Large Kernel Convolution

Large kernel convolutions are a technique in deep learning that aims to improve the receptive field of convolutional neural networks (CNNs), enabling them to capture broader contextual information within images or other data. Current research focuses on integrating large kernels into various architectures, including YOLO object detectors and U-Net-based segmentation models, often alongside techniques like attention mechanisms or structural reparameterization to mitigate computational costs. This approach has yielded significant performance gains in diverse applications such as medical image segmentation, object detection in remote sensing, and anomaly detection, demonstrating the effectiveness of expanding receptive fields for improved accuracy and efficiency.