  Zero-shot learning aims at recognizing unseen classes (no training example)
with knowledge transferred from seen classes. This is typically achieved by
exploiting a semantic feature space shared by both seen and unseen classes,
i.e., attribute or word vector, as the bridge. One common practice in zero-shot
learning is to train a projection between the visual and semantic feature
spaces with labeled seen classes examples. When inferring, this learned
projection is applied to unseen classes and recognizes the class labels by some
metrics. However, the visual and semantic feature spaces are mutually
independent and have quite different manifold structures. Under such a
paradigm, most existing methods easily suffer from the domain shift problem and
weaken the performance of zero-shot recognition. 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 upon an
autoencoder-based model to expand the semantic features from the visual inputs.
Additionally, the expansion is jointly guided by an embedded manifold extracted
from the visual feature space of the data. Our model is the first attempt to
align both feature spaces by expanding semantic features and derives two
benefits: first, we expand some auxiliary features that enhance the semantic
feature space; second and more importantly, we implicitly align the manifold
structures between the visual and semantic feature spaces; thus, the projection
can be better trained and mitigate the domain shift problem. Extensive
experiments show significant performance improvement, which verifies the
effectiveness of our model.
