Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Super Resolution Using Segmentation-Prior Self-Attention Generative
  Adversarial Network   Convolutional Neural Network (CNN) is intensively implemented to solve super
resolution (SR) tasks because of its superior performance. However, the problem
of super resolution is still challenging due to the lack of prior knowledge and
small receptive field of CNN. We propose the Segmentation-Piror Self-Attention
Generative Adversarial Network (SPSAGAN) to combine segmentation-priors and
feature attentions into a unified framework. This combination is led by a
carefully designed weighted addition to balance the influence of feature and
segmentation attentions, so that the network can emphasize textures in the same
segmentation category and meanwhile focus on the long-distance feature
relationship. We also propose a lightweight skip connection architecture called
Residual-in-Residual Sparse Block (RRSB) to further improve the
super-resolution performance and save computation. Extensive experiments show
that SPSAGAN can generate more realistic and visually pleasing textures
compared to state-of-the-art SFTGAN and ESRGAN on many SR datasets.

35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex