Multi-scale Orderless Pooling of Deep Convolutional Activation Features

Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective scheme called multi-scale orderless pooling (MOP-CNN). This scheme extracts CNN activations for local patches at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result. The resulting MOP-CNN representation can be used as a generic feature for either supervised or unsupervised recognition tasks, from image classification to instance-level retrieval; it consistently outperforms global CNN activations without requiring any joint training of prediction layers for a particular target dataset. In absolute terms, it achieves state-of-the-art results on the challenging SUN397 and MIT Indoor Scenes classification datasets, and competitive results on ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets

Risks linked to accidental inoculation of humans with veterinary vaccines: a 7-year prospective study

AIM: Accidental inoculation of humans with veterinary vaccines can lead to early and late complications. The aim of our study is to describe these complications and their risk factors. METHODS: Prospective observational study conducted from 2007 to 2014 at Angers University Hospital\u27s Poison Control Centre. The endpoints examined were: early and late locoregional complications, surgical treatment, and absence from work. The statistical analysis was based on a multivariate analysis. DISCUSSION: The presence of mineral oil adjuvants, the injection of the vaccine under pressure and injection in joint and tendon of the hand significantly increased early locoregional complications and surgery but only the presence of mineral oil adjuvant increased significantly late locoregional complications at one month. Absence from work is significantly correlated to the site of injection and the presence of mineral oil adjuvant. CONCLUSION: It is important to know about the contents of the veterinary vaccine in order to anticipate early and late complications that may arise (particularly due to the presence of mineral oil adjuvants). Special attention must also be given do the site of injection. We think that any accidental injection of veterinary vaccine into humans, especially those containing mineral oils, must lead to an early medical consultation. This must also be indicated on the product

Cross-dimensional Weighting for Aggregated Deep Convolutional Features

We propose a simple and straightforward way of creating powerful image representations via cross-dimensional weighting and aggregation of deep convolutional neural network layer outputs. We first present a generalized framework that encompasses a broad family of approaches and includes cross-dimensional pooling and weighting steps. We then propose specific non-parametric schemes for both spatial- and channel-wise weighting that boost the effect of highly active spatial responses and at the same time regulate burstiness effects. We experiment on different public datasets for image search and show that our approach outperforms the current state-of-the-art for approaches based on pre-trained networks. We also provide an easy-to-use, open source implementation that reproduces our results.Comment: Accepted for publications at the 4th Workshop on Web-scale Vision and Social Media (VSM), ECCV 201

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art image representations for object category retrieval over standard benchmark datasets containing 1M+ images; (ii) we show that ConvNets can be used to obtain features which are incredibly performant, and yet much lower dimensional than previous state-of-the-art image representations, and that their dimensionality can be reduced further without loss in performance by compression using product quantization or binarization. Consequently, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; (iii) we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking. The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU.Comment: Published in proceedings of ACCV 201

Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval

Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods. To this end, we introduce an objective that optimises instead a smoothed approximation of AP, coined Smooth-AP. Smooth-AP is a plug-and-play objective function that allows for end-to-end training of deep networks with a simple and elegant implementation. We also present an analysis for why directly optimising the ranking based metric of AP offers benefits over other deep metric learning losses. We apply Smooth-AP to standard retrieval benchmarks: Stanford Online products and VehicleID, and also evaluate on larger-scale datasets: INaturalist for fine-grained category retrieval, and VGGFace2 and IJB-C for face retrieval. In all cases, we improve the performance over the state-of-the-art, especially for larger-scale datasets, thus demonstrating the effectiveness and scalability of Smooth-AP to real-world scenarios.Comment: Accepted at ECCV 202

Chemical composition of nano-phases studied by anomalous small-angle X-ray scattering (ASAXS)

Anomalous small-angle X-ray scattering (ASAXS) is a technique developed in the 1980s. It offers the opportunity to go further in the investigation of nano-objects by providing chemical information besides characteristic features like size and volume fraction given by classical SAXS. ASAXS is an element-selective technique based on the anomalous variation of the scattering factor near the absorption edge of one chosen element. This technique requires a tunable wavelength of the incident beam that is available on synchrotron radiation sources. In this study, a simple approach is proposed and detailed to extract chemical information from anomalous SAXS data. To illustrate the procedure, two examples are treated by applying this data processing. The first one aims to discriminate between different possible phases in the Y- Ti-O system that may form nano-oxides in oxide-dispersion-strenghtened (ODS) steels, materials for future nuclear plants. The second one deals with the composition of nano- precipitates formed in the diffusion layer of nitrided steels. Such information is of prime importance to evaluate the maximum nitrogen that can be introduced by such a surface treatment and thus the mechanical properties that can be achieved

The breakdown of the municipality as caring platform: lessons for co-design and co-learning in the age of platform capitalism

If municipalities were the caring platforms of the 19-20th century sharing economy, how does care manifest in civic structures of the current period? We consider how platforms - from the local initiatives of communities transforming neighbourhoods, to the city, in the form of the local authority - are involved, trusted and/or relied on in the design of shared services and amenities for the public good. We use contrasting cases of interaction between local government and civil society organisations in Sweden and the UK to explore trends in public service provision. We look at how care can manifest between state and citizens and at the roles that co-design and co-learning play in developing contextually sensitive opportunities for caring platforms. In this way, we seek to learn from platforms in transition about the importance of co-learning in political and structural contexts and make recommendations for the co-design of (digital) platforms to care with and for civil society

Observation of a J^PC = 1-+ exotic resonance in diffractive dissociation of 190 GeV/c pi- into pi- pi- pi+

The COMPASS experiment at the CERN SPS has studied the diffractive dissociation of negative pions into the pi- pi- pi+ final state using a 190 GeV/c pion beam hitting a lead target. A partial wave analysis has been performed on a sample of 420000 events taken at values of the squared 4-momentum transfer t' between 0.1 and 1 GeV^2/c^2. The well-known resonances a1(1260), a2(1320), and pi2(1670) are clearly observed. In addition, the data show a significant natural parity exchange production of a resonance with spin-exotic quantum numbers J^PC = 1-+ at 1.66 GeV/c^2 decaying to rho pi. The resonant nature of this wave is evident from the mass-dependent phase differences to the J^PC = 2-+ and 1++ waves. From a mass-dependent fit a resonance mass of 1660 +- 10+0-64 MeV/c^2 and a width of 269+-21+42-64 MeV/c^2 is deduced.Comment: 7 page, 3 figures; version 2 gives some more details, data unchanged; version 3 updated authors, text shortened, data unchange

First Measurement of Chiral Dynamics in \pi^- \gamma -> \pi^- \pi^- \pi^+

The COMPASS collaboration at CERN has investigated the \pi^- \gamma -> \pi^- \pi^- \pi^+ reaction at center-of-momentum energy below five pion masses, sqrt(s) < 5 m(\pi), embedded in the Primakoff reaction of 190 GeV pions impinging on a lead target. Exchange of quasi-real photons is selected by isolating the sharp Coulomb peak observed at smallest momentum transfers, t' < 0.001 (GeV/c)^2. Using partial-wave analysis techniques, the scattering intensity of Coulomb production described in terms of chiral dynamics and its dependence on the 3\pi-invariant mass m(3\pi) = sqrt(s) were extracted. The absolute cross section was determined in seven bins of $\sqrt{s}$ with an overall precision of 20%. At leading order, the result is found to be in good agreement with the prediction of chiral perturbation theory over the whole energy range investigated.Comment: 10 pages, 5 figure