pwc-archive/papers-with-abstracts huggingface dataset:
Body Joint guided 3D Deep Convolutional Descriptors for Action Recognition
Authors: Congqi Cao, Yifan Zhang, Chunjie Zhang, Hanqing Lu
Date: 2017-04-24 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Three dimensional convolutional neural networks (3D CNNs) have been
established as a powerful tool to simultaneously learn features from both
spatial and temporal dimensions, which is suitable to be applied to video-based
action recognition. In this work, we propose not to directly use the
activations of fully-connected layers of a 3D CNN as the video feature, but to
use selective convolutional layer activations to form a discriminative
descriptor for video. It pools the feature on the convolutional layers under
the guidance of body joint positions. Two schemes of mapping body joints into
convolutional feature maps for pooling are discussed. The body joint positions
can be obtained from any off-the-shelf skeleton estimation algorithm. The
helpfulness of the body joint guided feature pooling with inaccurate skeleton
estimation is systematically evaluated. To make it end-to-end and do not rely
on any sophisticated body joint detection algorithm, we further propose a
two-stream bilinear model which can learn the guidance from the body joints and
capture the spatio-temporal features simultaneously. In this model, the body
joint guided feature pooling is conveniently formulated as a bilinear product
operation. Experimental results on three real-world datasets demonstrate the
effectiveness of body joint guided pooling which achieves promising
performance.
An ADMM Approach to Masked Signal Decomposition Using Subspace Representation
Authors: Shervin Minaee, Yao Wang
Date: 2017-04-25 00:00:00

Signal decomposition is a classical problem in signal processing, which aims
to separate an observed signal into two or more components each with its own
property. Usually each component is described by its own subspace or
dictionary. Extensive research has been done for the case where the components
are additive, but in real world applications, the components are often
non-additive. For example, an image may consist of a foreground object overlaid
on a background, where each pixel either belongs to the foreground or the
background. In such a situation, to separate signal components, we need to find
a binary mask which shows the location of each component. Therefore it requires
to solve a binary optimization problem. Since most of the binary optimization
problems are intractable, we relax this problem to the approximated continuous
problem, and solve it by alternating optimization technique. We show the
application of the proposed algorithm for three applications: separation of
text from background in images, separation of moving objects from a background
undergoing global camera motion in videos, separation of sinusoidal and spike
components in one dimensional signals. We demonstrate in each case that
considering the non-additive nature of the problem can lead to significant
improvement.
Inception Recurrent Convolutional Neural Network for Object Recognition
Authors: Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M. Taha
Date: 2017-04-25 00:00:00
Proceeding: CVPR 2015
Tasks: Object, Object Recognition

Deep convolutional neural networks (DCNNs) are an influential tool for
solving various problems in the machine learning and computer vision fields. In
this paper, we introduce a new deep learning model called an Inception-
Recurrent Convolutional Neural Network (IRCNN), which utilizes the power of an
inception network combined with recurrent layers in DCNN architecture. We have
empirically evaluated the recognition performance of the proposed IRCNN model
using different benchmark datasets such as MNIST, CIFAR-10, CIFAR- 100, and
SVHN. Experimental results show similar or higher recognition accuracy when
compared to most of the popular DCNNs including the RCNN. Furthermore, we have
investigated IRCNN performance against equivalent Inception Networks and
Inception-Residual Networks using the CIFAR-100 dataset. We report about 3.5%,
3.47% and 2.54% improvement in classification accuracy when compared to the
RCNN, equivalent Inception Networks, and Inception- Residual Networks on the
augmented CIFAR- 100 dataset respectively.
Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering
Authors: Lucia Ballerini, Ruggiero Lovreglio, Maria del C. Valdes-Hernandez, Joel Ramirez, Bradley J. MacIntosh, Sandra E. Black, Joanna M. Wardlaw
Date: 2017-04-25 00:00:00
Tasks: Segmentation

