Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PAG-Net: Progressive Attention Guided Depth Super-resolution Network   In this paper, we propose a novel method for the challenging problem of
guided depth map super-resolution, called PAGNet. It is based on residual dense
networks and involves the attention mechanism to suppress the texture copying
problem arises due to improper guidance by RGB images. The attention module
mainly involves providing the spatial attention to guidance image based on the
depth features. We evaluate the proposed trained models on test dataset and
provide comparisons with the state-of-the-art depth super-resolution methods.

10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: nlin
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn