Abstract
Unsupervised domain adaptation (UDA) enables semantic segmentation models to generalize from a labeled source domain to an unlabeled target domain. However, existing UDA methods still struggle to bridge the domain gap due to cross-domain contextual ambiguity, inconsistent feature representations, and class-wise pseudo-label noise. To address these challenges, we propose Omni-level Masking for Unsupervised Domain Adaptation (OMUDA), a unified framework that introduces hierarchical masking strategies across distinct representation levels. Specifically, OMUDA comprises: 1) a Context-Aware Masking (CAM) strategy that adaptively distinguishes foreground from background to balance global context and local details; 2) a Feature Distillation Masking (FDM) strategy that enhances robust and consistent feature learning through knowledge transfer from pre-trained models; and 3) a Class Decoupling Masking (CDM) strategy that mitigates the impact of noisy pseudo-labels by explicitly modeling class-wise uncertainty. This hierarchical masking paradigm effectively reduces the domain shift at the contextual, representational, and categorical levels, providing a unified solution beyond existing approaches. Extensive experiments on multiple challenging cross-domain semantic segmentation benchmarks validate the effectiveness of OMUDA. Notably, on the SYNTHIA->Cityscapes and GTA5->Cityscapes tasks, OMUDA can be seamlessly integrated into existing UDA methods and consistently achieving state-of-the-art results with an average improvement of 7%.
Abstract (translated)
无监督领域适应（UDA）使语义分割模型能够从有标签的源域泛化到无标签的目标域。然而，现有的UDA方法仍然难以弥合因跨域上下文模糊性、特征表示不一致以及类别伪标签噪声等原因导致的领域差距。为了解决这些挑战，我们提出了Omni-level Masking for Unsupervised Domain Adaptation（OMUDA），这是一种统一框架，引入了在不同表示层次上的分层掩码策略。具体来说，OMUDA包括： 1. 上下文感知掩码（CAM）策略：该策略能够自适应地区分前景和背景，并平衡全局上下文与局部细节； 2. 特征蒸馏掩码（FDM）策略：通过从预训练模型的知识转移来增强鲁棒且一致的特征学习； 3. 类别解耦掩码（CDM）策略：该策略通过显式建模类别不确定性，减轻了噪声伪标签的影响。 这种分层掩码范例在上下文、表示和分类级别上有效地减少了领域偏移，并提供了一种超越现有方法的统一解决方案。多项具有挑战性的跨域语义分割基准实验验证了OMUDA的有效性。特别地，在SYNTHIA->Cityscapes和GTA5->Cityscapes任务中，OMUDA可以无缝集成到现有的UDA方法中，并始终取得最先进的结果，平均改进达到7%。
URL
https://arxiv.org/abs/2512.12303
