  Few-shot semantic segmentation aims to learn to segment new object classes
with only a few annotated examples, which has a wide range of real-world
applications. Most existing methods either focus on the restrictive setting of
one-way few-shot segmentation or suffer from incomplete coverage of object
regions. In this paper, we propose a novel few-shot semantic segmentation
framework based on the prototype representation. Our key idea is to decompose
the holistic class representation into a set of part-aware prototypes, capable
of capturing diverse and fine-grained object features. In addition, we propose
to leverage unlabeled data to enrich our part-aware prototypes, resulting in
better modeling of intra-class variations of semantic objects. We develop a
novel graph neural network model to generate and enhance the proposed
part-aware prototypes based on labeled and unlabeled images. Extensive
experimental evaluations on two benchmarks show that our method outperforms the
prior art with a sizable margin.
