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

arXiv: https://arxiv.org/abs/2106.07550 · HTML: https://mlsanity.com/paper/2106.07550/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 74 of its 111 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 30 of 74)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Survey on Machine Learning Driven Material Defect Detection](https://mlsanity.com/paper/2406.07880/) (2024)
- [Automated Discovery of Functional Actual Causes in Complex Environments](https://mlsanity.com/paper/2404.10883/) (2024)
- [WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep Imaging Ultrasound](https://mlsanity.com/paper/2311.10857/) (2023)
- [Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction](https://mlsanity.com/paper/2301.11986/) (2023)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects](https://mlsanity.com/paper/2405.04093/) (2024)
- [A Second-Order Attention Mechanism For Prostate Cancer Segmentation and Detection in Bi-Parametric MRI](https://mlsanity.com/paper/2511.05760/) (2025)
- [Gradient-Skipping Relevance Propagation for Efficient Explainability of Vision Transformers](https://mlsanity.com/paper/2607.10365/) (2026)

## Abstract (arXiv)

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.