Multi Scale Attention

Multi-scale attention mechanisms in deep learning aim to improve model performance by processing information at multiple resolutions and scales, capturing both fine-grained details and broader contextual information. Current research focuses on integrating these mechanisms into various architectures, including transformers and convolutional neural networks, for tasks such as image restoration, medical image segmentation, and time series forecasting. This approach enhances the ability of models to handle complex data with varying levels of detail, leading to improved accuracy and robustness across diverse applications in computer vision, medical imaging, and signal processing.