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arXiv · 2203.05321

StyleBabel: Artistic Style Tagging and Captioning

Dan Ruta·Andrew Gilbert·Pranav Aggarwal·Naveen Marri·Ajinkya Kale·Jo Briggs·Chris Speed·Hailin Jin·Baldo Faieta·Alex Filipkowski·Zhe Lin·John Collomosse
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Abstract

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.

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Dan Ruta, Andrew Gilbert, Pranav Aggarwal, Naveen Marri, Ajinkya Kale, Jo Briggs, Chris Speed, Hailin Jin, Baldo Faieta, Alex Filipkowski, Zhe Lin, John Collomosse. 2022-03-11. StyleBabel: Artistic Style Tagging and Captioning. https://arxiv.org/abs/2203.05321

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