  Mixup-based data augmentation has been validated to be a critical stage in
the self-training framework for unsupervised domain adaptive semantic
segmentation (UDA-SS), which aims to transfer knowledge from a well-annotated
(source) domain to an unlabeled (target) domain. Existing self-training methods
usually adopt the popular region-based mixup techniques with a random sampling
strategy, which unfortunately ignores the dynamic evolution of different
semantics across various domains as training proceeds. To improve the UDA-SS
performance, we propose an Informed Domain Adaptation (IDA) model, a
self-training framework that mixes the data based on class-level segmentation
performance, which aims to emphasize small-region semantics during mixup. In
our IDA model, the class-level performance is tracked by an expected confidence
score (ECS). We then use a dynamic schedule to determine the mixing ratio for
data in different domains. Extensive experimental results reveal that our
proposed method is able to outperform the state-of-the-art UDA-SS method by a
margin of 1.1 mIoU in the adaptation of GTA-V to Cityscapes and of 0.9 mIoU in
the adaptation of SYNTHIA to Cityscapes.
