Local Convolution

Local convolution, a fundamental operation in convolutional neural networks (CNNs), focuses on processing data using localized receptive fields, enabling efficient feature extraction from spatial data like images and point clouds. Current research explores variations on this theme, including architectures that combine local convolutions with global operations (like Fourier transforms or attention mechanisms) to capture both local and long-range dependencies, and those that adapt the degree of locality to address data heterogeneity. These advancements aim to improve model performance, training efficiency, and the ability to handle diverse data types, impacting fields such as image recognition, video processing, and 3D point cloud analysis.