CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale

2024-05-27

AI Summary

To address the cross-modal identification challenge of insect species—including unknown taxa—in large-scale biodiversity monitoring, this paper proposes the first multimodal framework integrating images, DNA barcodes, and taxonomic text. Methodologically, it innovatively adapts CLIP-style contrastive learning for image–DNA cross-modal alignment, enabling zero-shot species recognition without task-specific fine-tuning. The framework jointly encodes visual features via ResNet/ViT, models DNA sequences using k-mer representations and Transformers, and embeds taxonomic labels, all unified within a shared multimodal embedding space to achieve semantic alignment across the three modalities. Evaluated on real-world field data, the approach achieves a zero-shot classification accuracy 8.3% higher than the best unimodal baseline, significantly improving generalization to both known and novel insect species. This work establishes a new paradigm for automated, scalable, and dynamic biodiversity monitoring.

Technology Category

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

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Measuring biodiversity is crucial for understanding ecosystem health. While prior works have developed machine learning models for taxonomic classification of photographic images and DNA separately, in this work, we introduce a multimodal approach combining both, using CLIP-style contrastive learning to align images, barcode DNA, and text-based representations of taxonomic labels in a unified embedding space. This allows for accurate classification of both known and unknown insect species without task-specific fine-tuning, leveraging contrastive learning for the first time to fuse barcode DNA and image data. Our method surpasses previous single-modality approaches in accuracy by over 8% on zero-shot learning tasks, showcasing its effectiveness in biodiversity studies.

Problem

• Combining images and DNA for biodiversity monitoring
• Improving species classification accuracy with multimodal learning
• Enabling zero-shot learning for unknown species identification

Innovation

• Multimodal approach combining images and DNA
• CLIP-style contrastive learning for alignment
• Zero-shot classification without fine-tuning