Compositionality with Primitives: A Unified View from Euclidean to Hyperbolic Vision-Language Models

Puru Vaish, Meelad Dashti, Christoph Brune, Pascal Mettes, Tatiana Tommasi, Nicola Strisciuglio · 2026 · arXiv

DOI: 10.48550/arXiv.2610.29811 · open-access PDF

Abstract

Compositional zero-shot learning (CZSL) aims to recognize novel combinations of previously observed objects and attributes. Prior work showed that large-scale vision-language models encode decomposable semantic structures that can be recomposed to enable generalization. The Geodesically Decomposable Embeddings (GDE) framework formalizes this by identifying directions in the tangent space at the Frechet mean of the embedding distribution, which encode primitive visual concepts. Unseen attribute-object compositions can be synthesized through vector addition of these primitives. We revisit the as

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