The enhanced representational power and broad applicability of deep learning models have attracted significant interest from the research community in recent years. However, these models often struggle to perform effectively under domain shift conditions, where the training data (the source domain) is related to but exhibits different distributions from the testing data (the target domain). To address this challenge, previous studies have attempted to reduce the domain gap between source and target data by incorporating a few labeled target samples during training - a technique known as semi-supervised domain adaptation (SSDA). While this strategy has demonstrated notable improvements in classification performance, the network architectures used in these approaches primarily focus on exploiting the features of individual images, leaving room for improvement in capturing rich representations. In this study, we introduce a Hierarchical Graph of Nodes designed to simultaneously present representations at both feature and category levels. At the feature level, we introduce a local graph to identify the most relevant patches within an image, facilitating adaptability to defined main object representations. At the category level, we employ a global graph to aggregate the features from samples within the same category, thereby enriching overall representations. Extensive experiments on widely used SSDA benchmark datasets, including Office-Home, DomainNet, and VisDA2017, demonstrate that both quantitative and qualitative results substantiate the effectiveness of HiGDA, establishing it as a new state-of-the-art method.

翻译：近年来，深度学习模型增强的表征能力与广泛适用性已引起研究界的极大兴趣。然而，这些模型在领域偏移条件下往往难以有效工作，即训练数据（源域）与测试数据（目标域）相关但呈现不同分布。为应对这一挑战，先前研究尝试通过在训练过程中引入少量带标签的目标样本来缩小源域与目标数据间的领域差距——这一技术被称为半监督域自适应（SSDA）。尽管该策略在分类性能上已展现出显著提升，但现有方法采用的网络架构主要聚焦于利用单张图像的特征，在捕获丰富表征方面仍有改进空间。本研究提出一种层次节点图，旨在同时呈现特征级与类别级的表征。在特征层面，我们引入局部图以识别图像中最相关的图像块，从而增强对已定义主要对象表征的自适应能力。在类别层面，我们采用全局图聚合同一类别内样本的特征，以此丰富整体表征。通过在Office-Home、DomainNet和VisDA2017等广泛使用的SSDA基准数据集上进行大量实验，定量与定性结果均证实了HiGDA的有效性，确立了其作为新一代最先进方法的地位。