The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

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 …

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 …

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 …

The recent flourish of deep learning in various tasks is largely accredited to the rich and
accessible labeled data. Nonetheless, massive supervision remains a luxury for many real …

Generalized zero-shot learning (GZSL) requires a classifier trained on seen classes that can
recognize objects from both seen and unseen classes. Due to the absence of unseen …

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic
knowledge from seen classes to unseen classes. Since semantic knowledge is built on …

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

Generalized zero-shot learning aims to recognize images from seen and unseen domains.
Recent methods focus on learning a unified semantic-aligned visual representation to …