94,657 research outputs found
An efficient rate control algorithm for a wavelet video codec
Rate control plays an essential role in video coding and transmission to provide the best video quality at the receiver's end given the constraint of certain network conditions. In this paper, a rate control algorithm using the Quality Factor (QF) optimization method is proposed for the wavelet-based video codec and implemented on an open source Dirac video encoder. A mathematical model which we call Rate-QF (R - QF) model is derived to generate the optimum QF for the current coding frame according to the target bitrate. The proposed algorithm is a complete one pass process and does not require complex mathematical calculation. The process of calculating the QF is quite simple and further calculation is not required for each coded frame. The experimental results show that the proposed algorithm can control the bitrate precisely (within 1% of target bitrate in average). Moreover, the variation of bitrate over each Group of Pictures (GOPs) is lower than that of H.264. This is an advantage in preventing the buffer overflow and underflow for real-time multimedia data streaming
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images
Current CNN-based algorithms for recovering the 3D pose of an object in an
image assume knowledge about both the object category and its 2D localization
in the image. In this paper, we relax one of these constraints and propose to
solve the task of joint object category and 3D pose estimation from an image
assuming known 2D localization. We design a new architecture for this task
composed of a feature network that is shared between subtasks, an object
categorization network built on top of the feature network, and a collection of
category dependent pose regression networks. We also introduce suitable loss
functions and a training method for the new architecture. Experiments on the
challenging PASCAL3D+ dataset show state-of-the-art performance in the joint
categorization and pose estimation task. Moreover, our performance on the joint
task is comparable to the performance of state-of-the-art methods on the
simpler 3D pose estimation with known object category task
Coherent states engineering with linear optics: Possible and impossible tasks
The general transformation of the product of coherent states
to the output state (
or ), which is realizable with linear optical circuit, is
characterized with a linear map from the vector
to
. A correspondence between the
transformations of a product of coherent states and those of a single photon
state is established with such linear maps. It is convenient to apply this
linear transformation method to design any linear optical scheme working with
coherent states. The examples include message encoding and quantum database
searching. The limitation of manipulating entangled coherent states with linear
optics is also discussed.Comment: 6 pages, 2 figure
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Learning deeper convolutional neural networks becomes a tendency in recent
years. However, many empirical evidences suggest that performance improvement
cannot be gained by simply stacking more layers. In this paper, we consider the
issue from an information theoretical perspective, and propose a novel method
Relay Backpropagation, that encourages the propagation of effective information
through the network in training stage. By virtue of the method, we achieved the
first place in ILSVRC 2015 Scene Classification Challenge. Extensive
experiments on two challenging large scale datasets demonstrate the
effectiveness of our method is not restricted to a specific dataset or network
architecture. Our models will be available to the research community later.Comment: Technical report for our submissions to the ILSVRC 2015 Scene
Classification Challenge, where we won the first plac
S-OHEM: Stratified Online Hard Example Mining for Object Detection
One of the major challenges in object detection is to propose detectors with
highly accurate localization of objects. The online sampling of high-loss
region proposals (hard examples) uses the multitask loss with equal weight
settings across all loss types (e.g, classification and localization, rigid and
non-rigid categories) and ignores the influence of different loss distributions
throughout the training process, which we find essential to the training
efficacy. In this paper, we present the Stratified Online Hard Example Mining
(S-OHEM) algorithm for training higher efficiency and accuracy detectors.
S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling
technique, to choose the training examples according to this influence during
hard example mining, and thus enhance the performance of object detectors. We
show through systematic experiments that S-OHEM yields an average precision
(AP) improvement of 0.5% on rigid categories of PASCAL VOC 2007 for both the
IoU threshold of 0.6 and 0.7. For KITTI 2012, both results of the same metric
are 1.6%. Regarding the mean average precision (mAP), a relative increase of
0.3% and 0.5% (1% and 0.5%) is observed for VOC07 (KITTI12) using the same set
of IoU threshold. Also, S-OHEM is easy to integrate with existing region-based
detectors and is capable of acting with post-recognition level regressors.Comment: 9 pages, 3 figures, accepted by CCCV 201
Selective Equal-Spin Andreev Reflections Induced by Majorana Fermions
In this work, we find that Majorana fermions induce selective equal spin
Andreev reflections (SESARs), in which incoming electrons with certain spin
polarization in the lead are reflected as counter propagating holes with the
same spin. The spin polarization direction of the electrons of this Andreev
reflected channel is selected by the Majorana fermions. Moreover, electrons
with opposite spin polarization are always reflected as electrons with
unchanged spin. As a result, the charge current in the lead is spin-polarized.
Therefore, a topological superconductor which supports Majorana fermions can be
used as a novel device to create fully spin-polarized currents in paramagnetic
leads. We point out that SESARs can also be used to detect Majorana fermions in
topological superconductors.Comment: 5 pages, 3 figures. Comments are welcome. Title changed to match
published versio
The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study
A neural network-based chart pattern represents adaptive parametric features,
including non-linear transformations, and a template that can be applied in the
feature space. The search of neural network-based chart patterns has been
unexplored despite its potential expressiveness. In this paper, we formulate a
general chart pattern search problem to enable cross-representational
quantitative comparison of various search schemes. We suggest a HyperNEAT
framework applying state-of-the-art deep neural network techniques to find
attractive neural network-based chart patterns; These techniques enable a fast
evaluation and search of robust patterns, as well as bringing a performance
gain. The proposed framework successfully found attractive patterns on the
Korean stock market. We compared newly found patterns with those found by
different search schemes, showing the proposed approach has potential.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation
Conference (GECCO 2017), Berlin, German
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