  Deep networks trained on the source domain show degraded performance when
tested on unseen target domain data. To enhance the model's generalization
ability, most existing domain generalization methods learn domain invariant
features by suppressing domain sensitive features. Different from them, we
propose a Domain Projection and Contrastive Learning (DPCL) approach for
generalized semantic segmentation, which includes two modules: Self-supervised
Source Domain Projection (SSDP) and Multi-level Contrastive Learning (MLCL).
SSDP aims to reduce domain gap by projecting data to the source domain, while
MLCL is a learning scheme to learn discriminative and generalizable features on
the projected data. During test time, we first project the target data by SSDP
to mitigate domain shift, then generate the segmentation results by the learned
segmentation network based on MLCL. At test time, we can update the projected
data by minimizing our proposed pixel-to-pixel contrastive loss to obtain
better results. Extensive experiments for semantic segmentation demonstrate the
favorable generalization capability of our method on benchmark datasets.
