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Humans can naturally and effectively find salient regions in complex scenes. Motivated by thisobservation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multi-modal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research.


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Attention mechanisms in computer vision: A survey

Show Author's information Meng-Hao Guo1Tian-Xing Xu1Jiang-Jiang Liu2Zheng-Ning Liu1Peng-Tao Jiang2Tai-Jiang Mu1Song-Hai Zhang1Ralph R. Martin3Ming-Ming Cheng2Shi-Min Hu1( )
BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
TKLNDST, College of Computer Science, Nankai University, Tianjin 300350, China
School of Computer Science and Informatics, Cardiff University, Cardiff, UK

Abstract

Humans can naturally and effectively find salient regions in complex scenes. Motivated by thisobservation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multi-modal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research.

Keywords:

attention, transformer, computer vision, deep learning, salience
Received: 31 December 2021 Accepted: 18 January 2022 Published: 15 March 2022 Issue date: September 2022
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Received: 31 December 2021
Accepted: 18 January 2022
Published: 15 March 2022
Issue date: September 2022

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61521002 and 62132012). We would like to thank Cheng-Ze Lu, Zhengyang Geng, Shilong Liu, He Wang, Huiying Lu, and Chenxi Huang for their helpful discussions and insightful suggestions.

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