Excitation Network

Excitation networks are neural network architectures designed to enhance feature representation by selectively emphasizing important information within data. Current research focuses on applying these networks across diverse applications, including image enhancement (e.g., low-light image processing and MRI reconstruction), action recognition (e.g., hand-to-hand and human-to-human interaction), and classification tasks (e.g., apple foliar disease identification). Key model architectures involve variations of the Squeeze and Excitation (SE) block, often integrated with other techniques like graph convolutions or multi-scale feature fusion, to improve accuracy and efficiency. These advancements have demonstrably improved performance in various fields, highlighting the broad utility of excitation networks for enhancing the capabilities of deep learning models.