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With the advent of state of the art nature-inspired pure attention basedmodels i.e. transformers, and their success in natural language processing(NLP), their extension to machine vision (MV) tasks was inevitable and muchfelt. Subsequently, vision transformers (ViTs) were introduced which are givingquite a challenge to the established deep learning based machine visiontechniques. However, pure attention based models/architectures liketransformers require huge data, large training times and large computationalresources. Some recent works suggest that combinations of these two variedfields can prove to build systems which have the advantages of both thesefields. Accordingly, this state of the art survey paper is introduced whichhopefully will help readers get useful information about this interesting andpotential research area. A gentle introduction to attention mechanisms isgiven, followed by a discussion of the popular attention based deeparchitectures. Subsequently, the major categories of the intersection ofattention mechanisms and deep learning for machine vision (MV) based arediscussed. Afterwards, the major algorithms, issues and trends within the scopeof the paper are discussed.

Attention mechanisms and deep learning for machine vision: A survey of the state of the art