Residual Attention

Residual attention mechanisms enhance deep learning models by selectively focusing on important features within data, improving accuracy and efficiency. Current research emphasizes integrating residual attention into various architectures, including convolutional neural networks (CNNs) and transformers, often within lightweight designs for resource-constrained applications. This focus is driven by the need for improved performance in diverse tasks such as image super-resolution, object detection, and medical image analysis, leading to more accurate and efficient solutions across multiple fields. The resulting models demonstrate improved accuracy and reduced computational cost compared to their predecessors.