Active learning aims to address the paucity of labeled data by finding the
most informative samples. However, when applying to semantic segmentation,
existing methods ignore the segmentation difficulty of different semantic
areas, which leads to poor performance on those hard semantic areas such as
tiny or slender objects. To deal with this problem, we propose a semantic
Difficulty-awarE Active Learning (DEAL) network composed of two branches: the
common segmentation branch and the semantic difficulty branch. For the latter
branch, with the supervision of segmentation error between the segmentation
result and GT, a pixel-wise probability attention module is introduced to learn
the semantic difficulty scores for different semantic areas. Finally, two
acquisition functions are devised to select the most valuable samples with
semantic difficulty. Competitive results on semantic segmentation benchmarks
demonstrate that DEAL achieves state-of-the-art active learning performance and
improves the performance of the hard semantic areas in particular.

该论文提出了一个基于难度感知的主动学习网络（DEAL），通过引入像素级概率注意力模块和两个获取函数来学习不同语义区域的难度得分，并在语义分割基准测试中取得了最新的主动学习性能，并特别提高了难语义区域的性能。