Semantic segmentation often suffers from significant performance degradation when the trained network is applied to a different domain. To address this issue, unsupervised domain adaptation (UDA) has been extensively studied. Existing methods introduce the domain bridging techniques to mitigate substantial domain gap, which construct intermediate domains to facilitate the gradual transfer of knowledge across different domains. However, these strategies often require dataset-specific designs and may generate unnatural intermediate distributions that lead to semantic shift. In this paper, we propose DiDA, a universal degradation-based bridging technique formalized as a diffusion forward process. DiDA consists of two key modules: (1) Degradation-based Intermediate Domain Construction, which creates continuous intermediate domains through simple image degradation operations to encourage learning domain-invariant features as domain differences gradually diminish; (2) Semantic Shift Compensation, which leverages a diffusion encoder to encode and compensate for semantic shift information with degraded time-steps, preserving discriminative representations in the intermediate domains. As a plug-and-play solution, DiDA supports various degradation operations and seamlessly integrates with existing UDA methods. Extensive experiments on prevalent synthetic-to-real semantic segmentation benchmarks demonstrate that DiDA consistently improves performance across different settings and achieves new state-of-the-art results when combined with existing methods.

翻译：语义分割任务中，当训练好的网络应用于不同领域时，常出现显著的性能下降。为解决这一问题，无监督域自适应（UDA）方法得到了广泛研究。现有方法通过引入域桥接技术来缓解显著的域差异，这些技术通过构建中间域来促进知识在不同域间的渐进迁移。然而，这些策略通常需要针对特定数据集进行设计，且可能生成非自然的中间分布，导致语义偏移。本文提出DiDA，一种基于通用退化的桥接技术，其形式化为扩散前向过程。DiDA包含两个关键模块：（1）基于退化的中间域构建模块，通过简单的图像退化操作创建连续的中间域，以鼓励在域差异逐渐减小的过程中学习域不变特征；（2）语义偏移补偿模块，利用扩散编码器对语义偏移信息进行编码，并结合退化时间步长进行补偿，从而在中间域中保持判别性表征。作为一种即插即用方案，DiDA支持多种退化操作，并能与现有UDA方法无缝集成。在主流合成到真实场景的语义分割基准测试上的大量实验表明，DiDA在不同设置下均能持续提升性能，且与现有方法结合时取得了新的最先进结果。