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.

翻译：尽管语义分割近期取得了进展，但实际应用中域偏移导致的性能下降仍是不可避免的挑战。当前解决该问题的主流方法是无监督领域自适应（UDA），但UDA缺乏目标域标注数据的约束过于严格，限制了其性能。为克服这一局限，更贴近实际的半监督领域自适应（SSDA）场景被提出。现有SSDA方法源自UDA范式，主要聚焦于利用未标注的目标域数据和源域数据。本文强调挖掘标注目标域数据与未标注目标域数据之间域内信息的重要性。为突破仅使用稀缺标注目标域数据进行监督的限制，我们提出了融合域间混合与域内混合（IIDM）的新型SSDA框架：域间混合可缩小源-目标域差异，域内混合能丰富可用目标域信息，使网络捕获更多域不变特征。我们还探索了不同域混合策略以更充分挖掘目标域信息。在GTA5→Cityscapes和SYNTHIA→Cityscapes基准上的全面实验表明，IIDM方法性能大幅超越现有方法。