  In this paper, we introduce a novel network that generates semantic,
instance, and part segmentation using a shared encoder and effectively fuses
them to achieve panoptic-part segmentation. Unifying these three segmentation
problems allows for mutually improved and consistent representation learning.
To fuse the predictions of all three heads efficiently, we introduce a
parameter-free joint fusion module that dynamically balances the logits and
fuses them to create panoptic-part segmentation. Our method is evaluated on the
Cityscapes Panoptic Parts (CPP) and Pascal Panoptic Parts (PPP) datasets. For
CPP, the PartPQ of our proposed model with joint fusion surpasses the previous
state-of-the-art by 1.6 and 4.7 percentage points for all areas and segments
with parts, respectively. On PPP, our joint fusion outperforms a model using
the previous top-down merging strategy by 3.3 percentage points in PartPQ and
10.5 percentage points in PartPQ for partitionable classes.
