Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

本文通过对文献的元分析，发现大多数计算机视觉应用都没有使用恰当的认识不确定性量化方法，从而忽略了模型自身的局限性。我们描述了使用没有适当的不确定性量化的模型所产生的后果，并激励社区采用具有适当校准的认知不确定度的模型版本，以实现让模型具有识别未知数据集的能力。在本文中，我们还总结了计算机视觉应用程序中不确定性估计的挑战和建议。

计算机视觉中缺乏不确定性的发现令人不安