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arXiv · 2609.11673

Multimodal Taxonomic Conditioning for Generative Plankton Imagery

Daniela Ivanova·Ozgu Goksu·Nicolas Pugeault
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Abstract

Automated plankton imaging produces severely long-tailed datasets, where the rare taxa of greatest ecological interest have too few images to train or evaluate classifiers reliably. We generate synthetic plankton imagery conditioned on taxonomy: a CLIP encoder is adapted on a large plankton corpus with a ranked contrastive objective extended to deep, ragged taxonomies, then frozen to condition a parameter-efficient diffusion transformer. We evaluate synthetic sample quality on distributional fidelity and downstream classifier utility.

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Daniela Ivanova, Ozgu Goksu, Nicolas Pugeault. 2026-09-10. Multimodal Taxonomic Conditioning for Generative Plankton Imagery. https://arxiv.org/abs/2609.11673

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