Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MSG-Transformer: Exchanging Local Spatial Information by Manipulating
  Messenger Tokens   Transformers have offered a new methodology of designing neural networks for
visual recognition. Compared to convolutional networks, Transformers enjoy the
ability of referring to global features at each stage, yet the attention module
brings higher computational overhead that obstructs the application of
Transformers to process high-resolution visual data. This paper aims to
alleviate the conflict between efficiency and flexibility, for which we propose
a specialized token for each region that serves as a messenger (MSG). Hence, by
manipulating these MSG tokens, one can flexibly exchange visual information
across regions and the computational complexity is reduced. We then integrate
the MSG token into a multi-scale architecture named MSG-Transformer. In
standard image classification and object detection, MSG-Transformer achieves
competitive performance and the inference on both GPU and CPU is accelerated.
Code is available at https://github.com/hustvl/MSG-Transformer.

19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con