Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Channel Attention and Multi-level Features Fusion for Single Image
  Super-Resolution   Convolutional neural networks (CNNs) have demonstrated superior performance
in super-resolution (SR). However, most CNN-based SR methods neglect the
different importance among feature channels or fail to take full advantage of
the hierarchical features. To address these issues, this paper presents a novel
recursive unit. Firstly, at the beginning of each unit, we adopt a compact
channel attention mechanism to adaptively recalibrate the channel importance of
input features. Then, the multi-level features, rather than only deep-level
features, are extracted and fused. Additionally, we find that it will force our
model to learn more details by using the learnable upsampling method (i.e.,
transposed convolution) only on residual branch (instead of using it both on
residual branch and identity branch) while using the bicubic interpolation on
the other branch. Analytic experiments show that our method achieves
competitive results compared with the state-of-the-art methods and maintains
faster speed as well.

19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg