An Automated Artistic Image Aesthetic Evaluation Framework via Expert Knowledge Injection into Large Vision-Language Models
TL;DR
The proposed AEKI framework translates formal art principles into structured, machine-executable instructions and indicates that domain knowledge can be incorporated into LVLM evaluation through a transparent rule-based layer, supporting applications in digital curation, computational aesthetics, and art education.
Abstract
Image aesthetic assessment (IAA) has progressed from handcrafted visual features to deep neural models, yet fine-art evaluation remains difficult because aesthetic judgment depends on style, historical context, and formal composition. Large Vision-Language Models (LVLMs) offer strong multimodal reasoning capabilities, but their direct application to art critique can produce generic descriptions, unstable scores, and weakly interpretable judgments. We therefore propose the Aesthetic Expert Knowledge Injection (AEKI) framework, which translates formal art principles into structured, machine-executable instructions. AEKI operationalizes four dimensions—Contrast & Harmony, Rhythm & Flow, Symmetry & Balance, and Variety & Unity—and assigns style-dependent weights w* across 16 artistic categories. The resulting three-stage pipeline performs style anchoring, expert-weight allocation, and structured instruction compilation before LVLM inference. We evaluate the framework on a multi-category painting collection and a balanced subset annotated by human evaluators. Comparisons with zero-shot LVLM baselines show improved alignment in both numerical scoring and critique professionalism, while ablation experiments clarify the contributions of style anchoring and dynamic weighting. These results indicate that domain knowledge can be incorporated into LVLM evaluation through a transparent rule-based layer, supporting applications in digital curation, computational aesthetics, and art education.