WP-CLIP：利用CLIP预测视觉艺术中的沃尔夫林原则
WP-CLIP: Leveraging CLIP to Predict Wölfflin's Principles in Visual Art
AI总结：
本文提出WP-CLIP，通过微调CLIP预测沃尔夫林五项原则得分，并在GAN绘画和Pandora-18K数据集上验证其跨风格泛化能力，展示了视觉-语言模型用于自动化艺术分析的潜力。
摘要：
沃尔夫林的五项原则为形式分析中的风格差异研究提供了一种结构化方法。然而，目前尚不存在能够有效预测视觉艺术中全部五项原则的度量指标。从计算角度评估一幅绘画的视觉特征，需要一种能够解读色彩、构图和主题选择等关键要素的度量方法。视觉-语言模型（VLMs）的最新进展表明，它们具备评估抽象图像属性的能力，这使其成为该任务的有力候选方案。在本工作中，我们探究了在大规模数据上预训练的CLIP是否能够理解并预测沃尔夫林原则。我们的研究结果表明，CLIP本身并不能固有地捕捉这类细微的风格要素。为解决这一问题，我们在真实艺术图像的标注数据集上对CLIP进行微调，以预测每项原则的得分。我们在GAN生成的绘画和Pandora-18K艺术数据集上评估了我们的模型WP-CLIP，证明了其在不同艺术风格间的泛化能力。我们的结果凸显了视觉-语言模型在自动化艺术分析中的潜力。
英文摘要：
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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