pwc-archive/papers-with-abstracts huggingface dataset:
Learning Robust Hash Codes for Multiple Instance Image Retrieval
Authors: Sailesh Conjeti, Magdalini Paschali, Amin Katouzian, Nassir Navab
Date: 2017-03-16 00:00:00
Tasks: Deep Hashing, Image Retrieval, Pose Retrieval, Retrieval

In this paper, for the first time, we introduce a multiple instance (MI) deep
hashing technique for learning discriminative hash codes with weak bag-level
supervision suited for large-scale retrieval. We learn such hash codes by
aggregating deeply learnt hierarchical representations across bag members
through a dedicated MI pool layer. For better trainability and retrieval
quality, we propose a two-pronged approach that includes robust optimization
and training with an auxiliary single instance hashing arm which is
down-regulated gradually. We pose retrieval for tumor assessment as an MI
problem because tumors often coexist with benign masses and could exhibit
complementary signatures when scanned from different anatomical views.
Experimental validations on benchmark mammography and histology datasets
demonstrate improved retrieval performance over the state-of-the-art methods.
Optimal Detection of Faulty Traffic Sensors Used in Route Planning
Authors: Amin Ghafouri, Aron Laszka, Abhishek Dubey, Xenofon Koutsoukos
Date: 2017-02-08 00:00:00
Tasks: Change Point Detection, Gaussian Processes

In a smart city, real-time traffic sensors may be deployed for various
applications, such as route planning. Unfortunately, sensors are prone to
failures, which result in erroneous traffic data. Erroneous data can adversely
affect applications such as route planning, and can cause increased travel
time. To minimize the impact of sensor failures, we must detect them promptly
and accurately. However, typical detection algorithms may lead to a large
number of false positives (i.e., false alarms) and false negatives (i.e.,
missed detections), which can result in suboptimal route planning. In this
paper, we devise an effective detector for identifying faulty traffic sensors
using a prediction model based on Gaussian Processes. Further, we present an
approach for computing the optimal parameters of the detector which minimize
losses due to false-positive and false-negative errors. We also characterize
critical sensors, whose failure can have high impact on the route planning
application. Finally, we implement our method and evaluate it numerically using
a real-world dataset and the route planning platform OpenTripPlanner.
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
Authors: Sohail Bahmani, Justin Romberg
Date: 2016-10-13 00:00:00
Tasks: Learning Theory, Retrieval

We propose a flexible convex relaxation for the phase retrieval problem that
operates in the natural domain of the signal. Therefore, we avoid the
prohibitive computational cost associated with "lifting" and semidefinite
programming (SDP) in methods such as PhaseLift and compete with recently
developed non-convex techniques for phase retrieval. We relax the quadratic
equations for phaseless measurements to inequality constraints each of which
representing a symmetric "slab". Through a simple convex program, our proposed
estimator finds an extreme point of the intersection of these slabs that is
best aligned with a given anchor vector. We characterize geometric conditions
that certify success of the proposed estimator. Furthermore, using classic
results in statistical learning theory, we show that for random measurements
the geometric certificates hold with high probability at an optimal sample
complexity. Phase transition of our estimator is evaluated through simulations.
Our numerical experiments also suggest that the proposed method can solve phase
retrieval problems with coded diffraction measurements as well.
Segmented and Directional Impact Detection for Parked Vehicles using Mobile Devices
Authors: Andre Ebert, Sebastian Feld, Florian Dorfmeister
Date: 2017-03-16 00:00:00

Mutual usage of vehicles as well as car sharing became more and more
attractive during the last years. Especially in urban environments with limited
parking possibilities and a higher risk for traffic jams, car rentals and
sharing services may save time and money. But when renting a vehicle it could
already be damaged (e.g., scratches or bumps inflicted by a previous user)
without the damage being perceived by the service provider. In order to address
such problems, we present an automated, motion-based system for impact
detection, that facilitates a common smartphone as a sensor platform. The
system is capable of detecting the impact segment and the point of time of an
impact event on a vehicle's surface, as well as its direction of origin. With
this additional specific knowledge, it may be possible to reconstruct the
circumstances of an impact event, e.g., to prove possible innocence of a
service's customer.
End-to-end training of object class detectors for mean average precision
Authors: Paul Henderson, Vittorio Ferrari
Date: 2016-07-12 00:00:00
Tasks: General Classification

