CLIBD: A Multimodal Biodiversity Monitoring Model Connecting Vision and Genomics

CLIBD (Contrastive Learning for Image-Barcode Diversity) is a multimodal biodiversity monitoring model connecting vision and genomics. It maps biological images, DNA barcodes, and text classification labels into a unified latent space via contrastive learning, enabling cross-modal retrieval and classification, and providing a new paradigm for large-scale biodiversity monitoring. Keywords: CLIBD, biodiversity, multimodal learning, contrastive learning, DNA barcode, species identification, computer vision, genomics.

Biodiversity monitoring is crucial for ecosystem health assessment and conservation strategy formulation. However, traditional manual identification is time-consuming and labor-intensive, making it difficult to handle large-scale samples. Current mainstream DNA barcode technology has high accuracy but is costly and time-consuming; image recognition is convenient but has limited accuracy when dealing with similar species or organisms lacking visual features. CLIBD aims to integrate the advantages of both modalities to solve the problem of complementary fusion in the field of biodiversity monitoring.

CLIBD core adopts a contrastive learning framework, mapping images, DNA barcodes, and text labels into a unified latent space. Multimodal encoder design: The image encoder is based on the Vision Transformer (ViT) pre-trained model; the DNA encoder uses BarcodeBERT (a pre-trained language model specifically designed for DNA sequences); the text encoder uses BERT-small to process taxonomic labels. Training uses a contrastive learning loss function, and parameter-efficient fine-tuning is performed via LoRA technology to reduce computational resource requirements and prevent overfitting.

CLIBD was trained and evaluated on the BIOSCAN-1M and BIOSCAN-5M insect datasets. A strict data partitioning strategy was used: the training set includes unlabeled records and some seen species, while the validation/test set includes both seen and unseen species, simulating the open-world recognition problem. Experimental results: Single-modal classification outperforms traditional methods; cross-modal retrieval (image-to-DNA, DNA-to-image) performs excellently; three-modal alignment further improves performance.

CLIBD's application scenarios include: 1. Rapid field surveys: Retrieve similar DNA records to assist identification after taking photos; 2. Museum specimen digitization: Establish associations between image and DNA databases; 3. Ecological monitoring and conservation assessment: Track population dynamics and evaluate conservation effects; 4. Citizen science projects: Lower the threshold for identification and support crowdsourced data collection.

CLIBD has the following limitations and future directions: 1. Data bias: Need to verify applicability to other biological groups such as plants and fungi; 2. Geographic distribution bias: Need to establish a globally balanced dataset; 3. Rare species identification: Can combine few-shot/meta-learning techniques; 4. Real-time inference optimization: Need to achieve edge device deployment via model compression and other technologies.

CLIBD integrates visual and genomic information, improves species identification accuracy, creates a flexible and efficient multimodal monitoring paradigm, and provides strong support for biodiversity research and conservation. The open-source implementation of the project provides resources for the research community and promotes innovative development in related fields.