  Despite recent advances in semantic segmentation, an inevitable challenge is
the performance degradation caused by the domain shift in real applications.
Current dominant approach to solve this problem is unsupervised domain
adaptation (UDA). However, the absence of labeled target data in UDA is overly
restrictive and limits performance. To overcome this limitation, a more
practical scenario called semi-supervised domain adaptation (SSDA) has been
proposed. Existing SSDA methods are derived from the UDA paradigm and primarily
focus on leveraging the unlabeled target data and source data. In this paper,
we highlight the significance of exploiting the intra-domain information
between the labeled target data and unlabeled target data. Instead of solely
using the scarce labeled target data for supervision, we propose a novel SSDA
framework that incorporates both Inter and Intra Domain Mixing (IIDM), where
inter-domain mixing mitigates the source-target domain gap and intra-domain
mixing enriches the available target domain information, and the network can
capture more domain-invariant features. We also explore different domain mixing
strategies to better exploit the target domain information. Comprehensive
experiments conducted on the GTA5 to Cityscapes and SYNTHIA to Cityscapes
benchmarks demonstrate the effectiveness of IIDM, surpassing previous methods
by a large margin.
