Harnessing Self-Supervised Features for Art Classification
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909055055822848 |
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| author | Melis, Federico Bilardello, Davide Prato, Emanuele Turri, Evelyn Baraldi, Lorenzo |
| author_facet | Melis, Federico Bilardello, Davide Prato, Emanuele Turri, Evelyn Baraldi, Lorenzo |
| contents | Classifying artworks presents a significant challenge due to the complex interplay of fine-grained details and abstract features that condition the style or genre of an artwork. This paper presents a systematic investigation of the effectiveness of supervised and self-supervised backbones as feature extractors for both artwork classification and retrieval, with a particular focus on paintings. We conduct an extensive experimental evaluation using the DINO family and CLIP models, assessing multiple classification strategies and feature representations. Our results demonstrate that employing a self-supervised backbone leads to consistent improvements in artwork classification performance. Moreover, our work provides insights into the applicability of classification and retrieval modules in real-world applications, such as virtual reality (VR) applications that support museum navigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18974 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Harnessing Self-Supervised Features for Art Classification Melis, Federico Bilardello, Davide Prato, Emanuele Turri, Evelyn Baraldi, Lorenzo Computer Vision and Pattern Recognition Artificial Intelligence Multimedia Classifying artworks presents a significant challenge due to the complex interplay of fine-grained details and abstract features that condition the style or genre of an artwork. This paper presents a systematic investigation of the effectiveness of supervised and self-supervised backbones as feature extractors for both artwork classification and retrieval, with a particular focus on paintings. We conduct an extensive experimental evaluation using the DINO family and CLIP models, assessing multiple classification strategies and feature representations. Our results demonstrate that employing a self-supervised backbone leads to consistent improvements in artwork classification performance. Moreover, our work provides insights into the applicability of classification and retrieval modules in real-world applications, such as virtual reality (VR) applications that support museum navigation. |
| title | Harnessing Self-Supervised Features for Art Classification |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Multimedia |
| url | https://arxiv.org/abs/2605.18974 |