论文标题
人口统计学对当代艺术的影响无监督的风格嵌入
Demographic Influences on Contemporary Art with Unsupervised Style Embeddings
论文作者
论文摘要
计算艺术分析通过依赖分类任务的优先级历史数据集，其中艺术品已经对必要的注释进行了很好的分类。另一方面，如今，通过专业和业余艺术家使用的互联网和社交网络来展示自己的作品，今天生产的艺术是众多且易于访问的。尽管这种艺术在样式和流派方面却没有分类，但不适合监督分析，但数据源带有新颖的信息，可以帮助以同样新颖的方式构建视觉内容。作为朝这个方向发展的第一步，我们介绍了当代的当代数据集，这是当代艺术品的多模式数据集。当代是绘画和图纸的集合，是基于Instagram上社交联系的详细图形网络以及其他社会人口统计学信息；在职业生涯开始时，所有这些都与442位艺术家有关。我们评估了适合生成图像的无监督样式嵌入的三种方法，并将其与其余数据相关联。一方面，我们发现视觉风格与另一方面的社会接近性，性别和国籍之间没有联系。
Computational art analysis has, through its reliance on classification tasks, prioritised historical datasets in which the artworks are already well sorted with the necessary annotations. Art produced today, on the other hand, is numerous and easily accessible, through the internet and social networks that are used by professional and amateur artists alike to display their work. Although this art, yet unsorted in terms of style and genre, is less suited for supervised analysis, the data sources come with novel information that may help frame the visual content in equally novel ways. As a first step in this direction, we present contempArt, a multi-modal dataset of exclusively contemporary artworks. contempArt is a collection of paintings and drawings, a detailed graph network based on social connections on Instagram and additional socio-demographic information; all attached to 442 artists at the beginning of their career. We evaluate three methods suited for generating unsupervised style embeddings of images and correlate them with the remaining data. We find no connections between visual style on the one hand and social proximity, gender, and nationality on the other.
