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Updated: Jul 26, 2025

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

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Published on: April 11, 2025

对比的表示学习视线估计.

Swati Jindal¹, Roberto Manduchi¹

¹University of California, Santa Cruz, Santa Cruz, CA, 95064, USA.

Proceedings of machine learning research

|June 16, 2023

概括

此摘要是机器生成的。

本研究介绍了凝视对比学习 (GazeCLR),这是一种新的自我监督的学习方法,用于凝视估计. GazeCLR增强了表示学习,以提高准确性,特别是在跨领域的场景中.

关键词:

凝视的估计估计.代表性学习学习学习自主监督学习学习

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科学领域:

计算机视觉计算机视觉
机器学习机器学习
人与计算机的交互

背景情况:

自主监督学习 (SSL) 通常使用对比学习进行视觉表示,专注于图像转换的不变性.
凝视估计需要外观不变性和几何转换等价性,传统的SSL方法可能无法完全解决这些问题.

研究的目的:

提出凝视对比学习 (GazeCLR),这是一个针对凝视估计的新型SSL框架.
开发一种方法,以促进视觉表征的不变性和等同性,以观察方向.

主要方法:

GazeCLR使用多视图数据来强制执行对几何转换的等价值.
采用特定的数据增强技术,在不改变视线方向的情况下保持外观变化的不变性.

主要成果:

GazeCLR在各种视线估计任务中表现出显著的有效性.
该框架在跨领域的目光估计中实现了高达17.2%的相对改进.
GazeCLR在几次拍摄评估中显示了与最先进的方法相比的竞争性表现.

结论:

GazeCLR提供了一种简单而有效的对比表示学习方法,用于视线估计.
该方法成功地平衡了不变性和等效性,在具有挑战性的场景中表现优于现有技术.
拟议的框架通过改进的表示学习,推进了目光估计领域.