  Although there is significant progress in supervised semantic segmentation,
it remains challenging to deploy the segmentation models to unseen domains due
to domain biases. Domain adaptation can help in this regard by transferring
knowledge from a labeled source domain to an unlabeled target domain. Previous
methods typically attempt to perform the adaptation on global features,
however, the local semantic affiliations accounting for each pixel in the
feature space are often ignored, resulting in less discriminability. To solve
this issue, we propose a novel semantic prototype-based contrastive learning
framework for fine-grained class alignment. Specifically, the semantic
prototypes provide supervisory signals for per-pixel discriminative
representation learning and each pixel of source and target domains in the
feature space is required to reflect the content of the corresponding semantic
prototype. In this way, our framework is able to explicitly make intra-class
pixel representations closer and inter-class pixel representations further
apart to improve the robustness of the segmentation model as well as alleviate
the domain shift problem. Our method is easy to implement and attains superior
results compared to state-of-the-art approaches, as is demonstrated with a
number of experiments. The code is publicly available at
https://github.com/BinhuiXie/SPCL.
