The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
FcaNet: Frequency Channel Attention Networks
Open accessAbstract
Attention mechanism, especially channel attention, has gained great success in the computer vision field. Many works focus on how to design efficient channel attention mechanisms while ignoring a fundamental problem, i.e., channel attention mechanism uses scalar to represent channel, which is difficult due to massive information loss. In this work, we start from a different view and regard the channel representation problem as a compression process using frequency analysis. Based on the frequency analysis, we mathematically prove that the conventional global average pooling is a special case of the feature decomposition in the frequency domain. With the proof, we naturally generalize the compression of the channel attention mechanism in the frequency domain and propose our method with multi-spectral channel attention, termed as FcaNet. FcaNet is simple but effective. We can change a few lines of code in the calculation to implement our method within existing channel attention methods. Moreover, the proposed method achieves state-of-the-art results compared with other channel attention methods on image classification, object detection, and instance segmentation tasks. Our method could consistently outperform the baseline SENet, with the same number of parameters and the same computational cost. Our code and models are publicly available at https://github.com/cfzd/FcaNet.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet Large Scale Visual Recognition Challenge
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Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
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“ECANet [36] introduces one-dimensional convolution layers to reduce the redundancy of fully connected layers and obtains more efficient results.”
BackgroundMethodInfluentialShow, Attend and Tell: Neural Image Caption Generation with Visual Attention
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Cited by
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566
“[57] rethought global information captured from the viewpoint of compression and analysed global average pooling in the frequency domain.”
BackgroundGuo, M.-H., Lu, C.-Z., Liu, Z.-N., Cheng, M.-M., & Hu, S.-M. (2023) · Computational Visual Media · doi:10.1007/s41095-023-0364-2 · Cited by 1,073
EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network
Zhang, H., Zu, K., Lu, J., Zou, Y., & Meng, D. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-26313-2_33 · Cited by 389
Detecting Camouflaged Object in Frequency Domain
Zhong, Y., Li, B., Tang, L., Kuang, S., Wu, S., & Ding, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00446 · Cited by 317
Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition
Wen, Z., Lin, W., Wang, T., & Xu, G. (2023) · Biomimetics · doi:10.3390/biomimetics8020199 · Cited by 313
Metadata
- Authors
- Qin, Z., Zhang, P., Wu, F., & Li, X.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 763-772
- OpenAlex
- W3202579583
- Cited by
- 1,367
- References
- 30
- FWCI
- 68.01
- Type
- conference paper
- Language
- en
- Open access
- green
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