PerForm-Lab-RIT/domain-adaptation-eye-tracking
Official Implementation for the paper Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking System.
This project helps eye-tracking researchers and developers improve the accuracy of eye image segmentation models. It takes both synthetic and real-world eye image datasets and processes them to reduce the differences between simulated and actual images. The output is a more robust segmentation model that performs better on real-world eye-tracking data, benefiting those who work with eye-gaze estimation and analysis.
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Use this if you are developing or evaluating eye-tracking systems and need to improve the performance of your eye image segmentation models, especially when working with a mix of synthetic and real-world eye data.
Not ideal if your primary focus is not eye-tracking or if you only work with perfectly labeled, homogeneous real-world datasets.
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Python
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Last pushed
Jun 07, 2024
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