# Multitask Painting Categorization by Deep Multibranch Neural Network

arXiv: https://arxiv.org/abs/1812.08052 · HTML: https://mlsanity.com/paper/1812.08052/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 8 of 8)

- [Understanding and Creating Art with AI: Review and Outlook](https://mlsanity.com/paper/2102.09109/) (2021)
- [Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings](https://mlsanity.com/paper/2105.08190/) (2021)
- [Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History](https://mlsanity.com/paper/2301.05124/) (2023)
- [WP-CLIP: Leveraging CLIP to Predict W"olfflin's Principles in Visual Art](https://mlsanity.com/paper/2508.12668/) (2025)
- [Level of agreement between emotions generated by Artificial Intelligence and human evaluation: a methodological proposal](https://mlsanity.com/paper/2410.08332/) (2024)
- [CompNet: A Designated Model to Handle Combinations of Images and Designed features](https://mlsanity.com/paper/2209.14454/) (2022)
- [FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art Commissions](https://mlsanity.com/paper/2404.16336/) (2024)
- [Abstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art](https://mlsanity.com/paper/2608.28339/) (2026)

## Abstract (arXiv)

In this work we propose a new deep multibranch neural network to solve the tasks of artist, style, and genre categorization in a multitask formulation. In order to gather clues from low-level texture details and, at the same time, exploit the coarse layout of the painting, the branches of the proposed networks are fed with crops at different resolutions. We propose and compare two different crop strategies: the first one is a random-crop strategy that permits to manage the tradeoff between accuracy and speed; the second one is a smart extractor based on Spatial Transformer Networks trained to extract the most representative subregions. Furthermore, inspired by the results obtained in other domains, we experiment the joint use of hand-crafted features directly computed on the input images along with neural ones. Experiments are performed on a new dataset originally sourced from wikiart.org and hosted by Kaggle, and made suitable for artist, style and genre multitask learning. The dataset here proposed, named MultitaskPainting100k, is composed by 100K paintings, 1508 artists, 125 styles and 41 genres. Our best method, tested on the MultitaskPainting100k dataset, achieves accuracy levels of 56.5%, 57.2%, and 63.6% on the tasks of artist, style and genre prediction respectively.