Feature Contrastive Transfer Learning for Few-Shot Long-Tail Sonar Image Classification

Abstract

Sonar image classification is challenging due to the limited availability and long-tail distribution of labeled sonar samples. In this work, a Feature Contrastive Transfer Learning (FCTL) framework is proposed for few-shot long-tailed sonar image classification. The proposed framework combines transfer learning and contrastive learning to improve model performance under limited labeled data. First, a deep convolutional neural network (CNN) is pre-trained on a large-scale image dataset to learn general feature representations. Then, contrastive learning is employed to maximize the similarity between positive sample pairs and minimize the similarity between positive and negative sample pairs. Specifically, positive samples are generated through a Gaussian feature enhancement method, while the remaining samples in a batch are negative. In addition, a balanced sampling strategy is employed to optimize the unbalanced feature distribution of long-tailed samples. Experiments on two different sonar image datasets demonstrate that the FCTL framework outperforms existing methods in few-shot long-tailed sonar image classification tasks.