  State-of-the-art semantic segmentation methods require sufficient labeled
data to achieve good results and hardly work on unseen classes without
fine-tuning. Few-shot segmentation is thus proposed to tackle this problem by
learning a model that quickly adapts to new classes with a few labeled support
samples. Theses frameworks still face the challenge of generalization ability
reduction on unseen classes due to inappropriate use of high-level semantic
information of training classes and spatial inconsistency between query and
support targets. To alleviate these issues, we propose the Prior Guided Feature
Enrichment Network (PFENet). It consists of novel designs of (1) a
training-free prior mask generation method that not only retains generalization
power but also improves model performance and (2) Feature Enrichment Module
(FEM) that overcomes spatial inconsistency by adaptively enriching query
features with support features and prior masks. Extensive experiments on
PASCAL-5$^i$ and COCO prove that the proposed prior generation method and FEM
both improve the baseline method significantly. Our PFENet also outperforms
state-of-the-art methods by a large margin without efficiency loss. It is
surprising that our model even generalizes to cases without labeled support
samples. Our code is available at https://github.com/Jia-Research-Lab/PFENet/.
