We introduce ProPanDL, a family of networks capable of uncertainty-aware
panoptic segmentation. Unlike existing segmentation methods, ProPanDL is
capable of estimating full probability distributions for both the semantic and
spatial aspects of panoptic segmentation. We implement and evaluate ProPanDL
variants capable of estimating both parametric (Variance Network) and
parameter-free (SampleNet) distributions quantifying pixel-wise spatial
uncertainty. We couple these approaches with two methods (Temperature Scaling
and Evidential Deep Learning) for semantic uncertainty estimation. To evaluate
the uncertainty-aware panoptic segmentation task, we address limitations with
existing approaches by proposing new metrics that enable separate evaluation of
spatial and semantic uncertainty. We additionally propose the use of the energy
score, a proper scoring rule, for more robust evaluation of spatial output
distributions. Using these metrics, we conduct an extensive evaluation of
ProPanDL variants. Our results demonstrate that ProPanDL is capable of
estimating well-calibrated and meaningful output distributions while still
retaining strong performance on the base panoptic segmentation task.

ProPanDL 是一种能够进行不确定性感知的全景分割网络家族，能够估计全景分割的语义和空间因素的完整概率分布，通过采用样本网络和方差网络进行像素级别的空间不确定性的估计以及采用温度缩放和证据深度学习等方法进行语义不确定性的估计来提高不确定性感知的全景分割任务，并建立了新的度量标准来进行空间和语义不确定性的单独评估和通过能量分数进行空间输出分布的更健壮评估，数值实验表明其在保持基础全景分割任务的强大性能的同时又能够估计良好校准且具有意义的输出分布。