arXiv:2604.22824v2 Announce Type: replace-cross
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.
Based on dual perspectives of management and ethics: exploring challenges and governance approaches for new media applications in psychiatric specialty hospitals
The further promotion and application of new media technologies present new opportunities for psychiatric specialty hospitals in areas such as health education, doctor-patient communication, service