Lightweight Attention

Lightweight attention mechanisms in deep learning aim to reduce computational cost and memory footprint while maintaining or improving performance in various tasks, including image and video processing, natural language processing, and biomedical applications. Current research focuses on developing novel attention modules, often incorporating techniques like selective attention, multi-scale feature fusion, and adaptive filtering, within lightweight convolutional neural networks or transformer-based architectures. These advancements enable deployment on resource-constrained devices and improve efficiency in large-scale applications, impacting fields ranging from mobile device applications to medical image analysis. The resulting models often achieve state-of-the-art or comparable performance with significantly fewer parameters than their heavier counterparts.