Cross Vector Convolution

Cross-vector convolution (CVC) is a neural network technique designed to enhance feature extraction by considering relationships between different feature channels or spatial locations within data. Current research focuses on developing CVC-based architectures, such as moment channel attention networks and sliding cross-vector convolution networks, to improve performance in diverse applications like image classification, object detection, and EEG-based cognitive workload recognition. These methods aim to capture richer contextual information and improve accuracy by leveraging inter-channel or inter-spatial dependencies, often incorporating mechanisms like channel attention and multi-scale feature fusion. The resulting improvements in accuracy and efficiency demonstrate the significant potential of CVC for various signal and image processing tasks.