Large Kernel

Large kernel convolutional neural networks (CNNs) are experiencing a resurgence in computer vision and related fields, driven by their ability to capture long-range dependencies and improve model performance, particularly in tasks where global context is crucial. Current research focuses on optimizing large kernel architectures for efficiency, exploring novel designs like peripheral convolutions and decomposition techniques to mitigate the computational cost associated with large kernel sizes, and investigating their application across diverse modalities beyond image processing, including audio, video, and point clouds. This renewed interest in large kernel CNNs offers a compelling alternative to transformer-based models, potentially leading to more efficient and robust solutions for various applications, including image classification, object detection, and medical image segmentation.