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

Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits

We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly difficult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p=N(N−1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is affordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge

Evolving weighting schemes for the Bag of Visual Words

The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g., term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method

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

Improvements to the X-ray Spectrometer at the Aerosol Laboratory, Instituto de Física, UNAM

Due to the demands of better (accurate and precise) analytical results using X-ray Fluorescence (XRF) at the Aerosol Laboratory, Instituto de Física, UNAM, it was necessary to carry out improvements in instrumentation and analytical procedures in the x-ray spectrometer located in this facility. A new turbomolecular vacuum system was installed, which allows reaching the working pressure in a shorter time. Characteristic x-rays are registered with a Silicon Drift Detector, or SDD, (8 mm thick Be window, 140 eV at 5.9 keV resolution), working directly in a high-vacuum, permitting the detection of x-rays with energies as low as 1 keV (Na Ka) and higher counting rates than in the past. Due to the interference produced by the Rh L x-rays emitted by the tube normally used for atmospheric and food analysis with Cl K x-rays, another tube with a W anode was mounted in the spectrometer to avoid this interference, with the possibility to select operation with any of these tubes. Examples of applications in atmospheric aerosols and other samples are presented, to demonstrate the enhanced function of the spectrometer. Other future modifications are also explained

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

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

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

phase i ii study of spartalizumab pdr001 an anti pd1 mab in patients with advanced melanoma or non small cell lung cancer

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Optical properties of ZnMgO films grown by spray pyrolysis and their application to UV photodetection

This work presents a comprehensive optical characterization of Zn1−xMgxO thin films grown by spray pyrolysis (SP). Absorption measurements show the high potential of this technique to tune the bandgap from 3.30 to 4.11 eV by changing the Mg acetate content in the precursor solution, leading to a change of the Mg-content ranging from 0 up to 35%, as measured by transmission electron microscopy-energy dispersive x-ray spectroscopy. The optical emission of the films obtained by cathodoluminescence and photoluminescence spectroscopy shows a blue shift of the peak position from 3.26 to 3.89 eV with increasing Mg incorporation, with a clear excitonic contribution even at high Mg contents. The linewidth broadening of the absorption and emission spectra as well as the magnitude of the observed Stokes shift are found to significantly increase with the Mg content. This is shown to be related to both potential fluctuations induced by pure statistical alloy disorder and the presence of a tail of band states, the latter dominating for medium Mg contents. Finally, metal–semiconductor–metal photodiodes were fabricated showing a high sensitivity and a blue shift in the cut-off energy from 3.32 to 4.02 eV, i.e., down to 308 nm. The photodiodes present large UV/dark contrast ratios (102 − 107), indicating the viability of SP as a growth technique to fabricate low cost (Zn, Mg)O-based UV photodetectors reaching short wavelengths