Cross Classroom Domain Adaptive Object Detector for Student's Heads

Chunhui Li, Haoze Yang, Kunyao Lan, Liping Shen

Abstract: Training on a label-rich dataset and test on another label-scarce dataset usually leads to a poor performance because of the domain shift. Unsupervised domain adaptation is proved to be effective on this problem in recent researches. Unsupervised domain adaptive object detection of students’ heads between different classrooms has becoming an important task with the development of Smart Classroom. However, few cross-classroom models for students’ heads have been proposed despite the rapid development of domain adaptive object detection. In this paper, we propose two adaptations which focus on the challenges of domain adaptive object detection of students’ heads between different classrooms, including the adaptation based on the numbers of students and the adaptation based on the locations of students. Based on Unbiased Mean Teacher framework, our Cross Classroom Domain Adaptive Object Detector achieves an average precision of 50.2% on the cross-classroom students’ heads dataset called SCUT_HEAD, which outperforms the existing state-of-the-art methods.

External IDs:dblp:conf/icann/LiYLS23