Exploiting CLIP-based Multi-modal Approach for Artwork Classification and Retrieval
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Abstract (arXiv)
Given the recent advances in multimodal image pretraining where visual models trained with semantically dense textual supervision tend to have better generalization capabilities than those trained using categorical attributes or through unsupervised techniques, in this work we investigate how recent CLIP model can be applied in several tasks in artwork domain. We perform exhaustive experiments on the NoisyArt dataset which is a dataset of artwork images crawled from public resources on the web. On such dataset CLIP achieves impressive results on (zero-shot) classification and promising results in both artwork-to-artwork and description-to-artwork domain.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 21 Sep 2023.
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17 of 29 references.
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5 more
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Citing papers per year: 2026: 2.
2 papers.