Publications (8)
RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
Aayush K. Chaudhary, Rakshit Kothari, Manoj Acharya +6
Accurate eye segmentation can improve eye-gaze estimation and support interactive computing based on visual attention; however, existing eye segmentation methods suffer from issues…
Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret
Viet Dung Nguyen, Yuhang Song, Anh Nguyen +3
Robot reinforcement learning from demonstrations (RLfD) assumes that expert data is abundant; this is usually unrealistic in the real world given data scarcity as well as high coll…
Differential Privacy for Eye-Tracking Data
Ao Liu, Lirong Xia, Andrew Duchowski +3
As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datas…
Enhancing Eye Feature Estimation from Event Data Streams through Adaptive Inference State Space Modeling
Viet Dung Nguyen, Mobina Ghorbaninejad, Chengyi Ma +5
Eye feature extraction from event-based data streams can be performed efficiently and with low energy consumption, offering great utility to real-world eye tracking pipelines. Howe…
Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual Reality
Kevin Barkevich, Reynold Bailey, Gabriel J. Diaz
Algorithms for the estimation of gaze direction from mobile and video-based eye trackers typically involve tracking a feature of the eye that moves through the eye camera image in…
A Neural Active Inference Model of Perceptual-Motor Learning
Zhizhuo Yang, Gabriel J. Diaz, Brett R. Fajen +2
The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience that can produce human-like behavior through reward-based lear…