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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2211.09480 |
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| _version_ | 1866917570852945920 |
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| author | Hayon, Offry Münger, Stefan Shimshoni, Ilan Tal, Ayellet |
| author_facet | Hayon, Offry Münger, Stefan Shimshoni, Ilan Tal, Ayellet |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_09480 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | ArcAid: Analysis of Archaeological Artifacts using Drawings Hayon, Offry Münger, Stefan Shimshoni, Ilan Tal, Ayellet Computer Vision and Pattern Recognition 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. |
| title | ArcAid: Analysis of Archaeological Artifacts using Drawings |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2211.09480 |