Aligning Understanding and Scoring for Data-Efficient Photographic Aesthetic Assessment

In this work, we study Photography Aesthetics Assessment (PAA), which aims to provide comprehensive aesthetic evaluation of photographs through both qualitative understanding and quantitative scoring. However, reliable photography aesthetic scoring remains challenging because large-scale aesthetic analyses can be obtained relatively easily, while high-quality and consistently calibrated human ratings are costly and difficult to scale. To address these challenges, we propose AesAlign, a data-efficient framework that decouples photography aesthetic understanding from quantitative score calibration. First, we establish a unified photography aesthetic evaluation protocol and construct large-scale fine-grained aesthetic analyses for 500K smartphone photographs. We further collect high-quality human-verified overall and dimension-level ratings for 50K images to provide reliable quantitative supervision. Based on these complementary supervision signals, AesAlign learns photography aesthetic knowledge from scalable analyses and subsequently calibrates the learned representations to human rating scales using a lightweight scoring probe. Extensive experiments demonstrate that AesAlign achieves strong performance under limited human supervision and exhibits robust cross-dataset generalization, enabling comprehensive and interpretable photography aesthetics assessment with reliable overall and multidimensional scores. Our models, training code, and dataset will be publicly available.