pwc-archive/papers-with-abstracts huggingface dataset:
End-to-end Convolutional Network for Saliency Prediction
Authors: Junting Pan, Xavier Giró-i-Nieto
Date: 2015-07-06 00:00:00
Tasks: Prediction, Saliency Prediction

The prediction of saliency areas in images has been traditionally addressed
with hand crafted features based on neuroscience principles. This paper however
addresses the problem with a completely data-driven approach by training a
convolutional network. The learning process is formulated as a minimization of
a loss function that measures the Euclidean distance of the predicted saliency
map with the provided ground truth. The recent publication of large datasets of
saliency prediction has provided enough data to train a not very deep
architecture which is both fast and accurate. The convolutional network in this
paper, named JuntingNet, won the LSUN 2015 challenge on saliency prediction
with a superior performance in all considered metrics.
A linear approach for sparse coding by a two-layer neural network
Authors: Alessandro Montalto, Giovanni Tessitore, Roberto Prevete
Date: 2015-07-06 00:00:00
Tasks: BIG-bench Machine Learning

Many approaches to transform classification problems from non-linear to
linear by feature transformation have been recently presented in the
literature. These notably include sparse coding methods and deep neural
networks. However, many of these approaches require the repeated application of
a learning process upon the presentation of unseen data input vectors, or else
involve the use of large numbers of parameters and hyper-parameters, which must
be chosen through cross-validation, thus increasing running time dramatically.
In this paper, we propose and experimentally investigate a new approach for the
purpose of overcoming limitations of both kinds. The proposed approach makes
use of a linear auto-associative network (called SCNN) with just one hidden
layer. The combination of this architecture with a specific error function to
be minimized enables one to learn a linear encoder computing a sparse code
which turns out to be as similar as possible to the sparse coding that one
obtains by re-training the neural network. Importantly, the linearity of SCNN
and the choice of the error function allow one to achieve reduced running time
in the learning phase. The proposed architecture is evaluated on the basis of
two standard machine learning tasks. Its performances are compared with those
of recently proposed non-linear auto-associative neural networks. The overall
results suggest that linear encoders can be profitably used to obtain sparse
data representations in the context of machine learning problems, provided that
an appropriate error function is used during the learning phase.
Implicit Contextual Integrity in Online Social Networks
Authors: Natalia Criado, Jose M. Such
Date: 2015-02-09 00:00:00

Many real incidents demonstrate that users of Online Social Networks need
mechanisms that help them manage their interactions by increasing the awareness
of the different contexts that coexist in Online Social Networks and preventing
them from exchanging inappropriate information in those contexts or
disseminating sensitive information from some contexts to others. Contextual
integrity is a privacy theory that conceptualises the appropriateness of
information sharing based on the contexts in which this information is to be
shared. Computational models of Contextual Integrity assume the existence of
well-defined contexts, in which individuals enact pre-defined roles and
information sharing is governed by an explicit set of norms. However, contexts
in Online Social Networks are known to be implicit, unknown a priori and ever
changing; users relationships are constantly evolving; and the information
sharing norms are implicit. This makes current Contextual Integrity models not
suitable for Online Social Networks.
  In this paper, we propose the first computational model of Implicit
Contextual Integrity, presenting an information model and an Information
Assistant Agent that uses the information model to learn implicit contexts,
relationships and the information sharing norms to help users avoid
inappropriate information exchanges and undesired information disseminations.
Through an experimental evaluation, we validate the properties of Information
Assistant Agents, which are shown to: infer the information sharing norms even
if a small proportion of the users follow the norms and in presence of
malicious users; help reduce the exchange of inappropriate information and the
dissemination of sensitive information with only a partial view of the system
and the information received and sent by their users; and minimise the burden
to the users in terms of raising unnecessary alerts.
Early Recognition of Human Activities from First-Person Videos Using Onset Representations
Authors: M. S. Ryoo, Thomas J. Fuchs, Lu Xia, J. K. Aggarwal, Larry Matthies
Date: 2014-06-20 00:00:00
Tasks: Activity Prediction, Person Recognition, Time Series, Time Series Analysis

