  Few-shot segmentation~(FSS) performance has been extensively promoted by
introducing episodic training and class-wise prototypes. However, the FSS
problem remains challenging due to three limitations: (1) Models are distracted
by task-unrelated information; (2) The representation ability of a single
prototype is limited; (3) Class-related prototypes ignore the prior knowledge
of base classes. We propose the Prior-Enhanced network with Meta-Prototypes to
tackle these limitations. The prior-enhanced network leverages the support and
query (pseudo-) labels in feature extraction, which guides the model to focus
on the task-related features of the foreground objects, and suppress much noise
due to the lack of supervised knowledge. Moreover, we introduce multiple
meta-prototypes to encode hierarchical features and learn class-agnostic
structural information. The hierarchical features help the model highlight the
decision boundary and focus on hard pixels, and the structural information
learned from base classes is treated as the prior knowledge for novel classes.
Experiments show that our method achieves the mean-IoU scores of 60.79% and
41.16% on PASCAL-$5^i$ and COCO-$20^i$, outperforming the state-of-the-art
method by 3.49% and 5.64% in the 5-shot setting. Moreover, comparing with
1-shot results, our method promotes 5-shot accuracy by 3.73% and 10.32% on the
above two benchmarks. The source code of our method is available at
https://github.com/Jarvis73/PEMP.
