  We present StyleBabel, a unique open access dataset of natural language
captions and free-form tags describing the artistic style of over 135K digital
artworks, collected via a novel participatory method from experts studying at
specialist art and design schools. StyleBabel was collected via an iterative
method, inspired by `Grounded Theory': a qualitative approach that enables
annotation while co-evolving a shared language for fine-grained artistic style
attribute description. We demonstrate several downstream tasks for StyleBabel,
adapting the recent ALADIN architecture for fine-grained style similarity, to
train cross-modal embeddings for: 1) free-form tag generation; 2) natural
language description of artistic style; 3) fine-grained text search of style.
To do so, we extend ALADIN with recent advances in Visual Transformer (ViT) and
cross-modal representation learning, achieving a state of the art accuracy in
fine-grained style retrieval.
