Coordinate Attention for Efficient Mobile Network Design
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Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention) for lifting model performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps. In this paper, we propose a novel attention mechanism for mobile networks by embedding positional information into channel attention, which we call "coordinate attention". Unlike channel attention that transforms a feature tensor to a single feature vector via 2D global pooling, the coordinate attention factorizes channel attention into two 1D feature encoding processes that aggregate features along the two spatial directions, respectively. In this way, long-range dependencies can be captured along one spatial direction and meanwhile precise positional information can be preserved along the other spatial direction. The resulting feature maps are then encoded separately into a pair of direction-aware and position-sensitive attention maps that can be complementarily applied to the input feature map to augment the representations of the objects of interest. Our coordinate attention is simple and can be flexibly plugged into classic mobile networks, such as MobileNetV2, MobileNeXt, and EfficientNet with nearly no computational overhead. Extensive experiments demonstrate that our coordinate attention is not only beneficial to ImageNet classification but more interestingly, behaves better in down-stream tasks, such as object detection and semantic segmentation. Code is available at https://github.com/Andrew-Qibin/CoordAttention.
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The coordinate attention mechanism intensifies focus on image features at both spatial and channel dimensions, minimizing feature information loss and enabling more precise anomaly identification and localization.
“introduces a coordinate attention (CA) mechanism to intensify focus on image features at both spatial and channel dimensions, minimizing feature information loss and thereby enabling more precise anomaly identification and localization.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [33]
Coordinate Attention integrates coordinate information into channel attention, capturing long-range dependencies while preserving precise positional information, facilitating more accurate target location and recognition.
“This mechanism integrates coordinate information into channel attention, capturing long-range dependencies while preserving precise positional information, facilitating more accurate target location and recognition by the model.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [33]
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- Authors
- Hou, Q., Zhou, D., & Feng, J.
- Affiliations
- National University of Singapore (SG)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 13708-13717
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3177052299
- Semantic Scholar
- 70cf7c785952375e8061c92235aa20e94b02ecd4
- Cited by
- 6,961
- References
- 48
- Citations by year
- 2026: 1204 · 2025: 1853 · 2024: 1832 · 2023: 1368 · 2022: 624 · 2021: 80
- FWCI
- 319.21
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Ministry of Education, India
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- coordinate attention, channel attention, positional information, attention mechanism, cellular networks, MobileNetV2, EfficientNet, long-range dependencies, image classification, object detection, semantic segmentation
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