ArcAid: Analysis of Archaeological Artifacts using Drawings
This addresses data scarcity and damage challenges in archaeology, offering a tool for artifact analysis, but it is incremental as it adapts semi-supervised learning to a specific domain.
The paper tackles the problem of classifying and retrieving images of archaeological artifacts, which are often damaged and have limited labeled data, by proposing a semi-supervised model that uses manual drawings to transfer domain knowledge and improve classification results, achieving unspecified gains while also learning to generate drawings for documentation.
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.