发表机构
Xidian University（西安电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究个性化图像美学评估问题，提出基于多模态大语言模型的PRAC方法，通过偏好丰富样本挖掘和美学共鸣群组合并建模个体美学偏好，经实验验证该方法优于现有技术。
AI 中文摘要
个性化图像美学评估（PIAA）旨在预测因人而异的图像美学评分。美学偏好在不同视觉刺激中表现程度不同且呈现群组特定模式。本文提出基于多模态大语言模型（MLLM）的方法，通过偏好丰富样本挖掘和美学共鸣群组合并（PRAC）来建模个体美学偏好。PRAC先通过分析图像的集体争议和个性化偏差识别偏好丰富样本，基于此通过比较偏好嵌入测量跨用户偏好相似度，再提出群组模型合并策略。在四个基准PIAA数据库上的大量实验和比较表明PRAC模型优于现有技术。代码和模型将公开。
英文摘要
Personalized Image Aesthetic Assessment (PIAA) aims to predict aesthetic ratings of images that vary across individuals. The aesthetic preferences manifest to different extents across distinct visual stimuli and exhibit cohort-specific patterns. Motivated by the above fact, this paper presents a Multimodal Large Language Model (MLLM)-based approach, which models individual aesthetic preferences by Preference-Rich sample mining and Aesthetically-resonant Cohort merging (PRAC). Specifically, PRAC first identifies preference-rich samples by analyzing both Collective Controversy and Personalized Deviation of images, maximizing the utility of limited user data. Based upon the preference-rich samples, cross-user preference similarities are measured by comparing preference embeddings. Then, a cohort-based model merging strategy, is proposed by aggregating preference patterns from aesthetically-resonant users, which further enhances the personalization for the target individual. Extensive experiments and comparisons on four benchmark PIAA databases demonstrate the superiority of the proposed PRAC model over the state-of-the-arts. The code and model will be public at https://github.com/yzc-ippl/PRAC.
CommentsThe paper has been accepted by ACM MM 2026
