  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. This paper
extends their approach by proposing several schemes to control the generation
of the negative pairs, resulting in a significant improvement of the
performance and giving above state-of-the-art results on three challenging ZSC
datasets.
