  Semantic, instance, and panoptic segmentations have been addressed using
different and specialized frameworks despite their underlying connections. This
paper presents a unified, simple, and effective framework for these essentially
similar tasks. The framework, named K-Net, segments both instances and semantic
categories consistently by a group of learnable kernels, where each kernel is
responsible for generating a mask for either a potential instance or a stuff
class. To remedy the difficulties of distinguishing various instances, we
propose a kernel update strategy that enables each kernel dynamic and
conditional on its meaningful group in the input image. K-Net can be trained in
an end-to-end manner with bipartite matching, and its training and inference
are naturally NMS-free and box-free. Without bells and whistles, K-Net
surpasses all previous published state-of-the-art single-model results of
panoptic segmentation on MS COCO test-dev split and semantic segmentation on
ADE20K val split with 55.2% PQ and 54.3% mIoU, respectively. Its instance
segmentation performance is also on par with Cascade Mask R-CNN on MS COCO with
60%-90% faster inference speeds. Code and models will be released at
https://github.com/ZwwWayne/K-Net/.
