A review of few-shot fine-grained image classification

Few-shot fine-grained image classification presents a significant challenge in computer vision due to its need for distinguishing subtle differences among visually similar categories with limited labeled data. This review paper provides a comprehensive overview of current methodologies and advances in this field. It examines various approaches including metric-based, data augmentation-based, knowledge distillation, self-supervised learning, and hybrid-based approaches that integrate multiple strategies. Metric-based methods focus on optimizing similarity metrics to enhance classification accuracy with few samples. Data augmentation approaches generate synthetic samples to expand training datasets and address data scarcity. Knowledge distillation leverages the transfer of knowledge from large teacher models to smaller student models, improving their performance. Self-supervised learning utilizes unlabeled data to pre-train models, thereby reducing dependence on labeled datasets. Hybrid approaches combine these techniques to address their individual limitations and enhance model robustness and adaptability. In addition, this paper also discusses the current limitations of these approaches, such as data scarcity, interpretability issues, and challenges in domain adaptation. Furthermore, key areas for future research, including multimodal learning, scalability and efficiency, domain adaptability, novel data augmentation techniques, and the interpretability and explainability of few-shot fine-grained models, are identified. The review highlights the broader implications of advancements in this field, emphasizing the potential impact on applications like object recognition, medical imaging, and species identification. By summarizing the state-of-the-art techniques and suggesting directions for future work, this paper aims to contribute to the advancement of few-shot fine-grained image classification and its practical applications.