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View all- Zhuang CYuan XGu LWei ZFan YGuo X(2025)Frequency Regulated Channel-Spatial Attention module for improved image classificationExpert Systems with Applications10.1016/j.eswa.2024.125463260(125463)Online publication date: Jan-2025
Improved Convolutional Neural Networks by Integrating High-frequency Information for Image Classification
Authors: 
Chengyuan Zhuang, Xiaohui Yuan, Xuan Guo, Zhenchun Wei, Juan Xu, Yuqi FanAuthors Info & Claims
Chengyuan Zhuang, Xiaohui Yuan, Xuan Guo, Zhenchun Wei, Juan Xu, Yuqi FanAuthors Info & ClaimsAbstract
Deep convolutional neural networks are powerful and popular tools as deep learning emerges in recent years for image classification in computer vision. However, it is difficult to learn convolutional filters from the examples. The innate frequency property of the data has not been well considered. To address this problem, we find high-frequency information import within deep networks and therefore propose our high-pass attention method (HPA) to help the learning process. HPA explicitly generates high-frequency information via a stage-wise high-pass filter to alleviate the burden of learning such information. Strengthened by channel attention on the concatenated features, our method demonstrates consistent improvements upon ResNet-18/ResNet-50 by 1.36%/1.60% and 1.47%/1.39% on the ImageNet-1K dataset and the Food-101 dataset, respectively, as well as the effectiveness over a variety of modules.
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Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
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Published: 29 May 2023
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CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
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CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
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View all- Zhuang CYuan XGu LWei ZFan YGuo X(2025)Frequency Regulated Channel-Spatial Attention module for improved image classificationExpert Systems with Applications10.1016/j.eswa.2024.125463260(125463)Online publication date: Jan-2025
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