Abstract

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

Keywords

Computer scienceCategorizationArtificial intelligenceComputer graphicsProcess (computing)SalientMechanism (biology)Computer visionHuman–computer interactionHuman visual system modelImage (mathematics)

Affiliated Institutions

Related Publications

Publication Info

Year
2022
Type
article
Volume
8
Issue
3
Pages
331-368
Citations
2040
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

2040
OpenAlex
29
Influential

Cite This

Meng-Hao Guo, Tian-Xing Xu, Jiangjiang Liu et al. (2022). Attention mechanisms in computer vision: A survey. Computational Visual Media , 8 (3) , 331-368. https://doi.org/10.1007/s41095-022-0271-y

Identifiers

DOI
10.1007/s41095-022-0271-y
arXiv
2111.07624

Data Quality

Data completeness: 84%