Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Local Multi-Head Channel Self-Attention for Facial Expression
  Recognition   Since the Transformer architecture was introduced in 2017 there has been many
attempts to bring the self-attention paradigm in the field of computer vision.
In this paper we propose a novel self-attention module that can be easily
integrated in virtually every convolutional neural network and that is
specifically designed for computer vision, the LHC: Local (multi) Head Channel
(self-attention). LHC is based on two main ideas: first, we think that in
computer vision the best way to leverage the self-attention paradigm is the
channel-wise application instead of the more explored spatial attention and
that convolution will not be replaced by attention modules like recurrent
networks were in NLP; second, a local approach has the potential to better
overcome the limitations of convolution than global attention. With LHC-Net we
managed to achieve a new state of the art in the famous FER2013 dataset with a
significantly lower complexity and impact on the "host" architecture in terms
of computational cost when compared with the previous SOTA.

29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph