  As deep learning-based computer vision algorithms continue to improve and
advance the state of the art, their robustness to real-world data continues to
lag their performance on datasets. This makes it difficult to bring an
algorithm from the lab to the real world. Ensemble-based uncertainty estimation
approaches such as Monte Carlo Dropout have been successfully used in many
applications in an attempt to address this robustness issue. Unfortunately, it
is not always clear if such ensemble-based approaches can be applied to a new
problem domain. This is the case with panoptic segmentation, where the
structure of the problem and architectures designed to solve it means that
unlike image classification or even semantic segmentation, the typical solution
of using a mean across samples cannot be directly applied. In this paper, we
demonstrate how ensemble-based uncertainty estimation approaches such as Monte
Carlo Dropout can be used in the panoptic segmentation domain with no changes
to an existing network, providing both improved performance and more
importantly a better measure of uncertainty for predictions made by the
network. Results are demonstrated quantitatively and qualitatively on the COCO,
KITTI-STEP and VIPER datasets.
