203 research outputs found
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images
We propose a simple and efficient method for exploiting synthetic images when
training a Deep Network to predict a 3D pose from an image. The ability of
using synthetic images for training a Deep Network is extremely valuable as it
is easy to create a virtually infinite training set made of such images, while
capturing and annotating real images can be very cumbersome. However, synthetic
images do not resemble real images exactly, and using them for training can
result in suboptimal performance. It was recently shown that for exemplar-based
approaches, it is possible to learn a mapping from the exemplar representations
of real images to the exemplar representations of synthetic images. In this
paper, we show that this approach is more general, and that a network can also
be applied after the mapping to infer a 3D pose: At run time, given a real
image of the target object, we first compute the features for the image, map
them to the feature space of synthetic images, and finally use the resulting
features as input to another network which predicts the 3D pose. Since this
network can be trained very effectively by using synthetic images, it performs
very well in practice, and inference is faster and more accurate than with an
exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for
3D object pose estimation from color images, and the NYU dataset for 3D hand
pose estimation from depth maps. We show that it allows us to outperform the
state-of-the-art on both datasets.Comment: CVPR 201
Efficient Rewirings for Enhancing Synchronizability of Dynamical Networks
In this paper, we present an algorithm for optimizing synchronizability of
complex dynamical networks. Based on some network properties, rewirings, i.e.
eliminating an edge and creating a new edge elsewhere, are performed
iteratively avoiding always self-loops and multiple edges between the same
nodes. We show that the method is able to enhance the synchronizability of
networks of any size and topological properties in a small number of steps that
scales with the network size.Although we take the eigenratio of the Laplacian
as the target function for optimization, we will show that it is also possible
to choose other appropriate target functions exhibiting almost the same
performance. The optimized networks are Ramanujan graphs, and thus, this
rewiring algorithm could be used to produce Ramanujan graphs of any size and
average degree
Extending Demand Response to Tenants in Cloud Data Centers via Non-intrusive Workload Flexibility Pricing
Participating in demand response programs is a promising tool for reducing
energy costs in data centers by modulating energy consumption. Towards this
end, data centers can employ a rich set of resource management knobs, such as
workload shifting and dynamic server provisioning. Nonetheless, these knobs may
not be readily available in a cloud data center (CDC) that serves cloud
tenants/users, because workloads in CDCs are managed by tenants themselves who
are typically charged based on a usage-based or flat-rate pricing and often
have no incentive to cooperate with the CDC operator for demand response and
cost saving. Towards breaking such "split incentive" hurdle, a few recent
studies have tried market-based mechanisms, such as dynamic pricing, inside
CDCs. However, such mechanisms often rely on complex designs that are hard to
implement and difficult to cope with by tenants. To address this limitation, we
propose a novel incentive mechanism that is not dynamic, i.e., it keeps pricing
for cloud resources unchanged for a long period. While it charges tenants based
on a Usage-based Pricing (UP) as used by today's major cloud operators, it
rewards tenants proportionally based on the time length that tenants set as
deadlines for completing their workloads. This new mechanism is called
Usage-based Pricing with Monetary Reward (UPMR). We demonstrate the
effectiveness of UPMR both analytically and empirically. We show that UPMR can
reduce the CDC operator's energy cost by 12.9% while increasing its profit by
4.9%, compared to the state-of-the-art approaches used by today's CDC operators
to charge their tenants
Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation
We propose a robust and accurate method for estimating the 3D poses of two
hands in close interaction from a single color image. This is a very
challenging problem, as large occlusions and many confusions between the joints
may happen. State-of-the-art methods solve this problem by regressing a heatmap
for each joint, which requires solving two problems simultaneously: localizing
the joints and recognizing them. In this work, we propose to separate these
tasks by relying on a CNN to first localize joints as 2D keypoints, and on
self-attention between the CNN features at these keypoints to associate them
with the corresponding hand joint. The resulting architecture, which we call
"Keypoint Transformer", is highly efficient as it achieves state-of-the-art
performance with roughly half the number of model parameters on the
InterHand2.6M dataset. We also show it can be easily extended to estimate the
3D pose of an object manipulated by one or two hands with high performance.
Moreover, we created a new dataset of more than 75,000 images of two hands
manipulating an object fully annotated in 3D and will make it publicly
available.Comment: Accepted at CVPR202
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