  This paper proposes a cross-modal retrieval system that leverages on image
and text encoding. Most multimodal architectures employ separate networks for
each modality to capture the semantic relationship between them. However, in
our work image-text encoding can achieve comparable results in terms of
cross-modal retrieval without having to use a separate network for each
modality. We show that text encodings can capture semantic relationships
between multiple modalities. In our knowledge, this work is the first of its
kind in terms of employing a single network and fused image-text embedding for
cross-modal retrieval. We evaluate our approach on two famous multimodal
datasets: MS-COCO and Flickr30K.
