Self-supervised transfer learning for few-shot classification on marine plankton images

The classification of marine plankton images is of great significance in ecological studies and environmental monitoring. In practical applications, plankton image classification faces several challenges, including sample imbalance, distinguishing between-class and within-class differences, and recognizing fine-grained features. To address these issues, we propose a few-shot self-supervised transfer learning (FSTL) framework. In FSTL, we design a new loss function that incorporates both supervised and self-supervised learning. The core of FSTL is a hybrid learning objective that integrates self-supervised contrastive learning for robust feature representation. Operating within a transfer learning paradigm, FSTL effectively adapts knowledge from head-classes to boost the few-shot classification performance on tail-classes. We applied FSTL to two datasets, in which plankton images were collected from Daya Bay and provided by the Woods Hole Oceanographic Institution (WHOI) datasets respectively. The experimental results demonstrated that our method showed better adaptability in the classification of plankton images. The findings of this study not only apply to the classification of plankton images but also offer the potential for classifying small-sample categories within long-tailed datasets.