Self-Supervised Pretraining for Fine-Grained Plankton Recognition

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Self-Supervised Pretraining for Fine-Grained Plankton Recognition
Original language description
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.
Czech name
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Czech description
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Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISBN
9798331599942
ISSN
2160-7508
e-ISSN
2160-7516
Number of pages
11
Pages from-to
2122-2132
Publisher name
IEEE Computer Society
Place of publication
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Event location
Salt Lake City
Event date
Jun 18, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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