Hard Negative Mining for Metric Learning Based Zero-Shot Classification

Understand

Zero-Shot learning has been shown to be an efficient strategy for domain adaptation.

• In this context, this paper builds on the recent work of Bucher et al.
• [1], which proposed an approach to solve Zero-Shot classification problems (ZSC) by introducing a novel metric learning based objective function.
• This objective function allows to learn an optimal embedding of the attributes jointly with a measure of similarity between images and attributes.