Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object
  Detection   A key problem in salient object detection is how to effectively model the
semantic properties of salient objects in a data-driven manner. In this paper,
we propose a multi-task deep saliency model based on a fully convolutional
neural network (FCNN) with global input (whole raw images) and global output
(whole saliency maps). In principle, the proposed saliency model takes a
data-driven strategy for encoding the underlying saliency prior information,
and then sets up a multi-task learning scheme for exploring the intrinsic
correlations between saliency detection and semantic image segmentation.
Through collaborative feature learning from such two correlated tasks, the
shared fully convolutional layers produce effective features for object
perception. Moreover, it is capable of capturing the semantic information on
salient objects across different levels using the fully convolutional layers,
which investigate the feature-sharing properties of salient object detection
with great feature redundancy reduction. Finally, we present a graph Laplacian
regularized nonlinear regression model for saliency refinement. Experimental
results demonstrate the effectiveness of our approach in comparison with the
state-of-the-art approaches.

0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph