  Understanding sequential information is a fundamental task for artificial
intelligence. Current neural networks attempt to learn spatial and temporal
information as a whole, limited their abilities to represent large scale
spatial representations over long-range sequences. Here, we introduce a new
modeling strategy called Semi-Coupled Structure (SCS), which consists of deep
neural networks that decouple the complex spatial and temporal concepts
learning. Semi-Coupled Structure can learn to implicitly separate input
information into independent parts and process these parts respectively.
Experiments demonstrate that a Semi-Coupled Structure can successfully annotate
the outline of an object in images sequentially and perform video action
recognition. For sequence-to-sequence problems, a Semi-Coupled Structure can
predict future meteorological radar echo images based on observed images. Taken
together, our results demonstrate that a Semi-Coupled Structure has the
capacity to improve the performance of LSTM-like models on large scale
sequential tasks.
