A Hybrid Few-Shot Image Classification Framework Combining Gaussian Modeling and Label Propagation

Humans possess the remarkable ability to recognize new objects with merely a handful of labeled examples, whereas contemporary deep learning models continue to face challenges in few-shot learning scenarios, primarily due to the scarcity of training data. In this study, we concentrate on addressing the challenges associated with transductive and semi-supervised few-shot image classification, both methods permitting the incorporation of unlabeled data during the training phase. To fully leverage the potential of unlabeled data, we explore a variety of unsupervised and semi-supervised learning approaches, including manifold learning, aimed at uncovering the intrinsic properties of the data. Specifically, we employ the locality preserving projection method as a powerful enabling technique for discriminative feature learning. The features learned are integrated into our proposed hybrid few-shot learning (FSL) framework, collaboratively augmenting the performance of few-shot image classification. Our proposed hybrid FSL framework capitalizes on the synergistic capabilities of both the parametric Gaussian model and the non-parametric label propagation model through a straightforward score-level ensemble learning approach. Consequently, our methodology yields superior outcomes on four benchmark datasets (miniImageNet, tieredImageNet, CUB, and CIFAR-FS), for both transductive and semi-supervised few-shot image classification tasks.