  Deep Learning has greatly advanced the performance of semantic segmentation,
however, its success relies on the availability of large amounts of annotated
data for training. Hence, many efforts have been devoted to domain adaptive
semantic segmentation that focuses on transferring semantic knowledge from a
labeled source domain to an unlabeled target domain. Existing self-training
methods typically require multiple rounds of training, while another popular
framework based on adversarial training is known to be sensitive to
hyper-parameters. In this paper, we present an easy-to-train framework that
learns domain-invariant prototypes for domain adaptive semantic segmentation.
In particular, we show that domain adaptation shares a common character with
few-shot learning in that both aim to recognize some types of unseen data with
knowledge learned from large amounts of seen data. Thus, we propose a unified
framework for domain adaptation and few-shot learning. The core idea is to use
the class prototypes extracted from few-shot annotated target images to
classify pixels of both source images and target images. Our method involves
only one-stage training and does not need to be trained on large-scale
un-annotated target images. Moreover, our method can be extended to variants of
both domain adaptation and few-shot learning. Experiments on adapting
GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes show that our method achieves
competitive performance to state-of-the-art.
