  The leading segmentation methods represent the output map as a pixel grid. We
study an alternative representation in which the object edges are modeled, per
image patch, as a polygon with $k$ vertices that is coupled with per-patch
label probabilities. The vertices are optimized by employing a differentiable
neural renderer to create a raster image. The delineated region is then
compared with the ground truth segmentation. Our method obtains multiple
state-of-the-art results: 76.26\% mIoU on the Cityscapes validation, 90.92\%
IoU on the Vaihingen building segmentation benchmark, 66.82\% IoU for the MoNU
microscopy dataset, and 90.91\% for the bird benchmark CUB. Our code for
training and reproducing these results is attached as supplementary.

Computer Vision and Pattern Recognition