pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Saliency Prediction in Natural Video
Authors: Souad Chaabouni, Jenny Benois-Pineau, Ofer Hadar, Chokri Ben Amar
Date: 2016-04-27 00:00:00
Tasks: Deep Learning, Prediction, Saliency Prediction, Specificity

The purpose of this paper is the detection of salient areas in natural video
by using the new deep learning techniques. Salient patches in video frames are
predicted first. Then the predicted visual fixation maps are built upon them.
We design the deep architecture on the basis of CaffeNet implemented with Caffe
toolkit. We show that changing the way of data selection for optimisation of
network parameters, we can save computation cost up to 12 times. We extend deep
learning approaches for saliency prediction in still images with RGB values to
specificity of video using the sensitivity of the human visual system to
residual motion. Furthermore, we complete primary colour pixel values by
contrast features proposed in classical visual attention prediction models. The
experiments are conducted on two publicly available datasets. The first is
IRCCYN video database containing 31 videos with an overall amount of 7300
frames and eye fixations of 37 subjects. The second one is HOLLYWOOD2 provided
2517 movie clips with the eye fixations of 19 subjects. On IRCYYN dataset, the
accuracy obtained is of 89.51%. On HOLLYWOOD2 dataset, results in prediction of
saliency of patches show the improvement up to 2% with regard to RGB use only.
The resulting accuracy of 76, 6% is obtained. The AUC metric in comparison of
predicted saliency maps with visual fixation maps shows the increase up to 16%
on a sample of video clips from this dataset.
Probabilistic Graphical Models on Multi-Core CPUs using Java 8
Authors: Andres R. Masegosa, Ana M. Martinez, Hanen Borchani
Date: 2016-04-27 00:00:00

In this paper, we discuss software design issues related to the development
of parallel computational intelligence algorithms on multi-core CPUs, using the
new Java 8 functional programming features. In particular, we focus on
probabilistic graphical models (PGMs) and present the parallelisation of a
collection of algorithms that deal with inference and learning of PGMs from
data. Namely, maximum likelihood estimation, importance sampling, and greedy
search for solving combinatorial optimisation problems. Through these concrete
examples, we tackle the problem of defining efficient data structures for PGMs
and parallel processing of same-size batches of data sets using Java 8
features. We also provide straightforward techniques to code parallel
algorithms that seamlessly exploit multi-core processors. The experimental
analysis, carried out using our open source AMIDST (Analysis of MassIve Data
STreams) Java toolbox, shows the merits of the proposed solutions.
Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss
Authors: Cesare Alippi, Giacomo Boracchi, Diego Carrera, Manuel Roveri
Date: 2015-10-16 00:00:00
Tasks: Change Detection

We address the problem of detecting changes in multivariate datastreams, and
we investigate the intrinsic difficulty that change-detection methods have to
face when the data dimension scales. In particular, we consider a general
approach where changes are detected by comparing the distribution of the
log-likelihood of the datastream over different time windows. Despite the fact
that this approach constitutes the frame of several change-detection methods,
its effectiveness when data dimension scales has never been investigated, which
is indeed the goal of our paper. We show that the magnitude of the change can
be naturally measured by the symmetric Kullback-Leibler divergence between the
pre- and post-change distributions, and that the detectability of a change of a
given magnitude worsens when the data dimension increases. This problem, which
we refer to as \emph{detectability loss}, is due to the linear relationship
between the variance of the log-likelihood and the data dimension. We
analytically derive the detectability loss on Gaussian-distributed datastreams,
and empirically demonstrate that this problem holds also on real-world datasets
and that can be harmful even at low data-dimensions (say, 10).
A Richly Annotated Dataset for Pedestrian Attribute Recognition
Authors: Dangwei Li, Zhang Zhang, Xiaotang Chen, Haibin Ling, Kaiqi Huang
Date: 2016-03-23 00:00:00
Tasks: Attribute, Pedestrian Attribute Recognition

