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Found 7 papers, 3 papers with code
Jitter Does Matter: Adapting Gaze Estimation to New Domains
We evaluate the proposed method on four cross-domain gaze estimation tasks, and experimental results demonstrate that it significantly reduces the gaze jitter and improves the gaze estimation performance in target domains.
Generalizing Gaze Estimation With Rotation Consistency
Consequently, we propose the Rotation-enhanced Unsupervised Domain Adaptation (RUDA) for gaze estimation.
Separating Content and Style for Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples.
Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation
Deep neural networks have significantly improved appearance-based gaze estimation accuracy.
Assessment of Deep Learning-based Heart Rate Estimation using Remote Photoplethysmography under Different Illuminations
We evaluate the performance of three deep learning-based methods (Deepphys, rPPGNet, and Physnet) to that of four traditional methods (CHROM, GREEN, ICA, and POS) using two public datasets: the UBFC-rPPG dataset and the BH-rPPG dataset.
Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark
This paper serves not only as a reference to develop deep learning-based gaze estimation methods, but also a guideline for future gaze estimation research.
Vulnerability of Appearance-based Gaze Estimation
However, many deep learning-based methods suffer from the vulnerability property, i. e., perturbing the raw image using noise confuses the gaze estimation models.