Perivascular Spaces (PVS) are a recently recognised feature of Small Vessel
Disease (SVD), also indicating neuroinflammation, and are an important part of
the brain's circulation and glymphatic drainage system. Quantitative analysis
of PVS on Magnetic Resonance Images (MRI) is important for understanding their
relationship with neurological diseases. In this work, we propose a
segmentation technique based on the 3D Frangi filtering for extraction of PVS
from MRI. Based on prior knowledge from neuroradiological ratings of PVS, we
used ordered logit models to optimise Frangi filter parameters in response to
the variability in the scanner's parameters and study protocols. We optimized
and validated our proposed models on two independent cohorts, a dementia sample
(N=20) and patients who previously had mild to moderate stroke (N=48). Results
demonstrate the robustness and generalisability of our segmentation method.
Segmentation-based PVS burden estimates correlated with neuroradiological
assessments (Spearman's $\rho$ = 0.74, p $<$ 0.001), suggesting the great
potential of our proposed method
Robust Cardiac Motion Estimation using Ultrafast Ultrasound Data: A Low-Rank-Topology-Preserving Approach
Authors: Angelica I. Aviles, Thomas Widlak, Alicia Casals, Maartje M. Nillesen, Habib Ammari
Date: 2016-10-26 00:00:00
Tasks: Diagnostic, Motion Estimation

Cardiac motion estimation is an important diagnostic tool to detect heart
diseases and it has been explored with modalities such as MRI and conventional
ultrasound (US) sequences. US cardiac motion estimation still presents
challenges because of the complex motion patterns and the presence of noise. In
this work, we propose a novel approach to estimate the cardiac motion using
ultrafast ultrasound data. -- Our solution is based on a variational
formulation characterized by the L2-regularized class. The displacement is
represented by a lattice of b-splines and we ensure robustness by applying a
maximum likelihood type estimator. While this is an important part of our
solution, the main highlight of this paper is to combine a low-rank data
representation with topology preservation. Low-rank data representation
(achieved by finding the k-dominant singular values of a Casorati Matrix
arranged from the data sequence) speeds up the global solution and achieves
noise reduction. On the other hand, topology preservation (achieved by
monitoring the Jacobian determinant) allows to radically rule out distortions
while carefully controlling the size of allowed expansions and contractions.
Our variational approach is carried out on a realistic dataset as well as on a
simulated one. We demonstrate how our proposed variational solution deals with
complex deformations through careful numerical experiments. While maintaining
the accuracy of the solution, the low-rank preprocessing is shown to speed up
the convergence of the variational problem. Beyond cardiac motion estimation,
our approach is promising for the analysis of other organs that experience
motion.
Single-Pass PCA of Large High-Dimensional Data
Authors: Wenjian Yu, Yu Gu, Jian Li, Shenghua Liu, Yaohang Li
Date: 2017-04-25 00:00:00
Tasks: Dimensionality Reduction, Vocal Bursts Intensity Prediction

Principal component analysis (PCA) is a fundamental dimension reduction tool
in statistics and machine learning. For large and high-dimensional data,
computing the PCA (i.e., the singular vectors corresponding to a number of
dominant singular values of the data matrix) becomes a challenging task. In
this work, a single-pass randomized algorithm is proposed to compute PCA with
only one pass over the data. It is suitable for processing extremely large and
high-dimensional data stored in slow memory (hard disk) or the data generated
in a streaming fashion. Experiments with synthetic and real data validate the
algorithm's accuracy, which has orders of magnitude smaller error than an
existing single-pass algorithm. For a set of high-dimensional data stored as a
150 GB file, the proposed algorithm is able to compute the first 50 principal
components in just 24 minutes on a typical 24-core computer, with less than 1
GB memory cost.
Event Stream-Based Process Discovery using Abstract Representations
Authors: Sebastiaan J. van Zelst, Boudewijn F. van Dongen, Wil M. P. van der Aalst
Date: 2017-04-25 00:00:00

The aim of process discovery, originating from the area of process mining, is
to discover a process model based on business process execution data. A
majority of process discovery techniques relies on an event log as an input. An
event log is a static source of historical data capturing the execution of a
business process. In this paper we focus on process discovery relying on online
streams of business process execution events. Learning process models from
event streams poses both challenges and opportunities, i.e. we need to handle
unlimited amounts of data using finite memory and, preferably, constant time.
We propose a generic architecture that allows for adopting several classes of
existing process discovery techniques in context of event streams. Moreover, we
provide several instantiations of the architecture, accompanied by
implementations in the process mining tool-kit ProM (http://promtools.org).
Using these instantiations, we evaluate several dimensions of stream-based
process discovery. The evaluation shows that the proposed architecture allows
us to lift process discovery to the streaming domain.
Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web
Authors: Sina Sajadmanesh, Sina Jafarzadeh, Seyed Ali Osia, Hamid R. Rabiee, Hamed Haddadi, Yelena Mejova, Mirco Musolesi, Emiliano De Cristofaro, Gianluca Stringhini
Date: 2016-10-26 00:00:00
Tasks: Nutrition

