OmniArt: Multi-task Deep Learning for Artistic Data Analysis

TL;DR

This paper introduces OmniArt, a multi-task deep learning approach for analyzing large-scale artistic data, outperforming traditional methods and fostering further research with a new extensive dataset.

Contribution

The paper presents a novel multi-task learning method tailored for artistic data, demonstrating superior performance over existing handcrafted and CNN-based approaches.

Findings

Multi-task learning improves artistic data analysis accuracy.

The method outperforms traditional handcrafted features and CNNs.

A new large-scale artistic dataset is introduced for research.

Abstract

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.