A multi-grained unsupervised domain adaptation approach for semantic segmentation.

When transferring knowledge between different datasets, domain mismatch greatly hinders model's perfor-mance. So domain adaption has been brought up to tackle the problem. Traditional methods focusing either on global or local alignment play a limited role in improving model's performance. In this paper, we propose a multi-grained unsupervised domain adaptation approach (Muda) for semantic segmentation. Muda aims to enforce multi-grained semantic consistency between domains by aligning domains at both global and category level. Specifically, coarse-grained adaptation uses global adversarial learning on an image translation model and a main segmentation model, which respectively attempts to eliminate appearance differences and to get similar segmentation maps from two domains. While fine-grained adaptation employs an auxiliary model to adapt category information to refine pseudo labels of target data. Experiments and ablation studies are conducted on two synthetic-to-real benchmarks: GTA5-* Cityscapes and SYNTHIA-* Cityscapes, which show that our model outperforms the state-of-the-art methods.