46,156 research outputs found
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
Matching pedestrians across disjoint camera views, known as person
re-identification (re-id), is a challenging problem that is of importance to
visual recognition and surveillance. Most existing methods exploit local
regions within spatial manipulation to perform matching in local
correspondence. However, they essentially extract \emph{fixed} representations
from pre-divided regions for each image and perform matching based on the
extracted representation subsequently. For models in this pipeline, local finer
patterns that are crucial to distinguish positive pairs from negative ones
cannot be captured, and thus making them underperformed. In this paper, we
propose a novel deep multiplicative integration gating function, which answers
the question of \emph{what-and-where to match} for effective person re-id. To
address \emph{what} to match, our deep network emphasizes common local patterns
by learning joint representations in a multiplicative way. The network
comprises two Convolutional Neural Networks (CNNs) to extract convolutional
activations, and generates relevant descriptors for pedestrian matching. This
thus, leads to flexible representations for pair-wise images. To address
\emph{where} to match, we combat the spatial misalignment by performing
spatially recurrent pooling via a four-directional recurrent neural network to
impose spatial dependency over all positions with respect to the entire image.
The proposed network is designed to be end-to-end trainable to characterize
local pairwise feature interactions in a spatially aligned manner. To
demonstrate the superiority of our method, extensive experiments are conducted
over three benchmark data sets: VIPeR, CUHK03 and Market-1501.Comment: Published at Pattern Recognition, Elsevie
Calculation of the Branching Ratio of in PQCD
The branching ratio of is re-evaluated in the PQCD approach.
In this theoretical framework all the phenomenological parameters in the
wavefunctions and Sudakov factor are priori fixed by fitting other experimental
data, and in the whole numerical computations we do not introduce any new
parameter. Our results are consistent with the upper bounds set by the Babar
and Belle measurements.Comment: 12 pages, 1 figure, version to appear in Phys. Rev.
Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates
We propose generalized additive partial linear models for complex data which
allow one to capture nonlinear patterns of some covariates, in the presence of
linear components. The proposed method improves estimation efficiency and
increases statistical power for correlated data through incorporating the
correlation information. A unique feature of the proposed method is its
capability of handling model selection in cases where it is difficult to
specify the likelihood function. We derive the quadratic inference
function-based estimators for the linear coefficients and the nonparametric
functions when the dimension of covariates diverges, and establish asymptotic
normality for the linear coefficient estimators and the rates of convergence
for the nonparametric functions estimators for both finite and high-dimensional
cases. The proposed method and theoretical development are quite challenging
since the numbers of linear covariates and nonlinear components both increase
as the sample size increases. We also propose a doubly penalized procedure for
variable selection which can simultaneously identify nonzero linear and
nonparametric components, and which has an asymptotic oracle property.
Extensive Monte Carlo studies have been conducted and show that the proposed
procedure works effectively even with moderate sample sizes. A pharmacokinetics
study on renal cancer data is illustrated using the proposed method.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1194 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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