Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Graph Generation Network: Generating Relational Knowledge from
  Diagrams   In this work, we introduce a new algorithm for analyzing a diagram, which
contains visual and textual information in an abstract and integrated way.
Whereas diagrams contain richer information compared with individual
image-based or language-based data, proper solutions for automatically
understanding them have not been proposed due to their innate characteristics
of multi-modality and arbitrariness of layouts. To tackle this problem, we
propose a unified diagram-parsing network for generating knowledge from
diagrams based on an object detector and a recurrent neural network designed
for a graphical structure. Specifically, we propose a dynamic graph-generation
network that is based on dynamic memory and graph theory. We explore the
dynamics of information in a diagram with activation of gates in gated
recurrent unit (GRU) cells. On publicly available diagram datasets, our model
demonstrates a state-of-the-art result that outperforms other baselines.
Moreover, further experiments on question answering shows potentials of the
proposed method for various applications.

10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph