Abstract
To tackle the cross-domain model performance degradation challenge, a gaze estimation method based on proxy tuning is proposed, called PTGaze. In PTGaze, an base model is used to learn the gaze representation of the baseline model in the source domain, and an adapt model is used to learn the gaze representation of the baseline model in the target domain. The gaze difference between the base and adapt models is utilized to guide the final output, ensuring the method’s accuracy in the target domain. Experimental results show that the proposed method achieves higher cross-domain gaze estimation accuracy on five public datasets, using RT-Gene, Full-face, and Gaze360 as baseline models.
Original language | English |
---|---|
Title of host publication | Proceedings of the 2025 symposium on eye tracking research and applications (ETRA) |
Editors | Yusuke Sugano, Mohamed Khamis, Aladine Chetouani, Ludwig Sidenmark, Alessandro Bruno |
Place of Publication | New York |
Publisher | ACM |
Pages | 1-2 |
Number of pages | 2 |
ISBN (Print) | 9798400714870 |
DOIs | |
Publication status | Published - 25 May 2025 |