  We introduce an efficient computational framework for hashing data belonging
to multiple modalities into a single representation space where they become
mutually comparable. The proposed approach is based on a novel coupled siamese
neural network architecture and allows unified treatment of intra- and
inter-modality similarity learning. Unlike existing cross-modality similarity
learning approaches, our hashing functions are not limited to binarized linear
projections and can assume arbitrarily complex forms. We show experimentally
that our method significantly outperforms state-of-the-art hashing approaches
on multimedia retrieval tasks.
