We propose an embarrassingly simple point annotation scheme to collect weak
supervision for instance segmentation. In addition to bounding boxes, we
collect binary labels for a set of points uniformly sampled inside each
bounding box. We show that the existing instance segmentation models developed
for full mask supervision can be seamlessly trained with point-based
supervision collected via our scheme. Remarkably, Mask R-CNN trained on COCO,
PASCAL VOC, Cityscapes, and LVIS with only 10 annotated random points per
object achieves 94%--98% of its fully-supervised performance, setting a strong
baseline for weakly-supervised instance segmentation. The new point annotation
scheme is approximately 5 times faster than annotating full object masks,
making high-quality instance segmentation more accessible in practice.
Inspired by the point-based 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 point-based supervision.

提出了基于单点标注的弱监督实例分割方法，通过该方法，仅标注每个物体的随机 10 个点就能够达到 94% 至 98% 的全监督效果，比全物体标注的方法快 5 倍，同时提出了一种新的基于点标注的 PointRend 实例分割模型，称为 Implicit PointRend。