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

Abstract

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.

CVPR 2020

References in corpus (18)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Rethinking Atrous Convolution for Semantic Image Segmentation
• ParseNet: Looking Wider to See Better
• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
• Associative Embedding: End-to-End Learning for Joint Detection and Grouping
• Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
• Semantic Instance Segmentation with a Discriminative Loss Function
• Deep High-Resolution Representation Learning for Visual Recognition
• Semantic Instance Segmentation via Deep Metric Learning
• DeeperLab: Single-Shot Image Parser
• Bridging Category-level and Instance-level Semantic Image Segmentation
• Learning to Fuse Things and Stuff
• Fast Scene Understanding for Autonomous Driving
• Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
• GFF: Gated Fully Fusion for Semantic Segmentation
• Instance Segmentation by Deep Coloring

Cited by in corpus (6)

• Deep High-Resolution Representation Learning for Visual Recognition
• Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
• CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation
• Multi-domain semantic segmentation with pyramidal fusion
• Deep Neural Network Perception Models and Robust Autonomous Driving Systems
• Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation