With the recent renaissance of deep convolutional neural networks (CNNs), encouraging
breakthroughs have been achieved on the supervised recognition tasks, where each class …

The goal of few-shot learning is to learn a classifier that generalizes well even when trained
with a limited number of training instances per class. The recently introduced meta-learning …

In few-shot classification, we are interested in learning algorithms that train a classifier from
only a handful of labeled examples. Recent progress in few-shot classification has featured …

Suffering from the extreme training data imbalance between seen and unseen classes, most
of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging …

Due to the importance of zero-shot learning, ie, classifying images where there is a lack of
labeled training data, the number of proposed approaches has recently increased steadily …

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for
automatic medical image segmentation. However, they have not demonstrated sufficiently …

Learning to classify new categories based on just one or a few examples is a long-standing
challenge in modern computer vision. In this work, we propose a simple yet effective method …

We propose a novel framework called Semantics-Preserving Adversarial Embedding
Network (SP-AEN) for zero-shot visual recognition (ZSL), where test images and their …

Zero shot learning in Image Classification refers to the setting where images from some
novel classes are absent in the training data but other information such as natural language …

A technical challenge of deep learning is recognizing target classes without seen data. Zero-
shot learning leverages semantic representations such as attributes or class prototypes to …