The Latest Research Progress of Attention Mechanism in Deep Learning

With the development of artificial intelligence and deep learning, the attention mechanism has become a key technology for enhancing the performance of complex tasks. This paper reviews the evolution of attention mechanisms, including soft attention, hard attention, and recent innovations such as multi-head latent attention and cross-attention. It focuses on the latest research outcomes, such as lightning attention, the PADRe polynomial attention replacement algorithm, the context anchor attention module, and improvements in attention mechanisms for large models. These advancements improve the efficiency and accuracy of models, expanding the application potential of attention mechanisms in fields such as computer vision, natural language processing, and remote sensing object detection, aiming to provide readers with a comprehensive understanding and stimulate innovative thinking.