DAT: Domain Adaptive Transformer for Domain Adaptive Semantic Segmentation

Abstract

Unsupervised domain adaptation (UDA) for semantic segmentation aims to predict class annotations on an unlabeled target dataset by training on a rich labeled source dataset. It is crucial in UDA semantic segmentation to decrease the domain gap by learning domain invariant feature representations across both domains. In this paper, we propose a novel transformer-based network, called a domain adaptive transformer (DAT), using a self-training scheme. We introduce domain invariant attention (DIA), which enables the DAT to exploit high-level domain invariant features at the patch level. Moreover, an entropy-based selective pseudo-labeling algorithm provides the DAT with reliable pseudo-labels of target samples for domain adaptive self-training, which corrects the noisy pseudo-labels online. We show that our DAT greatly improves the domain adaptability and achieves state-of-the-art results on the SYNTHIA-to-Cityscapes benchmark.