  Domain adaptive panoptic segmentation promises to resolve the long tail of
corner cases in natural scene understanding. Previous state of the art
addresses this problem with cross-task consistency, careful system-level
optimization and heuristic improvement of teacher predictions. In contrast, we
propose to build upon remarkable capability of mask transformers to estimate
their own prediction uncertainty. Our method avoids noise amplification by
leveraging fine-grained confidence of panoptic teacher predictions. In
particular, we modulate the loss with mask-wide confidence and discourage
back-propagation in pixels with uncertain teacher or confident student.
Experimental evaluation on standard benchmarks reveals a substantial
contribution of the proposed selection techniques. We report 47.4 PQ on Synthia
to Cityscapes, which corresponds to an improvement of 6.2 percentage points
over the state of the art. The source code is available at
https://github.com/helen1c/MC-PanDA.
