  Although Domain Adaptation in Semantic Scene Segmentation has shown
impressive improvement in recent years, the fairness concerns in the domain
adaptation have yet to be well defined and addressed. In addition, fairness is
one of the most critical aspects when deploying the segmentation models into
human-related real-world applications, e.g., autonomous driving, as any unfair
predictions could influence human safety. In this paper, we propose a novel
Fairness Domain Adaptation (FREDOM) approach to semantic scene segmentation. In
particular, from the proposed formulated fairness objective, a new adaptation
framework will be introduced based on the fair treatment of class
distributions. Moreover, to generally model the context of structural
dependency, a new conditional structural constraint is introduced to impose the
consistency of predicted segmentation. Thanks to the proposed Conditional
Structure Network, the self-attention mechanism has sufficiently modeled the
structural information of segmentation. Through the ablation studies, the
proposed method has shown the performance improvement of the segmentation
models and promoted fairness in the model predictions. The experimental results
on the two standard benchmarks, i.e., SYNTHIA $\to$ Cityscapes and GTA5 $\to$
Cityscapes, have shown that our method achieved State-of-the-Art (SOTA)
performance.
