225 research outputs found
Compression of Deep Neural Networks on the Fly
Thanks to their state-of-the-art performance, deep neural networks are
increasingly used for object recognition. To achieve these results, they use
millions of parameters to be trained. However, when targeting embedded
applications the size of these models becomes problematic. As a consequence,
their usage on smartphones or other resource limited devices is prohibited. In
this paper we introduce a novel compression method for deep neural networks
that is performed during the learning phase. It consists in adding an extra
regularization term to the cost function of fully-connected layers. We combine
this method with Product Quantization (PQ) of the trained weights for higher
savings in storage consumption. We evaluate our method on two data sets (MNIST
and CIFAR10), on which we achieve significantly larger compression rates than
state-of-the-art methods
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
PlaNet - Photo Geolocation with Convolutional Neural Networks
Is it possible to build a system to determine the location where a photo was
taken using just its pixels? In general, the problem seems exceptionally
difficult: it is trivial to construct situations where no location can be
inferred. Yet images often contain informative cues such as landmarks, weather
patterns, vegetation, road markings, and architectural details, which in
combination may allow one to determine an approximate location and occasionally
an exact location. Websites such as GeoGuessr and View from your Window suggest
that humans are relatively good at integrating these cues to geolocate images,
especially en-masse. In computer vision, the photo geolocation problem is
usually approached using image retrieval methods. In contrast, we pose the
problem as one of classification by subdividing the surface of the earth into
thousands of multi-scale geographic cells, and train a deep network using
millions of geotagged images. While previous approaches only recognize
landmarks or perform approximate matching using global image descriptors, our
model is able to use and integrate multiple visible cues. We show that the
resulting model, called PlaNet, outperforms previous approaches and even
attains superhuman levels of accuracy in some cases. Moreover, we extend our
model to photo albums by combining it with a long short-term memory (LSTM)
architecture. By learning to exploit temporal coherence to geolocate uncertain
photos, we demonstrate that this model achieves a 50% performance improvement
over the single-image model
Visual Link Retrieval in a Database of Paintings
This paper examines how far state-of-the-art machine vision algorithms can be used to retrieve common visual patterns shared by series of paintings. The research of such visual patterns, central to Art History Research, is challenging because of the diversity of similarity criteria that could relevantly demonstrate genealogical links. We design a methodology and a tool to annotate efficiently clusters of similar paintings and test various algorithms in a retrieval task. We show that pretrained convolutional neural network can perform better for this task than other machine vision methods aimed at photograph analysis. We also show that retrieval performance can be significantly improved by fine-tuning a network specifically for this task
Low-Resolution Face Recognition
Whilst recent face-recognition (FR) techniques have made significant progress
on recognising constrained high-resolution web images, the same cannot be said
on natively unconstrained low-resolution images at large scales. In this work,
we examine systematically this under-studied FR problem, and introduce a novel
Complement Super-Resolution and Identity (CSRI) joint deep learning method with
a unified end-to-end network architecture. We further construct a new
large-scale dataset TinyFace of native unconstrained low-resolution face images
from selected public datasets, because none benchmark of this nature exists in
the literature. With extensive experiments we show there is a significant gap
between the reported FR performances on popular benchmarks and the results on
TinyFace, and the advantages of the proposed CSRI over a variety of
state-of-the-art FR and super-resolution deep models on solving this largely
ignored FR scenario. The TinyFace dataset is released publicly at:
https://qmul-tinyface.github.io/.Comment: Accepted by 14th Asian Conference on Computer Visio
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
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