Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity
  Distribution and COMEX Relation Learning   The detection of facial action units (AUs) has been studied as it has the
competition due to the wide-ranging applications thereof. In this paper, we
propose a novel framework for the AU detection from a single input image by
grasping the \textbf{c}o-\textbf{o}ccurrence and \textbf{m}utual
\textbf{ex}clusion (COMEX) as well as the intensity distribution among AUs. Our
algorithm uses facial landmarks to detect the features of local AUs. The
features are input to a bidirectional long short-term memory (BiLSTM) layer for
learning the intensity distribution. Afterwards, the new AU feature
continuously passed through a self-attention encoding layer and a
continuous-state modern Hopfield layer for learning the COMEX relationships.
Our experiments on the challenging BP4D and DISFA benchmarks without any
external data or pre-trained models yield F1-scores of 63.7\% and 61.8\%
respectively, which shows our proposed networks can lead to performance
improvement in the AU detection task.

20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg