  We consider the problem of zero-shot recognition: learning a visual
classifier for a category with zero training examples, just using the word
embedding of the category and its relationship to other categories, which
visual data are provided. The key to dealing with the unfamiliar or novel
category is to transfer knowledge obtained from familiar classes to describe
the unfamiliar class. In this paper, we build upon the recently introduced
Graph Convolutional Network (GCN) and propose an approach that uses both
semantic embeddings and the categorical relationships to predict the
classifiers. Given a learned knowledge graph (KG), our approach takes as input
semantic embeddings for each node (representing visual category). After a
series of graph convolutions, we predict the visual classifier for each
category. During training, the visual classifiers for a few categories are
given to learn the GCN parameters. At test time, these filters are used to
predict the visual classifiers of unseen categories. We show that our approach
is robust to noise in the KG. More importantly, our approach provides
significant improvement in performance compared to the current state-of-the-art
results (from 2 ~ 3% on some metrics to whopping 20% on a few).
