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 …

Few-shot learning is one of the most challenging problems in computer vision due to the
difficulty of sample collection in many real-world applications. It aims at classifying a sample …

The goal of few-shot learning is to recognize new visual concepts with just a few amount of
labeled samples in each class. Recent effective metric-based few-shot approaches employ …

Few-shot learning (FSL) aims at recognizing a novel object under limited training samples. A
robust feature extractor (backbone) can significantly improve the recognition performance of …

Few-shot classification requires deep neural networks to learn generalized representations
only from limited training images, which is challenging but significant in low-data regimes …

Few-shot learning aims to recognize instances from previously unseen classes based on a
very limited number of examples. However, models often face the challenge of overfitting …

In Few-Shot Learning (FSL), the objective is to correctly recognize new samples from novel
classes with only a few available samples per class. Existing methods in FSL primarily focus …

Few-shot learning is the key step towards human-level intelligence. Prototypical Network is
a promising approach to address the key issue of over-fitting for few-shot learning …

Few-shot classification aims to adapt classifiers to novel classes with a few training samples.
However, the insufficiency of training data may cause a biased estimation of feature …

In image classification, it is common practice to train deep networks to extract a single
feature vector per input image. Few-shot classification methods also mostly follow this trend …