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OmniArt: Multi-task Deep Learning for Artistic Data Analysis

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arxiv 1708.00684 v1 pith:G5DDLF35 submitted 2017-08-02 cs.MM cs.CV

Gjorgji Strezoski , Marcel Worring This is my paper · ORCID
classification cs.MMcs.CV
keywords artisticdataanalysismethodmulti-taskdomainlearningpropose
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ArtiFact: A Large-Scale Multi-Modal Cultural Heritage Dataset

    cs.DB 2026-06 unverdicted novelty 7.0 of 10

    ArtiFact is a new multi-modal dataset of 651k museum records used to benchmark cross-modal error detection with seven error categories and semantic query processing challenges.

  2. Art Beyond Semantics: Sheaf-Informed Contrastive Learning for Multi-Relational Representations

    cs.CV 2026-07 conditional novelty 6.0 of 10

    CANVAS learns a separate embedding subspace for each art-historical relation and, across three art datasets, beats single-space CLIP models on most retrieval and classification benchmarks.

  3. Automating Iconclass: LLMs and RAG for Large-Scale Classification of Religious Woodcuts

    cs.IR 2025-10 conditional novelty 5.0 of 10

    Full-page LLM descriptions matched to Iconclass via vector search and RAG classify 590 early-modern religious woodcuts at 87-92% precision, roughly tripling the F1 of the image-only baseline (0.30 to 0.82).

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