S4Net: Single Stage Salient-Instance Segmentation
Ruochen Fan
and
Ming-Ming Cheng
and
Qibin Hou
and
Tai-Jiang Mu
and
Jingdong Wang
and
Shi-Min Hu
arXiv e-Print archive - 2017 via arXiv
Keywords:
cs.CV
First published: 2017/11/21 (6 years ago) Abstract: We consider an interesting problem-salient instance segmentation in this
paper. Other than producing bounding boxes, our network also outputs
high-quality instance-level segments. Taking into account the
category-independent property of each target, we design a single stage salient
instance segmentation framework, with a novel segmentation branch. Our new
branch regards not only local context inside each detection window but also its
surrounding context, enabling us to distinguish the instances in the same scope
even with obstruction. Our network is end-to-end trainable and runs at a fast
speed (40 fps when processing an image with resolution 320x320). We evaluate
our approach on a publicly available benchmark and show that it outperforms
other alternative solutions. We also provide a thorough analysis of the design
choices to help readers better understand the functions of each part of our
network. The source code can be found at
\url{https://github.com/RuochenFan/S4Net}.
It's like mask rcnn but for salient instances.
code will be available at https://github.com/RuochenFan/S4Net.
They invented a layer "mask pooling" that they claim is better than ROI pooling and ROI align.
>As can be seen, our proposed
binary RoIMasking and ternary RoIMasking both outperform
RoIPool and RoIAlign in mAP0.7
. Specifically, our
ternary RoIMasking result improves the RoIAlign result by
around 2.5 points. This reflects that considering more context
information outside the proposals does help for salient
instance segmentation
Important benchmark attached:
https://i.imgur.com/wOF2Ovz.png