Convolution Based

Convolution-based neural networks (CNNs) remain a cornerstone of computer vision, aiming to efficiently extract and process spatial information from images and other data. Current research focuses on enhancing CNN performance through architectural innovations like deformable convolutions and large kernel sizes, as well as exploring techniques such as knowledge distillation and multi-fidelity optimization to improve training efficiency and generalization. These advancements are driving improvements in diverse applications, including medical image analysis (e.g., skin cancer detection, Alzheimer's diagnosis), autonomous driving, and document processing, demonstrating the continued relevance and impact of CNNs in various fields.