Prototypical Contrastive Learning for Domain Adaptive Semantic Segmentation

Abstract

The goal of domain adaptive semantic segmentation is to train a model using labeled source domain data and produce accurate dense predictions on the unlabeled target domain. Previous methods adopt self-training, where reliable target domain predictions are used as pseudo labels for training. However, intra-class variations across domains, such as the varying visual appearance in each category, have not been fully explored, leading to misalignment in feature distribution between the source and target domains. In this paper, we propose to optimize the feature space with representative prototypes shared across domains. Specifically, we first adopt the non-parametric clustering to model multiple prototypes for each category feature space. Then, category-discriminative feature space is obtained via pixel-to-prototype contrastive learning. Through extensive experiments, our proposed method demonstrates competitive performance on GTA5→Cityscapes and Synthia→Cityscapes benchmark. It is noteworthy that our method is compatible with the existing UDA methods.