OmniArt: Multi-task Deep Learning for Artistic Data Analysis

2017/08/02 by Gjorgji Strezoski, Strezoski, Gjorgji, Marcel Worring +1 · 2 citations
Computer Science · Neuroscience · #Aesthetic Perception and Analysis #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Multimedia (cs.MM)

paper · pdf · doi:10.48550/arxiv.1708.00684

openalex publication_date 2017/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
• Quantifying Creativity in Art Networks
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ImageNet classification with deep convolutional neural networks

Cited by

• Automating Iconclass: LLMs and RAG for Large-Scale Classification of Religious Woodcuts
• Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning

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