Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is no guarantee that a predicted mask is close to the ground truth. To address this limitation, we introduce a conformal prediction algorithm to generate adaptive confidence sets for instance segmentation. Given an image and a pixel coordinate query, our algorithm generates a confidence set of instance predictions for that pixel, with a provable guarantee for the probability that at least one of the predictions has high Intersection-Over-Union (IoU) with the true object instance mask. We apply our algorithm to instance segmentation examples in agricultural field delineation, cell segmentation, and vehicle detection. Empirically, we find that our prediction sets vary in size based on query difficulty and attain the target coverage, outperforming existing baselines such as Learn Then Test, Conformal Risk Control, and morphological dilation-based methods. We provide versions of the algorithm with asymptotic and finite sample guarantees.

翻译：现有实例分割模型在平均预测上取得了较高性能，但缺乏系统化的不确定性量化：其输出未经校准，且无法保证预测掩码接近真实标注。为克服这一局限性，我们提出一种保形预测算法，用于生成实例分割的自适应置信集。给定图像与像素坐标查询，该算法可为该像素生成实例预测的置信集，并严格保证至少一个预测与真实物体实例掩码具有高交并比的概率。我们将算法应用于农田边界划分、细胞分割及车辆检测等实例分割场景。实证结果表明，我们的预测集规模能根据查询难度自适应调整，在达到目标覆盖度的同时，其性能优于现有基线方法（如"先学习后检验"、保形风险控制及基于形态学膨胀的方法）。我们提供了具有渐近保证与有限样本保证的算法版本。