Preserving Label-Related Domain-Specific Information for Cross-Domain Semantic Segmentation

Unsupervised domain adaptation semantic segmentation (UDASS) methods aim to learn domain-invariant information for alleviating the distribution shift problem between the source and target domains. However, ignoring the learning of domain-specific information that is label-related may limit the class discriminability on the target domain. We argue that a good representation for the UDASS task not only contains domain-invariant information but also preserves label-related domain-specific information. In this paper, a novel frequency spectrum domain adaptation approach via meta-learning (ML-FSDA) is proposed to achieve this goal for improving the class discriminability and generalization ability. ML-FSDA contains a frequency-spectrum meta-learning framework (FMF) and a class-aware domain-specific memory bank (CDMB). Specifically, first, inspired by the observation that the high-frequency component is consistent across different domains while the low-frequency component is much more domain-specific, the FMF aims to respectively learn label-related domain-specific and domain-invariant information from low-frequency and high-frequency images in a unified framework via the meta-learning strategy. Second, the CDMB is designed to preserve the label-related domain-specific information of each class in an external memory bank while the CDMB is updated in every iteration of the meta-training stage. Finally, the CDMB is utilized to embed the label-related domain-specific information into domain-invariant information at the class level during the meta-testing stage to enhance the class discriminability on the target domain. Extensive experiments demonstrate the effectiveness of ML-FSDA on two challenging cross-domain semantic segmentation benchmarks. Notably, for the GTA5 to Cityscapes task and the SYNTHIA to Cityscapes task, the proposed ML-FSDA achieves superior performance with 77.3% mIoU and 68.8% mIoU, respectively. The source code is released at https://github.com/seabearlmx/FSL.