CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale

• URL: http://arxiv.org/abs/2405.17537v3
• Date: Wed, 06 Nov 2024 15:56:04 GMT
• Title: CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale
• Authors: ZeMing Gong, Austin T. Wang, Xiaoliang Huo, Joakim Bruslund Haurum, Scott C. Lowe, Graham W. Taylor, Angel X. Chang,
• Abstract summary: We use contrastive learning to align images, barcode DNA, and text-based representations of taxonomic labels in a unified embedding space. Our method surpasses previous single-modality approaches in accuracy by over 8% on zero-shot learning tasks.
• Score: 21.995678534789615
• License:
• 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 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.

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