  Label noise and ambiguities between similar classes are challenging problems
in developing new models and annotating new data for semantic segmentation. In
this paper, we propose Compensation Learning in Semantic Segmentation, a
framework to identify and compensate ambiguities as well as label noise. More
specifically, we add a ground truth depending and globally learned bias to the
classification logits and introduce a novel uncertainty branch for neural
networks to induce the compensation bias only to relevant regions. Our method
is employed into state-of-the-art segmentation frameworks and several
experiments demonstrate that our proposed compensation learns inter-class
relations that allow global identification of challenging ambiguities as well
as the exact localization of subsequent label noise. Additionally, it enlarges
robustness against label noise during training and allows target-oriented
manipulation during inference. We evaluate the proposed method on %the widely
used datasets Cityscapes, KITTI-STEP, ADE20k, and COCO-stuff10k.
