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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…
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation
Huiling Wang, Tapani Raiko, Lasse Lensu +2
Deep convolutional neural networks (CNNs) have been immensely successful in many high-level computer vision tasks given large labeled datasets. However, for video semantic object s…