  Obtaining human per-pixel labels for semantic segmentation is incredibly
laborious, often making labeled dataset construction prohibitively expensive.
Here, we endeavor to overcome this problem with a novel algorithm that combines
semi-supervised and active learning, resulting in the ability to train an
effective semantic segmentation algorithm with significantly lesser labeled
data. To do this, we extend the prior state-of-the-art S4AL algorithm by
replacing its mean teacher approach for semi-supervised learning with a
self-training approach that improves learning with noisy labels. We further
boost the neural network's ability to query useful data by adding a contrastive
learning head, which leads to better understanding of the objects in the scene,
and hence, better queries for active learning. We evaluate our method on CamVid
and CityScapes datasets, the de-facto standards for active learning for
semantic segmentation. We achieve more than 95% of the network's performance on
CamVid and CityScapes datasets, utilizing only 12.1% and 15.1% of the labeled
data, respectively. We also benchmark our method across existing stand-alone
semi-supervised learning methods on the CityScapes dataset and achieve superior
performance without any bells or whistles.
