Gaze4HRI benchmark shows all tested zero-shot gaze estimation methods fail under at least one HRI condition, with training data diversity as the main driver of robustness.
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First large-scale fairness audit of gaze estimators reveals sizable accuracy disparities by ethnicity and gender, with existing mitigation methods providing only marginal fairness gains.
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Gaze4HRI: Zero-shot Benchmarking Gaze Estimation Neural-Networks for Human-Robot Interaction
Gaze4HRI benchmark shows all tested zero-shot gaze estimation methods fail under at least one HRI condition, with training data diversity as the main driver of robustness.
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Investigating Bias and Fairness in Appearance-based Gaze Estimation
First large-scale fairness audit of gaze estimators reveals sizable accuracy disparities by ethnicity and gender, with existing mitigation methods providing only marginal fairness gains.