Learning to Reuse Visual Knowledge

Zero-Shot Learning (ZSL) is an extreme form of transfer learning that aims at learning from a few “seen classes” to have an understanding about the “unseen classes” in the wild. Given a dataset in ZSL research, most existing works use a predetermined, ...

Zero-shot learning (ZSL) aims to bridge the knowledge transfer via available semantic representations (e.g., attributes) between labeled source instances of seen classes and unlabelled target instances of unseen classes. Most existing ZSL approaches ...

Generalized zero-shot extreme multi-label learning (GZXML) aims to predict relevant labels for unknown instances from a set of seen and unseen labels and is widely used in engineering applications. Since the supervisory information of the ...