  Panoptic segmentation requires segments of both "things" (countable object
instances) and "stuff" (uncountable and amorphous regions) within a single
output. A common approach involves the fusion of instance segmentation (for
"things") and semantic segmentation (for "stuff") into a non-overlapping
placement of segments, and resolves overlaps. However, instance ordering with
detection confidence do not correlate well with natural occlusion relationship.
To resolve this issue, we propose a branch that is tasked with modeling how two
instance masks should overlap one another as a binary relation. Our method,
named OCFusion, is lightweight but particularly effective in the instance
fusion process. OCFusion is trained with the ground truth relation derived
automatically from the existing dataset annotations. We obtain state-of-the-art
results on COCO and show competitive results on the Cityscapes panoptic
segmentation benchmark.
