  Unsupervised Domain Adaptation (UDA) aims to adapt models from labeled source
domains to unlabeled target domains. When adapting to adverse scenes, existing
UDA methods fail to perform well due to the lack of instructions, leading their
models to overlook discrepancies within all adverse scenes. To tackle this, we
propose CoDA which instructs models to distinguish, focus, and learn from these
discrepancies at scene and image levels. Specifically, CoDA consists of a
Chain-of-Domain (CoD) strategy and a Severity-Aware Visual Prompt Tuning
(SAVPT) mechanism. CoD focuses on scene-level instructions to divide all
adverse scenes into easy and hard scenes, guiding models to adapt from source
to easy domains with easy scene images, and then to hard domains with hard
scene images, thereby laying a solid foundation for whole adaptations. Building
upon this foundation, we employ SAVPT to dive into more detailed image-level
instructions to boost performance. SAVPT features a novel metric Severity that
divides all adverse scene images into low-severity and high-severity images.
Then Severity directs visual prompts and adapters, instructing models to
concentrate on unified severity features instead of scene-specific features,
without adding complexity to the model architecture. CoDA achieves SOTA
performances on widely-used benchmarks under all adverse scenes. Notably, CoDA
outperforms the existing ones by 4.6%, and 10.3% mIoU on the Foggy Driving, and
Foggy Zurich benchmarks, respectively. Our code is available at
https://github.com/Cuzyoung/CoDA
