Modeling Art Evaluations from Comparative Judgments: A Deep Learning Approach to Predicting Aesthetic Preferences

2026-01-30

AI Summary

This study addresses the challenges of modeling human aesthetic judgments of visual art—namely, high inter-individual variability and the high cost of obtaining direct aesthetic ratings. To overcome these limitations, the work introduces the Law of Comparative Judgment into aesthetic modeling and proposes a contrastive learning framework based on pairwise preferences, circumventing the need for absolute scoring. The approach leverages ResNet-50 for image feature extraction and integrates both a deep regression model and a dual-branch contrastive model. Experimental results demonstrate that the regression model achieves a 328% improvement in R² over the baseline, while the contrastive model attains comparable performance without requiring direct ratings. Furthermore, the proposed method reduces human annotation time by 60%, substantially alleviating cognitive burden and confirming the efficacy and feasibility of contrastive learning for modeling aesthetic preferences.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Learning Human Values and Preferences

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Modeling human aesthetic judgments in visual art presents significant challenges due to individual preference variability and the high cost of obtaining labeled data. To reduce cost of acquiring such labels, we propose to apply a comparative learning framework based on pairwise preference assessments rather than direct ratings. This approach leverages the Law of Comparative Judgment, which posits that relative choices exhibit less cognitive burden and greater cognitive consistency than direct scoring. We extract deep convolutional features from painting images using ResNet-50 and develop both a deep neural network regression model and a dual-branch pairwise comparison model. We explored four research questions: (RQ1) How does the proposed deep neural network regression model with CNN features compare to the baseline linear regression model using hand-crafted features? (RQ2) How does pairwise comparative learning compare to regression-based prediction when lacking access to direct rating values? (RQ3) Can we predict individual rater preferences through within-rater and cross-rater analysis? (RQ4) What is the annotation cost trade-off between direct ratings and comparative judgments in terms of human time and effort? Our results show that the deep regression model substantially outperforms the baseline, achieving up to $328\%$ improvement in $R^2$. The comparative model approaches regression performance despite having no access to direct rating values, validating the practical utility of pairwise comparisons. However, predicting individual preferences remains challenging, with both within-rater and cross-rater performance significantly lower than average rating prediction. Human subject experiments reveal that comparative judgments require $60\%$ less annotation time per item, demonstrating superior annotation efficiency for large-scale preference modeling.

Problem

• aesthetic preference
• comparative judgment
• art evaluation
• label cost
• individual variability

Innovation

• comparative learning
• aesthetic preference prediction
• deep convolutional features
• pairwise comparison
• annotation efficiency