  In this paper, we propose a unified panoptic segmentation network (UPSNet)
for tackling the newly proposed panoptic segmentation task. On top of a single
backbone residual network, we first design a deformable convolution based
semantic segmentation head and a Mask R-CNN style instance segmentation head
which solve these two subtasks simultaneously. More importantly, we introduce a
parameter-free panoptic head which solves the panoptic segmentation via
pixel-wise classification. It first leverages the logits from the previous two
heads and then innovatively expands the representation for enabling prediction
of an extra unknown class which helps better resolve the conflicts between
semantic and instance segmentation. Additionally, it handles the challenge
caused by the varying number of instances and permits back propagation to the
bottom modules in an end-to-end manner. Extensive experimental results on
Cityscapes, COCO and our internal dataset demonstrate that our UPSNet achieves
state-of-the-art performance with much faster inference. Code has been made
available at: https://github.com/uber-research/UPSNet
