@article{fdi:010082144,
  title = {{A}utomatic underwater fish species classification with limited data using few-shot learning},
  author = {{V}illon, {S}. and {I}ovan, {C}orina and {M}angeas, {M}organ and {C}laverie, {T}. and {M}ouillot, {D}. and {V}illeger, {S}. and {V}igliola, {L}aurent},
  editor = {},
  language = {{ENG}},
  abstract = {{U}nderwater cameras are widely used to monitor marine biodiversity, and the trend is increasing due to the availability of cheap action cameras. {T}he main bottleneck of video methods now resides in the manual processing of images, a time-consuming task requiring trained experts. {R}ecently, several solutions based on {D}eep {L}earning ({DL}) have been proposed to automatically process underwater videos. {T}he main limitation of such algorithms is that they require thousands of annotated images in order to learn to discriminate classes (here species). {T}his limitation implies two issues: 1) the annotation of hundreds of common species requires a lot of efforts 2) many species are too rare to gather enough data to train a classic {DL} algorithm. {H}ere, we propose to explore how fewshot learning ({FSL}), an emerging research field, could overcome {DL} limitations. {F}ew-shot learning is based on the principle of training a {D}eep {L}earning algorithm on "how to learn a new classification problem with only few images". {I}n our case-study, we assess the robustness of {FSL} to discriminate 20 coral reef fish species with a range of training databases from 1 image per class to 30 images per class, and compare {FSL} to a classic {DL} approach with thousands of images per class. {W}e found that {FSL} outperform classic {DL} approach in situations where annotated images are limited, yet still providing good classification accuracy.},
  keywords = {{F}ew-shot learning ; {D}eep learning ; {V}ideo ; {M}arine biodiversity},
  booktitle = {},
  journal = {{E}cological {I}nformatics},
  volume = {63},
  numero = {},
  pages = {101320 [6 ]},
  ISSN = {1574-9541},
  year = {2021},
  DOI = {10.1016/j.ecoinf.2021.101320},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082144},
}