Review of Attention Mechanisms in Image Processing

Abstract

Attention mechanism in image processing has become one of the popular and important techniques in the field of deep learning, and is widely used in various deep learning models in image processing because of its excellent plug-and-play convenience. By weighting the input features, the attention mechanism focuses the model’s attention on the most important regions to improve the accuracy and performance of image processing tasks. Firstly, this paper divides the development process of attention mechanism into four stages, and on this basis, reviews and summarizes the research status and progress of four aspects: channel attention, spatial attention, channel and spatial mixed attention, and self-attention. Secondly, this paper provides a detailed discussion on the core idea, key structure and specific implementation of attention mechanism, and further summarizes the advantages and disadvantages of used models. Finally, by comparing the current mainstream attention mechanisms and analyzing the results, this paper discusses the problems of attention mechanisms in the image processing field at this stage, and provides an outlook on the future development of attention mechanisms in image processing, so as to provide references for further research.

Keywords

• attention mechanism; core idea; key structures; image processing