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Multimodal Taxonomic Conditioning for Generative Plankton Imagery

arxiv.org/abs/2609.11673

Updated 21 min ago · first seen 11 Sept 2026

paper_01M294FR74HAW84DC4VFKPMBRM

Published
11 Sept 2026
T1 · 22 min ago
arXiv
2609.11673
T1 · 22 min ago
Category
cs.CV
T1 · 22 min ago

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.

Authors 3

Daniela Ivanova, Ozgu Goksu, Nicolas Pugeault

Specification

Official page

Source:arXiv (Atom API + RSS)T1observed 22 min agohigh

Arxiv announce type
new

Source:arXiv (Atom API + RSS)T1observed 21 min agohigh

arXiv id
2609.11673

Source:arXiv (Atom API + RSS)T1observed 22 min agohigh

Categories
cs.CV, cs.LG

Source:arXiv (Atom API + RSS)T1observed 22 min agohigh

PDF

Source:arXiv (Atom API + RSS)T1observed 22 min agohigh

Primary category
cs.CV

Source:arXiv (Atom API + RSS)T1observed 22 min agohigh

Published
11 Sept 2026

Source:arXiv (Atom API + RSS)T1observed 22 min agohigh

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Attributed facts

9

Source tiers

T19

Freshest observation

21 min ago

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None