Learning User Embeddings from Human Gaze for Personalised Saliency Prediction
CoRR(2024)
摘要
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.
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