Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CPNet: A Context Preserver Convolutional Neural Network for Detecting
  Shadows in Single RGB Images   Automatic detection of shadow regions in an image is a difficult task due to
the lack of prior information about the illumination source and the dynamic of
the scene objects. To address this problem, in this paper, a deep-learning
based segmentation method is proposed that identifies shadow regions at the
pixel-level in a single RGB image. We exploit a novel Convolutional Neural
Network (CNN) architecture to identify and extract shadow features in an
end-to-end manner. This network preserves learned contexts during the training
and observes the entire image to detect global and local shadow patterns
simultaneously. The proposed method is evaluated on two publicly available
datasets of SBU and UCF. We have improved the state-of-the-art Balanced Error
Rate (BER) on these datasets by 22\% and 14\%, respectively.

26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg