对大型视觉-语言模型进行微调以用于艺术品的评分与批评
Fine-Tuning a Large Vision-Language Model for Artwork's Scoring and Critique
AI总结：
本文提出通过微调Qwen2-VL-7B模型，结合多任务学习实现对人类绘画的自动化创造力评估，通过生成与评分标准一致的反馈，提高评分准确性和反馈质量。
摘要：
评估艺术创造力是创造力研究和艺术教育的基础，但大规模的手动评分（例如托兰斯创造性思维测试）是劳动密集型的。先前的机器学习方法在视觉创造力评分方面显示出潜力，但许多方法主要依赖图像特征，并提供有限或没有解释性反馈。我们提出了一种通过微调视觉语言模型Qwen2-VL-7B进行多任务学习的框架，以实现人类绘画的自动化创造力评估。我们的数据集包含1000幅人类创作的绘画，这些绘画在1-100分的尺度上评分，并配以简短的人类撰写描述（内容或艺术家解释）。两名专家评分者使用五维评分标准（原创性、颜色、纹理、构图、内容）对每件作品进行评估，并提供书面批评；我们采用80/20的训练测试划分。我们添加了一个轻量级的回归头到视觉编码器的输出，使模型能够在单次前向传递中预测数值评分并生成与评分标准一致的反馈。通过将结构化的评分标准和艺术作品描述嵌入系统提示中，我们限制生成的文本与定量预测相匹配。实验显示了强大的准确性，达到皮尔逊相关系数大于0.97，100分制上的平均绝对误差约为3.95。定性评估表明生成的反馈在语义上接近专家批评（平均SBERT余弦相似度=0.798）。所提出的方法连接了计算机视觉和艺术评估，并提供了一种可扩展的工具用于创造力研究和课堂反馈。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
