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.

翻译：在语义分割模型开发和新数据标注过程中，标签噪声与相似类别间的歧义性构成显著挑战。本文提出语义分割中的补偿学习框架，该框架能够识别并补偿歧义区域与标签噪声。具体而言，我们向分类logits中添加一个依赖真实标注且全局学习得到的偏置项，并引入新型不确定性分支，使得神经网络仅对相关区域施加补偿偏置。将本方法集成到当前最优分割框架后，多项实验表明：所提出的补偿机制能够学习类间关系，从而全局识别具有挑战性的歧义区域，同时精确定位后续出现的标签噪声。此外，该方法增强了训练阶段对标签噪声的鲁棒性，并允许推理阶段进行目标导向的调整。我们在Cityscapes、KITTI-STEP、ADE20k及COCO-stuff10k等广泛使用的数据集上验证了所提方法的有效性。