9,273 research outputs found
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
Joint Multi-Person Pose Estimation and Semantic Part Segmentation
Human pose estimation and semantic part segmentation are two complementary
tasks in computer vision. In this paper, we propose to solve the two tasks
jointly for natural multi-person images, in which the estimated pose provides
object-level shape prior to regularize part segments while the part-level
segments constrain the variation of pose locations. Specifically, we first
train two fully convolutional neural networks (FCNs), namely Pose FCN and Part
FCN, to provide initial estimation of pose joint potential and semantic part
potential. Then, to refine pose joint location, the two types of potentials are
fused with a fully-connected conditional random field (FCRF), where a novel
segment-joint smoothness term is used to encourage semantic and spatial
consistency between parts and joints. To refine part segments, the refined pose
and the original part potential are integrated through a Part FCN, where the
skeleton feature from pose serves as additional regularization cues for part
segments. Finally, to reduce the complexity of the FCRF, we induce human
detection boxes and infer the graph inside each box, making the inference forty
times faster.
Since there's no dataset that contains both part segments and pose labels, we
extend the PASCAL VOC part dataset with human pose joints and perform extensive
experiments to compare our method against several most recent strategies. We
show that on this dataset our algorithm surpasses competing methods by a large
margin in both tasks.Comment: This paper has been accepted by CVPR 201
Learning Fully Dense Neural Networks for Image Semantic Segmentation
Semantic segmentation is pixel-wise classification which retains critical
spatial information. The "feature map reuse" has been commonly adopted in CNN
based approaches to take advantage of feature maps in the early layers for the
later spatial reconstruction. Along this direction, we go a step further by
proposing a fully dense neural network with an encoder-decoder structure that
we abbreviate as FDNet. For each stage in the decoder module, feature maps of
all the previous blocks are adaptively aggregated to feed-forward as input. On
the one hand, it reconstructs the spatial boundaries accurately. On the other
hand, it learns more efficiently with the more efficient gradient
backpropagation. In addition, we propose the boundary-aware loss function to
focus more attention on the pixels near the boundary, which boosts the "hard
examples" labeling. We have demonstrated the best performance of the FDNet on
the two benchmark datasets: PASCAL VOC 2012, NYUDv2 over previous works when
not considering training on other datasets
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous convolution allows us to explicitly control the
resolution at which feature responses are computed within Deep Convolutional
Neural Networks. It also allows us to effectively enlarge the field of view of
filters to incorporate larger context without increasing the number of
parameters or the amount of computation. Second, we propose atrous spatial
pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP
probes an incoming convolutional feature layer with filters at multiple
sampling rates and effective fields-of-views, thus capturing objects as well as
image context at multiple scales. Third, we improve the localization of object
boundaries by combining methods from DCNNs and probabilistic graphical models.
The commonly deployed combination of max-pooling and downsampling in DCNNs
achieves invariance but has a toll on localization accuracy. We overcome this
by combining the responses at the final DCNN layer with a fully connected
Conditional Random Field (CRF), which is shown both qualitatively and
quantitatively to improve localization performance. Our proposed "DeepLab"
system sets the new state-of-art at the PASCAL VOC-2012 semantic image
segmentation task, reaching 79.7% mIOU in the test set, and advances the
results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and
Cityscapes. All of our code is made publicly available online.Comment: Accepted by TPAM
Reversible Recursive Instance-level Object Segmentation
In this work, we propose a novel Reversible Recursive Instance-level Object
Segmentation (R2-IOS) framework to address the challenging instance-level
object segmentation task. R2-IOS consists of a reversible proposal refinement
sub-network that predicts bounding box offsets for refining the object proposal
locations, and an instance-level segmentation sub-network that generates the
foreground mask of the dominant object instance in each proposal. By being
recursive, R2-IOS iteratively optimizes the two sub-networks during joint
training, in which the refined object proposals and improved segmentation
predictions are alternately fed into each other to progressively increase the
network capabilities. By being reversible, the proposal refinement sub-network
adaptively determines an optimal number of refinement iterations required for
each proposal during both training and testing. Furthermore, to handle multiple
overlapped instances within a proposal, an instance-aware denoising autoencoder
is introduced into the segmentation sub-network to distinguish the dominant
object from other distracting instances. Extensive experiments on the
challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of
R2-IOS over other state-of-the-art methods. In particular, the
over classes at IoU achieves , which significantly
outperforms the results of by PFN~\cite{PFN} and
by~\cite{liu2015multi}.Comment: 9 page
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