  Most existing algorithms for cross-modal Information Retrieval are based on a
supervised train-test setup, where a model learns to align the mode of the
query (e.g., text) to the mode of the documents (e.g., images) from a given
training set. Such a setup assumes that the training set contains an exhaustive
representation of all possible classes of queries. In reality, a retrieval
model may need to be deployed on previously unseen classes, which implies a
zero-shot IR setup. In this paper, we propose a novel GAN-based model for
zero-shot text to image retrieval. When given a textual description as the
query, our model can retrieve relevant images in a zero-shot setup. The
proposed model is trained using an Expectation-Maximization framework.
Experiments on multiple benchmark datasets show that our proposed model
comfortably outperforms several state-of-the-art zero-shot text to image
retrieval models, as well as zero-shot classification and hashing models
suitably used for retrieval.
