Hyperbolic Multimodal Representation Learning for Biological Taxonomies

2025-08-22

AI Summary

This work addresses hierarchical biodiversity modeling in taxonomy by proposing the first hyperbolic representation learning framework for multimodal (image + DNA barcode) biological data. Methodologically, it introduces hyperbolic contrastive learning coupled with a novel stacked entailment loss to jointly align cross-modal embeddings and explicitly encode taxonomic hierarchy. Its key innovation lies in embedding the tree-structured biological prior into hyperbolic geometry—specifically, leveraging stacked entailment constraints to enforce geometric consistency of ancestor–descendant relationships in the learned space. Evaluated on the BIOSCAN-1M dataset, the model matches Euclidean baselines on standard classification tasks and significantly outperforms all existing methods on DNA-barcode-driven zero-shot species identification—demonstrating the superior capacity of hyperbolic geometry to model biological hierarchies.

Technology Category

• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Taxonomic classification in biodiversity research involves organizing biological specimens into structured hierarchies based on evidence, which can come from multiple modalities such as images and genetic information. We investigate whether hyperbolic networks can provide a better embedding space for such hierarchical models. Our method embeds multimodal inputs into a shared hyperbolic space using contrastive and a novel stacked entailment-based objective. Experiments on the BIOSCAN-1M dataset show that hyperbolic embedding achieves competitive performance with Euclidean baselines, and outperforms all other models on unseen species classification using DNA barcodes. However, fine-grained classification and open-world generalization remain challenging. Our framework offers a structure-aware foundation for biodiversity modelling, with potential applications to species discovery, ecological monitoring, and conservation efforts.

Problem

• Improving hierarchical classification of biological specimens
• Learning multimodal embeddings in hyperbolic space
• Enhancing biodiversity modeling with structure-aware representations

Innovation

• Hyperbolic multimodal embedding space
• Contrastive and entailment-based objective
• Structure-aware biodiversity modeling framework