Abstract: Due to the superior representation capability of the Transformer’s Self-Attention mechanism,several researchers have developed Self-Attention mechanism-based image processing model and achieved great success.However,the traditional network for image classification based on Self-Attention cannot take into account global information and computational complexity,which limits the wide application of Self-Attention.This paper proposes an efficient and scalable attention module,Local Neighbor Glo-bal Self-Attention(LNG-SA),that may interact with local,neighbor,and global information at any stage.By cascading LNG-SA module,a brand-new network called LNG-Transformer is created.LNG-Transformer adopts a hierarchical structure that provides excellent flexibility,and has a computational complexity proportional to image resolution.The features of LNG-SA enable LNG-Transformer to interact with local information,neighbor information,and global information even in the early stage of high-resolution,resulting in increased efficiency and enhanced learning capacity.Experimental results show that LNG-Transformer performs well at image classification.

Key words: Image classification, Self-Attention, Multiple scales, Transformer