We present a method for training CNN-based object class detectors directly
using mean average precision (mAP) as the training loss, in a truly end-to-end
fashion that includes non-maximum suppression (NMS) at training time. This
contrasts with the traditional approach of training a CNN for a window
classification loss, then applying NMS only at test time, when mAP is used as
the evaluation metric in place of classification accuracy. However, mAP
following NMS forms a piecewise-constant structured loss over thousands of
windows, with gradients that do not convey useful information for gradient
descent. Hence, we define new, general gradient-like quantities for piecewise
constant functions, which have wide applicability. We describe how to calculate
these efficiently for mAP following NMS, enabling to train a detector based on
Fast R-CNN directly for mAP. This model achieves equivalent performance to the
standard Fast R-CNN on the PASCAL VOC 2007 and 2012 datasets, while being
conceptually more appealing as the very same model and loss are used at both
training and test time.
Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval
Authors: Li Liu, Fumin Shen, Yuming Shen, Xianglong Liu, Ling Shao
Date: 2017-03-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Retrieval, Retrieval, Sketch-Based Image Retrieval

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view
retrieval task, in which queries are abstract and ambiguous sketches while the
retrieval database is formed with natural images. Work in this area mainly
focuses on extracting representative and shared features for sketches and
natural images. However, these can neither cope well with the geometric
distortion between sketches and images nor be feasible for large-scale SBIR due
to the heavy continuous-valued distance computation. In this paper, we speed up
SBIR by introducing a novel binary coding method, named \textbf{Deep Sketch
Hashing} (DSH), where a semi-heterogeneous deep architecture is proposed and
incorporated into an end-to-end binary coding framework. Specifically, three
convolutional neural networks are utilized to encode free-hand sketches,
natural images and, especially, the auxiliary sketch-tokens which are adopted
as bridges to mitigate the sketch-image geometric distortion. The learned DSH
codes can effectively capture the cross-view similarities as well as the
intrinsic semantic correlations between different categories. To the best of
our knowledge, DSH is the first hashing work specifically designed for
category-level SBIR with an end-to-end deep architecture. The proposed DSH is
comprehensively evaluated on two large-scale datasets of TU-Berlin Extension
and Sketchy, and the experiments consistently show DSH's superior SBIR
accuracies over several state-of-the-art methods, while achieving significantly
reduced retrieval time and memory footprint.
Generalised Reichenbachian Common Cause Systems
Authors: Claudio Mazzola
Date: 2017-03-16 00:00:00

The principle of the common cause claims that if an improbable coincidence
has occurred, there must exist a common cause. This is generally taken to mean
that positive correlations between non-causally related events should disappear
when conditioning on the action of some underlying common cause. The extended
interpretation of the principle, by contrast, urges that common causes should
be called for in order to explain positive deviations between the estimated
correlation of two events and the expected value of their correlation. The aim
of this paper is to provide the extended reading of the principle with a
general probabilistic model, capturing the simultaneous action of a system of
multiple common causes. To this end, two distinct models are elaborated, and
the necessary and sufficient conditions for their existence are determined.
A Visual Web Tool to Perform What-If Analysis of Optimization Approaches
Authors: Sascha Van Cauwelaert, Michele Lombardi, Pierre Schaus
Date: 2017-03-16 00:00:00

In Operation Research, practical evaluation is essential to validate the
efficacy of optimization approaches. This paper promotes the usage of
performance profiles as a standard practice to visualize and analyze
experimental results. It introduces a Web tool to construct and export
performance profiles as SVG or HTML files. In addition, the application relies
on a methodology to estimate the benefit of hypothetical solver improvements.
Therefore, the tool allows one to employ what-if analysis to screen possible
research directions, and identify those having the best potential. The approach
is showcased on two Operation Research technologies: Constraint Programming and
Mixed Integer Linear Programming.
Finding Statistically Significant Attribute Interactions
Authors: Andreas Henelius, Antti Ukkonen, Kai Puolamäki
Date: 2016-12-22 00:00:00
Tasks: Attribute, feature selection

In many data exploration tasks it is meaningful to identify groups of
attribute interactions that are specific to a variable of interest. For
instance, in a dataset where the attributes are medical markers and the
variable of interest (class variable) is binary indicating presence/absence of
disease, we would like to know which medical markers interact with respect to
the binary class label. These interactions are useful in several practical
applications, for example, to gain insight into the structure of the data, in
feature selection, and in data anonymisation. We present a novel method, based
on statistical significance testing, that can be used to verify if the data set
has been created by a given factorised class-conditional joint distribution,
where the distribution is parametrised by a partition of its attributes.
Furthermore, we provide a method, named ASTRID, for automatically finding a
partition of attributes describing the distribution that has generated the
data. State-of-the-art classifiers are utilised to capture the interactions
present in the data by systematically breaking attribute interactions and
observing the effect of this breaking on classifier performance. We empirically
demonstrate the utility of the proposed method with examples using real and
synthetic data.
From visual words to a visual grammar: using language modelling for image classification
Authors: Antonio Foncubierta-Rodríguez, Henning Müller, Adrien Depeursinge
Date: 2017-03-16 00:00:00
Tasks: General Classification, image-classification, Image Classification, Language Modelling

