Art Beyond Semantics: Sheaf-Informed Contrastive Learning for Multi-Relational Representations
Understanding a painting is never a single act. Art historians may analyze the same work through concepts of style, iconography, or historical context, dimensions that are not interchangeable, and each carries distinct semantic relationships between the visual and the textual. Vision-Language Models (VLMs) like CLIP, which learn a single shared embedding space, collapse this richness into a single homogeneous alignment, thereby losing the multi-relational structure that defines art-historical reasoning. We introduce CANVAS (Contrastive Art-aware Network for Vision-Language Alignment with Sheaves), a framework for learning relation-aware multimodal representations inspired by sheaf theory....
| Authors | Ludovica Schaerf, Antonio Purificato, Piera Riccio et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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- antoniopuri/SemArtPlusDataset · cited by
- antoniopuri/WikiArtPlusDataset · cited by
Models and datasets referencing this paper
- antoniopuri/WikiArtPlusDataset
- antoniopuri/SemArtPlusDataset
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