Abstract
Zero-shot learning has gained popularity due to its potential to scale recognition models without requiring additional training data. This is usually achieved by associating categories with their semantic information like attributes. However, we believe that the potential offered
by this paradigm is not yet fully exploited. In this work,
we propose to utilize the structure of the space spanned
by the attributes using a set of relations. We devise objective functions to preserve these relations in the embedding space, thereby inducing semanticity to the embedding
space. Through extensive experimental evaluation on five
benchmark datasets, we demonstrate that inducing semanticity to the embedding space is beneficial for zero-shot
learning. The proposed approach outperforms the state-ofthe-art on the standard zero-shot setting as well as the more
realistic generalized zero-shot setting. We also demonstrate
how the proposed approach can be useful for making approximate semantic inferences about an image belonging to
a category for which attribute information is not available.