Convolutional Operation

Convolutional operations, the cornerstone of many deep learning models, aim to extract features from data by applying learned filters to localized regions. Current research focuses on improving efficiency and addressing limitations of traditional convolutional approaches, particularly their inherent locality, by integrating them with transformer architectures or developing novel alternatives like MLP mixers and "kervolutional" operations. These advancements are driving improvements in various applications, including medical image segmentation, object detection, and image restoration, by enabling more efficient and accurate feature extraction and modeling of both local and global patterns within data.