Despite recent advances in semantic segmentation, an inevitable challenge is
the performance degradation caused by the domain shift in real application.
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 limited labeled target data and unlabeled target data, as it
greatly benefits domain adaptation. Instead of solely using the scarce labeled
data for supervision, we propose a novel SSDA framework that incorporates both
inter-domain mixing and intra-domain mixing, where inter-domain mixing
mitigates the source-target domain gap and intra-domain mixing enriches the
available target domain information. By simultaneously learning from
inter-domain mixing and intra-domain mixing, the network can capture more
domain-invariant features and promote its performance on the target domain. We
also explore different domain mixing operations to better exploit the target
domain information. Comprehensive experiments conducted on the GTA5toCityscapes
and SYNTHIA2Cityscapes benchmarks demonstrate the effectiveness of our method,
surpassing previous methods by a large margin.

本文介绍了一种新的半监督领域适应框架，该框架结合了跨域混合和内域混合，实现了更好地利用目标域信息，并在 GTA5toCityscapes 和 SYNTHIA2Cityscapes 基准数据集上取得了显著的效果优于之前的方法。