PMDA: Domain Alignment with Prototype Matching for Cross-Domain Adaptive Segmentation

Cross-domain adaptive segmentation is a practical solution for the scenario that lacks expensive annotations or is inaccessible to ground truth. Prior works have tried to improve cross-domain adaptive segmentation with domain alignment, but most of them ignore the problem of training target deviation of distance-regularizing based domain alignment method. To address this, we propose a novel domain alignment mechanism that unifies the two optimization objectives, domain alignment, and segmentation performance, into one. In addition, existing methods are hard to apply under the source-free setting. We introduce a novel domain adaptive segmentation framework suitable for vanilla Unsupervised Domain Adaptation (UDA) and source-free UDA settings. Experiments show the proposed method outperforms competitive works with much more complicated mechanisms and achieves the state-of-the-art performance on both GTA -> Cityscapes and Synthia -> Cityscapes benchmarks. Our work can be easily added to existing methods and boost their performance.