  Zero-shot learning (ZSL) aims to recognize unseen classes accurately by
learning seen classes and known attributes, but correlations in attributes were
ignored by previous study which lead to classification results confused. To
solve this problem, we build an Attribute Correlation Potential Space
Generation (ACPSG) model which uses a graph convolution network and attribute
correlation to generate a more discriminating potential space. Combining
potential discrimination space and user-defined attribute space, we can better
classify unseen classes. Our approach outperforms some existing
state-of-the-art methods on several benchmark datasets, whether it is
conventional ZSL or generalized ZSL.
