# DEArt: Dataset of European Art

arXiv: https://arxiv.org/abs/2211.01226 · HTML: https://mlsanity.com/paper/2211.01226/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](https://mlsanity.com/paper/1810.09617/) (2018)
- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://mlsanity.com/paper/1609.08667/) (2016)
- [The Met Dataset: Instance-level Recognition for Artworks](https://mlsanity.com/paper/2202.01747/) (2022)
- [Weakly Supervised Object Detection in Artworks](https://mlsanity.com/paper/1810.02569/) (2018)
- [Detecting People in Artwork with CNNs](https://mlsanity.com/paper/1610.08871/) (2016)
- [A Data Set and a Convolutional Model for Iconography Classification in Paintings](https://mlsanity.com/paper/2010.11697/) (2020)
- [Improving Object Detection in Art Images Using Only Style Transfer](https://mlsanity.com/paper/2102.06529/) (2021)
- [Detecting People in Cubist Art](https://mlsanity.com/paper/1409.6235/) (2014)

## Built on by (most cited first, 9 of 9)

- [Synthesize Privacy-Preserving High-Resolution Images via Private Textual Intermediaries](https://mlsanity.com/paper/2506.07555/) (2025)
- [Benchmarking Vision-Language and Multimodal Large Language Models in Zero-shot and Few-shot Scenarios: A study on Christian Iconography](https://mlsanity.com/paper/2509.18839/) (2025)
- [No Annotations for Object Detection in Art through Stable Diffusion](https://mlsanity.com/paper/2412.06286/) (2024)
- [Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset](https://mlsanity.com/paper/2507.08384/) (2025)
- [On the Explainability of Vision-Language Models in Art History](https://mlsanity.com/paper/2602.20853/) (2026)
- [Gesture Classification in Artworks Using Contextual Image Features](https://mlsanity.com/paper/2412.03456/) (2024)
- [Automating Iconclass: LLMs and RAG for Large-Scale Classification of Religious Woodcuts](https://mlsanity.com/paper/2510.19986/) (2025)
- [Data Augmentation via Latent Diffusion Models for Detecting Smell-Related Objects in Historical Artworks](https://mlsanity.com/paper/2509.14755/) (2025)
- [ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation](https://mlsanity.com/paper/2608.05026/) (2026)

## Abstract (arXiv)

Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations. Our results show that object detectors for the cultural heritage domain can achieve a level of precision comparable to state-of-art models for generic images via transfer learning.