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.

基于等级方法定义多视图数据分布的成熟度感知分解的主动学习方法 (MADBAL) 在像素级的语义分割中具有显著的性能提升，减轻了训练负担，通过包括不确定性检测模块来感知特征成熟度并持续影响不确定性检测，验证实验证明了其在 Cityscapes 和 PASCAL VOC 数据集上优于现有方法。