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Personality-assisted Multi-task Learning for Generic and Personalized Image Aesthetics Assessment.

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    This study introduces a novel deep learning framework that uses personality traits to personalize image aesthetics assessment. The method accurately predicts both general image appeal and individual preferences, outperforming existing approaches.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Psychology

    Background:

    • Traditional image aesthetics assessment (IAA) focuses on average scores, neglecting individual taste.
    • Individual aesthetic preferences are subjective and influenced by personality traits.
    • Personality, particularly the Big-Five (BF) traits, is a key factor in modeling subjective preferences.

    Purpose of the Study:

    • To develop a personality-assisted multi-task deep learning framework for both generic and personalized IAA.
    • To model the relationship between image aesthetics, aesthetic distribution, and personality traits.
    • To generate personalized aesthetic scores tailored to individual user preferences.

    Main Methods:

    • A two-stage deep learning framework was proposed.
    • Stage 1: Multi-task learning network to predict image aesthetics distribution and Big-Five (BF) personality traits. A Siamese network was trained jointly on aesthetics and personality data.
    • Stage 2: Inter-task fusion to generate personalized aesthetic scores based on predicted personality traits and generic aesthetics.

    Main Results:

    • The proposed framework accurately predicts image aesthetics distribution and personality traits.
    • The Siamese network effectively captures common representations between image aesthetics and personality.
    • The inter-task fusion successfully generates personalized aesthetic scores.
    • The method outperforms state-of-the-art approaches in both generic and personalized IAA tasks.

    Conclusions:

    • Personality traits are crucial for understanding and modeling individual image aesthetic preferences.
    • The proposed personality-assisted framework offers a significant advancement in personalized image aesthetics assessment.
    • This approach has the potential to enhance user experience in image-related applications by providing tailored aesthetic evaluations.