  Zero-Shot Learning has been a highlighted research topic in both vision and
language areas. Recently, most existing methods adopt structured knowledge
information to model explicit correlations among categories and use deep graph
convolutional network to propagate information between different categories.
However, it is difficult to add new categories to existing structured knowledge
graph, and deep graph convolutional network suffers from over-smoothing
problem. In this paper, we provide a new semantic enhanced knowledge graph that
contains both expert knowledge and categories semantic correlation. Our
semantic enhanced knowledge graph can further enhance the correlations among
categories and make it easy to absorb new categories. To propagate information
on the knowledge graph, we propose a novel Residual Graph Convolutional Network
(ResGCN), which can effectively alleviate the problem of over-smoothing.
Experiments conducted on the widely used large-scale ImageNet-21K dataset and
AWA2 dataset show the effectiveness of our method, and establish a new
state-of-the-art on zero-shot learning. Moreover, our results on the
large-scale ImageNet-21K with various feature extraction networks show that our
method has better generalization and robustness.
