Jingsi Zhang, Xiaosheng Yu, Xiaoliang Lei, and Chengdong WuThis email address is being protected from spambots. You need JavaScript enabled to view it.
Faculty of Robot Science and Engineering, Northeastern University Shenyang 110819, China
Received: October 9, 2023 Accepted: October 30, 2023 Publication Date: November 16, 2023
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Image classification tasks often compress the neural network model to reduce the number of parameters, which leads to a decrease in classification accuracy. Therefore, we propose a novel ResNet50-based attention mechanism for image classification. ResNet50 network is used to extract image features and input the features into the graph neural network as node features. Then, packet convolution and depth-separable convolution are used to compress the residual network. The attention mechanism is introduced into the network backbone to make it focus on the important part of the neighborhood and help the branch network to extract key information. The accuracy of 5-way 1-shot task classification on three publicly available datasets reaches 86.32%, 92.21% and 92.19%, respectively. The proposed method has achieved remarkable results in image classification tasks.
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