Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and
unseen classes, when only the labeled examples from seen classes are provided. Recent …

This paper focuses on the unsupervised domain adaptation of transferring the knowledge
from the source domain to the target domain in the context of semantic segmentation …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

When labeled training data is scarce, a promising data augmentation approach is to
generate visual features of unknown classes using their attributes. To learn the 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 …