In this paper, we propose a methodology for early recognition of human
activities from videos taken with a first-person viewpoint. Early recognition,
which is also known as activity prediction, is an ability to infer an ongoing
activity at its early stage. We present an algorithm to perform recognition of
activities targeted at the camera from streaming videos, making the system to
predict intended activities of the interacting person and avoid harmful events
before they actually happen. We introduce the novel concept of 'onset' that
efficiently summarizes pre-activity observations, and design an approach to
consider event history in addition to ongoing video observation for early
first-person recognition of activities. We propose to represent onset using
cascade histograms of time series gradients, and we describe a novel
algorithmic setup to take advantage of onset for early recognition of
activities. The experimental results clearly illustrate that the proposed
concept of onset enables better/earlier recognition of human activities from
first-person videos.
Visual Data Deblocking using Structural Layer Priors
Authors: Xiaojie Guo
Date: 2015-07-06 00:00:00
Tasks: Blocking

The blocking artifact frequently appears in compressed real-world images or
video sequences, especially coded at low bit rates, which is visually annoying
and likely hurts the performance of many computer vision algorithms. A
compressed frame can be viewed as the superimposition of an intrinsic layer and
an artifact one. Recovering the two layers from such frames seems to be a
severely ill-posed problem since the number of unknowns to recover is twice as
many as the given measurements. In this paper, we propose a simple and robust
method to separate these two layers, which exploits structural layer priors
including the gradient sparsity of the intrinsic layer, and the independence of
the gradient fields of the two layers. A novel Augmented Lagrangian Multiplier
based algorithm is designed to efficiently and effectively solve the recovery
problem. Extensive experimental results demonstrate the superior performance of
our method over the state of the arts, in terms of visual quality and
simplicity.
Deep Image: Scaling up Image Recognition
Authors: Ren Wu, Shengen Yan, Yi Shan, Qingqing Dang, Gang Sun
Date: 2015-01-13 00:00:00
Tasks: Data Augmentation, Deep Learning

We present a state-of-the-art image recognition system, Deep Image, developed
using end-to-end deep learning. The key components are a custom-built
supercomputer dedicated to deep learning, a highly optimized parallel algorithm
using new strategies for data partitioning and communication, larger deep
neural network models, novel data augmentation approaches, and usage of
multi-scale high-resolution images. Our method achieves excellent results on
multiple challenging computer vision benchmarks.
Subspace-Sparse Representation
Authors: C. You, R. Vidal
Date: 2015-07-06 00:00:00
Tasks: Sparse Representation-based Classification

Given an overcomplete dictionary $A$ and a signal $b$ that is a linear
combination of a few linearly independent columns of $A$, classical sparse
recovery theory deals with the problem of recovering the unique sparse
representation $x$ such that $b = A x$. It is known that under certain
conditions on $A$, $x$ can be recovered by the Basis Pursuit (BP) and the
Orthogonal Matching Pursuit (OMP) algorithms. In this work, we consider the
more general case where $b$ lies in a low-dimensional subspace spanned by some
columns of $A$, which are possibly linearly dependent. In this case, the
sparsest solution $x$ is generally not unique, and we study the problem that
the representation $x$ identifies the subspace, i.e. the nonzero entries of $x$
correspond to dictionary atoms that are in the subspace. Such a representation
$x$ is called subspace-sparse. We present sufficient conditions for
guaranteeing subspace-sparse recovery, which have clear geometric
interpretations and explain properties of subspace-sparse recovery. We also
show that the sufficient conditions can be satisfied under a randomized model.
Our results are applicable to the traditional sparse recovery problem and we
get conditions for sparse recovery that are less restrictive than the canonical
mutual coherent condition. We also use the results to analyze the sparse
representation based classification (SRC) method, for which we get conditions
to show its correctness.
Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination
Authors: Tianpei Xie, Nasser M. Nasrabadi, Alfred O. Hero III
Date: 2015-07-05 00:00:00
Tasks: General Classification, MULTI-VIEW LEARNING

