mlsanityData to 7 Oct 2026

Weakly Supervised Object Detection in Artworks

arXiv 1810.02569 ↗First posted 5 Oct 2018cs.CVcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 3; 2023: 6; 2024: 5; 2025: 4; 2026: 2.

27 papers.

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  1. 2021Weakly Supervised Object Localization and Detection: A Surveycited by 84
  2. 2023Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenescited by 68
  3. 2021Understanding and Creating Art with AI: Review and Outlookcited by 60
  4. 2021Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generationcited by 30
  5. 2021Self-supervised object detection from audio-visual correspondencecited by 29
  6. 2023Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysiscited by 18
  7. 2023ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniquescited by 10
  8. 2021Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintingscited by 9
  9. 2022DEArt: Dataset of European Artcited by 9
  10. 2023Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art Historycited by 6
  11. 2023SniffyArt: The Dataset of Smelling Personscited by 6
  12. 2021A deep learning approach to clustering visual artscited by 4
15 more
  1. 2024KALE: An Artwork Image Captioning System Augmented with Heterogeneous Graphcited by 4
  2. 2022Unsupervised Clustering of Roman Potsherds via Variational Autoencoderscited by 3
  3. 2022How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classificationcited by 3
  4. 2023Transfer Learning for Olfactory Object Detectioncited by 3
  5. 2021Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Votingcited by 2
  6. 2024Context-Infused Visual Grounding for Artcited by 2
  7. 2024No Annotations for Object Detection in Art through Stable Diffusioncited by 2
  8. 2025Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) datasetcited by 2
  9. 2025Benchmarking Vision-Language and Multimodal Large Language Models in Zero-shot and Few-shot Scenarios: A study on Christian Iconographycited by 2
  10. 2026On the Explainability of Vision-Language Models in Art Historycited by 2
  11. 2024Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Surveycited by 1
  12. 2024Visual Motif Identification: Elaboration of a Curated Comparative Dataset and Classification Methodscited by 0
  13. 2025BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networkscited by 0
  14. 2025Data Augmentation via Latent Diffusion Models for Detecting Smell-Related Objects in Historical Artworkscited by 0
  15. 2026High-Resolution Artwork Outpainting with Global Blueprint Guidance and Layout Controlcited by 0