  This paper introduces a novel approach to learning instance segmentation
using extreme points, i.e., the topmost, leftmost, bottommost, and rightmost
points, of each object. These points are readily available in the modern
bounding box annotation process while offering strong clues for precise
segmentation, and thus allows to improve performance at the same annotation
cost with box-supervised methods. Our work considers extreme points as a part
of the true instance mask and propagates them to identify potential foreground
and background points, which are all together used for training a pseudo label
generator. Then pseudo labels given by the generator are in turn used for
supervised learning of our final model. On three public benchmarks, our method
significantly outperforms existing box-supervised methods, further narrowing
the gap with its fully supervised counterpart. In particular, our model
generates high-quality masks when a target object is separated into multiple
parts, where previous box-supervised methods often fail.
