Unsupervised Contrastive Domain Adaptation for Semantic Segmentation

• URL: http://arxiv.org/abs/2204.08399v1
• Date: Mon, 18 Apr 2022 16:50:46 GMT
• Title: Unsupervised Contrastive Domain Adaptation for Semantic Segmentation
• Authors: Feihu Zhang, Vladlen Koltun, Philip Torr, Ren\'e Ranftl, Stephan R. Richter
• Abstract summary: We introduce contrastive learning for feature alignment in cross-domain adaptation. The proposed approach consistently outperforms state-of-the-art methods for domain adaptation. It achieves 60.2% mIoU on the Cityscapes dataset.
• Score: 75.37470873764855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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