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 ADeADA, 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 the label efficiency, we design an adaptive budget allocation policy, which dynamically balances the labeling budgets among different categories as well as between density-aware and uncertainty-based methods. 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.

翻译：在领域适应领域,模型性能与目标域说明数量之间存在一种权衡。积极学习,尽量扩大模型性能,加上很少的信息标签数据,对于这种情景是有用的。在这项工作中,我们介绍了用于语义分化的通用主动域适应框架ADEADA,这是一个通用的主动域适应框架。为了使模型适应目标域,并贴上最小的标签,我们提议获取目标域高概率密度样本的标签,但来源域的概率密度较低,与现有的源域标记数据相辅相成。为进一步促进标签效率,我们设计了适应性预算分配政策,以动态方式平衡不同类别之间以及密度意识和不确定性方法之间的标记预算。广泛的实验表明,我们的方法超越了GTA5 - > 城景和 SYNTHIA - > 城景这两个基准的现有积极学习和领域适应基线。在不到5%的目标域说明的情况下,我们的方法与全面监督的结果相似。