  Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to
unseen ones so that the latter can be recognised without any training samples.
This is made possible by learning a projection function between a feature space
and a semantic space (e.g. attribute space). Considering the seen and unseen
classes as two domains, a big domain gap often exists which challenges ZSL.
Inspired by the fact that an unseen class is not exactly `unseen' if it belongs
to the same superclass as a seen class, we propose a novel inductive ZSL model
that leverages superclasses as the bridge between seen and unseen classes to
narrow the domain gap. Specifically, we first build a class hierarchy of
multiple superclass layers and a single class layer, where the superclasses are
automatically generated by data-driven clustering over the semantic
representations of all seen and unseen class names. We then exploit the
superclasses from the class hierarchy to tackle the domain gap challenge in two
aspects: deep feature learning and projection function learning. First, to
narrow the domain gap in the feature space, we integrate a recurrent neural
network (RNN) defined with the superclasses into a convolutional neural network
(CNN), in order to enforce the superclass hierarchy. Second, to further learn a
transferrable projection function for ZSL, a novel projection function learning
method is proposed by exploiting the superclasses to align the two domains.
Importantly, our transferrable feature and projection learning methods can be
easily extended to a closely related task -- few-shot learning (FSL). Extensive
experiments show that the proposed model significantly outperforms the
state-of-the-art alternatives in both ZSL and FSL tasks.
