Traditional Convolutional

Traditional convolutional neural networks (CNNs) are a cornerstone of computer vision, aiming to efficiently extract features from image data through hierarchical filtering. Current research focuses on addressing CNN limitations, such as handling long-range dependencies and adapting to varying data scales, through hybrid architectures combining CNNs with transformers or other novel approaches like state space models and specialized convolutional units. These advancements improve performance in various applications, including medical image segmentation, speech processing, and object detection, by enhancing robustness, efficiency, and accuracy.