- Nanqing Dong, E. Xing. 2018. Few-Shot Semantic Segmentation with Prototype Learning. Abstract: Semantic segmentation assigns a class label to each image pixel. This dense prediction problem requires large amounts of manually annotated data, which is often un-available. Few-shot learning aims to learn the pattern of a new category with only a few annotated examples. In this paper, we formulate the few-shot semantic segmentation problem from 1-way (class) to N -way (classes). Inspired by few-shot classiﬁcation, we propose a generalized framework for few-shot semantic segmentation with an alterna-tive training scheme. The framework is based on prototype learning and metric learning. Our approach outperforms the baselines by a large margin and shows comparable performance for 1-way few-shot semantic segmentation on PASCAL VOC 2012 dataset.
