  Image segmentation methods are usually trained with pixel-level annotations,
which require significant human effort to collect. The most common solution to
address this constraint is to implement weakly-supervised pipelines trained
with lower forms of supervision, such as bounding boxes or scribbles. Another
option are semi-supervised methods, which leverage a large amount of unlabeled
data and a limited number of strongly-labeled samples. In this second setup,
samples to be strongly-annotated can be selected randomly or with an active
learning mechanism that chooses the ones that will maximize the model
performance. In this work, we propose a sample selection approach to decide
which samples to annotate for semi-supervised instance segmentation. Our method
consists in first predicting pseudo-masks for the unlabeled pool of samples,
together with a score predicting the quality of the mask. This score is an
estimate of the Intersection Over Union (IoU) of the segment with the ground
truth mask. We study which samples are better to annotate given the quality
score, and show how our approach outperforms a random selection, leading to
improved performance for semi-supervised instance segmentation with low
annotation budgets.
