WeatherSeg: Weather-Robust Image Segmentation using Teacher-Student Dual Learning and Classifier-Updating Attention

• URL: http://arxiv.org/abs/2604.22824v2
• Date: Tue, 28 Apr 2026 05:54:22 GMT
• Title: WeatherSeg: Weather-Robust Image Segmentation using Teacher-Student Dual Learning and Classifier-Updating Attention
• Abstract summary: WeatherSeg is an advanced semi-affected segmentation framework.<n>It addresses autonomous driving's environmental perception challenges in adverse weather.<n>It is an effective solution for all-weather semantic segmentation in autonomous driving.
• Score: 6.69288555047147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: WeatherSeg, an advanced semi-supervised segmentation framework, addresses autonomous driving's environmental perception challenges in adverse weather while reducing annotation costs. This framework integrates a Dual Teacher-Student Weight-Sharing Model (DTSWSM) that enables knowledge distillation from weather-affected images, and a Classifier Weight Updating Attention Mechanism (CWUAM) that dynamically adjusts classifier weights based on environmental attributes. Comprehensive evaluations demonstrate that WeatherSeg significantly outperforms baseline models in both accuracy and robustness across various weather conditions, including clear, rainy, cloudy, and foggy scenarios, establishing it as an effective solution for all-weather semantic segmentation in autonomous driving and related applications.

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