  To further reduce the cost of semi-supervised domain adaptation (SSDA)
labeling, a more effective way is to use active learning (AL) to annotate a
selected subset with specific properties. However, domain adaptation tasks are
always addressed in two interactive aspects: domain transfer and the
enhancement of discrimination, which requires the selected data to be both
uncertain under the model and diverse in feature space. Contrary to active
learning in classification tasks, it is usually challenging to select pixels
that contain both the above properties in segmentation tasks, leading to the
complex design of pixel selection strategy. To address such an issue, we
propose a novel Active Domain Adaptation scheme with Multi-level Contrastive
Units (ADA-MCU) for semantic image segmentation. A simple pixel selection
strategy followed with the construction of multi-level contrastive units is
introduced to optimize the model for both domain adaptation and active
supervised learning. In practice, MCUs are constructed from intra-image,
cross-image, and cross-domain levels by using both labeled and unlabeled
pixels. At each level, we define contrastive losses from center-to-center and
pixel-to-pixel manners, with the aim of jointly aligning the category centers
and reducing outliers near the decision boundaries. In addition, we also
introduce a categories correlation matrix to implicitly describe the
relationship between categories, which are used to adjust the weights of the
losses for MCUs. Extensive experimental results on standard benchmarks show
that the proposed method achieves competitive performance against
state-of-the-art SSDA methods with 50% fewer labeled pixels and significantly
outperforms state-of-the-art with a large margin by using the same level of
annotation cost.
