584 research outputs found
Exploiting saliency for object segmentation from image level labels
There have been remarkable improvements in the semantic labelling task in the
recent years. However, the state of the art methods rely on large-scale
pixel-level annotations. This paper studies the problem of training a
pixel-wise semantic labeller network from image-level annotations of the
present object classes. Recently, it has been shown that high quality seeds
indicating discriminative object regions can be obtained from image-level
labels. Without additional information, obtaining the full extent of the object
is an inherently ill-posed problem due to co-occurrences. We propose using a
saliency model as additional information and hereby exploit prior knowledge on
the object extent and image statistics. We show how to combine both information
sources in order to recover 80% of the fully supervised performance - which is
the new state of the art in weakly supervised training for pixel-wise semantic
labelling. The code is available at https://goo.gl/KygSeb.Comment: CVPR 201
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
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