Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models

Taxonomies provide key information about the semantic relationships between concepts and the inherent organization of vision and language. Despite their impressive capabilities, large multimodal models (LMMs) often lack taxonomic knowledge, leading to low hierarchical visual recognition (HVR) consistency. These models typically only rely on language modeling objectives during fine-tuning and lack explicit taxonomy-aware regularization. To address this, we propose Hierarchical Representation Regularization (HiR$^2$), a simple plug-and-play regularizer that improves hierarchical consistency in LMMs. Specifically, we introduce a semantic-aware visual tree construction framework that extracts coarse-to-fine visual features from intermediate LLM layers guided by textual cues. The regularizer combines two complementary objectives: a taxonomic entailment loss that enforces hierarchy via hyperbolic entailment cones in the Lorentz model, and a discriminative dispersive loss that promotes angular separation of semantically similar embeddings on the unit sphere without disturbing the radial hierarchical structure. Extensive experiments demonstrate that HiR$^2$ effectively captures taxonomic structures across diverse LMMs and fine-tuning methods. Code is available at https://github.com/PKUICST-MIPL/HiR2_ICML2026.

Large multimodal models can recognize images and answer questions about them, but they often struggle to understand category relationships, such as knowing that a “rose” is also a “flower” and a “plant.” This paper proposes $HiR^2$, a simple add-on for model training that helps these models learn such tree-like category structures more consistently. $HiR^2$ guides the model’s internal visual representations to respect parent–child relationships between concepts while still distinguishing similar categories. Experiments on biological image datasets show that $HiR^2$ improves hierarchical recognition across different models and training methods.