2020/09/30 by Nikolai Huckle, Huckle, Nikolai, Noa García +4
Arts and Humanities · Computer Science · Neuroscience · #Aesthetic Perception and Analysis #Art History and Market Analysis #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Social and Information Networks (cs.SI) #cs.CV #cs.SI
paper · pdf · doi:10.48550/arxiv.2009.14545
To be published in Proceedings of the European Conference in Computer Vision Workshops 2020
openalex publication_date 2020/09/30 · arxiv created 2020/12/01 · arxiv updated 2020/12/02 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.