Abstract
Distribution shifts between training and testing data are a critical bottleneck limiting the practical utility of models, especially in real-world test-time scenarios. To adapt models when the source domain is unknown and the target domain is unlabeled, previous works constructed pseudo-source domains via data generation and translation, then aligned the target domain with them. However, significant discrepancies exist between the pseudo-source and the original source domain, leading to potential divergence when correcting the target directly. From this perspective, we propose a Stepwise Semantic Alignment (SSA) method, viewing the pseudo-source as a semantic bridge connecting the source and target, rather than a direct substitute for the source. Specifically, we leverage easily accessible universal semantics to rectify the semantic features of the pseudo-source, and then align the target domain using the corrected pseudo-source semantics. Additionally, we introduce a Hierarchical Feature Aggregation (HFA) module and a Confidence-Aware Complementary Learning (CACL) strategy to enhance the semantic quality of the SSA process in the absence of source and ground truth of target domains. We evaluated our approach on tasks like semantic segmentation and image classification, achieving a 5.2% performance boost on GTA2Cityscapes over the state-of-the-art.
Abstract (translated)
训练数据和测试数据之间的分布变化是限制模型实际应用的关键瓶颈，尤其是在现实世界的测试场景中。为了在源域未知且目标域未标记的情况下适应模型，以前的研究通过生成和转换构建了伪源域，并将目标域与它们对齐。然而，伪源域与原始源域之间存在显著差异，这可能导致直接纠正目标时的潜在偏差。从这个角度来看，我们提出了逐步语义对齐（SSA）方法，将伪源视作连接源域和目标域的语义桥梁，而不是直接替代品。具体来说，我们利用易于获取的通用语义来修正伪源的语义特征，并使用修正后的伪源语义对齐目标域。此外，我们引入了一个层次特征聚合（HFA）模块和一种基于信心感知互补学习（CACL）策略，在没有来源数据和目标域真实标签的情况下提升SSA过程中的语义质量。我们在诸如语义分割和图像分类等任务上评估了我们的方法，在GTA2Cityscapes上的表现比现有最佳方法高出5.2%。
URL
https://arxiv.org/abs/2603.03844
