Self-Supervised Pretraining for Fine-Grained Plankton Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0200638" target="_blank" >RIV/00216305:26230/26:0200638 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CVPRW67362.2025.00200" target="_blank" >http://dx.doi.org/10.1109/CVPRW67362.2025.00200</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPRW67362.2025.00200" target="_blank" >10.1109/CVPRW67362.2025.00200</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-Supervised Pretraining for Fine-Grained Plankton Recognition

Popis výsledku v původním jazyce

Plankton recognition is an important computer vision problem due to plankton's essential role in ocean food webs and carbon capture, highlighting the need for species-level monitoring. However, this task is challenging due to its fine-grained nature and dataset shifts caused by different imaging instruments and varying species distributions. As new plankton image datasets are collected at an increasing pace, there is a need for general plankton recognition models that require minimal expert effort for data labeling. In this work, we study large-scale self-supervised pretraining for fine-grained plankton recognition. We first employ masked autoencoding and a large volume of diverse plankton image data to pretrain a general-purpose plankton image encoder. Then, we utilize fine-tuning to obtain accurate plankton recognition models for new datasets with a very limited number of labeled training images. Our experiments show that self-supervised pretraining with diverse plankton data clearly increases plankton recognition accuracy compared to standard ImageNet pretraining when the amount of training data is limited. Moreover, the accuracy can be further improved when unlabeled target data is available and utilized during the pretraining.

Název v anglickém jazyce

Self-Supervised Pretraining for Fine-Grained Plankton Recognition

Popis výsledku anglicky

Plankton recognition is an important computer vision problem due to plankton's essential role in ocean food webs and carbon capture, highlighting the need for species-level monitoring. However, this task is challenging due to its fine-grained nature and dataset shifts caused by different imaging instruments and varying species distributions. As new plankton image datasets are collected at an increasing pace, there is a need for general plankton recognition models that require minimal expert effort for data labeling. In this work, we study large-scale self-supervised pretraining for fine-grained plankton recognition. We first employ masked autoencoding and a large volume of diverse plankton image data to pretrain a general-purpose plankton image encoder. Then, we utilize fine-tuning to obtain accurate plankton recognition models for new datasets with a very limited number of labeled training images. Our experiments show that self-supervised pretraining with diverse plankton data clearly increases plankton recognition accuracy compared to standard ImageNet pretraining when the amount of training data is limited. Moreover, the accuracy can be further improved when unlabeled target data is available and utilized during the pretraining.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

ISBN

9798331599942

ISSN

2160-7508

e-ISSN

2160-7516

Počet stran výsledku

11

Strana od-do

2122-2132

Název nakladatele

IEEE Computer Society

Místo vydání

—

Místo konání akce

Salt Lake City

Datum konání akce

18. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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