Learning-based approaches for semantic segmentation have two inherent
challenges. First, acquiring pixel-wise labels is expensive and time-consuming.
Second, realistic segmentation datasets are highly unbalanced: some categories
are much more abundant than others, biasing the performance to the most
represented ones. In this paper, we are interested in focusing human labelling
effort on a small subset of a larger pool of data, minimizing this effort while
maximizing performance of a segmentation model on a hold-out set. We present a
new active learning strategy for semantic segmentation based on deep
reinforcement learning (RL). An agent learns a policy to select a subset of
small informative image regions -- opposed to entire images -- to be labeled,
from a pool of unlabeled data. The region selection decision is made based on
predictions and uncertainties of the segmentation model being trained. Our
method proposes a new modification of the deep Q-network (DQN) formulation for
active learning, adapting it to the large-scale nature of semantic segmentation
problems. We test the proof of concept in CamVid and provide results in the
large-scale dataset Cityscapes. On Cityscapes, our deep RL region-based DQN
approach requires roughly 30% less additional labeled data than our most
competitive baseline to reach the same performance. Moreover, we find that our
method asks for more labels of under-represented categories compared to the
baselines, improving their performance and helping to mitigate class imbalance.

策略是通过使用深度强化学习对语义分割进行主动学习实现的，该方法比最有竞争力的基线数量少约 30％的附加标签数据，提高模型对不足表达类别的分类性能。