  With the development of web technology, multi-modal or multi-view data has
surged as a major stream for big data, where each modal/view encodes individual
property of data objects. Often, different modalities are complementary to each
other. Such fact motivated a lot of research attention on fusing the
multi-modal feature spaces to comprehensively characterize the data objects.
Most of the existing state-of-the-art focused on how to fuse the energy or
information from multi-modal spaces to deliver a superior performance over
their counterparts with single modal. Recently, deep neural networks have
exhibited as a powerful architecture to well capture the nonlinear distribution
of high-dimensional multimedia data, so naturally does for multi-modal data.
Substantial empirical studies are carried out to demonstrate its advantages
that are benefited from deep multi-modal methods, which can essentially deepen
the fusion from multi-modal deep feature spaces. In this paper, we provide a
substantial overview of the existing state-of-the-arts on the filed of
multi-modal data analytics from shallow to deep spaces. Throughout this survey,
we further indicate that the critical components for this field go to
collaboration, adversarial competition and fusion over multi-modal spaces.
Finally, we share our viewpoints regarding some future directions on this
field.
