Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning

• URL: http://arxiv.org/abs/2104.11056v1
• Date: Thu, 22 Apr 2021 13:39:12 GMT
• Title: Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning
• Authors: Weizhe Liu, David Ferstl, Samuel Schulter, Lukas Zebedin, Pascal Fua, Christian Leistner
• Abstract summary: We leverage contrastive learning to bridge the domain gap by aligning the features of structurally similar label patches across domains. Our approach consistently outperforms state-of-the-art unsupervised and semi-supervised methods on two challenging domain adaptive segmentation tasks.
• Score: 62.7588467386166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel approach to unsupervised and semi-supervised domain adaptation for semantic segmentation. Unlike many earlier methods that rely on adversarial learning for feature alignment, we leverage contrastive learning to bridge the domain gap by aligning the features of structurally similar label patches across domains. As a result, the networks are easier to train and deliver better performance. Our approach consistently outperforms state-of-the-art unsupervised and semi-supervised methods on two challenging domain adaptive segmentation tasks, particularly with a small number of target domain annotations. It can also be naturally extended to weakly-supervised domain adaptation, where only a minor drop in accuracy can save up to 75% of annotation cost.

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