ArcAid: Analysis of Archaeological Artifacts using Drawings

Offry Hayon, Stefan Munger, Ilan Shimshoni, Ayellet Tal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 code1 and dataset2 are publicly available.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7249-7259
Number of pages11
ISBN (Electronic)9798350318920
DOIs
StatePublished - 2024
Event2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 - Waikoloa, United States
Duration: 4 Jan 20248 Jan 2024

Publication series

NameProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024

Conference

Conference2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Country/TerritoryUnited States
CityWaikoloa
Period4/01/248/01/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Algorithms
  • Algorithms
  • and algorithms
  • Applications
  • Arts / games / social media
  • Datasets and evaluations
  • formulations
  • Machine learning architectures

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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