# Weakly Supervised Object Detection in Artworks

arXiv: https://arxiv.org/abs/1810.02569 · HTML: https://mlsanity.com/paper/1810.02569/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2018.

## Built on by (most cited first, 27 of 27)

- [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (2021)
- [Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes](https://mlsanity.com/paper/2303.02760/) (2023)
- [Understanding and Creating Art with AI: Review and Outlook](https://mlsanity.com/paper/2102.09109/) (2021)
- [Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation](https://mlsanity.com/paper/2109.05743/) (2021)
- [Self-supervised object detection from audio-visual correspondence](https://mlsanity.com/paper/2104.06401/) (2021)
- [Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis](https://mlsanity.com/paper/2304.10278/) (2023)
- [ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques](https://mlsanity.com/paper/2308.15284/) (2023)
- [Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings](https://mlsanity.com/paper/2105.08190/) (2021)
- [DEArt: Dataset of European Art](https://mlsanity.com/paper/2211.01226/) (2022)
- [Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History](https://mlsanity.com/paper/2301.05124/) (2023)
- [SniffyArt: The Dataset of Smelling Persons](https://mlsanity.com/paper/2311.11888/) (2023)
- [A deep learning approach to clustering visual arts](https://mlsanity.com/paper/2106.06234/) (2021)
- [KALE: An Artwork Image Captioning System Augmented with Heterogeneous Graph](https://mlsanity.com/paper/2409.10921/) (2024)
- [Unsupervised Clustering of Roman Potsherds via Variational Autoencoders](https://mlsanity.com/paper/2203.07437/) (2022)
- [How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classification](https://mlsanity.com/paper/2208.04693/) (2022)
- [Transfer Learning for Olfactory Object Detection](https://mlsanity.com/paper/2301.09906/) (2023)
- [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)
- [Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting](https://mlsanity.com/paper/2107.06935/) (2021)
- [Context-Infused Visual Grounding for Art](https://mlsanity.com/paper/2410.12369/) (2024)
- [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)
- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [Visual Motif Identification: Elaboration of a Curated Comparative Dataset and Classification Methods](https://mlsanity.com/paper/2410.15866/) (2024)
- [BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks](https://mlsanity.com/paper/2507.07134/) (2025)
- [Data Augmentation via Latent Diffusion Models for Detecting Smell-Related Objects in Historical Artworks](https://mlsanity.com/paper/2509.14755/) (2025)
- [High-Resolution Artwork Outpainting with Global Blueprint Guidance and Layout Control](https://mlsanity.com/paper/2607.06162/) (2026)

## Abstract (arXiv)

We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of our multiple-instance learning method, enables one to learn new classes on-the-fly from globally annotated databases, avoiding the tedious task of manually marking objects. We show on several databases that dropping the instance-level annotations only yields mild performance losses. We also introduce a new database, IconArt, on which we perform detection experiments on classes that could not be learned on photographs, such as Jesus Child or Saint Sebastian. To the best of our knowledge, these are the first experiments dealing with the automatic (and in our case weakly supervised) detection of iconographic elements in paintings. We believe that such a method is of great benefit for helping art historians to explore large digital databases.