Class Balanced Dynamic Acquisition for Domain Adaptive Semantic
  Segmentation using Active Learning
Marc Schachtsiek Simone Rossi Thomas Hannagan
Domain adaptive active learning is leading the charge in label-efficient
training of neural networks. For semantic segmentation, state-of-the-art models
jointly use two criteria of uncertainty and diversity to select training
labels, combined with a pixel-wise acquisition strategy. However, we show that
such methods currently suffer from a class imbalance issue which degrades their
performance for larger active learning budgets. We then introduce Class
Balanced Dynamic Acquisition (CBDA), a novel active learning method that
mitigates this issue, especially in high-budget regimes. The more balanced
labels increase minority class performance, which in turn allows the model to
outperform the previous baseline by 0.6, 1.7, and 2.4 mIoU for budgets of 5%,
10%, and 20%, respectively. Additionally, the focus on minority classes leads
to improvements of the minimum class performance of 0.5, 2.9, and 4.6 IoU
respectively. The top-performing model even exceeds the fully supervised
baseline, showing that a more balanced label than the entire ground truth can
be beneficial.