@inproceedings{bibcite_15713,
  author = {Onur {\c C}opur and Mert Nakip and Simone Scardapane and J{\"u}rgen Slowack},
  title = {Engagement Detection with Multi-Task Training in E-Learning Environments},
  abstract = {<p>Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly improve the user experience and efficiency by providing valuable feedback. In this paper, we propose a novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes mean squared error and triplet loss together to determine the engagement level of students in an e-learning environment. The performance of this system is evaluated and compared against the state-ofthe-art on a publicly available dataset as well as videos collected from real-life scenarios. The results show that ED-MTT achieves 6\% lower MSE than the best state-of-the-art performance with highly acceptable training time and lightweight feature extraction.</p>
},
  year = {2022},
  journal = {International Conference on Image Analysis and Processing (ICIAP)},
  publisher = {Springer},
  doi = {10.1007/978-3-031-06433-3_35},
  language = {eng},
}