WARLearn: Weather-Adaptive Representation Learning

Shubham Agarwal, Raz Birman, Ofer Hadar; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4978-4987

This paper introduces WARLearn a novel framework designed for adaptive representation learning in challenging and adversarial weather conditions. Leveraging the in-variance principal used in Barlow Twins we demonstrate the capability to port the existing models initially trained on clear weather data to effectively handle adverse weather conditions. With minimal additional training our method exhibits remarkable performance gains in scenarios characterized by fog and low-light conditions. This adaptive framework extends its applicability beyond adverse weather settings offering a versatile solution for domains exhibiting variations in data distributions. Furthermore WARLearn is invaluable in scenarios where data distributions undergo significant shifts over time enabling models to remain updated and accurate. Our experimental findings reveal a remarkable performance with a mean average precision (mAP) of 52.6% on unseen real-world foggy dataset (RTTS). Similarly in low light conditions our framework achieves a mAP of 55.7% on unseen real-world low light dataset (ExDark). Notably WARLearn surpasses the performance of state-of-the-art frameworks including FeatEnHancer Image Adaptive YOLO DENet C2PNet PairLIE and ZeroDCE by a substantial margin in adverse weather improving the baseline performance in both foggy and low light conditions. The WARLearn code is available at https://github.com/ShubhamAgarwal12/WARLearn