WeatherSeg: Weather-Robust Image Segmentation using Teacher-Student Dual Learning and Classifier-Updating Attention
WeatherSeg is a semi-supervised image segmentation framework designed to improve environmental perception for autonomous driving in adverse weather conditions while reducing annotation costs. It uses a Dual Teacher-Student Weight-Sharing Model for knowledge distillation and a Classifier Weight Updating Attention Mechanism to dynamically adjust classifier weights based on weather. The framework demonstrates superior accuracy and robustness across clear, rainy, cloudy, and foggy conditions compared to baseline models. It is positioned as an effective solution for all-weather semantic segmentation in autonomous driving applications.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2604.22824 (cs) [Submitted on 19 Apr 2026 (v1), last revised 28 Apr 2026 (this version, v2)] Title:WeatherSeg: Weather-Robust Image Segmentation using Teacher-Student Dual Learning and Classifier-Updating Attention Authors:Zhang Zhang, Yifeng Zeng, Houshi Jiang, Yinghui Pan View a PDF of the paper titled WeatherSeg: Weather-Robust Image Segmentation using Teacher-Student Dual Learning and Classifier-Updating Attention, by Zhang Zhang and 3 other authors View PDF HTML (experimental) Abstract:WeatherSeg, an advanced semi-supervised segmentation framework, addresses autonomous driving's environmental perception challenges in adverse weather while reducing annotation costs.
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