  Part-aware panoptic segmentation is a problem of computer vision that aims to
provide a semantic understanding of the scene at multiple levels of
granularity. More precisely, semantic areas, object instances, and semantic
parts are predicted simultaneously. In this paper, we present our Joint
Panoptic Part Fusion (JPPF) that combines the three individual segmentations
effectively to obtain a panoptic-part segmentation. Two aspects are of utmost
importance for this: First, a unified model for the three problems is desired
that allows for mutually improved and consistent representation learning.
Second, balancing the combination so that it gives equal importance to all
individual results during fusion. Our proposed JPPF is parameter-free and
dynamically balances its input. The method is evaluated and compared on the
Cityscapes Panoptic Parts (CPP) and Pascal Panoptic Parts (PPP) datasets in
terms of PartPQ and Part-Whole Quality (PWQ). In extensive experiments, we
verify the importance of our fair fusion, highlight its most significant impact
for areas that can be further segmented into parts, and demonstrate the
generalization capabilities of our design without fine-tuning on 5 additional
datasets.
