Demographic Influences on Contemporary Art with Unsupervised Style Embeddings
This addresses the challenge of analyzing unsorted contemporary art for researchers in computational art analysis, though it is incremental as it applies existing methods to new data.
The authors tackled the problem of analyzing contemporary art without style annotations by creating contempArt, a multi-modal dataset of 442 early-career artists' works with social and demographic data, and found no correlation between visual style and social proximity, gender, or nationality using unsupervised embeddings.
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.