TEXMET: A Deep Learning Benchmark for Archaeological Textile Reconstruction
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The paper introduces STITCH, a self‑supervised compatibility measure that replaces traditional boundary‑dependent metrics for solving visual puzzles with eroded fragments. By comparing frozen ViT patch tokens from piece interiors using a lightweight cross‑attention head, STITCH can be plugged into existing solvers and achieves near‑perfect reconstruction on the JPwLEG‑5 benchmark, outperforms prior methods on a new ImageNet 7×7 benchmark, and remains robust across varying erosion levels and high missing‑piece scenarios. The authors demonstrate that improving the matching metric, rather than the solver itself, yields significant performance gains across multiple datasets.
The paper introduces an AI-assisted pipeline for retrieving goldsmith marks from silverware images, combining mark localization with metric‑learning fine‑tuning on three backbone models (ResNet‑50, ViT‑S/16, and DINOv2 ViT‑S/14). Systematic tests of cropping strategies show that manual cropping and metric‑learning fine‑tuning yield the best performance, with DINOv2 ViT‑S/14 achieving 62.63% mAP and 73.74% Top‑1 accuracy. The authors release a manually annotated dataset, code, and a public web interface to support reproducibility and adoption in digital humanities.
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