Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Merging
Open Access
- 9 November 2026
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 13423-13432
Abstract
No abstract availableFunding Information
- National Natural ScienceFoundation of China (62471349, 62171340, 62301378, and 625B2142)
- Fundamental Research Fundsfor the Central Universities (QTZX25076 and YJSJ25004)
- China Postdoctoral Science Foundation (2024M76255)
This publication has 27 references indexed in Scilit:
- LAPIS: A Novel Dataset for Personalized Image Aesthetic Assessment2025
- Teaching Large Language Models to Regress Accurate Image Quality Scores Using Score Distribution2025
- Beauty is in the eye of your cohort: Structured individual differences allow predictions of individualized aesthetic ratings of imagesCognition, 2024
- Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt Rewriting2024
- Personalized Image Aesthetics Assessment with Rich Attributes2022
- Personalized Image Aesthetics2017
- Photo Aesthetics Ranking Network with Attributes and Content Adaptation2016
- AVA: A large-scale database for aesthetic visual analysis2012
- Objectifying subjectivity in images through aesthetics: A Bergsonian approach2012
- Curious eyes: Individual differences in personality predict eye movement behavior in scene-viewingCognition, 2012