FADE Net

FADE, representing several distinct but related research efforts, broadly addresses the challenge of improving feature upsampling and anomaly detection in various contexts. Current research focuses on developing task-agnostic upsampling operators that effectively handle both region- and detail-sensitive tasks, and on leveraging large vision-language models for robust few-shot and zero-shot anomaly detection, particularly in industrial settings. These advancements are significant for improving the performance of image segmentation, object detection (including falling object detection in videos and 3D object detection using multi-sensor data), and wireless communication channel modeling, ultimately impacting fields like autonomous driving, quality control, and communication system design.