We introduce an assessment procedure for interactive segmentation models. Based on concepts from Bayesian Experimental Design, the procedure measures a model's understanding of point prompts and their correspondence with the desired segmentation mask. We show that Oracle Dice index measurements are insensitive or even misleading in measuring this property. We demonstrate the use of the proposed procedure on three interactive segmentation models and subsets of two large image segmentation datasets.

翻译：我们提出了一种针对交互式分割模型的评估流程。该流程基于贝叶斯实验设计概念，通过测量模型对点提示及其与期望分割掩码对应关系的理解程度，评估模型性能。我们证明，Oracle Dice指数测量在衡量这一特性时缺乏敏感性，甚至可能产生误导。我们展示了该流程在三种交互式分割模型及两个大型图像分割数据集子集上的应用效果。