Multi Weather

Multi-weather research focuses on developing robust computer vision and weather prediction models that can accurately handle diverse and often co-occurring weather conditions, overcoming limitations of systems trained primarily on clear-weather data. Current research emphasizes the use of transformer-based architectures and mixture-of-experts models, along with techniques like contrastive learning and uncertainty modeling, to improve accuracy and efficiency across various tasks, including crowd counting, weather forecasting, and image restoration. These advancements are significant for improving the reliability of applications ranging from autonomous driving and agricultural yield prediction to public health forecasting and climate modeling. The development of large-scale, multi-weather datasets is also a key area of focus, enabling the training of more robust and generalizable models.