  Zero-shot learning (ZSL) aims to recognize objects from novel unseen classes
without any training data. Recently, structure-transfer based methods are
proposed to implement ZSL by transferring structural knowledge from the
semantic embedding space to image feature space to classify testing images.
However, we observe that such a knowledge transfer framework may suffer from
the problem of the geometric inconsistency between the data in the training and
testing spaces. We call this problem as the space shift problem. In this paper,
we propose a novel graph based method to alleviate this space shift problem.
Specifically, a Shared Reconstruction Graph (SRG) is pursued to capture the
common structure of data in the two spaces. With the learned SRG, each unseen
class prototype (cluster center) in the image feature space can be synthesized
by the linear combination of other class prototypes, so that testing instances
can be classified based on the distance to these synthesized prototypes. The
SRG bridges the image feature space and semantic embedding space. By applying
spectral clustering on the learned SRG, many meaningful clusters can be
discovered, which interprets ZSL performance on the datasets. Our method can be
easily extended to the generalized zero-shot learning setting. Experiments on
three popular datasets show that our method outperforms other methods on all
datasets. Even with a small number of training samples, our method can achieve
the state-of-the-art performance.
