arXiv:2603. 03892v2 Announce Type: replace-cross Abstract: In this paper, we present a network structure for classifying metadata of cuneiform tablets.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
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.
Learning to read cuneiform tablets is an extremely demanding task; consequently, of the roughly half million excavated tablets, only a small fraction has been analysed by Assyriologists. Computer vision offers a promising avenue for decipherment but requires large, densely annotated datasets.
arXiv:2607. 09104v1 Announce Type: cross Abstract: While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming.
The paper evaluates whether incorporating the hierarchical structure of Tironian notes can improve automatic recognition of this complex Latin shorthand system. Experiments compare flat classifiers (ResNet18, ConvNeXt, Swin, ViT) with hierarchy‑aware models (HD‑CNN and routing approaches) on handwritten and manuscript samples, with and without few‑shot adaptation. Results show that hierarchical models outperform flat ones when no adaptation is applied, but flat models surpass them after few‑shot adaptation, indicating that hierarchy can aid recognition under non‑adapted conditions.
The paper introduces Biomedica, an open-source dataset sourced from PubMed Central that includes over 6 million scientific articles and 24 million image‑text pairs, along with 27 metadata fields and expert human annotations. To facilitate use, the authors provide scalable streaming and search APIs via a web server. They demonstrate the dataset’s value by training embedding models, chat‑style models, and retrieval‑augmented chat agents, all of which outperform previous open systems in their categories.
arXiv:2607. 04262v1 Announce Type: new Abstract: Convolutional Neural Network (CNN) and Vision Transformer (ViT) for image classification exploit a dense grid of pixels containing redundant information.