Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Complex Sequential Understanding through the Awareness of Spatial and
  Temporal Concepts   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.

22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th