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 …

Recent self-supervised methods for image representation learning are based on maximizing
the agreement between embedding vectors from different views of the same image. A trivial …

Continual Learning has inspired a plethora of approaches and evaluation settings; however,
the majority of them overlooks the properties of a practical scenario, where the data stream …

Zero-shot learning (ZSL), an emerging topic in recent years, targets at distinguishing unseen
class images by taking images from seen classes for training the classifier. Existing works …

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 …

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 key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge
between visual and attribute features on seen classes, and thus achieving a desirable …

From the beginning of zero-shot learning research, visual attributes have been shown to
play an important role. In order to better transfer attribute-based knowledge from known to …

Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring
semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable …

Recent works have shown that unstructured text (documents) from online sources can serve
as useful auxiliary information for zero-shot image classification. However, these methods …