In this paper, we aim to improve the dataset foundation for pedestrian
attribute recognition in real surveillance scenarios. Recognition of human
attributes, such as gender, and clothes types, has great prospects in real
applications. However, the development of suitable benchmark datasets for
attribute recognition remains lagged behind. Existing human attribute datasets
are collected from various sources or an integration of pedestrian
re-identification datasets. Such heterogeneous collection poses a big challenge
on developing high quality fine-grained attribute recognition algorithms.
Furthermore, human attribute recognition are generally severely affected by
environmental or contextual factors, such as viewpoints, occlusions and body
parts, while existing attribute datasets barely care about them. To tackle
these problems, we build a Richly Annotated Pedestrian (RAP) dataset from real
multi-camera surveillance scenarios with long term collection, where data
samples are annotated with not only fine-grained human attributes but also
environmental and contextual factors. RAP has in total 41,585 pedestrian
samples, each of which is annotated with 72 attributes as well as viewpoints,
occlusions, body parts information. To our knowledge, the RAP dataset is the
largest pedestrian attribute dataset, which is expected to greatly promote the
study of large-scale attribute recognition systems. Furthermore, we empirically
analyze the effects of different environmental and contextual factors on
pedestrian attribute recognition. Experimental results demonstrate that
viewpoints, occlusions and body parts information could assist attribute
recognition a lot in real applications.
DASC: Robust Dense Descriptor for Multi-modal and Multi-spectral Correspondence Estimation
Authors: Seungryong Kim, Dongbo Min, Bumsub Ham, Minh N. Do, Kwanghoon Sohn
Date: 2016-04-27 00:00:00

Establishing dense correspondences between multiple images is a fundamental
task in many applications. However, finding a reliable correspondence in
multi-modal or multi-spectral images still remains unsolved due to their
challenging photometric and geometric variations. In this paper, we propose a
novel dense descriptor, called dense adaptive self-correlation (DASC), to
estimate multi-modal and multi-spectral dense correspondences. Based on an
observation that self-similarity existing within images is robust to imaging
modality variations, we define the descriptor with a series of an adaptive
self-correlation similarity measure between patches sampled by a randomized
receptive field pooling, in which a sampling pattern is obtained using a
discriminative learning. The computational redundancy of dense descriptors is
dramatically reduced by applying fast edge-aware filtering. Furthermore, in
order to address geometric variations including scale and rotation, we propose
a geometry-invariant DASC (GI-DASC) descriptor that effectively leverages the
DASC through a superpixel-based representation. For a quantitative evaluation
of the GI-DASC, we build a novel multi-modal benchmark as varying photometric
and geometric conditions. Experimental results demonstrate the outstanding
performance of the DASC and GI-DASC in many cases of multi-modal and
multi-spectral dense correspondences.
Predictive No-Reference Assessment of Video Quality
Authors: Maria Torres Vega, Decebal Constantin Mocanu, Antonio Liotta
Date: 2016-04-25 00:00:00
Tasks: BIG-bench Machine Learning

Among the various means to evaluate the quality of video streams,
No-Reference (NR) methods have low computation and may be executed on thin
clients. Thus, NR algorithms would be perfect candidates in cases of real-time
quality assessment, automated quality control and, particularly, in adaptive
mobile streaming. Yet, existing NR approaches are often inaccurate, in
comparison to Full-Reference (FR) algorithms, especially under lossy network
conditions. In this work, we present an NR method that combines machine
learning with simple NR metrics to achieve a quality index comparably as
accurate as the Video Quality Metric (VQM) Full-Reference algorithm. Our method
is tested in an extensive dataset (960 videos), under lossy network conditions
and considering nine different machine learning algorithms. Overall, we achieve
an over 97% correlation with VQM, while allowing real-time assessment of video
quality of experience in realistic streaming scenarios.
Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Authors: Animashree Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan
Date: 2015-10-15 00:00:00
Tasks: Action Detection, Activity Detection, Tensor Decomposition

