Few-shot segmentation targets to segment new classes with few annotated
images provided. It is more challenging than traditional semantic segmentation
tasks that segment known classes with abundant annotated images. In this paper,
we propose a Prototype Refinement Network (PRNet) to attack the challenge of
few-shot segmentation. It firstly learns to bidirectionally extract prototypes
from both support and query images of the known classes. Furthermore, to
extract representative prototypes of the new classes, we use adaptation and
fusion for prototype refinement. The step of adaptation makes the model to
learn new concepts which is directly implemented by retraining. Prototype
fusion is firstly proposed which fuses support prototypes with query
prototypes, incorporating the knowledge from both sides. It is effective in
prototype refinement without importing extra learnable parameters. In this way,
the prototypes become more discriminative in low-data regimes. Experiments on
PASAL-$5^i$ and COCO-$20^i$ demonstrate the superiority of our method.
Especially on COCO-$20^i$, PRNet significantly outperforms existing methods by
a large margin of 13.1\% in 1-shot setting.

本文提出了一种原型精炼网络（PRNet），通过自适应和融合获取新类别的代表性原型，从而增强了低数据量情况下的原型区分度，实现了少样本分割的挑战，实验结果表明该方法显著优于现有方法。