Attention Module

Attention modules are mechanisms within neural networks designed to selectively focus on the most relevant information, improving efficiency and accuracy. Current research emphasizes developing more efficient attention mechanisms, particularly for long sequences and high-dimensional data, exploring variations like selective attention, frequency-aware attention, and low-rank approximations within architectures such as transformers and state-space models. These advancements are significantly impacting various fields, including computer vision (e.g., image and video analysis), natural language processing (e.g., large language models), and healthcare (e.g., medical image analysis), by enhancing model performance and reducing computational costs.