Robust tensor CP decomposition involves decomposing a tensor into low rank
and sparse components. We propose a novel non-convex iterative algorithm with
guaranteed recovery. It alternates between low-rank CP decomposition through
gradient ascent (a variant of the tensor power method), and hard thresholding
of the residual. We prove convergence to the globally optimal solution under
natural incoherence conditions on the low rank component, and bounded level of
sparse perturbations. We compare our method with natural baselines which apply
robust matrix PCA either to the {\em flattened} tensor, or to the matrix slices
of the tensor. Our method can provably handle a far greater level of
perturbation when the sparse tensor is block-structured. This naturally occurs
in many applications such as the activity detection task in videos. Our
experiments validate these findings. Thus, we establish that tensor methods can
tolerate a higher level of gross corruptions compared to matrix methods.
Smoothing parameter estimation framework for IBM word alignment models
Authors: Vuong Van Bui, Cuong Anh Le
Date: 2016-01-14 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, parameter estimation, Translation, Word Alignment

IBM models are very important word alignment models in Machine Translation.
Following the Maximum Likelihood Estimation principle to estimate their
parameters, the models will easily overfit the training data when the data are
sparse. While smoothing is a very popular solution in Language Model, there
still lacks studies on smoothing for word alignment. In this paper, we propose
a framework which generalizes the notable work Moore [2004] of applying
additive smoothing to word alignment models. The framework allows developers to
customize the smoothing amount for each pair of word. The added amount will be
scaled appropriately by a common factor which reflects how much the framework
trusts the adding strategy according to the performance on data. We also
carefully examine various performance criteria and propose a smoothened version
of the error count, which generally gives the best result.
Simple, Robust and Optimal Ranking from Pairwise Comparisons
Authors: Nihar B. Shah, Martin J. Wainwright
Date: 2015-12-30 00:00:00
Tasks: Computational Efficiency

We consider data in the form of pairwise comparisons of n items, with the
goal of precisely identifying the top k items for some value of k < n, or
alternatively, recovering a ranking of all the items. We analyze the Copeland
counting algorithm that ranks the items in order of the number of pairwise
comparisons won, and show it has three attractive features: (a) its
computational efficiency leads to speed-ups of several orders of magnitude in
computation time as compared to prior work; (b) it is robust in that
theoretical guarantees impose no conditions on the underlying matrix of
pairwise-comparison probabilities, in contrast to some prior work that applies
only to the BTL parametric model; and (c) it is an optimal method up to
constant factors, meaning that it achieves the information-theoretic limits for
recovering the top k-subset. We extend our results to obtain sharp guarantees
for approximate recovery under the Hamming distortion metric, and more
generally, to any arbitrary error requirement that satisfies a simple and
natural monotonicity condition.
Train and Test Tightness of LP Relaxations in Structured Prediction
Authors: Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag
Date: 2015-11-04 00:00:00
Tasks: Prediction, Structured Prediction

Structured prediction is used in areas such as computer vision and natural
language processing to predict structured outputs such as segmentations or
parse trees. In these settings, prediction is performed by MAP inference or,
equivalently, by solving an integer linear program. Because of the complex
scoring functions required to obtain accurate predictions, both learning and
inference typically require the use of approximate solvers. We propose a
theoretical explanation to the striking observation that approximations based
on linear programming (LP) relaxations are often tight on real-world instances.
In particular, we show that learning with LP relaxed inference encourages
integrality of training instances, and that tightness generalizes from train to
test data.
Audio Recording Device Identification Based on Deep Learning
Authors: Simeng Qi, Zheng Huang, Yan Li, Shaopei Shi
Date: 2016-02-18 00:00:00
Tasks: Deep Learning, Speech Enhancement

