Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Neural Networks for Image Understanding Based on Multiple Cues:
  Group Emotion Recognition and Event Recognition as Use Cases   A graph neural network (GNN) for image understanding based on multiple cues
is proposed in this paper. Compared to traditional feature and decision fusion
approaches that neglect the fact that features can interact and exchange
information, the proposed GNN is able to pass information among features
extracted from different models. Two image understanding tasks, namely
group-level emotion recognition (GER) and event recognition, which are highly
semantic and require the interaction of several deep models to synthesize
multiple cues, were selected to validate the performance of the proposed
method. It is shown through experiments that the proposed method achieves
state-of-the-art performance on the selected image understanding tasks. In
addition, a new group-level emotion recognition database is introduced and
shared in this paper.

13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