AdaDCP: Learning an Adapter with Discrete Cosine Prior for Clear-to-Adverse Domain Generalization

Vision Foundation Model (VFM) provides an inherent generalization ability to unseen domains for downstream tasks.However, fine-tuning VFM to parsing various adverse scenes (\eg, fog, snow, night) is particularly challenging, as these samples are difficult to collect and annotate.Using easy-to-acquire clear scenes as the source domain is a feasible solution, but a huge domain gap exists between them and clear scenes due to dramatically different scene appearance.In this paper, we propose \texttt{AdaDCP} to effectively fine-tune a VFM for adverse scene segmentation, by only generalizing from a clear source domain. Interestingly, the frequency bands from a VFM exhibit either variant or invariant properties on various adverse weather conditions after discerete cosine transform. Therefore, our \texttt{AdaDCP} is enpowered by three key components: (1) weather-invariant band adapation that provides a foundation to enhance the robustness to adverse scenes; (2) weather-variant band adapation that preceives the weather-specific information from each type of adverse scenes; (3) weather-invariant band alignment that implictly enforces the weather-variant bands to progressively incoperate the weather-invariant information, therefore mitigating the clear-to-adverse domain gap.Experiments conducted on eight unseen adverse scene segmentation datasets show its state-of-the-art performance.