结合ResNet和CBAM的静态图像行为识别方法

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中图分类号：TP391.4 文献标志码：A 文章编号：1671-6841（2025）03-0065-07

DOI：10.13705/j.issn.1671-6841.2023171

Still Image Action Recognition Method Combining ResNet and CBAM

GAO Han，WAN Fangjie，MA Mingxu （School of Cyber Science and Engineering， Zhengzhou University， Zhengzhou 4500O2， China）

Abstract： 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.

Key words： residual network；action recognition；convolutional block attention module； still image; transfer learning

0 引言

目前，行为识别研究主要集中在视频领域，基于静态图像的行为识别工作较少。（剩余9561字）