An integrated DL-LLM system using LLM-based interviews and semantic features predicts individual image aesthetic ratings more accurately than human predictors or the target's re-evaluations, with error below within-person variability.
Quantitative Analysis of Training Methods, Data Size, and User-Specific Effectiveness in DL-Based Personalized Aesthetic Evaluation,
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AI Outperforms Humans in Personalized Image Aesthetics Assessment via LLM-Based Interviews and Semantic Feature Extraction
An integrated DL-LLM system using LLM-based interviews and semantic features predicts individual image aesthetic ratings more accurately than human predictors or the target's re-evaluations, with error below within-person variability.