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.

翻译：用户行为的可复用嵌入在个性化显著性预测任务中展现出显著的性能提升。然而，现有方法需要将明确的用户特征和偏好作为输入，这些信息往往难以获取。我们提出一种新颖方法，从少量用户特定眼动追踪数据生成的自然图像及其对应显著性图对中提取用户嵌入。该方法的核心是一个孪生卷积神经编码器，通过对比不同用户的图像与个人显著性图对来学习用户嵌入。在两个公开显著性数据集上的评估表明，生成的嵌入具有高判别力，能有效将通用显著性图优化为个性化用户特征，并具备良好的跨用户和跨图像泛化能力。最后，基于模型对个体用户特征的编码能力，本研究揭示了可复用的注视行为嵌入在其他应用中的潜在价值。