Villon S. auteur aut IRD Iovan Corina auteur aut IRD Mangeas Morgan auteur aut IRD Claverie T. auteur aut IRD Mouillot D. auteur aut IRD Villeger S. auteur aut IRD Vigliola Laurent auteur aut IRD text journalArticle eng text/pdf born digital access Underwater cameras are widely used to monitor marine biodiversity, and the trend is increasing due to the availability of cheap action cameras. The main bottleneck of video methods now resides in the manual processing of images, a time-consuming task requiring trained experts. Recently, several solutions based on Deep Learning (DL) have been proposed to automatically process underwater videos. The main limitation of such algorithms is that they require thousands of annotated images in order to learn to discriminate classes (here species). This 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. Here, we propose to explore how fewshot learning (FSL), an emerging research field, could overcome DL limitations. Few-shot learning is based on the principle of training a Deep Learning algorithm on "how to learn a new classification problem with only few images". In 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. We found that FSL outperform classic DL approach in situations where annotated images are limited, yet still providing good classification accuracy. specialized Few-shot learning Deep learning Video Marine biodiversity 034 122 63 101320 [6 ] 2021 1574-9541 https://www.documentation.ird.fr/hor/fdi:010082144 10.1016/j.ecoinf.2021.101320 1574-9541 [F B010082144] https://www.documentation.ird.fr/hor/fdi:010082144 https://www.documentation.ird.fr/intranet/publi/2021-08/010082144.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2021-08-31 2025-02-24 fdi:010082144 fre