The Bag--of--Visual--Words (BoVW) is a visual description technique that aims
at shortening the semantic gap by partitioning a low--level feature space into
regions of the feature space that potentially correspond to visual concepts and
by giving more value to this space. In this paper we present a conceptual
analysis of three major properties of language grammar and how they can be
adapted to the computer vision and image understanding domain based on the bag
of visual words paradigm. Evaluation of the visual grammar shows that a
positive impact on classification accuracy and/or descriptor size is obtained
when the technique are applied when the proposed techniques are applied.
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking
Authors: Erwin Quiring, Daniel Arp, Konrad Rieck
Date: 2017-03-16 00:00:00
Tasks: Autonomous Driving, BIG-bench Machine Learning, Face Recognition, Malware Detection, Model extraction

Machine learning is increasingly used in security-critical applications, such
as autonomous driving, face recognition and malware detection. Most learning
methods, however, have not been designed with security in mind and thus are
vulnerable to different types of attacks. This problem has motivated the
research field of adversarial machine learning that is concerned with attacking
and defending learning methods. Concurrently, a different line of research has
tackled a very similar problem: In digital watermarking information are
embedded in a signal in the presence of an adversary. As a consequence, this
research field has also extensively studied techniques for attacking and
defending watermarking methods.
  The two research communities have worked in parallel so far, unnoticeably
developing similar attack and defense strategies. This paper is a first effort
to bring these communities together. To this end, we present a unified notation
of black-box attacks against machine learning and watermarking that reveals the
similarity of both settings. To demonstrate the efficacy of this unified view,
we apply concepts from watermarking to machine learning and vice versa. We show
that countermeasures from watermarking can mitigate recent model-extraction
attacks and, similarly, that techniques for hardening machine learning can fend
off oracle attacks against watermarks. Our work provides a conceptual link
between two research fields and thereby opens novel directions for improving
the security of both, machine learning and digital watermarking.
Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition
Authors: Leonie Zeune, Stephan A. van Gils, Leon W. M. M. Terstappen, Christoph Brune
Date: 2017-03-16 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

This paper focuses on multi-scale approaches for variational methods and
corresponding gradient flows. Recently, for convex regularization functionals
such as total variation, new theory and algorithms for nonlinear eigenvalue
problems via nonlinear spectral decompositions have been developed. Those
methods open new directions for advanced image filtering. However, for an
effective use in image segmentation and shape decomposition, a clear
interpretation of the spectral response regarding size and intensity scales is
needed but lacking in current approaches. In this context, $L^1$ data
fidelities are particularly helpful due to their interesting multi-scale
properties such as contrast invariance. Hence, the novelty of this work is the
combination of $L^1$-based multi-scale methods with nonlinear spectral
decompositions. We compare $L^1$ with $L^2$ scale-space methods in view of
spectral image representation and decomposition. We show that the contrast
invariant multi-scale behavior of $L^1-TV$ promotes sparsity in the spectral
response providing more informative decompositions. We provide a numerical
method and analyze synthetic and biomedical images at which decomposition leads
to improved segmentation.
Detection of Human Rights Violations in Images: Can Convolutional Neural Networks help?
Authors: Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier
Date: 2017-03-12 00:00:00

After setting the performance benchmarks for image, video, speech and audio
processing, deep convolutional networks have been core to the greatest advances
in image recognition tasks in recent times. This raises the question of whether
there are any benefit in targeting these remarkable deep architectures with the
unattempted task of recognising human rights violations through digital images.
Under this perspective, we introduce a new, well-sampled human rights-centric
dataset called Human Rights Understanding (HRUN). We conduct a rigorous
evaluation on a common ground by combining this dataset with different
state-of-the-art deep convolutional architectures in order to achieve
recognition of human rights violations. Experimental results on the HRUN
dataset have shown that the best performing CNN architectures can achieve up to
88.10\% mean average precision. Additionally, our experiments demonstrate that
increasing the size of the training samples is crucial for achieving an
improvement on mean average precision principally when utilising very deep
networks.
Evaluating Deep Convolutional Neural Networks for Material Classification
Authors: Grigorios Kalliatakis, Georgios Stamatiadis, Shoaib Ehsan, Ales Leonardis, Juergen Gall, Anca Sticlaru, Klaus D. McDonald-Maier
Date: 2017-03-12 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Material Classification, object-detection, Object Detection

