  Graph convolutional neural networks have recently shown great potential for
the task of zero-shot learning. These models are highly sample efficient as
related concepts in the graph structure share statistical strength allowing
generalization to new classes when faced with a lack of data. However,
multi-layer architectures, which are required to propagate knowledge to distant
nodes in the graph, dilute the knowledge by performing extensive Laplacian
smoothing at each layer and thereby consequently decrease performance. In order
to still enjoy the benefit brought by the graph structure while preventing
dilution of knowledge from distant nodes, we propose a Dense Graph Propagation
(DGP) module with carefully designed direct links among distant nodes. DGP
allows us to exploit the hierarchical graph structure of the knowledge graph
through additional connections. These connections are added based on a node's
relationship to its ancestors and descendants. A weighting scheme is further
used to weigh their contribution depending on the distance to the node to
improve information propagation in the graph. Combined with finetuning of the
representations in a two-stage training approach our method outperforms
state-of-the-art zero-shot learning approaches.
