  Active Domain Adaptation (ADA) queries the labels of a small number of
selected target samples to help adapting a model from a source domain to a
target domain. The local context of queried data is important, especially when
the domain gap is large. However, this has not been fully explored by existing
ADA works. In this paper, we propose a Local context-aware ADA framework, named
LADA, to address this issue. To select informative target samples, we devise a
novel criterion based on the local inconsistency of model predictions. Since
the labeling budget is usually small, fine-tuning model on only queried data
can be inefficient. We progressively augment labeled target data with the
confident neighbors in a class-balanced manner. Experiments validate that the
proposed criterion chooses more informative target samples than existing active
selection strategies. Furthermore, our full method clearly surpasses recent ADA
arts on various benchmarks. Code is available at https://github.com/tsun/LADA.

Machine Learning;Computer Vision and Pattern Recognition