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

Puru Vaish, Meelad Dashti, Christoph Brüne, Pascal Mettes, Tatiana Tommasi, Nicola Strisciuglio · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29811 · cited by 0 works (OpenAlex) · open-access PDF

German

Compositional Zero-Shot Learning (CZSL) zielt darauf ab, neue Kombinationen von zuvor beobachteten Objekten und Attributen zu erkennen. Vorherige Arbeiten zeigten, dass groß angelegte Vision-Sprachmodelle dekomponierbare semantische Strukturen kodieren, die rekonstituiert werden können, um eine Verallgemeinerung zu ermöglichen. Das Geodesically Decomposable Embeddings (GDE) Framework formalisiert dies, indem Richtungen im tangenten Raum am Frechet-Mittel der Embedded-Distribution identifiziert werden, die primitive visuelle Konzepte kodieren. Unsichtbare Attribute-Objekt-Kompositionen können durch Vektorzusammensetzung dieser Primitiven synthetisiert werden. Wir überprüfen die Annahme, dass die Kompositionserallgemeinerung eine expl

Machine translation (Meta M2M100); the English original follows.

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 assumption that compositional generalization requires explicit attribute-object pair re-composition. By means of new compositional scoring functions and extensive evaluations, we show that directly using the decomposed primitives suffices for recognizing unseen compositions. In that, we gain insights on how the geometry of the embedding manifold (Euclidean, spherical, or hyperbolic) and dataset structure affect compositional reasoning. Across models (CLIP, DINOv2, MERU, HyCoCLIP), we show that compositional structure consistently emerges as primitive representations, enabling inference on CZSL benchmarks (MIT-States, VAW-CZSL, UT-Zappos50K, Clothing16K) and group robustness on Waterbirds and CelebA. Code: https://github.com/nis-research/Compositionality-with-Primitives.

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Topics: Embedding, Principle of compositionality, ENCODE, Robustness (evolution), Manifold (fluid mechanics), Mathematics

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