  Deep convolutional neural networks generally perform well in underwater
object recognition tasks on both optical and sonar images. Many such methods
require hundreds, if not thousands, of images per class to generalize well to
unseen examples. However, obtaining and labeling sufficiently large volumes of
data can be relatively costly and time-consuming, especially when observing
rare objects or performing real-time operations. Few-Shot Learning (FSL)
efforts have produced many promising methods to deal with low data
availability. However, little attention has been given in the underwater
domain, where the style of images poses additional challenges for object
recognition algorithms. To the best of our knowledge, this is the first paper
to evaluate and compare several supervised and semi-supervised Few-Shot
Learning (FSL) methods using underwater optical and side-scan sonar imagery.
Our results show that FSL methods offer a significant advantage over the
traditional transfer learning methods that fine-tune pre-trained models. We
hope that our work will help apply FSL to autonomous underwater systems and
expand their learning capabilities.
