  In this paper, we introduce a novel learning scheme named weakly
semi-supervised instance segmentation (WSSIS) with point labels for
budget-efficient and high-performance instance segmentation. Namely, we
consider a dataset setting consisting of a few fully-labeled images and a lot
of point-labeled images. Motivated by the main challenge of semi-supervised
approaches mainly derives from the trade-off between false-negative and
false-positive instance proposals, we propose a method for WSSIS that can
effectively leverage the budget-friendly point labels as a powerful weak
supervision source to resolve the challenge. Furthermore, to deal with the hard
case where the amount of fully-labeled data is extremely limited, we propose a
MaskRefineNet that refines noise in rough masks. We conduct extensive
experiments on COCO and BDD100K datasets, and the proposed method achieves
promising results comparable to those of the fully-supervised model, even with
50% of the fully labeled COCO data (38.8% vs. 39.7%). Moreover, when using as
little as 5% of fully labeled COCO data, our method shows significantly
superior performance over the state-of-the-art semi-supervised learning method
(33.7% vs. 24.9%). The code is available at
https://github.com/clovaai/PointWSSIS.
