# OmniArt: Multi-task Deep Learning for Artistic Data Analysis

arXiv: https://arxiv.org/abs/1708.00684 · HTML: https://mlsanity.com/paper/1708.00684/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 16 of 16)

- [ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity](https://mlsanity.com/paper/2103.09776/) (2021)
- [Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings](https://mlsanity.com/paper/2105.08190/) (2021)
- [Compression ensembles quantify aesthetic complexity and the evolution of visual art](https://mlsanity.com/paper/2205.10271/) (2022)
- [Multimodal Metadata Assignment for Cultural Heritage Artifacts](https://mlsanity.com/paper/2406.00423/) (2024)
- [A deep learning approach to clustering visual arts](https://mlsanity.com/paper/2106.06234/) (2021)
- [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)
- [VULCA-Bench: A Multicultural Vision-Language Benchmark for Evaluating Cultural Understanding](https://mlsanity.com/paper/2601.07986/) (2026)
- [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)
- [Stylistic Multi-Task Analysis of Ukiyo-e Woodblock Prints](https://mlsanity.com/paper/2410.12379/) (2024)
- [Automating Iconclass: LLMs and RAG for Large-Scale Classification of Religious Woodcuts](https://mlsanity.com/paper/2510.19986/) (2025)
- [Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models](https://mlsanity.com/paper/2601.07984/) (2026)
- [Art Beyond Semantics: Sheaf-Informed Contrastive Learning for Multi-Relational Representations](https://mlsanity.com/paper/2607.16321/) (2026)
- [AACP: Aesthetics assessment of children's paintings based on self-supervised learning](https://mlsanity.com/paper/2403.07578/) (2024)
- [Detecting Media Clones in Cultural Repositories Using a Positive Unlabeled Learning Approach](https://mlsanity.com/paper/2604.04071/) (2026)
- [ArtiFact: A Large-Scale Multi-Modal Cultural Heritage Dataset](https://mlsanity.com/paper/2606.09648/) (2026)

## Abstract (arXiv)

Vast amounts of artistic data is scattered on-line from both museums and art applications. Collecting, processing and studying it with respect to all accompanying attributes is an expensive process. With a motivation to speed up and improve the quality of categorical analysis in the artistic domain, in this paper we propose an efficient and accurate method for multi-task learning with a shared representation applied in the artistic domain. We continue to show how different multi-task configurations of our method behave on artistic data and outperform handcrafted feature approaches as well as convolutional neural networks. In addition to the method and analysis, we propose a challenge like nature to the new aggregated data set with almost half a million samples and structured meta-data to encourage further research and societal engagement.