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 …

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 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 …

The last decade has witnessed a growing interest in video salient object detection (VSOD).
However, the research community long-term lacked a well-established VSOD dataset …

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

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic
knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute …

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 …

We address the problem of fine-grained generalized zero-shot recognition of visually similar
classes without training images for some classes. We propose a dense attribute-based …

Most of the existing Zero-Shot Learning (ZSL) approaches learn direct embeddings from
global features or image parts (regions) to the semantic space, which, however, fail to …