Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-level Texture Encoding and Representation (MuLTER) based on Deep
  Neural Networks   In this paper, we propose a multi-level texture encoding and representation
network (MuLTER) for texture-related applications. Based on a multi-level
pooling architecture, the MuLTER network simultaneously leverages low- and
high-level features to maintain both texture details and spatial information.
Such a pooling architecture involves few extra parameters and keeps feature
dimensions fixed despite of the changes of image sizes. In comparison with
state-of-the-art texture descriptors, the MuLTER network yields higher
recognition accuracy on typical texture datasets such as MINC-2500 and
GTOS-mobile with a discriminative and compact representation. In addition, we
analyze the impact of combining features from different levels, which supports
our claim that the fusion of multi-level features efficiently enhances
recognition performance. Our source code will be published on GitHub
(https://github.com/olivesgatech).

15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org