9 citations · 18 across the 6 of their papers we have counts for
6 papers
Cross-modal learning for plankton recognition
Joona Kareinen, Veikka Immonen, Tuomas Eerola +5
This paper considers self-supervised cross-modal coordination as a strategy enabling utilization of multiple modalities and large volumes of unlabeled plankton data to build models…
Open-Set Plankton Recognition
Joona Kareinen, Annaliina Skyttä, Tuomas Eerola +5
This paper considers open-set recognition (OSR) of plankton images. Plankton include a diverse range of microscopic aquatic organisms that have an important role in marine ecosyste…
Self-Supervised Pretraining for Fine-Grained Plankton Recognition
Joona Kareinen, Tuomas Eerola, Kaisa Kraft +3
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 monitor…
DAPlankton: Benchmark Dataset for Multi-instrument Plankton Recognition via Fine-grained Domain Adaptation
Daniel Batrakhanov, Tuomas Eerola, Kaisa Kraft +6
Plankton recognition provides novel possibilities to study various environmental aspects and an interesting real-world context to develop domain adaptation (DA) methods. Different…
Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectives
Tuomas Eerola, Daniel Batrakhanov, Nastaran Vatankhah Barazandeh +7
Planktonic organisms are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to understand the changes in th…
Towards Phytoplankton Parasite Detection Using Autoencoders
Simon Bilik, Daniel Batrakhanov, Tuomas Eerola +9
Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their i…