In this paper, we consider multi-sensor classification when there is a large
number of unlabeled samples. The problem is formulated under the multi-view
learning framework and a Consensus-based Multi-View Maximum Entropy
Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the
stochastic agreement between multiple classifiers on the unlabeled dataset, the
algorithm simultaneously learns multiple high accuracy classifiers. We
demonstrate that our proposed method can yield improved performance over
previous multi-view learning approaches by comparing performance on three real
multi-sensor data sets.
Autoencoding the Retrieval Relevance of Medical Images
Authors: Zehra Camlica, H. R. Tizhoosh, Farzad Khalvati
Date: 2015-07-05 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Content-based image retrieval (CBIR) of medical images is a crucial task that
can contribute to a more reliable diagnosis if applied to big data. Recent
advances in feature extraction and classification have enormously improved CBIR
results for digital images. However, considering the increasing accessibility
of big data in medical imaging, we are still in need of reducing both memory
requirements and computational expenses of image retrieval systems. This work
proposes to exclude the features of image blocks that exhibit a low encoding
error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the
histogram of autoendcoding errors of image blocks for each image class to
facilitate the decision which image regions, or roughly what percentage of an
image perhaps, shall be declared relevant for the retrieval task. This leads to
reduction of feature dimensionality and speeds up the retrieval process. To
validate the proposed scheme, we employ local binary patterns (LBP) and support
vector machines (SVM) which are both well-established approaches in CBIR
research community. As well, we use IRMA dataset with 14,410 x-ray images as
test data. The results show that the dimensionality of annotated feature
vectors can be reduced by up to 50% resulting in speedups greater than 27% at
expense of less than 1% decrease in the accuracy of retrieval when validating
the precision and recall of the top 20 hits.
Fast Label Embeddings via Randomized Linear Algebra
Authors: Paul Mineiro, Nikos Karampatziakis
Date: 2014-12-19 00:00:00

Many modern multiclass and multilabel problems are characterized by
increasingly large output spaces. For these problems, label embeddings have
been shown to be a useful primitive that can improve computational and
statistical efficiency. In this work we utilize a correspondence between rank
constrained estimation and low dimensional label embeddings that uncovers a
fast label embedding algorithm which works in both the multiclass and
multilabel settings. The result is a randomized algorithm whose running time is
exponentially faster than naive algorithms. We demonstrate our techniques on
two large-scale public datasets, from the Large Scale Hierarchical Text
Challenge and the Open Directory Project, where we obtain state of the art
results.
Confidence-based Reasoning in Stochastic Constraint Programming
Authors: Roberto Rossi, Brahim Hnich, S. Armagan Tarim, Steven Prestwich
Date: 2011-10-09 00:00:00

In this work we introduce a novel approach, based on sampling, for finding
assignments that are likely to be solutions to stochastic constraint
satisfaction problems and constraint optimisation problems. Our approach
reduces the size of the original problem being analysed; by solving this
reduced problem, with a given confidence probability, we obtain assignments
that satisfy the chance constraints in the original model within prescribed
error tolerance thresholds. To achieve this, we blend concepts from stochastic
constraint programming and statistics. We discuss both exact and approximate
variants of our method. The framework we introduce can be immediately employed
in concert with existing approaches for solving stochastic constraint programs.
A thorough computational study on a number of stochastic combinatorial
optimisation problems demonstrates the effectiveness of our approach.
TV News Commercials Detection using Success based Locally Weighted Kernel Combination
Authors: Raghvendra Kannao, Prithwijit Guha
Date: 2015-07-05 00:00:00

