  Semantic segmentation in adverse weather scenarios is a critical task for
autonomous driving systems. While foundation models have shown promise, the
need for specialized adaptors becomes evident for handling more challenging
scenarios. We introduce DiffPrompter, a novel differentiable visual and latent
prompting mechanism aimed at expanding the learning capabilities of existing
adaptors in foundation models. Our proposed $\nabla$HFC image processing block
excels particularly in adverse weather conditions, where conventional methods
often fall short. Furthermore, we investigate the advantages of jointly
training visual and latent prompts, demonstrating that this combined approach
significantly enhances performance in out-of-distribution scenarios. Our
differentiable visual prompts leverage parallel and series architectures to
generate prompts, effectively improving object segmentation tasks in adverse
conditions. Through a comprehensive series of experiments and evaluations, we
provide empirical evidence to support the efficacy of our approach. Project
page at https://diffprompter.github.io.
