跨领域应用下的注意力机制研究综述

In recent years, Attention Mechanism has been widely applied in various fields of deep learning. It enhances the model\'s attention to relevant information by introducing attention weights, thereby strengthening key information and weakening useless information. This improves some of the shortcomings of traditional deep learning methods and improves the efficiency of deep learning algorithms. This article provides an overview of the applications and related research of attention mechanisms in different fields such as natural language processing, computer vision, and data prediction. It provides a detailed introduction to the historical development, structure, and application scenarios of attention mechanisms, and conducts a thorough analysis of the current research status. It also explores possible future development directions of attention mechanisms.