PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

Understand

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.