  In multimodal machine learning tasks, it is due to the complexity of the
assignments that the network structure, in most cases, is assembled in a
sophisticated way. The holistic architecture can be separated into several
logical parts according to the respective ends that the modules are devised to
achieve. As the number of modalities of information representation increases,
constructing ad hoc subnetworks for processing the data from divergent
modalities while mediating the fusion of different information types has become
a cumbersome and expensive problem. In this paper, we present an approach to
facilitate the question with a reusable and composable neural unit; by
connecting the units in series or parallel, the arduous network constructing of
multimodal machine learning tasks will be accomplished in a much
straightforward way. Additionally, through parameter sharing (weights
replication) among the units, the space complexity will be significantly
reduced. We have conducted experiments on three commonly used datasets; our
method achieves impressive performance compared to several video QA baselines.
