Reusable embeddings of user behaviour have shown significant performance
improvements for the personalised saliency prediction task. However, prior
works require explicit user characteristics and preferences as input, which are
often difficult to obtain. We present a novel method to extract user embeddings
from pairs of natural images and corresponding saliency maps generated from a
small amount of user-specific eye tracking data. At the core of our method is a
Siamese convolutional neural encoder that learns the user embeddings by
contrasting the image and personal saliency map pairs of different users.
Evaluations on two public saliency datasets show that the generated embeddings
have high discriminative power, are effective at refining universal saliency
maps to the individual users, and generalise well across users and images.
Finally, based on our model's ability to encode individual user
characteristics, our work points towards other applications that can benefit
from reusable embeddings of gaze behaviour.

利用少量用户特定的眼动跟踪数据从自然图像和相应的显著性图中提取用户嵌入，创造了一个能够对个体用户细化通用显著性图的新方法。通过对不同用户的图像和个人显著性图对比，使用一个 Siamese 卷积神经编码器学习用户嵌入。在两个公开显著性数据集上的评估表明，产生的嵌入具有较高的区分能力，在用户和图像间具有良好的泛化能力。结论指出，基于模型编码个体用户特征的能力，我们的工作为其他能够受益于可重用的注视行为嵌入的应用提供了方向。

基于人眼注视的用户嵌入学习个性化显著性预测

Learning User Embeddings from Human Gaze for Personalised Saliency  Prediction

Recent work in XAI for eye tracking data has evaluated the suitability of
feature attribution methods to explain the output of deep neural sequence
models for the task of oculomotric biometric identification. These methods
provide saliency maps to highlight important input features of a specific eye
gaze sequence. However, to date, its localization analysis has been lacking a
quantitative approach across entire datasets. In this work, we employ
established gaze event detection algorithms for fixations and saccades and
quantitatively evaluate the impact of these events by determining their concept
influence. Input features that belong to saccades are shown to be substantially
more important than features that belong to fixations. By dissecting saccade
events into sub-events, we are able to show that gaze samples that are close to
the saccadic peak velocity are most influential. We further investigate the
effect of event properties like saccadic amplitude or fixational dispersion on
the resulting concept influence.

本研究旨在通过使用现有的凝视事件检测算法定量评估比较眼球运动中其不同成分的影响，从而展示 Saccades 事件对于数据集中输入特征的显著重要性， 并通过使用 Saccades 的子事件进一步研究样本的速度对于概念的影响。

弥合鸿沟：凝视事件作为可解释概念解释深度神经序列模型

Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep  Neural Sequence Models

Eye tracking data during reading is a useful source of information to
understand the cognitive processes that take place during language
comprehension processes. Different languages account for different brain
triggers , however there seems to be some uniform indicators. In this paper, we
describe our submission to the CMCL 2022 shared task on predicting human
reading patterns for multi-lingual dataset. Our model uses text representations
from transformers and some hand engineered features with a regression layer on
top to predict statistical measures of mean and standard deviation for 2 main
eye-tracking features. We train an end to end model to extract meaningful
information from different languages and test our model on two seperate
datasets. We compare different transformer models and show ablation studies
affecting model performance. Our final submission ranked 4th place for
SubTask-1 and 1st place for SubTask-2 for the shared task.

本文提出了一种基于 transformer 和回归模型的方法，利用眼动追踪数据预测多语言数据集中人类阅读模式的统计指标，通过模型训练和比较不同的 transformer 模型，我们在 CMCL 2022 共享任务中取得了显著的成果，获得了 SubTask-1 第 4 名和 SubTask-2 第 1 名的好成绩。