  Zero-shot learning relies on semantic class representations such as
hand-engineered attributes or learned embeddings to predict classes without any
labeled examples. We propose to learn class representations by embedding nodes
from common sense knowledge graphs in a vector space. Common sense knowledge
graphs are an untapped source of explicit high-level knowledge that requires
little human effort to apply to a range of tasks. To capture the knowledge in
the graph, we introduce ZSL-KG, a general-purpose framework with a novel
transformer graph convolutional network (TrGCN) for generating class
representations. Our proposed TrGCN architecture computes non-linear
combinations of node neighbourhoods. Our results show that ZSL-KG improves over
existing WordNet-based methods on five out of six zero-shot benchmark datasets
in language and vision.
