Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DMSANet: Dual Multi Scale Attention Network   Attention mechanism of late has been quite popular in the computer vision
community. A lot of work has been done to improve the performance of the
network, although almost always it results in increased computational
complexity. In this paper, we propose a new attention module that not only
achieves the best performance but also has lesser parameters compared to most
existing models. Our attention module can easily be integrated with other
convolutional neural networks because of its lightweight nature. The proposed
network named Dual Multi Scale Attention Network (DMSANet) is comprised of two
parts: the first part is used to extract features at various scales and
aggregate them, the second part uses spatial and channel attention modules in
parallel to adaptively integrate local features with their global dependencies.
We benchmark our network performance for Image Classification on ImageNet
dataset, Object Detection and Instance Segmentation both on MS COCO dataset.

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The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat