  We propose point-based instance-level annotation, a new form of weak
supervision for instance segmentation. It combines the standard bounding box
annotation with labeled points that are uniformly sampled inside each bounding
box. We show that the existing instance segmentation models developed for full
mask supervision, like Mask R-CNN, can be seamlessly trained with the
point-based annotation without any major modifications. In our experiments,
Mask R-CNN models trained on COCO, PASCAL VOC, Cityscapes, and LVIS with only
10 annotated points per object achieve 94%--98% of their fully-supervised
performance. The new point-based annotation is approximately 5 times faster to
collect than object masks, making high-quality instance segmentation more
accessible for new data.
  Inspired by the new annotation form, we propose a modification to PointRend
instance segmentation module. For each object, the new architecture, called
Implicit PointRend, generates parameters for a function that makes the final
point-level mask prediction. Implicit PointRend is more straightforward and
uses a single point-level mask loss. Our experiments show that the new module
is more suitable for the proposed point-based supervision.
