Convolutional Block

A convolutional block is a fundamental building block in convolutional neural networks (CNNs), designed to extract features from input data through convolutional operations. Current research focuses on improving efficiency and robustness of convolutional blocks, exploring variations such as attention mechanisms (e.g., CBAM), gated convolutions, and hybrid architectures combining CNNs with transformers or state space models to enhance feature extraction and reduce computational costs. These advancements are significantly impacting various applications, including image classification, object detection, medical image analysis, and time series forecasting, by improving accuracy and enabling real-time processing on resource-constrained devices.