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Related Experiment Video

Updated: Sep 10, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

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Unsupervised Gaze Representation Learning by Switching Features.

Yunjia Sun, Jiabei Zeng, Shiguang Shan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 19, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an unsupervised deep learning framework to improve 3D gaze estimation by disentangling gaze-relevant information. The novel Cross-Encoder methods effectively extract accurate gaze representations from eye and face images.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • Unsupervised learning is crucial for deep learning when annotated data is scarce.
    • Existing unsupervised methods struggle to differentiate subtle gaze cues from irrelevant information in 3D gaze estimation.

    Purpose of the Study:

    • To develop an unsupervised learning framework for 3D gaze estimation that disentangles gaze-relevant and gaze-irrelevant information.
    • To improve the accuracy of gaze estimation by effectively utilizing unlabeled data.

    Main Methods:

    • Proposing a novel unsupervised learning framework that seeks shared information between image pairs.
    • Utilizing encoders and decoders with feature switching for latent representation learning.
    • Deriving Cross-Encoder and Cross-Encoder++ models for gaze representation from eye and face images.

    Main Results:

    • The proposed framework theoretically guarantees the disentanglement of information into distinct latent feature parts.
    • Cross-Encoder and Cross-Encoder++ demonstrate superior performance compared to existing methods on public gaze datasets.
    • Ablation studies confirm the successful extraction of gaze features, both quantitatively and qualitatively.

    Conclusions:

    • The developed unsupervised framework effectively addresses the challenge of distinguishing gaze-relevant information in 3D gaze estimation.
    • The Cross-Encoder models offer a significant advancement in unsupervised gaze representation learning.
    • This work paves the way for more robust and accurate 3D gaze estimation systems using unlabeled data.