Papers › OmniArt: Multi-task Deep Learning for Artistic Data Analysis
OmniArt: Multi-task Deep Learning for Artistic Data Analysis
Gjorgji Strezoski, Marcel Worring
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Period Estimation | OmniArt | OmniArt | Mean absolute error | 77.9 | #1 of 2 | Archive leaderboard | report |
| Period Estimation | OmniArt | ResNet-50 | Mean absolute error | 79.3 | #2 of 2 | Archive leaderboard | report |
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