Scaling Up Personalized Aesthetic Assessment via Task Vector
  Customization
Authors: name: Jooyeol Yun, name: Jaegul Choo

The task of personalized image aesthetic assessment seeks to tailor aesthetic
score prediction models to match individual preferences with just a few
user-provided inputs. However, the scalability and generalization capabilities
of current approaches are considerably restricted by their reliance on an
expensive curated database. To overcome this long-standing scalability
challenge, we present a unique approach that leverages readily available
databases for general image aesthetic assessment and image quality assessment.
Specifically, we view each database as a distinct image score regression task
that exhibits varying degrees of personalization potential. By determining
optimal combinations of task vectors, known to represent specific traits of
each database, we successfully create personalized models for individuals. This
approach of integrating multiple models allows us to harness a substantial
amount of data. Our extensive experiments demonstrate the effectiveness of our
approach in generalizing to previously unseen domains-a challenge previous
approaches have struggled to achieve-making it highly applicable to real-world
scenarios. Our novel approach significantly advances the field by offering
scalable solutions for personalized aesthetic assessment and establishing high
standards for future research.
https://yeolj00.github.io/personal-projects/personalized-aesthetics/