  Domain adaptation for semantic segmentation aims to improve the model
performance in the presence of a distribution shift between source and target
domain. Leveraging the supervision from auxiliary tasks~(such as depth
estimation) has the potential to heal this shift because many visual tasks are
closely related to each other. However, such a supervision is not always
available. In this work, we leverage the guidance from self-supervised depth
estimation, which is available on both domains, to bridge the domain gap. On
the one hand, we propose to explicitly learn the task feature correlation to
strengthen the target semantic predictions with the help of target depth
estimation. On the other hand, we use the depth prediction discrepancy from
source and target depth decoders to approximate the pixel-wise adaptation
difficulty. The adaptation difficulty, inferred from depth, is then used to
refine the target semantic segmentation pseudo-labels. The proposed method can
be easily implemented into existing segmentation frameworks. We demonstrate the
effectiveness of our proposed approach on the benchmark tasks
SYNTHIA-to-Cityscapes and GTA-to-Cityscapes, on which we achieve the new
state-of-the-art performance of $55.0\%$ and $56.6\%$, respectively. Our code
is available at \url{https://github.com/qinenergy/corda}.
