  In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast
system for panoptic segmentation, aiming to establish a solid baseline for
bottom-up methods that can achieve comparable performance of two-stage methods
while yielding fast inference speed. In particular, Panoptic-DeepLab adopts 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. As a result, our single Panoptic-DeepLab simultaneously
ranks first at all three Cityscapes benchmarks, setting the new state-of-art of
84.2% mIoU, 39.0% AP, and 65.5% PQ on test set. Additionally, equipped with
MobileNetV3, Panoptic-DeepLab runs nearly in real-time with a single 1025x2049
image (15.8 frames per second), while achieving a competitive performance on
Cityscapes (54.1 PQ% on test set). On Mapillary Vistas test set, our ensemble
of six models attains 42.7% PQ, outperforming the challenge winner in 2018 by a
healthy margin of 1.5%. Finally, our Panoptic-DeepLab also performs on par with
several top-down approaches on the challenging COCO dataset. For the first
time, we demonstrate a bottom-up approach could deliver state-of-the-art
results on panoptic segmentation.
