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

TL;DR

This survey reviews recent advances in attention mechanisms and deep learning for machine vision, highlighting vision transformers, their challenges, and hybrid approaches combining attention with traditional methods.

Contribution

It provides a comprehensive overview of attention-based deep architectures in machine vision, discussing key algorithms, issues, and emerging trends in the field.

Findings

Vision transformers challenge traditional deep learning methods.

Hybrid models leverage advantages of attention and deep learning.

Attention mechanisms improve performance but require large data and resources.

Abstract

With the advent of state of the art nature-inspired pure attention based models i.e. transformers, and their success in natural language processing (NLP), their extension to machine vision (MV) tasks was inevitable and much felt. Subsequently, vision transformers (ViTs) were introduced which are giving quite a challenge to the established deep learning based machine vision techniques. However, pure attention based models/architectures like transformers require huge data, large training times and large computational resources. Some recent works suggest that combinations of these two varied fields can prove to build systems which have the advantages of both these fields. Accordingly, this state of the art survey paper is introduced which hopefully will help readers get useful information about this interesting and potential research area. A gentle introduction to attention mechanisms is given, followed by a discussion of the popular attention based deep architectures. Subsequently, the major categories of the intersection of attention mechanisms and deep learning for machine vision (MV) based are discussed. Afterwards, the major algorithms, issues and trends within the scope of the paper are discussed.