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This paper presents a novel framework to integrate both semantic and instancecontexts for panoptic segmentation. In existing works, it is common to use ashared backbone to extract features for both things (countable classes such asvehicles) and stuff (uncountable classes such as roads). This, however, failsto capture the rich relations among them, which can be utilized to enhancevisual understanding and segmentation performance. To address this shortcoming,we propose a novel Panoptic, Instance, and Semantic Relations (PISR) module toexploit such contexts. First, we generate panoptic encodings to summarize keyfeatures of the semantic classes and predicted instances. A Panoptic RelationalAttention (PRA) module is then applied to the encodings and the global featuremap from the backbone. It produces a feature map that captures 1) the relationsacross semantic classes and instances and 2) the relations between thesepanoptic categories and spatial features. PISR also automatically learns tofocus on the more important instances, making it robust to the number ofinstances used in the relational attention module. Moreover, PISR is a generalmodule that can be applied to any existing panoptic segmentation architecture.Through extensive evaluations on panoptic segmentation benchmarks likeCityscapes, COCO, and ADE20K, we show that PISR attains considerableimprovements over existing approaches.

Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation