Despite the great progress made by deep CNNs in image semantic segmentation,
they typically require a large number of densely-annotated images for training
and are difficult to generalize to unseen object categories. Few-shot
segmentation has thus been developed to learn to perform segmentation from only
a few annotated examples. In this paper, we tackle the challenging few-shot
segmentation problem from a metric learning perspective and present PANet, a
novel prototype alignment network to better utilize the information of the
support set. Our PANet learns class-specific prototype representations from a
few support images within an embedding space and then performs segmentation
over the query images through matching each pixel to the learned prototypes.
With non-parametric metric learning, PANet offers high-quality prototypes that
are representative for each semantic class and meanwhile discriminative for
different classes. Moreover, PANet introduces a prototype alignment
regularization between support and query. With this, PANet fully exploits
knowledge from the support and provides better generalization on few-shot
segmentation. Significantly, our model achieves the mIoU score of 48.1% and
55.7% on PASCAL-5i for 1-shot and 5-shot settings respectively, surpassing the
state-of-the-art method by 1.8% and 8.6%.

本研究提出了一种基于度量学习的原型对齐网络（PANet）来解决仅使用少量样本进行图像分割的问题，它通过学习每个语义类别的原型表示，并通过匹配每个像素到所学习的原型来执行查询图像的分割，最终在 PASCAL-5i 上超越了现有技术达到了 48.1% 和 55.7% 的 mIoU 得分（1-shot 和 5-shot 设置），是一种有效的 few-shot segmentation 方法。