Sanket Kalwar∗1 Mihir Ungarala∗1 Shruti Jain∗1 Aaron Monis1 Krishna Reddy Konda3 Sourav Garg2 K Madhava Krishna1
1 Robotics Research Center,KCIS, IIIT Hyderabad, India 2 Australian Institute for Machine Learning, University of Adelaide, Australia 3 ZF TCI, Hyderabad
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 ∇HFC (High Frequency Components) based 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 outof-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.