5,164 research outputs found
Multi-scale Deep Learning Architectures for Person Re-identification
Person Re-identification (re-id) aims to match people across non-overlapping
camera views in a public space. It is a challenging problem because many people
captured in surveillance videos wear similar clothes. Consequently, the
differences in their appearance are often subtle and only detectable at the
right location and scales. Existing re-id models, particularly the recently
proposed deep learning based ones match people at a single scale. In contrast,
in this paper, a novel multi-scale deep learning model is proposed. Our model
is able to learn deep discriminative feature representations at different
scales and automatically determine the most suitable scales for matching. The
importance of different spatial locations for extracting discriminative
features is also learned explicitly. Experiments are carried out to demonstrate
that the proposed model outperforms the state-of-the art on a number of
benchmarksComment: 9 pages, 3 figures, accepted by ICCV 201
Sketch-a-Net that Beats Humans
We propose a multi-scale multi-channel deep neural network framework that,
for the first time, yields sketch recognition performance surpassing that of
humans. Our superior performance is a result of explicitly embedding the unique
characteristics of sketches in our model: (i) a network architecture designed
for sketch rather than natural photo statistics, (ii) a multi-channel
generalisation that encodes sequential ordering in the sketching process, and
(iii) a multi-scale network ensemble with joint Bayesian fusion that accounts
for the different levels of abstraction exhibited in free-hand sketches. We
show that state-of-the-art deep networks specifically engineered for photos of
natural objects fail to perform well on sketch recognition, regardless whether
they are trained using photo or sketch. Our network on the other hand not only
delivers the best performance on the largest human sketch dataset to date, but
also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral
N-(4-ChloroÂphenÂyl)-4-(2-oxocycloÂpentÂyl)butyramide
In the title compound, C15H18ClNO2, the amide group is coplanar with the chloroÂphenyl group, making a dihedral angle of 1.71 (12)°. The cycloÂpentaÂnone ring adopts a twist conformation. A weak intraÂmolecular C—H⋯O hydrogen bond is observed. MolÂecules are linked into cyclic centrosymmetric dimers by paired N—H⋯O hydrogen bonds
The largest virialized dark halo in the universe
Using semi-analytic approach, we present an estimate of the properties of the
largest virialized dark halos in the present universe for three different
scenarios of structure formation: SCDM, LCDM and OCDM models. The resulting
virial mass and temperature increase from the lowest values of and 9.8 keV in OCDM, the mid-range values of and 31 keV in LCDM, to the highest values of
, 65 keV in SCDM. As compared with the
largest virialized object seen in the universe, the richest clusters of
galaxies, we can safely rule out the OCDM model. In addition, the SCDM model is
very unlikely because of the unreasonably high virial mass and temperature. Our
computation favors the prevailing LCDM model in which superclusters may be
marginally regarded as dynamically-virialized systems.Comment: 5 pages, Accepted by Int. J. Mod. Phys.
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