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IEEE 2017 Conference on
Computer Vision and Pattern
Recognition

Deeply Supervised Salient Object Detection with Short Connections
Qibin Hou, Ming-Ming Cheng, Xiaowei Hu, Ali Borji, Zhuowen Tu, Philip H. S. Torr
Additional Changes:

Observations:
Problem:
Following HED, we add a stack of side supervisions after each stage
to see the different behaviors brought by multi-level features.
Deeper layers are able
to accurately locate the
salient objects while
lower layers encode
rich detailed features
which are required for
refinement.

The goal in salient object
detection is to identify the
most visually distinctive
objects or regions in an
image and then segment
them out from the background.

Results and Failure Cases:

FCN-Based Methods:
Illustration of different architectures. (a) Hypercolumn [1], (b)
FCN-8s [2], (c) HED [3], (d) and (e) different patterns of our
proposed architecture.
A series of short connections are introduced in our architecture
for combining the
advantages of
both deeper layers
and
shallower
layers. While our
(c)
(a)
(b)
approach can be
extended
to
a
Hidden
variety of different
Layer
structures, we just
Loss
list two typical ones.
Layer
(d)

To enhance the ability
of each side output,
we also add another
two
convolutional
layers with different
kernel sizes to each
side output.

(e)

The Architecture of Our DSS:
Introducing short connections to the skip-layer
structure within the HED
architecture.
High-level features can be
transformed to shallower
side-output layers.
Shallower side-output can
help refine the sparse and
irregular prediction maps
from deeper side-output
layers.

Short connection
Fusion weights

Fusion loss

Loss

Side outputs

6464

11
conv

6464 CE
loss
2up

128128

11 128128 11
conv
conv

CE
loss

Source code: https://mmcheng.net/dss/

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