Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Region based Ensemble Learning Network for Fine-grained Classification   As an important research topic in computer vision, fine-grained
classification which aims to recognition subordinate-level categories has
attracted significant attention. We propose a novel region based ensemble
learning network for fine-grained classification. Our approach contains a
detection module and a module for classification. The detection module is based
on the faster R-CNN framework to locate the semantic regions of the object. The
classification module using an ensemble learning method, which trains a set of
sub-classifiers for different semantic regions and combines them together to
get a stronger classifier. In the evaluation, we implement experiments on the
CUB-2011 dataset and the result of experiments proves our method s efficient
for fine-grained classification. We also extend our approach to remote scene
recognition and evaluate it on the NWPU-RESISC45 dataset.

16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
18
The categories of this paper are: econ