Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation

Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation. Previous domain adaptation methods merely consider the alignment of the intra-class representational distributions across various domains, while the inter-class structural relationship is insufficiently explored, resulting in the aligned representations on the target domain might not be as easily discriminated as done on the source domain anymore. Instead, ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation. By considering the same class prototypes as positives and other class prototypes as negatives to achieve class-centered distribution alignment, ProCA achieves state-of-the-art performance on classical domain adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. Code is available at ProCA.

Automated summary

In this paper, the authors propose a simple and efficient contrastive learning method called ProCA for unsupervised domain adaptive semantic segmentation. ProCA incorporates inter-class information into class-wise prototypes and adopts a class-centered distribution alignment approach. Experimental results show that ProCA achieves state-of-the-art performance on classical domain adaptation tasks.