325 research outputs found
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
Influence of oxidizing and Nitriding parameters on nitrogen concentration of electrical steels
The influence of oxidizing and nitriding parameters on the nitrogen concentration of grain-oriented electrical steels preliminary to the development of the final Goss texture was explored. Results show that the nitrogen enrichment is driven by a ferrite to austenite transformation during thermochemical treatments. Such a trans- formation is promoted by (i) a redistribution of ferrite-forming elements close to the surface during oxidizing prior to nitriding, (ii) the oxygen content within the oxide layer prior to nitriding, (iii) the temperature of oxidizing and nitriding, and (iv) the nitrogen enrichment during nitriding. Optimization of the nitrogen content, and thus the precipitation kinetics of alloying elements nitrides (e.g. inhibitors) required for the development of the final Goss texture can be controlled by an optimization of the oxide layer growth, the temperatures and gas mixture of nitriding.collaboration thyssenkrupp Electrical Stee
Compact Deep Aggregation for Set Retrieval
The objective of this work is to learn a compact embedding of a set of
descriptors that is suitable for efficient retrieval and ranking, whilst
maintaining discriminability of the individual descriptors. We focus on a
specific example of this general problem -- that of retrieving images
containing multiple faces from a large scale dataset of images. Here the set
consists of the face descriptors in each image, and given a query for multiple
identities, the goal is then to retrieve, in order, images which contain all
the identities, all but one, \etc
To this end, we make the following contributions: first, we propose a CNN
architecture -- {\em SetNet} -- to achieve the objective: it learns face
descriptors and their aggregation over a set to produce a compact fixed length
descriptor designed for set retrieval, and the score of an image is a count of
the number of identities that match the query; second, we show that this
compact descriptor has minimal loss of discriminability up to two faces per
image, and degrades slowly after that -- far exceeding a number of baselines;
third, we explore the speed vs.\ retrieval quality trade-off for set retrieval
using this compact descriptor; and, finally, we collect and annotate a large
dataset of images containing various number of celebrities, which we use for
evaluation and is publicly released.Comment: 20 page
Cr cluster characterization in Cu-Cr-Zr alloy after ECAP processing and aging using SANS and HAADF-STEM
International audienceThe precipitation of nano-sized Cr clusters was investigated in a commercial Cu-1Cr-0.1Zr (wt.%) alloy processed by Equal-Channel Angular Pressing (ECAP) and subsequent aging at 550 °C for 4 hours using small angle neutron scattering (SANS) measurements and high-angle annular dark-field-scanning transmission electron microscopy (HAADF-STEM). The size and volume fraction of nano-sized Cr clusters were estimated using both techniques. These parameters assessed from SANS (d~3.2 nm, Fv~1.1 %) agreed reasonably with those from HAADF-STEM (d ~2.5 nm, Fv~2.3%). Besides nano-sized Cr clusters, HAADF-STEM technique evidenced the presence of rare cuboid and spheroid sub-micronic Cr particles about 380-620 nm mean size. Both techniques did not evidence the presence of intermetallic CuxZry phases within the aging conditions
The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search
This paper reconsiders common benchmarking approaches to nearest neighbor
search. It is shown that the concept of local intrinsic dimensionality (LID)
allows to choose query sets of a wide range of difficulty for real-world
datasets. Moreover, the effect of different LID distributions on the running
time performance of implementations is empirically studied. To this end,
different visualization concepts are introduced that allow to get a more
fine-grained overview of the inner workings of nearest neighbor search
principles. The paper closes with remarks about the diversity of datasets
commonly used for nearest neighbor search benchmarking. It is shown that such
real-world datasets are not diverse: results on a single dataset predict
results on all other datasets well.Comment: Preprint of the paper accepted at SISAP 201
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
© 2019, The Author(s). Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers
The Group Loss for Deep Metric Learning
Deep metric learning has yielded impressive results in tasks such as
clustering and image retrieval by leveraging neural networks to obtain highly
discriminative feature embeddings, which can be used to group samples into
different classes. Much research has been devoted to the design of smart loss
functions or data mining strategies for training such networks. Most methods
consider only pairs or triplets of samples within a mini-batch to compute the
loss function, which is commonly based on the distance between embeddings. We
propose Group Loss, a loss function based on a differentiable label-propagation
method that enforces embedding similarity across all samples of a group while
promoting, at the same time, low-density regions amongst data points belonging
to different groups. Guided by the smoothness assumption that "similar objects
should belong to the same group", the proposed loss trains the neural network
for a classification task, enforcing a consistent labelling amongst samples
within a class. We show state-of-the-art results on clustering and image
retrieval on several datasets, and show the potential of our method when
combined with other techniques such as ensemblesComment: Accepted to European Conference on Computer Vision (ECCV) 2020,
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