提升恶劣天气下图像分割精度，降低标注成本。
WeatherSeg: Weather-Robust Image Segmentation using Teacher-Student Dual Learning and Classifier-Updating Attention
- 采用师生双模型知识蒸馏，从有雨雾的图像中学习
- 动态调整分类器权重，适应不同天气条件，显著提优
- 适合自动驾驶等需全天候感知的场景
WeatherSeg是一种先进的半监督分割框架，旨在应对自动驾驶在恶劣天气下的环境感知挑战，同时降低标注成本。该框架结合了双教师-学生权共享模型（DTSWSM），实现从受天气影响图像中的知识蒸馏；以及分类器权重更新注意力机制（CWUAM），根据环境属性动态调整分类器权重。全面评估表明，WeatherSeg在清晰、雨天、多云和雾天等多种天气条件下，均显著优于基线模型，在准确性和鲁棒性上表现突出，为自动驾驶及相关应用中的全天候语义分割提供了有效解决方案。
原文摘要 · Abstract (English)
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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