{"ID":23507320,"CreatedAt":"2026-09-18T02:21:44.056544415Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.20509","arxiv_id":"2609.20509","title":"Automated Goldsmith's Mark Retrieval in Silverware","abstract":"For art historians, goldsmith marks play a critical role in the identification and dating of artifacts. In practice, experts must manually compare a query mark against hundreds of documented examples, a process that is both tedious and highly dependent on specialist knowledge. To address this, we present an AI-assisted retrieval pipeline that combines mark localization with metric-learning fine-tuning across three backbone architectures: an ImageNet-pretrained ResNet-50, a supervised ViT-S/16, and a self-supervised DINOv2 ViT-S/14. We conduct a systematic evaluation of cropping strategies, where we measure the impact of no cropping, manual ground-truth cropping, and learned detection-based cropping, and assess their interaction with each backbone. Our strongest configuration, DINOv2 ViT-S/14 with manual crop and metric-learning fine-tuning, achieves an mAP of 62.63% and a Top-1 accuracy of 73.74%. Our experiments show that self-supervised pretraining and mark localization are the two most impactful factors, with learned cropping recovering the majority of the gain from manual cropping without requiring ground-truth annotations at inference time. To enable reproducibility and adoption in the digital humanities, we release our manually annotated dataset and codebase, and deploy the system via a public web interface.","short_abstract":"For art historians, goldsmith marks play a critical role in the identification and dating of artifacts. In practice, experts must manually compare a query mark against hundreds of documented examples, a process that is both tedious and highly dependent on specialist knowledge. To address this, we present an AI-assisted...","url_abs":"https://arxiv.org/abs/2609.20509","url_pdf":"https://arxiv.org/pdf/2609.20509v1","authors":"[\"Atmik Tiwari\",\"Vincent Christlein\",\"Mark Fichtner\",\"Freya Gohlke\",\"Birgit Schübel\",\"Theresa Witting\",\"Heike Zech\",\"Mathias Zinnen\"]","published":"2026-09-17T14:51:46Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.DL\"]","methods":"[]","has_code":false}