Fully Convolutional

Fully convolutional networks (FCNs) are a class of deep learning architectures characterized by the exclusive use of convolutional layers, enabling efficient processing of spatial data like images and signals. Current research focuses on applying FCNs to diverse tasks, including image segmentation, object detection and pose estimation, and time-series forecasting, often employing variations like U-Nets, and integrating them with other architectures such as transformers. This versatility makes FCNs significant for various fields, from medical image analysis and autonomous navigation to industrial process monitoring and remote sensing, offering improved accuracy and efficiency in many applications.