DeepAI AI Chat
Log In Sign Up

Single-shot Path Integrated Panoptic Segmentation

by   Sukjun Hwang, et al.

Panoptic segmentation, which is a novel task of unifying instance segmentation and semantic segmentation, has attracted a lot of attention lately. However, most of the previous methods are composed of multiple pathways with each pathway specialized to a designated segmentation task. In this paper, we propose to resolve panoptic segmentation in single-shot by integrating the execution flows. With the integrated pathway, a unified feature map called Panoptic-Feature is generated, which includes the information of both things and stuffs. Panoptic-Feature becomes more sophisticated by auxiliary problems that guide to cluster pixels that belong to the same instance and differentiate between objects of different classes. A collection of convolutional filters, where each filter represents either a thing or stuff, is applied to Panoptic-Feature at once, materializing the single-shot panoptic segmentation. Taking the advantages of both top-down and bottom-up approaches, our method, named SPINet, enjoys high efficiency and accuracy on major panoptic segmentation benchmarks: COCO and Cityscapes.


page 3

page 8


SpatialFlow: Bridging All Tasks for Panoptic Segmentation

The newly proposed panoptic segmentation task, which aims to encompass t...

Learning to Fuse Things and Stuff

We propose an end-to-end learning approach for panoptic segmentation, a ...

Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation

Few-shot learning allows machines to classify novel classes using only a...

EOLO: Embedded Object Segmentation only Look Once

In this paper, we introduce an anchor-free and single-shot instance segm...

DeeperLab: Single-Shot Image Parser

We present a single-shot, bottom-up approach for whole image parsing. Wh...

Incremental Few-Shot Instance Segmentation

Few-shot instance segmentation methods are promising when labeled traini...

Path Aggregation Network for Instance Segmentation

The way that information propagates in neural networks is of great impor...