Multimodal Taxonomic Conditioning for Generative Plankton Imagery

The paper presents a method for generating synthetic plankton images conditioned on taxonomic labels to address the long‑tailed nature of automated plankton imaging datasets. A CLIP encoder is fine‑tuned on a large plankton corpus using a ranked contrastive objective that accommodates deep, ragged taxonomies, and then frozen to guide a parameter‑efficient diffusion transformer. The quality of the synthetic samples is evaluated both for distributional fidelity and for their usefulness in training downstream classifiers.

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• llms
• diffusion
• multimodal