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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
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Gaze Estimation by Attention-Induced Hierarchical Variational Auto-Encoder.

Guanhe Huang, Jingyue Shi, Jun Xu

    IEEE Transactions on Cybernetics
    |September 20, 2023
    PubMed
    Summary

    This study introduces a new generative framework for appearance-based gaze estimation, called variational gaze estimation network (VGE-Net). VGE-Net improves accuracy on challenging eye images by generating multiple gaze maps and using an attention mechanism for fusion.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Appearance-based gaze estimation is crucial for human-computer interaction.
    • Deterministic models struggle with low-resolution, dark, or occluded eye images.

    Purpose of the Study:

    • To develop a robust appearance-based gaze estimation method overcoming limitations of deterministic approaches.
    • To introduce a novel generative framework for improved gaze estimation accuracy.

    Main Methods:

    • Proposed Variational Gaze Estimation Network (VGE-Net) using variational inference.
    • Generated multiple complementary gaze maps supervised by ground-truth.
    • Employed an attention-based regression network for adaptive fusion of predictions.

    Main Results:

    • VGE-Net demonstrated superior performance over state-of-the-art methods on MPIIGaze, EYEDIAP, and Columbia benchmarks.
    • Significant improvements observed particularly in challenging gaze estimation scenarios.
    • Ablation studies confirmed the efficacy of proposed model components.

    Conclusions:

    • The generative framework and VGE-Net offer a robust solution for appearance-based gaze estimation.
    • The proposed attention-based fusion mechanism enhances estimation accuracy under adverse conditions.
    • Public release of code will facilitate further research and development in gaze estimation.