CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale

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.

@article{gong2025_2405.17537,
  title={ CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale },
  author={ ZeMing Gong and Austin T. Wang and Xiaoliang Huo and Joakim Bruslund Haurum and Scott C. Lowe and Graham W. Taylor and Angel X. Chang },
  journal={arXiv preprint arXiv:2405.17537},
  year={ 2025 }
}