A novel ResNet50-based attention mechanism for image classification
| Autor: | Jingsi Zhang, Xiaosheng Yu, Xiaoliang Lei, Chengdong Wu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Journal of Applied Science and Engineering, Vol 27, Iss 8, Pp 2961-2969 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2708-9967 2708-9975 |
| DOI: | 10.6180/jase.202408_27(8).0004 |
| Popis: | Image classification tasks often compress the neural network model to reduce the number of parameters, which leads to a decrease in classification accuracy. herefore, 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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