Instance segmentation has witnessed promising advancements through deep neural network-based algorithms. However, these models often exhibit incorrect predictions with unwarranted confidence levels. Consequently, evaluating prediction uncertainty becomes critical for informed decision-making. Existing methods primarily focus on quantifying uncertainty in classification or regression tasks, lacking emphasis on instance segmentation. Our research addresses the challenge of estimating spatial certainty associated with the location of instances with star-convex shapes. Two distinct clustering approaches are evaluated which compute spatial and fractional certainty per instance employing samples by the Monte-Carlo Dropout or Deep Ensemble technique. Our study demonstrates that combining spatial and fractional certainty scores yields improved calibrated estimation over individual certainty scores. Notably, our experimental results show that the Deep Ensemble technique alongside our novel radial clustering approach proves to be an effective strategy. Our findings emphasize the significance of evaluating the calibration of estimated certainties for model reliability and decision-making.

翻译：实例分割通过基于深度神经网络的算法取得了显著进展。然而，这些模型常常以过高的置信度做出错误预测。因此，评估预测不确定性对于明智决策至关重要。现有方法主要关注分类或回归任务中的不确定性量化，缺乏对实例分割的重视。我们的研究解决了与星凸形状实例位置相关的空间确定性估计这一挑战。我们评估了两种不同的聚类方法，这些方法采用蒙特卡洛丢弃或深度集成技术，通过样本计算每个实例的空间确定性和分数确定性。我们的研究表明，将空间确定性和分数确定性分数相结合，相较于单个确定性分数，能够产生更优的校准估计。值得注意的是，我们的实验结果表明，深度集成技术结合我们新颖的径向聚类方法是一种有效的策略。我们的发现强调了评估估计确定性的校准对于模型可靠性和决策的重要性。