  Self-training has greatly facilitated domain adaptive semantic segmentation,
which iteratively generates pseudo labels on unlabeled target data and retrains
the network. However, realistic segmentation datasets are highly imbalanced,
pseudo labels are typically biased to the majority classes and basically noisy,
leading to an error-prone and suboptimal model. In this paper, we propose a
simple region-based active learning approach for semantic segmentation under a
domain shift, aiming to automatically query a small partition of image regions
to be labeled while maximizing segmentation performance. Our algorithm, Region
Impurity and Prediction Uncertainty (RIPU), introduces a new acquisition
strategy characterizing the spatial adjacency of image regions along with the
prediction confidence. We show that the proposed region-based selection
strategy makes more efficient use of a limited budget than image-based or
point-based counterparts. Further, we enforce local prediction consistency
between a pixel and its nearest neighbors on a source image. Alongside, we
develop a negative learning loss to make the features more discriminative.
Extensive experiments demonstrate that our method only requires very few
annotations to almost reach the supervised performance and substantially
outperforms state-of-the-art methods. The code is available at
https://github.com/BIT-DA/RIPU.
