Learning User Embeddings from Human Gaze for Personalised Saliency Prediction

Core Concepts

Reusable user embeddings extracted from human gaze data improve personalised saliency prediction models.

Abstract

Introduction:

Saliency prediction identifies regions likely to attract gaze.

User Embeddings Extraction:

Siamese CNN contrasts image-saliency pairs for user embeddings.

Personalised Saliency Prediction:

User embeddings refine universal saliency maps for individuals.

Experiments:

Closed-set and open-set results on two datasets show the effectiveness of the method.

User Embeddings Analysis:

Accuracy increases with more examples, improving generalization.

Broader Impact:

Implications for various applications but potential misuse concerns highlighted.

Stats

"Model CC SIM AUC NSS KLD": "0.736 0.651 0.907 2.170 0.509"
"Model CC SIM AUC NSS KLD": "0.813 0.706 0.922 2.405 0.357"

Quotes

"Our method uses 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."

Key Insights Distilled From

Learning User Embeddings from Human Gaze for Personalised Saliency Prediction

by Flor... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13653.pdf

Deeper Inquiries

How can user embeddings be safeguarded against potential misuse

User embeddings can be safeguarded against potential misuse by implementing robust security measures and privacy protections. Some strategies to safeguard user embeddings include:

Anonymization: Ensure that the user embeddings are anonymized, removing any personally identifiable information.
Encryption: Implement encryption techniques to secure the storage and transmission of user embeddings.
Access Control: Limit access to user embeddings only to authorized personnel with a legitimate need for such data.
Data Minimization: Collect and store only the necessary data required for generating user embeddings, minimizing the risk of exposure.
Regular Auditing: Conduct regular audits to monitor access patterns and detect any unauthorized use or breaches.

What are the ethical considerations when using implicit user information like gaze behavior

When using implicit user information like gaze behavior, ethical considerations play a crucial role in ensuring responsible data usage:

Informed Consent: Users should be informed about how their gaze behavior data will be used and have the option to opt-out if they choose.
Transparency: Clearly communicate how gaze behavior data is collected, processed, and utilized in personalized saliency prediction models.
Data Security: Safeguard sensitive gaze behavior data through encryption, secure storage practices, and access controls to prevent unauthorized access or misuse.
Fairness & Bias Mitigation: Ensure that personalized saliency predictions do not perpetuate biases or discriminate against certain individuals based on their visual attention patterns.

How might advancements in personalized saliency prediction impact privacy concerns related to user profiling

Advancements in personalized saliency prediction could impact privacy concerns related to user profiling in several ways:
1.Increased Data Sensitivity: As personalized saliency predictions become more accurate, they may reveal deeper insights into individual preferences and behaviors, raising concerns about invasive profiling practices.
2Potential Misuse: Personalized saliency predictions could potentially be misused for targeted advertising or manipulation without users' consent or awareness
3Regulatory Compliance: Organizations utilizing personalized saliency prediction must adhere strictly to privacy regulations like GDPR (General Data Protection Regulation)to protect users' rights regarding their personal information
These advancements underscore the importance of maintaining strong ethical standards when leveraging implicit user information for predictive modeling purposes."