Fine-Tuning a Large Vision-Language Model for Artwork's Scoring and Critique

2026-02-09

AI Summary

This work addresses the high cost and subjectivity of human evaluation in assessing artistic creativity, as well as the limited interpretability of existing image-feature-based methods. The authors propose a multi-task fine-tuning framework leveraging Qwen2-VL-7B, which uniquely integrates a five-dimensional structured scoring rubric into system prompts. In a single forward pass, the model simultaneously generates highly accurate creativity scores—achieving a Pearson correlation coefficient exceeding 0.97 and a mean absolute error of approximately 3.95 on a 100-point scale—and produces aligned explanatory comments. Trained on a dataset of 1,000 paintings combining visual inputs, textual descriptions, and expert annotations, the model’s generated critiques achieve an SBERT similarity of 0.798 with human expert evaluations, substantially enhancing both the accuracy and interpretability of automated creativity assessment.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Assessing artistic creativity is foundational to creativity research and arts education, yet manual scoring (e.g., Torrance Tests of Creative Thinking) is labor-intensive at scale. Prior machine-learning approaches show promise for visual creativity scoring, but many rely mainly on image features and provide limited or no explanatory feedback. We propose a framework for automated creativity assessment of human paintings by fine-tuning the vision-language model Qwen2-VL-7B with multi-task learning. Our dataset contains 1000 human-created paintings scored on a 1-100 scale and paired with a short human-written description (content or artist explanation). Two expert raters evaluated each work using a five-dimension rubric (originality, color, texture, composition, content) and provided written critiques; we use an 80/20 train-test split. We add a lightweight regression head on the visual encoder output so the model can predict a numerical score and generate rubric-aligned feedback in a single forward pass. By embedding the structured rubric and the artwork description in the system prompt, we constrain the generated text to match the quantitative prediction. Experiments show strong accuracy, achieving Pearson r>0.97 and MAE about 3.95 on the 100-point scale. Qualitative evaluation indicates the generated feedback is semantically close to expert critiques (average SBERT cosine similarity = 0.798). The proposed approach bridges computer vision and art assessment and offers a scalable tool for creativity research and classroom feedback.

Problem

• artwork scoring
• creativity assessment
• automated critique
• vision-language model
• art education

Innovation

• vision-language model
• creativity assessment
• multi-task learning
• automated art critique
• fine-tuning