9 citations · 28 across the 8 of their papers we have counts for
8 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…
Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies
Teemu Härkönen, Erik M. Vartiainen, Lasse Lensu +2
We propose an approach utilizing gamma-distributed random variables, coupled with log-Gaussian modeling, to generate synthetic datasets suitable for training neural networks. This…
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…