Object location is fundamental to panoptic segmentation as it is related to
all things and stuff in the image scene. Knowing the locations of objects in
the image provides clues for segmenting and helps the network better understand
the scene. How to integrate object location in both thing and stuff
segmentation is a crucial problem. In this paper, we propose spatial
information flows to achieve this objective. The flows can bridge all sub-tasks
in panoptic segmentation by delivering the object's spatial context from the
box regression task to others. More importantly, we design four parallel
sub-networks to get a preferable adaptation of object spatial information in
sub-tasks. Upon the sub-networks and the flows, we present a location-aware and
unified framework for panoptic segmentation, denoted as SpatialFlow. We perform
a detailed ablation study on each component and conduct extensive experiments
to prove the effectiveness of SpatialFlow. Furthermore, we achieve
state-of-the-art results, which are $47.9$ PQ and $62.5$ PQ respectively on
MS-COCO and Cityscapes panoptic benchmarks. Code will be available at
this https URL

该研究论文提出了一种称为 SpatialFlow 的基于空间信息流的新型定位感知与统一框架，利用子网络和信息流来解决全景分割中物体位置与场景理解问题，其实现在 MS-COCO 和 Cityscapes 全景基准测试上获得了最先进的成果。