  Image captioning implies automatically generating textual descriptions of
images based only on the visual input. Although this has been an extensively
addressed research topic in recent years, not many contributions have been made
in the domain of art historical data. In this particular context, the task of
image captioning is confronted with various challenges such as the lack of
large-scale datasets of image-text pairs, the complexity of meaning associated
with describing artworks and the need for expert-level annotations. This work
aims to address some of those challenges by utilizing a novel large-scale
dataset of artwork images annotated with concepts from the Iconclass
classification system designed for art and iconography. The annotations are
processed into clean textual description to create a dataset suitable for
training a deep neural network model on the image captioning task. Motivated by
the state-of-the-art results achieved in generating captions for natural
images, a transformer-based vision-language pre-trained model is fine-tuned
using the artwork image dataset. Quantitative evaluation of the results is
performed using standard image captioning metrics. The quality of the generated
captions and the model's capacity to generalize to new data is explored by
employing the model on a new collection of paintings and performing an analysis
of the relation between commonly generated captions and the artistic genre. The
overall results suggest that the model can generate meaningful captions that
exhibit a stronger relevance to the art historical context, particularly in
comparison to captions obtained from models trained only on natural image
datasets.
