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

Gaze behaviour has been used as a way to gather cognitive information for a
number of years. In this paper, we discuss the use of gaze behaviour in solving
different tasks in natural language processing (NLP) without having to record
it at test time. This is because the collection of gaze behaviour is a costly
task, both in terms of time and money. Hence, in this paper, we focus on
research done to alleviate the need for recording gaze behaviour at run time.
We also mention different eye tracking corpora in multiple languages, which are
currently available and can be used in natural language processing. We conclude
our paper by discussing applications in a domain - education - and how learning
gaze behaviour can help in solving the tasks of complex word identification and
automatic essay grading.

本文讨论了在自然语言处理中解决不同任务时使用注视行为的方法，重点在于研究减轻运行时记录注视行为需求的方法，并提及了现有的多语言注视行为数据集，最后讨论了在教育领域中应用注视行为的方法与其可以帮助解决的问题。