Convolutional Structure

Convolutional structures are fundamental building blocks in many deep learning models, primarily used for their efficiency in extracting local features from data like images and time series. Current research focuses on improving convolutional networks' efficiency and performance through novel architectures like hierarchical gated convolutions and multi-structural designs, often incorporating attention mechanisms or integrating them with other architectures such as transformers to leverage both local and global information. These advancements are driving progress in diverse applications, including medical image analysis, object detection, and image super-resolution, by enabling faster and more accurate processing of complex data.