25,948 research outputs found
Target-Tailored Source-Transformation for Scene Graph Generation
Scene graph generation aims to provide a semantic and structural description
of an image, denoting the objects (with nodes) and their relationships (with
edges). The best performing works to date are based on exploiting the context
surrounding objects or relations,e.g., by passing information among objects. In
these approaches, to transform the representation of source objects is a
critical process for extracting information for the use by target objects. In
this work, we argue that a source object should give what tar-get object needs
and give different objects different information rather than contributing
common information to all targets. To achieve this goal, we propose a
Target-TailoredSource-Transformation (TTST) method to efficiently propagate
information among object proposals and relations. Particularly, for a source
object proposal which will contribute information to other target objects, we
transform the source object feature to the target object feature domain by
simultaneously taking both the source and target into account. We further
explore more powerful representations by integrating language prior with the
visual context in the transformation for the scene graph generation. By doing
so the target object is able to extract target-specific information from the
source object and source relation accordingly to refine its representation. Our
framework is validated on the Visual Genome bench-mark and demonstrated its
state-of-the-art performance for the scene graph generation. The experimental
results show that the performance of object detection and visual relation-ship
detection are promoted mutually by our method
Fast Deep Matting for Portrait Animation on Mobile Phone
Image matting plays an important role in image and video editing. However,
the formulation of image matting is inherently ill-posed. Traditional methods
usually employ interaction to deal with the image matting problem with trimaps
and strokes, and cannot run on the mobile phone in real-time. In this paper, we
propose a real-time automatic deep matting approach for mobile devices. By
leveraging the densely connected blocks and the dilated convolution, a light
full convolutional network is designed to predict a coarse binary mask for
portrait images. And a feathering block, which is edge-preserving and matting
adaptive, is further developed to learn the guided filter and transform the
binary mask into alpha matte. Finally, an automatic portrait animation system
based on fast deep matting is built on mobile devices, which does not need any
interaction and can realize real-time matting with 15 fps. The experiments show
that the proposed approach achieves comparable results with the
state-of-the-art matting solvers.Comment: ACM Multimedia Conference (MM) 2017 camera-read
Robust and fully automated segmentation of mandible from CT scans
Mandible bone segmentation from computed tomography (CT) scans is challenging
due to mandible's structural irregularities, complex shape patterns, and lack
of contrast in joints. Furthermore, connections of teeth to mandible and
mandible to remaining parts of the skull make it extremely difficult to
identify mandible boundary automatically. This study addresses these challenges
by proposing a novel framework where we define the segmentation as two
complementary tasks: recognition and delineation. For recognition, we use
random forest regression to localize mandible in 3D. For delineation, we
propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation
algorithm, operating on the recognized mandible sub-volume. Despite heavy CT
artifacts and dental fillings, consisting half of the CT image data in our
experiments, we have achieved highly accurate detection and delineation
results. Specifically, detection accuracy more than 96% (measured by union of
intersection (UoI)), the delineation accuracy of 91% (measured by dice
similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff
Distance) were found.Comment: 4 pages, 5 figures, IEEE International Symposium on Biomedical
Imaging (ISBI) 201
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
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