Commercial detection in news broadcast videos involves judicious selection of
meaningful audio-visual feature combinations and efficient classifiers. And,
this problem becomes much simpler if these combinations can be learned from the
data. To this end, we propose an Multiple Kernel Learning based method for
boosting successful kernel functions while ignoring the irrelevant ones. We
adopt a intermediate fusion approach where, a SVM is trained with a weighted
linear combination of different kernel functions instead of single kernel
function. Each kernel function is characterized by a feature set and kernel
type. We identify the feature sub-space locations of the prediction success of
a particular classifier trained only with particular kernel function. We
propose to estimate a weighing function using support vector regression (with
RBF kernel) for each kernel function which has high values (near 1.0) where the
classifier learned on kernel function succeeded and lower values (nearly 0.0)
otherwise. Second contribution of this work is TV News Commercials Dataset of
150 Hours of News videos. Classifier trained with our proposed scheme has
outperformed the baseline methods on 6 of 8 benchmark dataset and our own TV
commercials dataset.
Parsimonious Labeling
Authors: Puneet K. Dokania, M. Pawan Kumar
Date: 2015-07-05 00:00:00
Proceeding: ICCV 2015 12
Tasks: Denoising, Diversity, Image Denoising

We propose a new family of discrete energy minimization problems, which we
call parsimonious labeling. Specifically, our energy functional consists of
unary potentials and high-order clique potentials. While the unary potentials
are arbitrary, the clique potentials are proportional to the {\em diversity} of
set of the unique labels assigned to the clique. Intuitively, our energy
functional encourages the labeling to be parsimonious, that is, use as few
labels as possible. This in turn allows us to capture useful cues for important
computer vision applications such as stereo correspondence and image denoising.
Furthermore, we propose an efficient graph-cuts based algorithm for the
parsimonious labeling problem that provides strong theoretical guarantees on
the quality of the solution. Our algorithm consists of three steps. First, we
approximate a given diversity using a mixture of a novel hierarchical $P^n$
Potts model. Second, we use a divide-and-conquer approach for each mixture
component, where each subproblem is solved using an effficient
$\alpha$-expansion algorithm. This provides us with a small number of putative
labelings, one for each mixture component. Third, we choose the best putative
labeling in terms of the energy value. Using both sythetic and standard real
datasets, we show that our algorithm significantly outperforms other graph-cuts
based approaches.
Dependency Recurrent Neural Language Models for Sentence Completion
Authors: Piotr Mirowski, Andreas Vlachos
Date: 2015-07-05 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Language Modeling, Language Modelling, Sentence, Sentence Completion

Recent work on language modelling has shifted focus from count-based models
to neural models. In these works, the words in each sentence are always
considered in a left-to-right order. In this paper we show how we can improve
the performance of the recurrent neural network (RNN) language model by
incorporating the syntactic dependencies of a sentence, which have the effect
of bringing relevant contexts closer to the word being predicted. We evaluate
our approach on the Microsoft Research Sentence Completion Challenge and show
that the dependency RNN proposed improves over the RNN by about 10 points in
accuracy. Furthermore, we achieve results comparable with the state-of-the-art
models on this task.
Inference for determinantal point processes without spectral knowledge
Authors: Rémi Bardenet, Michalis K. Titsias
Date: 2015-07-04 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Point Processes, Variational Inference

