  Zero-shot learning (ZSL) aims to classify images of an unseen class only
based on a few attributes describing that class but no access to any training
sample. A popular strategy is to learn a mapping between the semantic space of
class attributes and the visual space of images based on the seen classes and
their data. Thus, an unseen class image can be ideally mapped to its
corresponding class attributes. The key challenge is how to align the
representations in the two spaces. For most ZSL settings, the attributes for
each seen/unseen class are only represented by a vector while the seen-class
data provide much more information. Thus, the imbalanced supervision from the
semantic and the visual space can make the learned mapping easily overfitting
to the seen classes. To resolve this problem, we propose Isometric Propagation
Network (IPN), which learns to strengthen the relation between classes within
each space and align the class dependency in the two spaces. Specifically, IPN
learns to propagate the class representations on an auto-generated graph within
each space. In contrast to only aligning the resulted static representation, we
regularize the two dynamic propagation procedures to be isometric in terms of
the two graphs' edge weights per step by minimizing a consistency loss between
them. IPN achieves state-of-the-art performance on three popular ZSL
benchmarks. To evaluate the generalization capability of IPN, we further build
two larger benchmarks with more diverse unseen classes and demonstrate the
advantages of IPN on them.
