In this paper, we address the challenge of unsupervised mistake detection in
egocentric video through the analysis of gaze signals, a critical component for
advancing user assistance in smart glasses. Traditional supervised methods,
reliant on manually labeled mistakes, suffer from domain-dependence and
scalability issues. This research introduces an unsupervised method for
detecting mistakes in videos of human activities, overcoming the challenges of
domain-specific requirements and the necessity for annotated data. By analyzing
unusual gaze patterns that signal user disorientation during tasks, we propose
a gaze completion model that forecasts eye gaze trajectories from incomplete
inputs. The difference between the anticipated and observed gaze paths acts as
an indicator for identifying errors. Our method is validated on the EPIC-Tent
dataset, showing its superiority compared to current one-class supervised and
unsupervised techniques.

本研究提出了一种无监督的方法，通过分析用户视线信号来检测机载视频中的错误，提升智能眼镜中的用户辅助功能，并通过分析异常视线模式预测眼睛视线轨迹以识别错误，该方法在 EPIC-Tent 数据集上验证了其在一类监督和无监督技术上的优越性。

睁大眼睛：通过检测不可预测注视实现自心视角视频中的无监督错误检测

Eyes Wide Unshut: Unsupervised Mistake Detection in Egocentric Video by  Detecting Unpredictable Gaze

Obtaining large-scale radiology reports can be difficult for medical images
due to various reasons, limiting the effectiveness of contrastive pre-training
in the medical image domain and underscoring the need for alternative methods.
In this paper, we propose eye-tracking as an alternative to text reports, as it
allows for the passive collection of gaze signals without disturbing
radiologist's routine diagnosis process. By tracking the gaze of radiologists
as they read and diagnose medical images, we can understand their visual
attention and clinical reasoning. When a radiologist has similar gazes for two
medical images, it may indicate semantic similarity for diagnosis, and these
images should be treated as positive pairs when pre-training a
computer-assisted diagnosis (CAD) network through contrastive learning.
Accordingly, we introduce the Medical contrastive Gaze Image Pre-training
(McGIP) as a plug-and-play module for contrastive learning frameworks. McGIP
uses radiologist's gaze to guide contrastive pre-training. We evaluate our
method using two representative types of medical images and two common types of
gaze data. The experimental results demonstrate the practicality of McGIP,
indicating its high potential for various clinical scenarios and applications.

通过眼动追踪作为替代文本报告的方法，我们可以 passively 收集放射科医师在阅读和诊断医学图像时的视觉关注和临床推理，从而在医学图像领域中改善对比度预训练的有效性。我们提出了医学对比度视线图像预训练（McGIP）作为对比度学习框架的即插即用模块，通过放射科医师的注视来指导对比度预训练，实验结果表明了 McGIP 的实用性，显示其在各种临床场景和应用中具有很高的潜力。