Single-shot Path Integrated Panoptic Segmentation

Описание: Authors: Sukjun Hwang (Yonsei University); Seoung Wug Oh (Adobe Research); Seon Joo Kim (Yonsei University)* Description: 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.