title: Scaling Up Personalized Aesthetic Assessment via Task Vector
    Customization
summary: 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/
paper_authors._id: 6690f984e760dfd88da43754
paper_authors.name: Jooyeol Yun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-12T09:42:10.900Z
paper_authors._id: 6369f693bf21b20c5692937b
paper_authors.avatarUrl: /avatars/e937dc8234b3e456149882bfce34841f.svg
paper_authors.fullname: Jooyeol Yun
paper_authors.type: user
paper_authors.user: YeolJoo
paper_authors._id: 6690f984e760dfd88da43755
paper_authors.name: Jaegul Choo
paper_title: Scaling Up Personalized Aesthetic Assessment via Task Vector
    Customization
paper_summary: 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/
paper_ai_summary: A novel approach integrates multiple image score regression tasks from readily available databases to create scalable and personalized aesthetic assessment models.
paper_ai_keywords: image score regression task task vectors personalized models personalized aesthetic assessment