Most existing zero-shot learning approaches exploit transfer learning via an intermediate-
level semantic representation shared between an annotated auxiliary dataset and a target …

Most existing zero-shot learning approaches exploit transfer learning via an intermediate
semantic representation shared between an annotated auxiliary dataset and a target dataset …

Most existing zero-shot learning approaches exploit transfer learning via an intermediate-
level semantic representation such as visual attributes or semantic word vectors. Such a …

Recently, feature generating methods have been successfully applied to zero-shot learning
(ZSL). However, most previous approaches only generate visual representations for zero …

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 …

Recently, zero-shot learning (ZSL) has received increasing interest. The key idea
underpinning existing ZSL approaches is to exploit knowledge transfer via an intermediate …

Our paper addresses the challenge of generalized zero-shot learning, where the label of a
target image may belong to either a seen or an unseen category. Previous methods for this …

Abstract Most existing Zero-Shot Learning (ZSL) methods have the strong bias problem, in
which instances of unseen (target) classes tend to be categorized as one of the seen …

Zero-shot learning (ZSL) aims at recognizing data as an unseen category, using information
learned from the training data of predefined (seen) labels or attributes. In this paper, we …

As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims
at automatically recognizing the instances from unseen object classes without training data …