  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.
