Can I read Towards Effective Deep Embedding for Zero-Shot Learning on EtoBox?

Towards Effective Deep Embedding for Zero-Shot Learning by Zhang, Lei; Wang, Peng; Liu, Lingqiao; Shen, Chunhua; Wei, Wei; Zhang, Yannning; Hengel, Anton Van Den is a scholarly article available to read on EtoBox.

What is Towards Effective Deep Embedding for Zero-Shot Learning about?

Zero-shot learning (ZSL) can be formulated as a cross-domain matching problem: after being projected into a joint embedding space, a visual sample will match against all candidate class-level semantic descriptions and be assigned to the nearest class. In this process, the embedding space underpins the success of such matching and is crucial for ZSL. In this paper, we conduct an in-depth study on the construction of embedding space for ZSL and posit that an ideal embedding space should satisfy two criteria: intra-class compactness and inter-class separability. While the former encourages the embeddings of visual samples of one class to distribute tightly close to the semantic description embedding of this class, the latter requires embeddings from different classes to be well separated from each other. Towards this goal, we present a simple but effective two-branch network to simultaneously map semantic descriptions and visual samples into a joint space, on which visual embeddings are forced to regress to their class-level semantic embeddings and the embeddings crossing classes are required to be distinguishable by a trainable classifier. Furthermore, we extend our method to a trans

Author

Zhang, Lei; Wang, Peng; Liu, Lingqiao; Shen, Chunhua; Wei, Wei; Zhang, Yannning; Hengel, Anton Van Den

Published

2018

Language

EN