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.

Panoptic-DeepLab 是一个用于全景分割的模型，具有简单、强大和快速的特点。通过采用语义和实例分割的双重 ASPP 和双重解码器结构，同时使用语义分割模型的典型设计和类别不可知的实例中心回归，Panoptic-DeepLab 超过了所有三个 Cityscapes 基准测试，取得了新的最佳表现，并在 Mapillary Vistas 测试集上提供了未来领先的 42.7％ PQ 精度.

Panoptic-DeepLab: 一种简单、强大、快速的自下而上全景分割基线

Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up  Panoptic Segmentation

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.

Panoptic-DeepLab 采取了双重 ASPP 和双重解码器结构，实现了单次扫描的全景分割，并在 Cityscapes 和 Mapillary Vistas 数据集上取得了最新的最优结果。