Historical Printed Ornaments: Dataset and Tasks
<|reference_start|>Historical Printed Ornaments: Dataset and Tasks: This paper aims to develop the study of historical printed ornaments with modern unsupervised computer vision. We highlight three complex tasks that are of critical interest to book historians: clustering, element discovery, and unsupervised change localization. For each of these tasks, we introduce an evaluation benchmark, and we adapt and evaluate state-of-the-art models. Our Rey's Ornaments dataset is designed to be a representative example of a set of ornaments historians would be interested in. It focuses on an XVIIIth century bookseller, Marc-Michel Rey, providing a consistent set of ornaments with a wide diversity and representative challenges. Our results highlight the limitations of state-of-the-art models when faced with real data and show simple baselines such as k-means or congealing can outperform more sophisticated approaches on such data. Our dataset and code can be found at https://printed-ornaments.github.io/.<|reference_end|>
@article{chaki2024historical,
    title={Historical Printed Ornaments: Dataset and Tasks},
    author={Sayan Kumar Chaki, Zeynep Sonat Baltaci, Elliot Vincent, Remi Emonet,
  Fabienne Vial-Bonacci, Christelle Bahier-Porte, Mathieu Aubry, Thierry
  Fournel},
    journal={arXiv preprint arXiv:2408.08633},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08633},
    primaryClass={cs.CV}
}