Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Single image depth estimation by dilated deep residual convolutional
  neural network and soft-weight-sum inference   This paper proposes a new residual convolutional neural network (CNN)
architecture for single image depth estimation. Compared with existing deep CNN
based methods, our method achieves much better results with fewer training
examples and model parameters. The advantages of our method come from the usage
of dilated convolution, skip connection architecture and soft-weight-sum
inference. Experimental evaluation on the NYU Depth V2 dataset shows that our
method outperforms other state-of-the-art methods by a margin.

16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph