Pyramid Network

Pyramid networks are a class of deep learning architectures designed to efficiently process multi-scale information in various data types, including images, videos, and time series. Current research focuses on optimizing these networks for specific tasks, such as object detection, semantic segmentation, and video interpolation, often incorporating techniques like feature pyramid networks (FPNs), transformer modules, and recurrent neural networks to improve accuracy and efficiency. These advancements are driving improvements in diverse applications, from medical image analysis and remote sensing to air quality prediction and efficient image compression, by enabling more accurate and computationally feasible solutions.