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.
Differential acceptance of a national digital health platform among community and frontline health workers in Cote d’Ivoire: a cross-sectional study
IntroductionMobile-based digital health solutions are critical technologies that play a significant role in improving the quality of healthcare services. Cote d’Ivoire is digitizing its community-based