  Generalized zero-shot learning (GZSL) tackles the problem of learning to
classify instances involving both seen classes and unseen ones. The key issue
is how to effectively transfer the model learned from seen classes to unseen
classes. Existing works in GZSL usually assume that some prior information
about unseen classes are available. However, such an assumption is unrealistic
when new unseen classes appear dynamically. To this end, we propose a novel
heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic
to unseen classes and instances, by leveraging graph neural network.
Specifically, a structured heterogeneous graph is constructed with high-level
representative nodes for seen classes, which are chosen through Wasserstein
barycenter in order to simultaneously capture inter-class and intra-class
relationship. The aggregation and embedding functions can be learned through
graph neural network, which can be used to compute the embeddings of unseen
classes by transferring the knowledge from their neighbors. Extensive
experiments on public benchmark datasets show that our method achieves
state-of-the-art results.
