  In the field of domain adaptation, a trade-off exists between the model
performance and the number of target domain annotations. Active learning,
maximizing model performance with few informative labeled data, comes in handy
for such a scenario. In this work, we present D2ADA, a general active domain
adaptation framework for semantic segmentation. To adapt the model to the
target domain with minimum queried labels, we propose acquiring labels of the
samples with high probability density in the target domain yet with low
probability density in the source domain, complementary to the existing source
domain labeled data. To further facilitate labeling efficiency, we design a
dynamic scheduling policy to adjust the labeling budgets between domain
exploration and model uncertainty over time. Extensive experiments show that
our method outperforms existing active learning and domain adaptation baselines
on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than
5% target domain annotations, our method reaches comparable results with that
of full supervision. Our code is publicly available at
https://github.com/tsunghan-wu/D2ADA.
