Hierarchy-Guided Multimodal Representation Learning for Taxonomic Inference

2026-03-26

AI Summary

This work addresses the limited robustness of multimodal species classification in large-scale wild datasets, which often stems from neglecting the hierarchical structure inherent in biological taxonomy. To this end, the authors propose an end-to-end hierarchical-aware multimodal learning framework that explicitly models taxonomic hierarchies. The approach introduces Hierarchical Information Regularization (HiR) to refine the geometric structure of the embedding space and incorporates a lightweight fusion predictor capable of both unimodal and joint inference. Evaluated on multiple large-scale biodiversity benchmarks, the method outperforms strong multimodal baselines by over 14% in accuracy, demonstrating particularly superior performance under challenging conditions such as missing modalities or degraded DNA barcodes.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate biodiversity identification from large-scale field data is a foundational problem with direct impact on ecology, conservation, and environmental monitoring. In practice, the core task is taxonomic prediction - inferring order, family, genus, or species from imperfect inputs such as specimen images, DNA barcodes, or both. Existing multimodal methods often treat taxonomy as a flat label space and therefore fail to encode the hierarchical structure of biological classification, which is critical for robustness under noise and missing modalities. We present two end-to-end variants for hierarchy-aware multimodal learning: CLiBD-HiR, which introduces Hierarchical Information Regularization (HiR) to shape embedding geometry across taxonomic levels, yielding structured and noise-robust representations; and CLiBD-HiR-Fuse, which additionally trains a lightweight fusion predictor that supports image-only, DNA-only, or joint inference and is resilient to modality corruption. Across large-scale biodiversity benchmarks, our approach improves taxonomic classification accuracy by over 14 percent compared to strong multimodal baselines, with particularly large gains under partial and corrupted DNA conditions. These results highlight that explicitly encoding biological hierarchy, together with flexible fusion, is key for practical biodiversity foundation models.

Problem

• taxonomic inference
• multimodal representation learning
• hierarchical classification
• biodiversity identification
• modality robustness

Innovation

• hierarchical representation learning
• multimodal fusion
• taxonomic inference
• noise-robust embedding
• biological classification