Unsupervised data labeling and incremental cross-domain training for enhanced hybrid eye gaze estimation-Reference-Cited by-同舟云学术

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Unsupervised data labeling and incremental cross-domain training for enhanced hybrid eye gaze estimation

Published:2024-06-04 Issue: Volume: Page:1-7
ISSN:
Container-title:Proceedings of the 2024 Symposium on Eye Tracking Research and Applications
language:
Short-container-title:

Author:

Garcia De La Santa Alejandro¹^ORCID,Muguerza Rivero Javier²^ORCID,Lopez David³^ORCID,Elordi Unai⁴^ORCID,Unzueta Luis⁴^ORCID,Villanueva Arantxa⁵^ORCID

Affiliation:

1. Computer Science Faculty, University of the Basque Country, UPV/EHU, Spain and Irisbond Crowdbonding SL, Spain

2. Computer Science Faculty, University of the Basque Country, UPV/EHU, Spain

3. Irisbond Crowdbonding SL, Spain

4. Fundación Vicomtech, Basque Research and Technology Alliance (BRTA), Spain

5. Electrical, Electronics and Communications Eng. Dept., Public University of Navarre, Spain

Funder

BIKAINTEK 2023

HAZITEK 2023

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3649902.3656367

Reference27 articles.

1. Nayan Adhikari Pedro G. Lind and Gustavo B. Moreno e Mello. 2021. Prototyping-Based Study of Designs for Eye-Tracking Interface in Augmentative and Alternative Communication Applications. https://doi.org/10.1007/978-3-030-90179-0_1

2. Convolutional Neural Network-Based Methods for Eye Gaze Estimation: A Survey

3. Alternative and Augmentative Communication for People with Disabilities and Language Problems: An Eye Gaze Tracking Approach

4. Toward Mobile Eye-Based Human-Computer Interaction

5. The Effect of Different Deep Network Architectures upon CNN-Based Gaze Tracking

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