Although there have been encouraging breakthroughs in supervised learning since the
renaissance of deep learning, the recognition of large-scale object classes remains a …

Traditional machine learning techniques, including deep learning, most assume that the
classes of testing samples belong to the subset of training samples. However, there are …

With the knowledge learned from some labelled training images, zero-shot learning (ZSL)
aims to recognize new visual concepts by leveraging some intermediate information for both …

Abstract Zero-Shot Learning (ZSL) aims to recognize images belonging to unseen classes
that are unavailable in the training process, while Generalized Zero-Shot Learning (GZSL) is …

We investigate the problem of generalized zero-shot learning (GZSL). GZSL relaxes the
unrealistic assumption in conventional zero-shot learning (ZSL) that test data belong only to …

Deep learning methods may decline in their performance when the number of labelled
training samples is limited, in a scenario known as few-shot learning. The methods may …

Zero-Shot Learning (ZSL) aims to recognise unseen object classes, which are not observed
during the training phase. The existing body of works on ZSL mostly relies on pretrained …

Deep learning models have the ability to extract empirical knowledge from large-scale
datasets. However, the sharing of data has become increasingly challenging due to …

Abstract Zero-Shot Learning (ZSL) aims at recognizing unseen classes that are absent
during the training stage. Unlike the existing approaches that learn a visual-semantic …

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so
that the latter can be recognised without any training samples. This is made possible by …