  Zero-shot learning (ZSL) is made possible by learning a projection function
between a feature space and a semantic space (e.g.,~an attribute space). Key to
ZSL is thus to learn a projection that is robust against the often large domain
gap between the seen and unseen class domains. In this work, this is achieved
by unseen class data synthesis and robust projection function learning.
Specifically, a novel semantic data synthesis strategy is proposed, by which
semantic class prototypes (e.g., attribute vectors) are used to simply perturb
seen class data for generating unseen class ones. As in any data
synthesis/hallucination approach, there are ambiguities and uncertainties on
how well the synthesised data can capture the targeted unseen class data
distribution. To cope with this, the second contribution of this work is a
novel projection learning model termed competitive bidirectional projection
learning (BPL) designed to best utilise the ambiguous synthesised data.
Specifically, we assume that each synthesised data point can belong to any
unseen class; and the most likely two class candidates are exploited to learn a
robust projection function in a competitive fashion. As a third contribution,
we show that the proposed ZSL model can be easily extended to few-shot learning
(FSL) by again exploiting semantic (class prototype guided) feature synthesis
and competitive BPL. Extensive experiments show that our model achieves the
state-of-the-art results on both problems.
