Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization

Unsupervised domain adaptation (UDA) for semantic seg-mentation has been well-studied in recent years. However, most existing works largely neglect the local regional consis-tency across different domains, and are less robust to changes in outdoor environments. In this paper, we propose a novel and fully end-to-end trainable approach, called regional contrastive consistency regularization (RCCR) for domain adaptive semantic segmentation. Our core idea is to pull the sim-ilar regional features extracted from the same location of dif-ferent images, i.e., the original image and augmented image, to be closer, and meanwhile push the features from the dif-ferent locations of the two images to be separated. We pro-pose a region-wise contrastive loss with two sampling strate-gies to realize effective regional consistency. Besides, we present momentum projection heads, where the teacher pro-jection head is the exponential moving average of the student. Finally, a memory bank mechanism is designed to learn more robust and stable region-wise features under varying environ-ments. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods.