29,389 research outputs found
S4Net: Single Stage Salient-Instance Segmentation
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}
Instance-Level Salient Object Segmentation
Image saliency detection has recently witnessed rapid progress due to deep
convolutional neural networks. However, none of the existing methods is able to
identify object instances in the detected salient regions. In this paper, we
present a salient instance segmentation method that produces a saliency mask
with distinct object instance labels for an input image. Our method consists of
three steps, estimating saliency map, detecting salient object contours and
identifying salient object instances. For the first two steps, we propose a
multiscale saliency refinement network, which generates high-quality salient
region masks and salient object contours. Once integrated with multiscale
combinatorial grouping and a MAP-based subset optimization framework, our
method can generate very promising salient object instance segmentation
results. To promote further research and evaluation of salient instance
segmentation, we also construct a new database of 1000 images and their
pixelwise salient instance annotations. Experimental results demonstrate that
our proposed method is capable of achieving state-of-the-art performance on all
public benchmarks for salient region detection as well as on our new dataset
for salient instance segmentation.Comment: To appear in CVPR201
An Iterative Co-Saliency Framework for RGBD Images
As a newly emerging and significant topic in computer vision community,
co-saliency detection aims at discovering the common salient objects in
multiple related images. The existing methods often generate the co-saliency
map through a direct forward pipeline which is based on the designed cues or
initialization, but lack the refinement-cycle scheme. Moreover, they mainly
focus on RGB image and ignore the depth information for RGBD images. In this
paper, we propose an iterative RGBD co-saliency framework, which utilizes the
existing single saliency maps as the initialization, and generates the final
RGBD cosaliency map by using a refinement-cycle model. Three schemes are
employed in the proposed RGBD co-saliency framework, which include the addition
scheme, deletion scheme, and iteration scheme. The addition scheme is used to
highlight the salient regions based on intra-image depth propagation and
saliency propagation, while the deletion scheme filters the saliency regions
and removes the non-common salient regions based on interimage constraint. The
iteration scheme is proposed to obtain more homogeneous and consistent
co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is
proposed in the addition scheme to introduce the depth information to enhance
identification of co-salient objects. The proposed method can effectively
exploit any existing 2D saliency model to work well in RGBD co-saliency
scenarios. The experiments on two RGBD cosaliency datasets demonstrate the
effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics
2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm
- …