  Using deep learning, we now have the ability to create exceptionally good
semantic segmentation systems; however, collecting the prerequisite pixel-wise
annotations for training images remains expensive and time-consuming.
Therefore, it would be ideal to minimize the number of human annotations needed
when creating a new dataset. Here, we address this problem by proposing a novel
algorithm that combines active learning and semi-supervised learning. Active
learning is an approach for identifying the best unlabeled samples to annotate.
While there has been work on active learning for segmentation, most methods
require annotating all pixel objects in each image, rather than only the most
informative regions. We argue that this is inefficient. Instead, our active
learning approach aims to minimize the number of annotations per-image. Our
method is enriched with semi-supervised learning, where we use pseudo labels
generated with a teacher-student framework to identify image regions that help
disambiguate confused classes. We also integrate mechanisms that enable better
performance on imbalanced label distributions, which have not been studied
previously for active learning in semantic segmentation. In experiments on the
CamVid and CityScapes datasets, our method obtains over 95% of the network's
performance on the full-training set using less than 17% of the training data,
whereas the previous state of the art required 40% of the training data.