In this paper we present a research on identification of audio recording
devices from background noise, thus providing a method for forensics. The audio
signal is the sum of speech signal and noise signal. Usually, people pay more
attention to speech signal, because it carries the information to deliver. So a
great amount of researches have been dedicated to getting higher
Signal-Noise-Ratio (SNR). There are many speech enhancement algorithms to
improve the quality of the speech, which can be seen as reducing the noise.
However, noises can be regarded as the intrinsic fingerprint traces of an audio
recording device. These digital traces can be characterized and identified by
new machine learning techniques. Therefore, in our research, we use the noise
as the intrinsic features. As for the identification, multiple classifiers of
deep learning methods are used and compared. The identification result shows
that the method of getting feature vector from the noise of each device and
identifying them with deep learning techniques is viable, and well-preformed.
Procedural Generation of Angry Birds Levels using Building Constructive Grammar with Chinese-Style and/or Japanese-Style Models
Authors: YuXuan Jiang, Misaki Kaidan, Chun Yin Chu, Tomohiro Harada, Ruck Thawonmas
Date: 2016-04-27 00:00:00

This paper presents a procedural generation method that creates visually
attractive levels for the Angry Birds game. Besides being an immensely popular
mobile game, Angry Birds has recently become a test bed for various artificial
intelligence technologies. We propose a new approach for procedurally
generating Angry Birds levels using Chinese style and Japanese style building
structures. A conducted experiment confirms the effectiveness of our approach
with statistical significance.
Image Colorization Using a Deep Convolutional Neural Network
Authors: Tung Nguyen, Kazuki Mori, Ruck Thawonmas
Date: 2016-04-27 00:00:00
Tasks: Colorization, General Classification, image-classification, Image Classification, Image Colorization, Semantic Similarity, Semantic Textual Similarity

In this paper, we present a novel approach that uses deep learning techniques
for colorizing grayscale images. By utilizing a pre-trained convolutional
neural network, which is originally designed for image classification, we are
able to separate content and style of different images and recombine them into
a single image. We then propose a method that can add colors to a grayscale
image by combining its content with style of a color image having semantic
similarity with the grayscale one. As an application, to our knowledge the
first of its kind, we use the proposed method to colorize images of ukiyo-e a
genre of Japanese painting?and obtain interesting results, showing the
potential of this method in the growing field of computer assisted art.
Interactive Bayesian Hierarchical Clustering
Authors: Sharad Vikram, Sanjoy Dasgupta
Date: 2016-02-10 00:00:00
Tasks: Clustering

Clustering is a powerful tool in data analysis, but it is often difficult to
find a grouping that aligns with a user's needs. To address this, several
methods incorporate constraints obtained from users into clustering algorithms,
but unfortunately do not apply to hierarchical clustering. We design an
interactive Bayesian algorithm that incorporates user interaction into
hierarchical clustering while still utilizing the geometry of the data by
sampling a constrained posterior distribution over hierarchies. We also suggest
several ways to intelligently query a user. The algorithm, along with the
querying schemes, shows promising results on real data.
Detection of epileptic seizure in EEG signals using linear least squares preprocessing
Authors: Z. Roshan Zamir
Date: 2016-04-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification

An epileptic seizure is a transient event of abnormal excessive neuronal
discharge in the brain. This unwanted event can be obstructed by detection of
electrical changes in the brain that happen before the seizure takes place. The
automatic detection of seizures is necessary since the visual screening of EEG
recordings is a time consuming task and requires experts to improve the
diagnosis. Four linear least squares-based preprocessing models are proposed to
extract key features of an EEG signal in order to detect seizures. The first
two models are newly developed. The original signal (EEG) is approximated by a
sinusoidal curve. Its amplitude is formed by a polynomial function and compared
with the pre developed spline function.Different statistical measures namely
classification accuracy, true positive and negative rates, false positive and
negative rates and precision are utilized to assess the performance of the
proposed models. These metrics are derived from confusion matrices obtained
from classifiers. Different classifiers are used over the original dataset and
the set of extracted features. The proposed models significantly reduce the
dimension of the classification problem and the computational time while the
classification accuracy is improved in most cases. The first and third models
are promising feature extraction methods. Logistic, LazyIB1, LazyIB5 and J48
are the best classifiers. Their true positive and negative rates are $1$ while
false positive and negative rates are zero and the corresponding precision
values are $1$. Numerical results suggest that these models are robust and
efficient for detecting epileptic seizure.
Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs
Authors: Ziyu Zhang, Sanja Fidler, Raquel Urtasun
Date: 2015-12-21 00:00:00
Proceeding: CVPR 2016 6
Tasks: Autonomous Driving

