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-Chloro­phen­yl)-4-(2-oxocyclo­pent­yl)butyramide

In the title compound, C15H18ClNO2, the amide group is coplanar with the chloro­phenyl group, making a dihedral angle of 1.71 (12)°. The cyclo­penta­none ring adopts a twist conformation. A weak intra­molecular C—H⋯O hydrogen bond is observed. Mol­ecules 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 $1.6 \times 10^{15}h^{-1}M_{\odot}$ and 9.8 keV in OCDM, the mid-range values of $9.0 \times 10^{15}h^{-1}M_{\odot}$ and 31 keV in LCDM, to the highest values of $20.9 \times 10^{15}h^{-1}M_{\odot}$, 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.