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To address the problem of poor recognition performance caused by the lack of large-scale datasets and the inability to utilize spatiotemporal features, a model that combined residual neural network(ResNet)and convolutional block attention module(CBAM)was proposed for still image action recognition. Specific data augmentation techniques were employed to extend the dataset. Transfer learning was applied to initialize the model, followed by fine-tuning to enhance feature representation of still image action recognition. The CBAM was embedded into the first convolutional layer of ResNet to adjust the model′s attention. The Grad-CAM method was utilized to extract and visualize the regions of interest in image which provided an explanation for the precision improvement. On the PPMI dataset, the proposed model achieved the average precision for instrument-playing, instrument-holding, and overall categories of 88.30%, 81.94% and 77.93%, respectively, which verified the effectiveness of the method.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2023171
China Classification Code:TP391.41
Citation Information:
[1]GAO Han,WAN Fangjie,MA Mingxu.Still Image Action Recognition Method Combining ResNet and CBAM[J].Journal of Zhengzhou University(Natural Science Edition),2025,57(03):65-71.DOI:10.13705/j.issn.1671-6841.2023171.
Fund Information:
河南省重大专项(221100210100)
2023-07-06
2023
2024-04-27
2025-06-17
2025
2
2024-06-24
2024-06-24
2024-06-24