  A comprehensive understanding of vision and language and their interrelation
are crucial to realize the underlying similarities and differences between
these modalities and to learn more generalized, meaningful representations. In
recent years, most of the works related to Text-to-Image synthesis and
Image-to-Text generation, focused on supervised generative deep architectures
to solve the problems, where very little interest was placed on learning the
similarities between the embedding spaces across modalities. In this paper, we
propose a novel self-supervised deep learning based approach towards learning
the cross-modal embedding spaces; for both image to text and text to image
generations. In our approach, we first obtain dense vector representations of
images using StackGAN-based autoencoder model and also dense vector
representations on sentence-level utilizing LSTM based text-autoencoder; then
we study the mapping from embedding space of one modality to embedding space of
the other modality utilizing GAN and maximum mean discrepancy based generative
networks. We, also demonstrate that our model learns to generate textual
description from image data as well as images from textual data both
qualitatively and quantitatively.
