Real-time panoptic segmentation with relationship between adjacent pixels and boundary prediction

and determine whether they belong to the same instance. Besides, we leverage boundary information to enhance supervision information and help our method distinguish adjacent objects. Combining predicted category labels for each pixel from the semantic segmentation branch, we can generate a unified panoptic segmentation mask in a parameter-free step. We demonstrate our method’s effectiveness on MS COCO dataset and Cityscapes dataset, which obtain competitive results.