Few-shot learning (FSL) aims to address the data-scarce problem. A standard FSL
framework is composed of two components:(1) Pre-train. Employ the base data to generate …

In recent years, researchers pay growing attention to the few-shot learning (FSL) task to
address the data-scarce problem. A standard FSL framework is composed of two …

Collecting a substantial number of labeled samples is infeasible in many real-world
scenarios, thereby bringing out challenges for supervised classification. The research on …

Few-shot learning (FSL), purposing to resolve the problem of data-scarce, has attracted
considerable attention in recent years. A popular FSL framework contains two phases:(i) the …

The few-shot classification (FSC) task aims to classify data with limited labeled examples
across different categories. Typically, researchers pre-train a feature extractor using base …

Recently, few-shot learning has attracted more and more attention. Generally, the fine-tuning-
based few-shot learning framework contains two stages: i) In the pre-training stage, using …

Deep dictionary learning (DDL) shows good performance in visual classification tasks.
However, almost all existing DDL methods ignore the locality relationships between the …

Off-the-shelf dictionary learning algorithms have achieved satisfactory results in small
sample face recognition applications. However, the achieved results depend on the facial …

Decoupled Few-shot learning (FSL) is an effective methodology that deals with the problem
of data-scarce. Its standard paradigm includes two phases:(1) Pre-train. Generating a CNN …

Few-shot classification (FSC) has become a significant area of research in recent years. A
prevalent method in this field is the separation of feature representations from classifiers …