检索结果-南通市图书馆

re-distributing facial features for Engagement Prediction with ModernTCN

Computers, Materials and Continua 2024年第1期81卷 369-391页

作者： Li X. Zhu W. Li Q. Hou C. Zhang Y. College of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang 330108 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan 430205 China School of Electronic Information Engineering Wuhan Donghu University Wuhan 430212 China

Automatically detecting learners’ engagement levels helps to develop more effective online teaching and assessment programs, allowing teachers to provide timely feedback and make personalized adjustments based on students’ needs to enhance teaching effectiveness. Traditional approaches mainly rely on single-frame multimodal facial spatial information, neglecting temporal emotional and behavioural features, with accuracy affected by significant pose variations. Additionally, convolutional padding can erode feature maps, affecting feature extraction’s representational capacity. To address these issues, we propose a hybrid neural network architecture, the redistributing facial features and temporal convolutional network (refEIP). This network consists of three key components: first, utilizing the spatial attention mechanism large kernel attention (LKA) to automatically capture local patches and mitigate the effects of pose variations; second, employing the feature organization and weight distribution (FOWD) module to redistribute feature weights and eliminate the impact of white features and enhancing representation in facial feature maps. Finally, we analyse the temporal changes in video frames through the modern temporal convolutional network (ModernTCN) module to detect engagement levels. We constructed a near-infrared engagement video dataset (NEVD) to better validate the efficiency of the refEIP network. Through extensive experiments and in-depth studies, we evaluated these methods on the NEVD and the Database for Affect in Situations of Elicitation (DAiSEE), achieving an accuracy of 90.8% on NEVD and 61.2% on DAiSEE in the four-class classification task, indicating significant advantages in addressing engagement video analysis problems. © 2024 The Authors. Published by Tech Science Press.

关键词： Engagement prediction re-distributing facial features spatiotemporal network temporal convolutional network

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：