  Panoptic segmentation unifies semantic segmentation and instance segmentation
which has been attracting increasing attention in recent years. However, most
existing research was conducted under a supervised learning setup whereas
unsupervised domain adaptive panoptic segmentation which is critical in
different tasks and applications is largely neglected. We design a domain
adaptive panoptic segmentation network that exploits inter-style consistency
and inter-task regularization for optimal domain adaptive panoptic
segmentation. The inter-style consistency leverages geometric invariance across
the same image of the different styles which fabricates certain
self-supervisions to guide the network to learn domain-invariant features. The
inter-task regularization exploits the complementary nature of instance
segmentation and semantic segmentation and uses it as a constraint for better
feature alignment across domains. Extensive experiments over multiple domain
adaptive panoptic segmentation tasks (e.g., synthetic-to-real and real-to-real)
show that our proposed network achieves superior segmentation performance as
compared with the state-of-the-art.
