23 research outputs found
Densely Deformable Efficient Salient Object Detection Network
Salient Object Detection (SOD) domain using RGB-D data has lately emerged
with some current models' adequately precise results. However, they have
restrained generalization abilities and intensive computational complexity. In
this paper, inspired by the best background/foreground separation abilities of
deformable convolutions, we employ them in our Densely Deformable Network
(DDNet) to achieve efficient SOD. The salient regions from densely deformable
convolutions are further refined using transposed convolutions to optimally
generate the saliency maps. Quantitative and qualitative evaluations using the
recent SOD dataset against 22 competing techniques show our method's efficiency
and effectiveness. We also offer evaluation using our own created
cross-dataset, surveillance-SOD (S-SOD), to check the trained models' validity
in terms of their applicability in diverse scenarios. The results indicate that
the current models have limited generalization potentials, demanding further
research in this direction. Our code and new dataset will be publicly available
at https://github.com/tanveer-hussain/EfficientSO
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
Bifurcated backbone strategy for RGB-D salient object detection
Multi-level feature fusion is a fundamental topic in computer vision. It has
been exploited to detect, segment and classify objects at various scales. When
multi-level features meet multi-modal cues, the optimal feature aggregation and
multi-modal learning strategy become a hot potato. In this paper, we leverage
the inherent multi-modal and multi-level nature of RGB-D salient object
detection to devise a novel cascaded refinement network. In particular, first,
we propose to regroup the multi-level features into teacher and student
features using a bifurcated backbone strategy (BBS). Second, we introduce a
depth-enhanced module (DEM) to excavate informative depth cues from the channel
and spatial views. Then, RGB and depth modalities are fused in a complementary
way. Our architecture, named Bifurcated Backbone Strategy Network (BBS-Net), is
simple, efficient, and backbone-independent. Extensive experiments show that
BBS-Net significantly outperforms eighteen SOTA models on eight challenging
datasets under five evaluation measures, demonstrating the superiority of our
approach ( improvement in S-measure the top-ranked model:
DMRA-iccv2019). In addition, we provide a comprehensive analysis on the
generalization ability of different RGB-D datasets and provide a powerful
training set for future research.Comment: A preliminary version of this work has been accepted in ECCV 202