  Previous feature alignment methods in Unsupervised domain adaptation(UDA)
mostly only align global features without considering the mismatch between
class-wise features. In this work, we propose a new coarse-to-fine feature
alignment method using contrastive learning called CFContra. It draws
class-wise features closer than coarse feature alignment or class-wise feature
alignment only, therefore improves the model's performance to a great extent.
We build it upon one of the most effective methods of UDA called entropy
minimization to further improve performance. In particular, to prevent
excessive memory occupation when applying contrastive loss in semantic
segmentation, we devise a new way to build and update the memory bank. In this
way, we make the algorithm more efficient and viable with limited memory.
Extensive experiments show the effectiveness of our method and model trained on
the GTA5 to Cityscapes dataset has boost mIOU by 3.5 compared to the MinEnt
algorithm. Our code will be publicly available.
