aiqbench analysis
This research addresses the critical challenge of long-tailed distributions in ecological computer vision by using taxonomy-conditioned diffusion models to generate synthetic training data for rare plankton species. By adapting a CLIP encoder with ranked contrastive objectives, the method enables parameter-efficient generation that improves downstream classifier utility without requiring massive new datasets. The approach demonstrates how generative AI can bridge data scarcity gaps in specialized scientific domains like automated imaging.
Multimodal Taxonomic Conditioning for Generative Plankton Imagery
arXiv:2609.11673v1 Announce Type: cross 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.
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.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2609.11673 [cs.CV]
(or arXiv:2609.11673v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.11673 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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