Few-shot image classification is a challenging problem that aims to achieve the human level
of recognition based only on a small number of training images. One main solution to few …

Metric-based methods are one of the most common methods to solve the problem of few-
shot image classification. However, traditional metric-based few-shot methods suffer from …

Few-shot classification requires adapting knowledge learned from a large annotated base
dataset to recognize novel unseen classes, each represented by few labeled examples. In …

Although modern object detectors rely heavily on a significant amount of training data,
humans can easily detect novel objects using a few training examples. The mechanism of …

Few-shot learning (FSL) aims at fast adaptation to novel classes with few training samples.
Among FSL methods, meta-learning and transfer learning-based methods are the most …

To improve the ability of the deep learning model to handle imbalanced data, a fault
diagnosis method based on improved gated convolutional neural network (IGCNN) is …

Annotated images for rare disease diagnosis are extremely hard to collect. Therefore,
identifying rare diseases under a few-shot learning (FSL) setting is significant. Existing FSL …

Meta-learning aims to train a classifier on collections of tasks, such that it can recognize new
classes given few samples from each. However, current approaches encounter overfitting …

Few-shot classification aims to identify novel categories using only a few labeled samples.
Generally, the metric-based few-shot classification methods compare the feature embedding …

Few-shot learning aims to recognize novel concepts with only a few examples. To this end,
previous studies resort to acquiring a strong inductive bias via meta-learning on a group of …