  Unsupervised domain adaptation (UDA) aims to enhance the generalization
capability of a certain model from a source domain to a target domain. Present
UDA models focus on alleviating the domain shift by minimizing the feature
discrepancy between the source domain and the target domain but usually ignore
the class confusion problem. In this work, we propose an Inter-class Separation
and Intra-class Aggregation (ISIA) mechanism. It encourages the cross-domain
representative consistency between the same categories and differentiation
among diverse categories. In this way, the features belonging to the same
categories are aligned together and the confusable categories are separated. By
measuring the align complexity of each category, we design an Adaptive-weighted
Instance Matching (AIM) strategy to further optimize the instance-level
adaptation. Based on our proposed methods, we also raise a hierarchical
unsupervised domain adaptation framework for cross-domain semantic segmentation
task. Through performing the image-level, feature-level, category-level and
instance-level alignment, our method achieves a stronger generalization
performance of the model from the source domain to the target domain. In two
typical cross-domain semantic segmentation tasks, i.e., GTA5 to Cityscapes and
SYNTHIA to Cityscapes, our method achieves the state-of-the-art segmentation
accuracy. We also build two cross-domain semantic segmentation datasets based
on the publicly available data, i.e., remote sensing building segmentation and
road segmentation, for domain adaptive segmentation.
