用神经风格表征聚类艺术作品，探索风格识别新方法
Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations
- 融合风格分类、迁移与多模态模型生成风格特征
- 在多个数据集上验证不同表征的聚类效果差异
- 为艺术风格分析和推荐提供可复用框架
基于风格对艺术作品进行聚类具有广泛实际应用价值，如艺术推荐、风格检索及艺术家或作品集风格演变研究。本文提出并深入探讨了‘基于风格的艺术作品聚类’这一尚未充分解决的问题。通过探索并设计多种神经特征表示方法——包括风格分类、风格迁移以及大语言视觉模型——用于实现风格聚类。我们的目标是通过定性与定量分析，评估这些方法在多个艺术作品语料库及人工合成风格数据集上的相对有效性。除构建一个完整的风格聚类与评估框架外，研究还揭示了若干关于特征表示、网络架构及其对风格聚类影响的新见解。
原文摘要 · Abstract (English)
Clustering artworks based on style can have many potential real-world applications like art recommendations, style-based search and retrieval, and the study of artistic style evolution of an artist or in an artwork corpus. We introduce and deliberate over the notion of 'Style-based clustering of visual artworks'. We argue that clustering artworks based on style is largely an unaddressed problem. We explore and devise different neural feature representations - from the style-classification, style-transfer to large language vision models - that can be then used for style-based clustering. Our objective is to assess the relative effectiveness of these devised style-based clustering approaches through qualitative and quantitative analysis by applying them to multiple artwork corpora and curated synthetically styled datasets. Besides providing a broad framework for style-based clustering and evaluation, our analysis provides some key novel insights on feature representations, architectures and implications for style-based clustering.
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