# iART: A Search Engine for Art-Historical Images to Support Research in the Humanities

arXiv: https://arxiv.org/abs/2108.01542 · HTML: https://mlsanity.com/paper/2108.01542/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2021.

## Built on by (most cited first, 6 of 6)

- [Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History](https://mlsanity.com/paper/2301.05124/) (2023)
- [On the Explainability of Vision-Language Models in Art History](https://mlsanity.com/paper/2602.20853/) (2026)
- [Studying Illustrations in Manuscripts: An Efficient Deep-Learning Approach](https://mlsanity.com/paper/2601.05269/) (2025)
- [There Is a Digital Art History](https://mlsanity.com/paper/2308.07464/) (2023)
- [Uncertainty-Aware Art-Historical Dating with Vision-Language Models](https://mlsanity.com/paper/2608.18984/) (2026)
- [Automated Goldsmith's Mark Retrieval in Silverware](https://mlsanity.com/paper/2609.20509/) (2026)

## Abstract (arXiv)

In this paper, we introduce iART: an open Web platform for art-historical research that facilitates the process of comparative vision. The system integrates various machine learning techniques for keyword- and content-based image retrieval as well as category formation via clustering. An intuitive GUI supports users to define queries and explore results. By using a state-of-the-art cross-modal deep learning approach, it is possible to search for concepts that were not previously detected by trained classification models. Art-historical objects from large, openly licensed collections such as Amsterdam Rijksmuseum and Wikidata are made available to users.