When employing deep neural networks (DNNs) for semantic segmentation in safety-critical applications like automotive perception or medical imaging, it is important to estimate their performance at runtime, e.g. via uncertainty estimates or prediction quality estimates. Previous works mostly performed uncertainty estimation on pixel-level. In a line of research, a connected-component-wise (segment-wise) perspective was taken, approaching uncertainty estimation on an object-level by performing so-called meta classification and regression to estimate uncertainty and prediction quality, respectively. In those works, each predicted segment is considered individually to estimate its uncertainty or prediction quality. However, the neighboring segments may provide additional hints on whether a given predicted segment is of high quality, which we study in the present work. On the basis of uncertainty indicating metrics on segment-level, we use graph neural networks (GNNs) to model the relationship of a given segment's quality as a function of the given segment's metrics as well as those of its neighboring segments. We compare different GNN architectures and achieve a notable performance improvement.

翻译：在自动驾驶感知或医学成像等安全关键应用中采用深度神经网络进行语义分割时，运行时性能估计至关重要，例如通过不确定性估计或预测质量估计。先前研究主要在像素层面进行不确定性估计。一系列研究采用连通分量（片段层面）的视角，通过执行所谓的元分类和回归分别估计不确定性和预测质量，从而在物体层面实现不确定性估计。这些工作中每个预测片段被独立考虑以估计其不确定性或预测质量。然而，相邻片段可能为判断给定预测片段是否具有高质量提供额外线索，这正是本研究探讨的核心问题。基于片段层面的不确定性指示指标，我们采用图神经网络将给定片段的质量关系建模为其自身指标及其相邻片段指标的函数。通过比较不同图神经网络架构，我们实现了显著的性能提升。