PRN: Panoptic Refinement Network

Bo Sun, Jason Kuen, Zhe Lin, Philippos Mordohai, Simon Su Chen

Abstract:

Panoptic segmentation is the task of uniquely assigning every pixel in an image to either a semantic label or an individual
object instance, generating a coherent and complete scene description. Many current panoptic segmentation methods, however, predict masks of semantic classes and object instances in separate branches, yielding inconsistent predictions. Moreover, because state-of-the-art panoptic segmentation models rely on box proposals, the instance masks predicted are often of low-resolution. To overcome these limitations, we propose the Panoptic Refinement Network (PRN), which takes masks from base panoptic segmentation models and refines them jointly to produce coherent results. PRN extends the offset map-based architecture of Panoptic-Deeplab with several novel ideas including a foreground mask and instance bounding box offsets, as well as coordinate convolutions for improved spatial prediction. Experimental results on COCO and Cityscapes show that PRN can significantly improve already accurate results from a variety of panoptic segmentation networks.