# Unsupervised Contrastive Domain Adaptation for Semantic Segmentation Source: [Unsupervised Contrastive Domain Adaptation for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:14:32.298Z Authors: Feihu Zhang, Vladlen Koltun, Philip Torr, Ren\\'e Ranftl, Stephan R. Richter Published: 2022-04-18T16:50:46.000Z ## Abstract Semantic segmentation models struggle to generalize in the presence of domain shift. In this paper, we introduce contrastive learning for feature alignment in cross-domain adaptation. We assemble both in-domain contrastive pairs and cross-domain contrastive pairs to learn discriminative features that align across domains. Based on the resulting well-aligned feature representations we introduce a label expansion approach that is able to discover samples from hard classes during the adaptation process to further boost performance. The proposed approach consistently outperforms state-of-the-art methods for domain adaptation. It achieves 60.2% mIoU on the Cityscapes dataset when training on the synthetic GTA5 dataset together with unlabeled Cityscapes images. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()