Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings

Gesina Schwalbe; Mert Keser; Moritz Bayerkuhnlein; Edgar Heinert; Annika Mütze; Marvin Keller; Sparsh Tiwari; Georgii Mikriukov; Diedrich Wolter; Jae Hee Lee; Matthias Rottmann

arXiv · 2603.26798

Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings

Gesina Schwalbe·Mert Keser·Moritz Bayerkuhnlein·Edgar Heinert·Annika Mütze·Marvin Keller·Sparsh Tiwari·Georgii Mikriukov·Diedrich Wolter·Jae Hee Lee·Matthias Rottmann

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Abstract

Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this space is rarely inspected. We present a post-hoc framework to explain, verify, and align the semantic hierarchies induced by a VLM over a given set of child classes. First, we extract a binary hierarchy by agglomerative clustering of class centroids and name internal nodes by dictionary-based matching to a concept bank. Second, we quantify plausibility by comparing the extracted tree against human ontologies using efficient tree- and edge-level consistency measures, and we evaluate utility via explainable hierarchical tree-traversal inference with uncertainty-aware early stopping (UAES). Third, we propose an ontology-guided post-hoc alignment method that learns a lightweight embedding-space transformation, using UMAP to generate target neighborhoods from a desired hierarchy. Across 13 pretrained VLMs and 4 image datasets, our method finds systematic modality differences: image encoders are more discriminative, while text encoders induce hierarchies that better match human taxonomies. Overall, the results reveal a persistent trade-off between zero-shot accuracy and ontological plausibility and suggest practical routes to improve semantic alignment in shared embedding spaces.

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Gesina Schwalbe, Mert Keser, Moritz Bayerkuhnlein, Edgar Heinert, Annika Mütze, Marvin Keller, Sparsh Tiwari, Georgii Mikriukov, Diedrich Wolter, Jae Hee Lee, Matthias Rottmann. 2026-08-09. Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings. https://arxiv.org/abs/2603.26798

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