  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%.
