  The core of our approach, Pixel Consensus Voting, is a framework for instance
segmentation based on the Generalized Hough transform. Pixels cast discretized,
probabilistic votes for the likely regions that contain instance centroids. At
the detected peaks that emerge in the voting heatmap, backprojection is applied
to collect pixels and produce instance masks. Unlike a sliding window detector
that densely enumerates object proposals, our method detects instances as a
result of the consensus among pixel-wise votes. We implement vote aggregation
and backprojection using native operators of a convolutional neural network.
The discretization of centroid voting reduces the training of instance
segmentation to pixel labeling, analogous and complementary to FCN-style
semantic segmentation, leading to an efficient and unified architecture that
jointly models things and stuff. We demonstrate the effectiveness of our
pipeline on COCO and Cityscapes Panoptic Segmentation and obtain competitive
results. Code will be open-sourced.
