There Is a Digital Art History
Leonardo Impett Fabian Offert
In this paper, we revisit Johanna Drucker's question, "Is there a digital art
history?" -- posed exactly a decade ago -- in the light of the emergence of
large-scale, transformer-based vision models. While more traditional types of
neural networks have long been part of digital art history, and digital
humanities projects have recently begun to use transformer models, their
epistemic implications and methodological affordances have not yet been
systematically analyzed. We focus our analysis on two main aspects that,
together, seem to suggest a coming paradigm shift towards a "digital" art
history in Drucker's sense. On the one hand, the visual-cultural repertoire
newly encoded in large-scale vision models has an outsized effect on digital
art history. The inclusion of significant numbers of non-photographic images
allows for the extraction and automation of different forms of visual logics.
Large-scale vision models have "seen" large parts of the Western visual canon
mediated by Net visual culture, and they continuously solidify and concretize
this canon through their already widespread application in all aspects of
digital life. On the other hand, based on two technical case studies of
utilizing a contemporary large-scale visual model to investigate basic
questions from the fields of art history and urbanism, we suggest that such
systems require a new critical methodology that takes into account the
epistemic entanglement of a model and its applications. This new methodology
reads its corpora through a neural model's training data, and vice versa: the
visual ideologies of research datasets and training datasets become entangled.