Deep Compact Person Re-Identification with Distractor Synthesis via Guided DC-GANs

We present a dual-stream CNN that learns both appearance and facial features in tandem from still images and, after feature fusion, infers person identities. We then describe an alternative architecture of a single, lightweight ID-CondenseNet where a face detector-guided DC-GAN is used to generate distractor person images for enhanced training. For evaluation, we test both architectures on FLIMA, a new extension of an existing person re-identification dataset with added frame-by-frame annotations of face presence. Although the dual-stream CNN can outperform the CondenseNet approach on FLIMA, we show that the latter surpasses all state-of-the-art architectures in top-1 ranking performance when applied to the largest existing person re-identification dataset, MSMT17. We conclude that whilst re-identification performance is highly sensitive to the structure of datasets, distractor augmentation and network compression have a role to play for enhancing performance characteristics for larger scale applications

ChaLearn Looking at People Challenge 2014: Dataset and Results

This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively

ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results

This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the first round of the competition. The goal of the competition was to automatically evaluate five “apparent” personality traits (the so-called “Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the final phase. Despite the difficulty of the task, the teams made great advances in this round of the challenge

Optical properties and microstructure of 2.02-3.30 eV ZnCdO nanowires: Effect of thermal annealing

International audienceZnCdO nanowires with up to 45% Cd are demonstrated showing room temperature photoluminescence (PL) down to 2.02 eV and a radiative efficiency similar to that of ZnO nanowires. Analysis of the microstructure in individual nanowires confirms the presence of a single wurtzite phase even at the highest Cd contents, with a homogeneous distribution of Cd both in the longitudinal and transverse directions. Thermal annealing at 550 C yields an overall improvement of the PL, which is blue-shifted as a result of the homogeneous decrease of Cd throughout the nanowire, but the single wurtzite structure is fully maintained

Why are cell populations maintained via multiple compartments?

We consider the maintenance of ‘product’ cell populations from ‘progenitor’ cells via a sequence of one or more cell types, or compartments, where each cell’s fate is chosen stochastically. If there is only one compartment then large amplification, that is, a large ratio of product cells to progenitors comes with disadvantages. The product cell population is dominated by large families (cells descended from the same progenitor) and many generations separate, on average, product cells from progenitors. These disadvantages are avoided using suitably constructed sequences of compartments: the amplification factor of a sequence is the product of the amplification factors of each compartment, while the average number of generations is a sum over contributions from each compartment. Passing through multiple compartments is, in fact, an efficient way to maintain a product cell population from a small flux of progenitors, avoiding excessive clonality and minimizing the number of rounds of division en route. We use division, exit and death rates, estimated from measurements of single-positive thymocytes, to choose illustrative parameter values in the single-compartment case. We also consider a five-compartment model of thymocyte differentiation, from double-negative precursors to single-positive product cells

Light polarization sensitive photodetectors with m- and r-plane homoepitaxial ZnO/ZnMgO quantum wells

Homoepitaxial ZnO/(Zn,Mg)O multiple quantum wells (MQWs) grown with m- and r-plane orientations are used to demonstrate Schottky photodiodes sensitive to the polarization state of light. In both orientations, the spectral photoresponse of the MQW photodiodes shows a sharp excitonic absorption edge at 3.48 eV with a very low Urbach tail, allowing the observation of the absorption from the A, B and C excitonic transitions. The absorption edge energy is shifted by ∼30 and ∼15 meV for the m- and r-plane MQW photodiodes, respectively, in full agreement with the calculated polarization of the A, B, and C excitonic transitions. The best figures of merit are obtained for the m-plane photodiodes, which present a quantum efficiency of ∼11%, and a specific detectivity D* of ∼6.4 × 1010 cm Hz1/2/W. In these photodiodes, the absorption polarization sensitivity contrast between the two orthogonal in-plane axes yields a maximum value of (R⊥/R||)max ∼ 9.9 with a narrow bandwidth of ∼33 meV

Effects of spatial resolution of terrain models on modelled discharge and soil loss in Oaxaca, Mexico

The effect of the spatial resolution of digital terrain models (DTMs) on topography and soil erosion modelling is well documented for low resolutions. Nowadays, the availability of high spatial resolution DTMs from unmanned aerial vehicles (UAVs) opens new horizons for detailed assessment of soil erosion with hydrological models, but the effects of DTM resolution on model outputs at this scale have not been systematically tested. This study combines plot-scale soil erosion measurements, UAV-derived DTMs, and spatially explicit soil erosion modelling to select an appropriate spatial resolution based on allowable loss of information. During 39 precipitation events, sediment and soil samples were collected on five bounded and unbounded plots and four land covers (forest, fallow, maize, and eroded bare land). Additional soil samples were collected across a 220ha watershed to generate soil maps. Precipitation was collected by two rain gauges and vegetation was mapped. A total of two UAV campaigns over the watershed resulted in a 0.60m spatial-resolution DTM used for resampling to 1, 2, 4, 8, and 15m and a multispectral orthomosaic to generate a land cover map. The OpenLISEM model was calibrated at plot level at 1m resolution and then extended to the watershed level at the different DTM resolutions. Resampling the 1m DTM to lower resolutions resulted in an overall reduction in slope. This reduction was driven by migration of pixels from higher to lower slope values; its magnitude was proportional to resolution. At the watershed outlet, 1 and 2m resolution models exhibited the largest hydrograph and sedigraph peaks, total runoff, and soil loss; they proportionally decreased with resolution. Sedigraphs were more sensitive than hydrographs to spatial resolution, particularly at the highest resolutions. The highest-resolution models exhibited a wider range of predicted soil loss due to their larger number of pixels and steeper slopes. The proposed evaluation method was shown to be appropriate and transferable for soil erosion modelling studies, indicating that 4m resolution (<5% loss of slope information) was sufficient for describing soil erosion variability at the study site

ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An overview

This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost 200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to allow researchers to keep making progress in the four tracks