Attention mechanisms in computer vision: A survey

Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, 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 adju...

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Published in:Computational visual media (Beijing) Vol. 8; no. 3; pp. 331 - 368
Main Authors: Guo, Meng-Hao, Xu, Tian-Xing, Liu, Jiang-Jiang, Liu, Zheng-Ning, Jiang, Peng-Tao, Mu, Tai-Jiang, Zhang, Song-Hai, Martin, Ralph R., Cheng, Ming-Ming, Hu, Shi-Min
Format: Journal Article
Language:English
Published: Beijing Tsinghua University Press 01.09.2022
Springer Nature B.V
Subjects:
Artificial Intelligence
Computer Graphics
Computer Science
Computer vision
Deep learning
Image classification
Image processing
Image Processing and Computer Vision
Image segmentation
Neural networks
Object recognition
Review Article
Semantics
User Interfaces and Human Computer Interaction
Video data
Visual aspects
Visual tasks
salience
transformer
deep learning
attention
computer vision
ISSN:2096-0433, 2096-0662
Online Access:Get full text
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Summary:Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, 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, multimodal 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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ISSN:2096-0433
2096-0662
DOI:10.1007/s41095-022-0271-y