The application of video text generation technology in assessing the effectiveness of teaching ethnic traditional sports

被引:0
作者
Tang Z. [1 ]
Wang D. [2 ]
机构
[1] College of Sports, Hunan University of Science and Engineering, Hunan, Yongzhou
[2] College of Sports Education, Guangzhou Sport University, Guangdong, Guangzhou
关键词
3D residual attention network; Ethnic traditional sports; Teaching effectiveness evaluation; Video text generation technique;
D O I
10.2478/amns.2023.2.00023
中图分类号
学科分类号
摘要
Ethnic traditional sports are forms of physical activity unique to one or more ethnic groups. Effective evaluation of the teaching effectiveness of ethnic traditional sports helps to promote the development of ethnic sports and the transmission of ethnic traditional sports. Currently, the evaluation of ethnic traditional sports is mainly a scoring system, which evaluates students' performance in ethnic traditional sports through teachers' scoring, and this evaluation method is difficult to assess the teaching effectiveness of ethnic traditional sports accurately. In this paper, based on the video text generation technology, the 3D residual attention network model is constructed by introducing the attention mechanism into the 3D residual module in the video feature extraction through the encoding-decoding video text description framework and improving the traditional deep residual network for evaluating the teaching effect of ethnic sports. After experimental validation, it is shown that the 3D residual attention network model can more accurately describe the evaluation of ethnic traditional sports teaching effectiveness using natural language. © 2023 Zhi Tang et al., published by Sciendo.
引用
收藏
页码:3085 / 3104
页数:19
相关论文
共 28 条
[1]  
He X., Tian S., Analysis of the Communication Method of National Traditional Sports Culture Based on Deep Learning, Scientific Programming, 2022, 1, (2022)
[2]  
Li C., Lyu S., Machine Learning-Based Classification and Evaluation of Regional Ethnic Traditional Sports Tourism Resources, Mobile Information Systems, 2022, 1, (2022)
[3]  
Huang G., Gong Y., Xu Q., A Convolutional Attention Residual Network for Stereo Matching, IEEE Access, 8, 1, pp. 50828-50842, (2020)
[4]  
Sun Z., Wang X., Zhang Q., Real-Time Video Saliency Prediction Via 3D Residual Convolutional Neural Network, IEEE Access, 7, 1, pp. 147743-147754, (2019)
[5]  
Qing Y., Liu W., Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism, Remote Sensing, 13, 3, (2021)
[6]  
Dong M., Fang Z., Li Y., AR3D: Attention Residual 3D Network for Human Action Recognition, Sensors, 21, 5, (2021)
[7]  
Cai J., Hu J., 3D RANs: 3D Residual Attention Networks for action recognition, Visual Computer, 36, 6, pp. 1261-1270, (2020)
[8]  
Li X., Zhou Z., Chen L., Residual attention-based LSTM for video captioning, World Wide Web-Internet and Web Information Systems, 22, 2, pp. 621-636, (2019)
[9]  
Saab S., Fu Y., Ray A., A Dynamically Stabilized Recurrent Neural Network, Neural Processing Letters, 54, 2, pp. 1195-1209, (2022)
[10]  
Lyu S., Liu J., Convolutional Recurrent Neural Networks for Text Classification, Journal of Database Management, 32, 4, pp. 65-82, (2021)