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

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.

Modeling Image-Caption Rating from Comparative Judgments

Rating the accuracy of captions in describing images is time-consuming and subjective for humans. In contrast, it is often easier for people to compare two captions and decide which one better matches a given image. In this work, we propose a machine learning framework that models such comparative judgments instead of direct ratings. The model can then be applied to rank unseen image-caption pairs in the same way as a regression model trained on direct ratings. Using the VICR dataset, we extract visual features with ResNet-50 and text features with MiniLM, then train both a regression model and a comparative learning model. While the regression model achieves better performance (Pearson's $ρ$: 0.7609 and Spearman's $r_s$: 0.7089), the comparative learning model steadily improves with more data and approaches the regression baseline. In addition, a small-scale human evaluation study comparing absolute rating, pairwise comparison, and same-image comparison shows that comparative annotation yields faster results and has greater agreement among human annotators. These results suggest that comparative learning can effectively model human preferences while significantly reducing the cost of human annotations.