  We present Panoptic-DeepLab, a bottom-up and single-shot approach for
panoptic segmentation. Our Panoptic-DeepLab is conceptually simple and delivers
state-of-the-art results. In particular, we adopt the dual-ASPP and
dual-decoder structures specific to semantic, and instance segmentation,
respectively. The semantic segmentation branch is the same as the typical
design of any semantic segmentation model (e.g., DeepLab), while the instance
segmentation branch is class-agnostic, involving a simple instance center
regression. Our single Panoptic-DeepLab sets the new state-of-art at all three
Cityscapes benchmarks, reaching 84.2% mIoU, 39.0% AP, and 65.5% PQ on test set,
and advances results on the other challenging Mapillary Vistas.
