SCouT Net

SCouT (and related models like SCOUT+) represents a family of algorithms leveraging deep learning, particularly transformer and convolutional neural network architectures, to address diverse problems involving spatiotemporal data analysis and prediction. Current research focuses on improving the accuracy and efficiency of these models in applications ranging from gaze prediction and organ dose estimation in medical imaging to multi-target tracking in robotics and flood detection from satellite imagery. These advancements offer significant potential for improving the accuracy and speed of various applications, from enhancing driver safety systems to optimizing medical procedures and disaster response.