Multimodal Taxonomic Conditioning for Generative Plankton Imagery — Paper

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: arxiv.org/abs/2609.11673
Source:arXiv (Atom API + RSS)T1observed 51 min agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
arXiv id: 2609.11673
Source:arXiv (Atom API + RSS)T1observed 51 min agohigh
Categories: cs.CV, cs.LG
Source:arXiv (Atom API + RSS)T1observed 51 min agohigh
PDF: arxiv.org/pdf/2609.11673
Source:arXiv (Atom API + RSS)T1observed 51 min agohigh
Primary category: cs.CV
Source:arXiv (Atom API + RSS)T1observed 51 min agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 51 min agohigh

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Attributed facts

9

Source tiers

T19

Freshest observation

50 min ago

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None

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Authors: Daniela Ivanova, Ozgu Goksu, Nicolas Pugeault

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Claim history · Abstract

1 claims · 1 propertiesShow all properties

Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
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.	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
Property changedPaperMultimodal Taxonomic Conditioning for Generative Plankton Imagery
Multimodal Taxonomic Conditioning for Generative Plankton Imagery: arxiv announce type changed from cross to new
Arxiv announce typecross→newarxiv
rss.arxiv.org
11 Sept 2026
New paperPaperMultimodal Taxonomic Conditioning for Generative Plankton Imagery
New paper: Multimodal Taxonomic Conditioning for Generative Plankton Imagery
arxiv
rss.arxiv.org

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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CV	feed	T1· Official	50 min ago	1
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	50 min ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.