PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
This addresses the challenge of segmenting unseen object categories with limited annotated examples, which is incremental as it builds on existing few-shot segmentation methods.
The paper tackles the few-shot image semantic segmentation problem by proposing PANet, a prototype alignment network that learns class-specific prototypes from support images and matches query pixels to them, achieving mIoU scores of 48.1% and 55.7% on PASCAL-5i for 1-shot and 5-shot settings, surpassing the state-of-the-art by 1.8% and 8.6%.
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%.