聚焦于在分布偏移环境中应用于预训练模型的事后置信度估计器。我们针对语义分割提出了一种新颖的图像级置信度度量方法，并通过三项医学影像任务的实验验证了其有效性。研究结果表明，事后置信度估计器为降低分布偏移的影响提供了一种经济高效的途径。