Selective Kernel

Selective kernel networks represent a recent advancement in convolutional neural networks (CNNs) focusing on dynamically adjusting the receptive field of convolutional kernels to improve model efficiency and performance across diverse tasks. Current research emphasizes the development of novel architectures like Large Selective Kernel Networks (LSKNets) and their applications in various domains, including remote sensing, medical image analysis, and 3D point cloud processing. This approach addresses limitations of fixed-kernel-size CNNs by enabling more efficient feature extraction and improved generalization, leading to significant advancements in accuracy and computational efficiency for a range of applications. The resulting models often achieve state-of-the-art results while requiring fewer parameters and less computational power than traditional methods.