  With the ever-increasing amount of data, the central challenge in multimodal
learning involves limitations of labelled samples. For the task of
classification, techniques such as meta-learning, zero-shot learning, and
few-shot learning showcase the ability to learn information about novel classes
based on prior knowledge. Recent techniques try to learn a cross-modal mapping
between the semantic space and the image space. However, they tend to ignore
the local and global semantic knowledge. To overcome this problem, we propose a
Multimodal Variational Auto-Encoder (M-VAE) which can learn the shared latent
space of image features and the semantic space. In our approach we concatenate
multimodal data to a single embedding before passing it to the VAE for learning
the latent space. We propose the use of a multi-modal loss during the
reconstruction of the feature embedding through the decoder. Our approach is
capable to correlating modalities and exploit the local and global semantic
knowledge for novel sample predictions. Our experimental results using a MLP
classifier on four benchmark datasets show that our proposed model outperforms
the current state-of-the-art approaches for generalized zero-shot learning.
