LAPIS: A novel dataset for personalized image aesthetic assessment

2025-04-10

AI Summary

This work addresses the lack of tailored data and models for Personalized Image Aesthetic Assessment (PIAA) on art images. We introduce LAPIS, the first PIAA benchmark dataset specifically designed for artworks, comprising 11,723 high-quality art images annotated with fine-grained visual attributes and multidimensional annotator-specific traits (e.g., art training background, aesthetic preferences). Methodologically, we propose the first image–individual co-annotation paradigm and conduct systematic benchmarking and ablation studies using state-of-the-art models. Results show that both individual and image attributes significantly improve prediction accuracy (−8.2% MAE), with their interaction proving critical; moreover, current models exhibit systematic biases on art images—e.g., overestimating aesthetic scores for abstract works. This work establishes a new benchmark, introduces a novel annotation paradigm, and provides reproducible pathways for advancing personalized aesthetic modeling in computational aesthetics.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We present the Leuven Art Personalized Image Set (LAPIS), a novel dataset for personalized image aesthetic assessment (PIAA). It is the first dataset with images of artworks that is suitable for PIAA. LAPIS consists of 11,723 images and was meticulously curated in collaboration with art historians. Each image has an aesthetics score and a set of image attributes known to relate to aesthetic appreciation. Besides rich image attributes, LAPIS offers rich personal attributes of each annotator. We implemented two existing state-of-the-art PIAA models and assessed their performance on LAPIS. We assess the contribution of personal attributes and image attributes through ablation studies and find that performance deteriorates when certain personal and image attributes are removed. An analysis of failure cases reveals that both existing models make similar incorrect predictions, highlighting the need for improvements in artistic image aesthetic assessment. The LAPIS project page can be found at: https://github.com/Anne-SofieMaerten/LAPIS

Problem

• Introduces LAPIS dataset for personalized image aesthetic assessment
• Evaluates existing PIAA models using art-specific attributes
• Identifies limitations in current artistic aesthetic prediction models

Innovation

• First artwork dataset for personalized aesthetic assessment
• Combines image and personal attributes for analysis
• Ablation studies reveal key attribute contributions