Attention Block

Attention blocks are modular components within neural networks designed to selectively focus on the most relevant information, improving efficiency and accuracy in various tasks. Current research emphasizes developing attention blocks tailored to specific applications, such as image compression, long sequence processing, and medical image analysis, often integrating them into transformer-based architectures or convolutional neural networks. These advancements lead to improved performance in diverse fields, including image processing, natural language processing, and time series forecasting, by reducing computational costs and enhancing feature extraction. The resulting models often achieve state-of-the-art results while requiring fewer resources.