# ArcAid: Analysis of Archaeological Artifacts using Drawings

arXiv: https://arxiv.org/abs/2211.09480 · HTML: https://mlsanity.com/paper/2211.09480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 65 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 24 of 24)

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- [DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation](https://mlsanity.com/paper/2103.13447/) (2021)
- [Unsupervised Domain Generalization by Learning a Bridge Across Domains](https://mlsanity.com/paper/2112.02300/) (2021)
- [Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation](https://mlsanity.com/paper/2111.14353/) (2021)
- [Solving Archaeological Puzzles](https://mlsanity.com/paper/1812.10553/) (2018)
- [Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach](https://mlsanity.com/paper/1908.10254/) (2019)
- [Computational Ceramicology](https://mlsanity.com/paper/1911.09960/) (2019)

## Abstract (arXiv)

Archaeology is an intriguing domain for computer vision. It suffers not only from shortage in (labeled) data, but also from highly-challenging data, which is often extremely abraded and damaged. This paper proposes a novel semi-supervised model for classification and retrieval of images of archaeological artifacts. This model utilizes unique data that exists in the domain -- manual drawings made by special artists. These are used during training to implicitly transfer the domain knowledge from the drawings to their corresponding images, improving their classification results. We show that while learning how to classify, our model also learns how to generate drawings of the artifacts, an important documentation task, which is currently performed manually. Last but not least, we collected a new dataset of stamp-seals of the Southern Levant. Our code and dataset are publicly available.