  Few shot segmentation (FSS) aims to learn pixel-level classification of a
target object in a query image using only a few annotated support samples. This
is challenging as it requires modeling appearance variations of target objects
and the diverse visual cues between query and support images with limited
information. To address this problem, we propose a semi-supervised FSS strategy
that leverages additional prototypes from unlabeled images with uncertainty
guided pseudo label refinement. To obtain reliable prototypes from unlabeled
images, we meta-train a neural network to jointly predict segmentation and
estimate the uncertainty of predictions. We employ the uncertainty estimates to
exclude predictions with high degrees of uncertainty for pseudo label
construction to obtain additional prototypes based on the refined pseudo
labels. During inference, query segmentation is predicted using prototypes from
both support and unlabeled images including low-level features of the query
images. Our approach is end-to-end and can easily supplement existing
approaches without the requirement of additional training to employ unlabeled
samples. Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$ demonstrate that
our model can effectively remove unreliable predictions to refine pseudo labels
and significantly improve upon state-of-the-art performances.
