Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Contrast Learning for Salient Object Detection   Salient object detection has recently witnessed substantial progress due to
powerful features extracted using deep convolutional neural networks (CNNs).
However, existing CNN-based methods operate at the patch level instead of the
pixel level. Resulting saliency maps are typically blurry, especially near the
boundary of salient objects. Furthermore, image patches are treated as
independent samples even when they are overlapping, giving rise to significant
redundancy in computation and storage. In this CVPR 2016 paper, we propose an
end-to-end deep contrast network to overcome the aforementioned limitations.
Our deep network consists of two complementary components, a pixel-level fully
convolutional stream and a segment-wise spatial pooling stream. The first
stream directly produces a saliency map with pixel-level accuracy from an input
image. The second stream extracts segment-wise features very efficiently, and
better models saliency discontinuities along object boundaries. Finally, a
fully connected CRF model can be optionally incorporated to improve spatial
coherence and contour localization in the fused result from these two streams.
Experimental results demonstrate that our deep model significantly improves the
state of the art.

18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys