  We investigated domain adaptive semantic segmentation in foggy weather
scenarios, which aims to enhance the utilization of unlabeled foggy data and
improve the model's adaptability to foggy conditions. Current methods rely on
clear images as references, jointly learning defogging and segmentation for
foggy images. Despite making some progress, there are still two main drawbacks:
(1) the coupling of segmentation and defogging feature representations,
resulting in a decrease in semantic representation capability, and (2) the
failure to leverage real fog priors in unlabeled foggy data, leading to
insufficient model generalization ability. To address these issues, we propose
a novel training framework, Decouple Defogging and Semantic learning, called
D2SL, aiming to alleviate the adverse impact of defogging tasks on the final
segmentation task. In this framework, we introduce a domain-consistent transfer
strategy to establish a connection between defogging and segmentation tasks.
Furthermore, we design a real fog transfer strategy to improve defogging
effects by fully leveraging the fog priors from real foggy images. Our approach
enhances the semantic representations required for segmentation during the
defogging learning process and maximizes the representation capability of fog
invariance by effectively utilizing real fog data. Comprehensive experiments
validate the effectiveness of the proposed method.
