19 research outputs found
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
Output consensus of multi-agent systems with delayed and sampled-data
This paper considers the output consensus problem of high-order leader-following multi-
agent systems with unknown nonlinear dynamics, in which the delayed and sampled outputs of the
system are the only available data. The unknown nonlinear dynamics are assumed to satisfy the
Lipschitz condition and the interconnected topologies are assumed to be undirected and connected.
A distributed observer-based output feedback controller is proposed for the system to reach output
consensus. Both of the bounds of the allowable delay and sampling period are also obtained. Sta-
bility analysis shows that the considered systems are globally exponentially stable under the output
feedback controller. Finally, a simulation example is given to validate our theoretical results.This work was supported in part by the National Natural Science
Foundation of China (61273183, 61374028, and 61304162).http://www.ietdl.orgIET-CTAhb2017Electrical, Electronic and Computer Engineerin
Hard superconducting gap in PbTe nanowires
Semiconductor nanowires coupled to a superconductor provide a powerful
testbed for quantum device physics such as Majorana zero modes and gate-tunable
hybrid qubits. The performance of these quantum devices heavily relies on the
quality of the induced superconducting gap. A hard gap, evident as vanishing
subgap conductance in tunneling spectroscopy, is both necessary and desired.
Previously, a hard gap has been achieved and extensively studied in III-V
semiconductor nanowires (InAs and InSb). In this study, we present the
observation of a hard superconducting gap in PbTe nanowires coupled to a
superconductor Pb. The gap size () is 1 meV (maximally 1.3 meV
in one device). Additionally, subgap Andreev bound states can also be created
and controlled through gate tuning. Tuning a device into the open regime can
reveal Andreev enhancement of the subgap conductance, suggesting a remarkable
transparent superconductor-semiconductor interface, with a transparency of
0.96. These results pave the way for diverse superconducting quantum
devices based on PbTe nanowires
Ballistic PbTe Nanowire Devices
Disorder is the primary obstacle in current Majorana nanowire experiments.
Reducing disorder or achieving ballistic transport is thus of paramount
importance. In clean and ballistic nanowire devices, quantized conductance is
expected with plateau quality serving as a benchmark for disorder assessment.
Here, we introduce ballistic PbTe nanowire devices grown using the
selective-area-growth (SAG) technique. Quantized conductance plateaus in units
of are observed at zero magnetic field. This observation represents an
advancement in diminishing disorder within SAG nanowires, as none of the
previously studied SAG nanowires (InSb or InAs) exhibit zero-field ballistic
transport. Notably, the plateau values indicate that the ubiquitous valley
degeneracy in PbTe is lifted in nanowire devices. This degeneracy lifting
addresses an additional concern in the pursuit of Majorana realization.
Moreover, these ballistic PbTe nanowires may enable the search for clean
signatures of the spin-orbit helical gap in future devices
Pose-Guided Human Parsing by an AND/OR Graph Using Pose-Context Features
Parsing human into semantic parts is crucial to human-centric analysis. In this paper, we propose a human parsing pipeline that uses pose cues, e.g., estimates of human joint locations, to provide pose-guided segment proposals for semantic parts. These segment proposals are ranked using standard appearance cues, deep-learned semantic feature, and a novel pose feature called pose-context. Then these proposals are selected and assembled using an And-Or graph to output a parse of the person. The And-Or graph is able to deal with large human appearance variability due to pose, choice of clothing, etc. We evaluate our approach on the popular Penn-Fudan pedestrian parsing dataset, showing that it significantly outperforms the state of the art, and perform diagnostics to demonstrate the effectiveness of different stages of our pipeline