Translating images from a source domain to a target domain for learning
target models is one of the most common strategies in domain adaptive semantic
segmentation (DASS). However, existing methods still struggle to preserve
semantically-consistent local details between the original and translated
images. In this work, we present an innovative approach that addresses this
challenge by using source-domain labels as explicit guidance during image
translation. Concretely, we formulate cross-domain image translation as a
denoising diffusion process and utilize a novel Semantic Gradient Guidance
(SGG) method to constrain the translation process, conditioning it on the
pixel-wise source labels. Additionally, a Progressive Translation Learning
(PTL) strategy is devised to enable the SGG method to work reliably across
domains with large gaps. Extensive experiments demonstrate the superiority of
our approach over state-of-the-art methods.

通过使用源领域标签作为图像翻译的明确指导，本文提出了一种创新的方法来解决领域自适应语义分割中的语义一致的局部细节保持问题，并利用了渐进翻译学习策略来实现在具有大差距领域中可靠工作的语义梯度引导方法。大量实验证明了我们方法相对于最先进的方法的优越性。