12,052 research outputs found
Region Refinement Network for Salient Object Detection
Albeit intensively studied, false prediction and unclear boundaries are still
major issues of salient object detection. In this paper, we propose a Region
Refinement Network (RRN), which recurrently filters redundant information and
explicitly models boundary information for saliency detection. Different from
existing refinement methods, we propose a Region Refinement Module (RRM) that
optimizes salient region prediction by incorporating supervised attention masks
in the intermediate refinement stages. The module only brings a minor increase
in model size and yet significantly reduces false predictions from the
background. To further refine boundary areas, we propose a Boundary Refinement
Loss (BRL) that adds extra supervision for better distinguishing foreground
from background. BRL is parameter free and easy to train. We further observe
that BRL helps retain the integrity in prediction by refining the boundary.
Extensive experiments on saliency detection datasets show that our refinement
module and loss bring significant improvement to the baseline and can be easily
applied to different frameworks. We also demonstrate that our proposed model
generalizes well to portrait segmentation and shadow detection tasks
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
Recurrent Attentional Networks for Saliency Detection
Convolutional-deconvolution networks can be adopted to perform end-to-end
saliency detection. But, they do not work well with objects of multiple scales.
To overcome such a limitation, in this work, we propose a recurrent attentional
convolutional-deconvolution network (RACDNN). Using spatial transformer and
recurrent network units, RACDNN is able to iteratively attend to selected image
sub-regions to perform saliency refinement progressively. Besides tackling the
scale problem, RACDNN can also learn context-aware features from past
iterations to enhance saliency refinement in future iterations. Experiments on
several challenging saliency detection datasets validate the effectiveness of
RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection
methods.Comment: CVPR 201
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