Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.05615 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929237868412928 |
|---|---|
| author | Batrakhanov, Daniel Eerola, Tuomas Kraft, Kaisa Haraguchi, Lumi Lensu, Lasse Suikkanen, Sanna Camarena-Gómez, María Teresa Seppälä, Jukka Kälviäinen, Heikki |
| author_facet | Batrakhanov, Daniel Eerola, Tuomas Kraft, Kaisa Haraguchi, Lumi Lensu, Lasse Suikkanen, Sanna Camarena-Gómez, María Teresa Seppälä, Jukka Kälviäinen, Heikki |
| contents | Plankton recognition provides novel possibilities to study various environmental aspects and an interesting real-world context to develop domain adaptation (DA) methods. Different imaging instruments cause domain shift between datasets hampering the development of general plankton recognition methods. A promising remedy for this is DA allowing to adapt a model trained on one instrument to other instruments. In this paper, we present a new DA dataset called DAPlankton which consists of phytoplankton images obtained with different instruments. Phytoplankton provides a challenging DA problem due to the fine-grained nature of the task and high class imbalance in real-world datasets. DAPlankton consists of two subsets. DAPlankton_LAB contains images of cultured phytoplankton providing a balanced dataset with minimal label uncertainty. DAPlankton_SEA consists of images collected from the Baltic Sea providing challenging real-world data with large intra-class variance and class imbalance. We further present a benchmark comparison of three widely used DA methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05615 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DAPlankton: Benchmark Dataset for Multi-instrument Plankton Recognition via Fine-grained Domain Adaptation Batrakhanov, Daniel Eerola, Tuomas Kraft, Kaisa Haraguchi, Lumi Lensu, Lasse Suikkanen, Sanna Camarena-Gómez, María Teresa Seppälä, Jukka Kälviäinen, Heikki Computer Vision and Pattern Recognition Plankton recognition provides novel possibilities to study various environmental aspects and an interesting real-world context to develop domain adaptation (DA) methods. Different imaging instruments cause domain shift between datasets hampering the development of general plankton recognition methods. A promising remedy for this is DA allowing to adapt a model trained on one instrument to other instruments. In this paper, we present a new DA dataset called DAPlankton which consists of phytoplankton images obtained with different instruments. Phytoplankton provides a challenging DA problem due to the fine-grained nature of the task and high class imbalance in real-world datasets. DAPlankton consists of two subsets. DAPlankton_LAB contains images of cultured phytoplankton providing a balanced dataset with minimal label uncertainty. DAPlankton_SEA consists of images collected from the Baltic Sea providing challenging real-world data with large intra-class variance and class imbalance. We further present a benchmark comparison of three widely used DA methods. |
| title | DAPlankton: Benchmark Dataset for Multi-instrument Plankton Recognition via Fine-grained Domain Adaptation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.05615 |