Determining the material category of a surface from an image is a demanding
task in perception that is drawing increasing attention. Following the recent
remarkable results achieved for image classification and object detection
utilising Convolutional Neural Networks (CNNs), we empirically study material
classification of everyday objects employing these techniques. More
specifically, we conduct a rigorous evaluation of how state-of-the art CNN
architectures compare on a common ground over widely used material databases.
Experimental results on three challenging material databases show that the best
performing CNN architectures can achieve up to 94.99\% mean average precision
when classifying materials.
Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data
Authors: Grigorios Kalliatakis, Nikolaos Vidakis, Georgios Triantafyllidis
Date: 2017-03-11 00:00:00
Tasks: Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Head Pose Estimation, Pose Estimation

Despite significant recent advances in the field of head pose estimation and
facial expression recognition, raising the cognitive level when analysing human
activity presents serious challenges to current concepts. Motivated by the need
of generating comprehensible visual representations from different sets of
data, we introduce a system capable of monitoring human activity through head
pose and facial expression changes, utilising an affordable 3D sensing
technology (Microsoft Kinect sensor). An approach build on discriminative
random regression forests was selected in order to rapidly and accurately
estimate head pose changes in unconstrained environment. In order to complete
the secondary process of recognising four universal dominant facial expressions
(happiness, anger, sadness and surprise), emotion recognition via facial
expressions (ERFE) was adopted. After that, a lightweight data exchange format
(JavaScript Object Notation-JSON) is employed, in order to manipulate the data
extracted from the two aforementioned settings. Such mechanism can yield a
platform for objective and effortless assessment of human activity within the
context of serious gaming and human-computer interaction.
Shift Aggregate Extract Networks
Authors: Francesco Orsini, Daniele Baracchi, Paolo Frasconi
Date: 2017-03-16 00:00:00
Tasks: General Classification, Graph Classification

We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in kernel methods, enabling nested part-of-part relations. Unlike recursive neural networks, which unroll a template on input graphs directly, we unroll a neural network template over the decomposition hierarchy, allowing us to deal with the high degree variability that typically characterize social network graphs. Deep hierarchical decompositions are also amenable to domain compression, a technique that reduces both space and time complexity by exploiting symmetries. We show empirically that our approach is able to outperform current state-of-the-art graph classification methods on large social network datasets, while at the same time being competitive on small chemobiological benchmark datasets.
Student-t Process Quadratures for Filtering of Non-Linear Systems with Heavy-Tailed Noise
Authors: Jakub Prüher, Filip Tronarp, Toni Karvonen, Simo Särkkä, Ondřej Straka
Date: 2017-03-15 00:00:00

The aim of this article is to design a moment transformation for Student- t
distributed random variables, which is able to account for the error in the
numerically computed mean. We employ Student-t process quadrature, an instance
of Bayesian quadrature, which allows us to treat the integral itself as a
random variable whose variance provides information about the incurred
integration error. Advantage of the Student- t process quadrature over the
traditional Gaussian process quadrature, is that the integral variance depends
also on the function values, allowing for a more robust modelling of the
integration error. The moment transform is applied in nonlinear sigma-point
filtering and evaluated on two numerical examples, where it is shown to
outperform the state-of-the-art moment transforms.
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Authors: Vincent Andrearczyk, Paul F. Whelan
Date: 2017-03-16 00:00:00
Tasks: General Classification, Retrieval, Texture Classification, Video Recognition

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.
Character-based Neural Embeddings for Tweet Clustering
Authors: Svitlana Vakulenko, Lyndon Nixon, Mihai Lupu
Date: 2017-03-15 00:00:00
Proceeding: WS 2017 4
Tasks: Clustering

In this paper we show how the performance of tweet clustering can be improved
by leveraging character-based neural networks. The proposed approach overcomes
the limitations related to the vocabulary explosion in the word-based models
and allows for the seamless processing of the multilingual content. Our
evaluation results and code are available on-line at
https://github.com/vendi12/tweet2vec_clustering
Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow
Authors: Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini
Date: 2017-03-10 00:00:00
Tasks: BIG-bench Machine Learning

This report provides an introduction to some Machine Learning tools within
the most common development environments. It mainly focuses on practical
problems, skipping any theoretical introduction. It is oriented to both
students trying to approach Machine Learning and experts looking for new
frameworks.