用CLIP模型预测艺术风格五原则，提升自动艺术分析能力
WP-CLIP: Leveraging CLIP to Predict Wölfflin's Principles in Visual Art
- 用标注的艺术图像微调CLIP，学习预测沃尔夫林五原则
- 在生成画作和1.8万张真实画作上验证，跨风格泛化能力强
- 为艺术风格量化分析提供新工具，适合艺术计算研究者
沃尔夫林的五个风格原则为形式分析提供了结构化框架，但现有度量方法无法有效预测全部原则。图像视觉属性的计算评估需能解读色彩、构图和主题等关键元素。近年来，视觉语言模型（VLM）在评估抽象图像属性方面表现突出，成为该任务的有力候选。本文探究预训练于大规模数据的CLIP是否能理解并预测沃尔夫林原则。结果表明，其原始模型无法捕捉此类细微风格特征。为此，我们在真实艺术图像的标注数据集上对CLIP进行微调，以预测每个原则的评分。所提出的WP-CLIP模型在GAN生成画作和Pandora-18K艺术数据集上进行评估，展示了跨多样艺术风格的泛化能力。结果证明，VLM在自动化艺术分析中具有巨大潜力。
原文摘要 · Abstract (English)
Wölfflin's five principles offer a structured approach to analyzing stylistic variations for formal analysis. However, no existing metric effectively predicts all five principles in visual art. Computationally evaluating the visual aspects of a painting requires a metric that can interpret key elements such as color, composition, and thematic choices. Recent advancements in vision-language models (VLMs) have demonstrated their ability to evaluate abstract image attributes, making them promising candidates for this task. In this work, we investigate whether CLIP, pre-trained on large-scale data, can understand and predict Wölfflin's principles. Our findings indicate that it does not inherently capture such nuanced stylistic elements. To address this, we fine-tune CLIP on annotated datasets of real art images to predict a score for each principle. We evaluate our model, WP-CLIP, on GAN-generated paintings and the Pandora-18K art dataset, demonstrating its ability to generalize across diverse artistic styles. Our results highlight the potential of VLMs for automated art analysis.
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