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.

翻译：深度网络在源域训练后，对未见过的目标域数据进行测试时性能会下降。为增强模型的泛化能力，现有的大多数域泛化方法通过抑制域敏感特征来学习域不变特征。与它们不同，我们提出了一种用于广义语义分割的域投影与对比学习（DPCL）方法，包含两个模块：自监督源域投影（SSDP）和多层次对比学习（MLCL）。SSDP旨在通过将数据投影到源域来缩小域间隙，而MLCL是一种学习方案，用于在投影数据上学习具有判别性和泛化能力的特征。在测试过程中，我们首先通过SSDP投影目标数据以减轻域偏移，然后基于MLCL利用已训练的分割网络生成分割结果。在测试阶段，我们可通过最小化提出的像素级对比损失来更新投影数据，从而获得更优结果。针对语义分割的大量实验表明，我们方法在基准数据集上具有优异的泛化能力。