  Zero-shot learning (ZSL) aims at recognizing unseen classes with knowledge
transferred from seen classes. This is typically achieved by exploiting a
semantic feature space (FS) shared by both seen and unseen classes, i.e.,
attributes or word vectors, as the bridge. However, due to the mutually
disjoint of training (seen) and testing (unseen) data, existing ZSL methods
easily and commonly suffer from the domain shift problem. To address this
issue, we propose a novel model called AMS-SFE. It considers the Alignment of
Manifold Structures by Semantic Feature Expansion. Specifically, we build up an
autoencoder based model to expand the semantic features and joint with an
alignment to an embedded manifold extracted from the visual FS of data. It is
the first attempt to align these two FSs by way of expanding semantic features.
Extensive experiments show the remarkable performance improvement of our model
compared with other existing methods.
