Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …

Zero-shot learning (ZSL) is one of the most promising problems where substantial progress
can potentially be achieved through unsupervised learning, due to distributional differences …

Zero-shot learning has been well explored to precisely identify new unobserved classes
through a visual-semantic function obtained from the existing objects. However, there exist …

Abstract Generalized Zero-Shot Learning (GZSL) has emerged as a pivotal research domain
in computer vision, owing to its capability to recognize objects that have not been seen …

Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that
some classes are not observable during training. To bridge the gap between the seen and …

Zero-shot learning (ZSL) aims to recognize objects of unseen classes with available training
data from another set of seen classes. Existing solutions are focused on exploring …

Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts
dedicated to overcoming the problems of visual-semantic domain gaps and seen-unseen …

The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that
were not seen during training. To address this challenging task, most ZSL methods relate …

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 …

We present a deep generative model for Zero-Shot Learning (ZSL). Unlike most existing
methods for this problem, that represent each class as a point (via a semantic embedding) …