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15 March 2022
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Published:
15 March 2022
Attention mechanisms in computer vision: A survey
Shi-Min Hu1(
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)
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Guo M-H, Xu T-X, Liu J-J, et al.
Attention mechanisms in computer vision: A survey.
Computational Visual Media,
2022, 8(3): 331-368.
https://doi.org/10.1007/s41095-022-0271-y
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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
Shi-Min Hu1(
)
)
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, computer vision, deep learning, transformer, salience
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Received: 31 December 2021
Accepted: 18 January 2022
Published:
15 March 2022
Issue date: September 2022
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© The Author(s) 2022.
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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