Our aim is to provide a pixel-wise instance-level labeling of a monocular
image in the context of autonomous driving. We build on recent work [Zhang et
al., ICCV15] that trained a convolutional neural net to predict instance
labeling in local image patches, extracted exhaustively in a stride from an
image. A simple Markov random field model using several heuristics was then
proposed in [Zhang et al., ICCV15] to derive a globally consistent instance
labeling of the image. In this paper, we formulate the global labeling problem
with a novel densely connected Markov random field and show how to encode
various intuitive potentials in a way that is amenable to efficient mean field
inference [Kr\"ahenb\"uhl et al., NIPS11]. Our potentials encode the
compatibility between the global labeling and the patch-level predictions,
contrast-sensitive smoothness as well as the fact that separate regions form
different instances. Our experiments on the challenging KITTI benchmark [Geiger
et al., CVPR12] demonstrate that our method achieves a significant performance
boost over the baseline [Zhang et al., ICCV15].
Inverting Visual Representations with Convolutional Networks
Authors: Alexey Dosovitskiy, Thomas Brox
Date: 2015-06-09 00:00:00
Proceeding: CVPR 2016 6

Feature representations, both hand-designed and learned ones, are often hard
to analyze and interpret, even when they are extracted from visual data. We
propose a new approach to study image representations by inverting them with an
up-convolutional neural network. We apply the method to shallow representations
(HOG, SIFT, LBP), as well as to deep networks. For shallow representations our
approach provides significantly better reconstructions than existing methods,
revealing that there is surprisingly rich information contained in these
features. Inverting a deep network trained on ImageNet provides several
insights into the properties of the feature representation learned by the
network. Most strikingly, the colors and the rough contours of an image can be
reconstructed from activations in higher network layers and even from the
predicted class probabilities.
Mixtures of Sparse Autoregressive Networks
Authors: Marc Goessling, Yali Amit
Date: 2015-11-15 00:00:00

We consider high-dimensional distribution estimation through autoregressive
networks. By combining the concepts of sparsity, mixtures and parameter sharing
we obtain a simple model which is fast to train and which achieves
state-of-the-art or better results on several standard benchmark datasets.
Specifically, we use an L1-penalty to regularize the conditional distributions
and introduce a procedure for automatic parameter sharing between mixture
components. Moreover, we propose a simple distributed representation which
permits exact likelihood evaluations since the latent variables are interleaved
with the observable variables and can be easily integrated out. Our model
achieves excellent generalization performance and scales well to extremely high
dimensions.
Evaluating the effect of topic consideration in identifying communities of rating-based social networks
Authors: Ali Reihanian, Behrouz Minaei-Bidgoli, Muhammad Yousefnezhad
Date: 2016-04-26 00:00:00
Tasks: Community Detection

Finding meaningful communities in social network has attracted the attentions
of many researchers. The community structure of complex networks reveals both
their organization and hidden relations among their constituents. Most of the
researches in the field of community detection mainly focus on the topological
structure of the network without performing any content analysis. Nowadays,
real world social networks are containing a vast range of information including
shared objects, comments, following information, etc. In recent years, a number
of researches have proposed approaches which consider both the contents that
are interchanged in the networks and the topological structures of the networks
in order to find more meaningful communities. In this research, the effect of
topic analysis in finding more meaningful communities in social networking
sites in which the users express their feelings toward different objects (like
movies) by the means of rating is demonstrated by performing extensive
experiments.