Determinantal point processes (DPPs) are point process models that naturally
encode diversity between the points of a given realization, through a positive
definite kernel $K$. DPPs possess desirable properties, such as exact sampling
or analyticity of the moments, but learning the parameters of kernel $K$
through likelihood-based inference is not straightforward. First, the kernel
that appears in the likelihood is not $K$, but another kernel $L$ related to
$K$ through an often intractable spectral decomposition. This issue is
typically bypassed in machine learning by directly parametrizing the kernel
$L$, at the price of some interpretability of the model parameters. We follow
this approach here. Second, the likelihood has an intractable normalizing
constant, which takes the form of a large determinant in the case of a DPP over
a finite set of objects, and the form of a Fredholm determinant in the case of
a DPP over a continuous domain. Our main contribution is to derive bounds on
the likelihood of a DPP, both for finite and continuous domains. Unlike
previous work, our bounds are cheap to evaluate since they do not rely on
approximating the spectrum of a large matrix or an operator. Through usual
arguments, these bounds thus yield cheap variational inference and moderately
expensive exact Markov chain Monte Carlo inference methods for DPPs.
Techniques for effective and efficient fire detection from social media images
Authors: Marcos Bedo, Gustavo Blanco, Willian Oliveira, Mirela Cazzolato, Alceu Costa, Jose Rodrigues, Agma Traina, Caetano Traina Jr
Date: 2015-06-11 00:00:00
Tasks: Content-Based Image Retrieval, Decision Making, Fire Detection, Image Retrieval, Management, Retrieval

Social media could provide valuable information to support decision making in
crisis management, such as in accidents, explosions and fires. However, much of
the data from social media are images, which are uploaded in a rate that makes
it impossible for human beings to analyze them. Despite the many works on image
analysis, there are no fire detection studies on social media. To fill this
gap, we propose the use and evaluation of a broad set of content-based image
retrieval and classification techniques for fire detection. Our main
contributions are: (i) the development of the Fast-Fire Detection method
(FFDnR), which combines feature extractor and evaluation functions to support
instance-based learning, (ii) the construction of an annotated set of images
with ground-truth depicting fire occurrences -- the FlickrFire dataset, and
(iii) the evaluation of 36 efficient image descriptors for fire detection.
Using real data from Flickr, our results showed that FFDnR was able to achieve
a precision for fire detection comparable to that of human annotators.
Therefore, our work shall provide a solid basis for further developments on
monitoring images from social media.
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
Authors: Sascha Rothe, Hinrich Schütze
Date: 2015-07-04 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Word Embeddings, Word Sense Disambiguation, Word Similarity

We present \textit{AutoExtend}, a system to learn embeddings for synsets and
lexemes. It is flexible in that it can take any word embeddings as input and
does not need an additional training corpus. The synset/lexeme embeddings
obtained live in the same vector space as the word embeddings. A sparse tensor
formalization guarantees efficiency and parallelizability. We use WordNet as a
lexical resource, but AutoExtend can be easily applied to other resources like
Freebase. AutoExtend achieves state-of-the-art performance on word similarity
and word sense disambiguation tasks.
Learning in the Presence of Corruption
Authors: Brendan van Rooyen, Robert C. Williamson
Date: 2015-04-01 00:00:00

In supervised learning one wishes to identify a pattern present in a joint
distribution $P$, of instances, label pairs, by providing a function $f$ from
instances to labels that has low risk $\mathbb{E}_{P}\ell(y,f(x))$. To do so,
the learner is given access to $n$ iid samples drawn from $P$. In many real
world problems clean samples are not available. Rather, the learner is given
access to samples from a corrupted distribution $\tilde{P}$ from which to
learn, while the goal of predicting the clean pattern remains. There are many
different types of corruption one can consider, and as of yet there is no
general means to compare the relative ease of learning under these different
corruption processes. In this paper we develop a general framework for tackling
such problems as well as introducing upper and lower bounds on the risk for
learning in the presence of corruption. Our ultimate goal is to be able to make
informed economic decisions in regards to the acquisition of data sets. For a
certain subclass of corruption processes (those that are
\emph{reconstructible}) we achieve this goal in a particular sense. Our lower
bounds are in terms of the coefficient of ergodicity, a simple to calculate
property of stochastic matrices. Our upper bounds proceed via a generalization
of the method of unbiased estimators appearing in recent work of Natarajan et
al and implicit in the earlier work of Kearns.