Maturity-Aware Active Learning for Semantic Segmentation with
  Hierarchically-Adaptive Sample Assessment
Amirsaeed Yazdani Xuelu Li Vishal Monga
Active Learning (AL) for semantic segmentation is challenging due to heavy
class imbalance and different ways of defining "sample" (pixels, areas, etc.),
leaving the interpretation of the data distribution ambiguous. We propose
"Maturity-Aware Distribution Breakdown-based Active Learning'' (MADBAL), an AL
method that benefits from a hierarchical approach to define a multiview data
distribution, which takes into account the different "sample" definitions
jointly, hence able to select the most impactful segmentation pixels with
comprehensive understanding. MADBAL also features a novel uncertainty
formulation, where AL supporting modules are included to sense the features'
maturity whose weighted influence continuously contributes to the uncertainty
detection. In this way, MADBAL makes significant performance leaps even in the
early AL stage, hence reducing the training burden significantly. It
outperforms state-of-the-art methods on Cityscapes and PASCAL VOC datasets as
verified in our extensive experiments.