  In this paper, we present a conceptually simple, strong, and efficient
framework for panoptic segmentation, called Panoptic FCN. Our approach aims to
represent and predict foreground things and background stuff in a unified fully
convolutional pipeline. In particular, Panoptic FCN encodes each object
instance or stuff category into a specific kernel weight with the proposed
kernel generator and produces the prediction by convolving the high-resolution
feature directly. With this approach, instance-aware and semantically
consistent properties for things and stuff can be respectively satisfied in a
simple generate-kernel-then-segment workflow. Without extra boxes for
localization or instance separation, the proposed approach outperforms previous
box-based and -free models with high efficiency on COCO, Cityscapes, and
Mapillary Vistas datasets with single scale input. Our code is made publicly
available at https://github.com/Jia-Research-Lab/PanopticFCN.

Computer Vision and Pattern Recognition