Food and nutrition occupy an increasingly prevalent space on the web, and
dishes and recipes shared online provide an invaluable mirror into culinary
cultures and attitudes around the world. More specifically, ingredients,
flavors, and nutrition information become strong signals of the taste
preferences of individuals and civilizations. However, there is little
understanding of these palate varieties. In this paper, we present a
large-scale study of recipes published on the web and their content, aiming to
understand cuisines and culinary habits around the world. Using a database of
more than 157K recipes from over 200 different cuisines, we analyze
ingredients, flavors, and nutritional values which distinguish dishes from
different regions, and use this knowledge to assess the predictability of
recipes from different cuisines. We then use country health statistics to
understand the relation between these factors and health indicators of
different nations, such as obesity, diabetes, migration, and health
expenditure. Our results confirm the strong effects of geographical and
cultural similarities on recipes, health indicators, and culinary preferences
across the globe.
Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
Authors: Arkaitz Zubiaga, Alex Voss, Rob Procter, Maria Liakata, Bo wang, Adam Tsakalidis
Date: 2016-04-25 00:00:00
Tasks: Classification, General Classification

In contrast to much previous work that has focused on location classification
of tweets restricted to a specific country, here we undertake the task in a
broader context by classifying global tweets at the country level, which is so
far unexplored in a real-time scenario. We analyse the extent to which a
tweet's country of origin can be determined by making use of eight
tweet-inherent features for classification. Furthermore, we use two datasets,
collected a year apart from each other, to analyse the extent to which a model
trained from historical tweets can still be leveraged for classification of new
tweets. With classification experiments on all 217 countries in our datasets,
as well as on the top 25 countries, we offer some insights into the best use of
tweet-inherent features for an accurate country-level classification of tweets.
We find that the use of a single feature, such as the use of tweet content
alone -- the most widely used feature in previous work -- leaves much to be
desired. Choosing an appropriate combination of both tweet content and metadata
can actually lead to substantial improvements of between 20\% and 50\%. We
observe that tweet content, the user's self-reported location and the user's
real name, all of which are inherent in a tweet and available in a real-time
scenario, are particularly useful to determine the country of origin. We also
experiment on the applicability of a model trained on historical tweets to
classify new tweets, finding that the choice of a particular combination of
features whose utility does not fade over time can actually lead to comparable
performance, avoiding the need to retrain. However, the difficulty of achieving
accurate classification increases slightly for countries with multiple
commonalities, especially for English and Spanish speaking countries.
Learning Control for Air Hockey Striking using Deep Reinforcement Learning
Authors: Ayal Taitler, Nahum Shimkin
Date: 2017-02-26 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the task of learning control policies for a robotic mechanism
striking a puck in an air hockey game. The control signal is a direct command
to the robot's motors. We employ a model free deep reinforcement learning
framework to learn the motoric skills of striking the puck accurately in order
to score. We propose certain improvements to the standard learning scheme which
make the deep Q-learning algorithm feasible when it might otherwise fail. Our
improvements include integrating prior knowledge into the learning scheme, and
accounting for the changing distribution of samples in the experience replay
buffer. Finally we present our simulation results for aimed striking which
demonstrate the successful learning of this task, and the improvement in
algorithm stability due to the proposed modifications.
Twenty (simple) questions
Authors: Yuval Dagan, Yuval Filmus, Ariel Gabizon, Shay Moran
Date: 2016-11-05 00:00:00

A basic combinatorial interpretation of Shannon's entropy function is via the
"20 questions" game. This cooperative game is played by two players, Alice and
Bob: Alice picks a distribution $\pi$ over the numbers $\{1,\ldots,n\}$, and
announces it to Bob. She then chooses a number $x$ according to $\pi$, and Bob
attempts to identify $x$ using as few Yes/No queries as possible, on average.
  An optimal strategy for the "20 questions" game is given by a Huffman code
for $\pi$: Bob's questions reveal the codeword for $x$ bit by bit. This
strategy finds $x$ using fewer than $H(\pi)+1$ questions on average. However,
the questions asked by Bob could be arbitrary. In this paper, we investigate
the following question: Are there restricted sets of questions that match the
performance of Huffman codes, either exactly or approximately?
  Our first main result shows that for every distribution $\pi$, Bob has a
strategy that uses only questions of the form "$x < c$?" and "$x = c$?", and
uncovers $x$ using at most $H(\pi)+1$ questions on average, matching the
performance of Huffman codes in this sense. We also give a natural set of
$O(rn^{1/r})$ questions that achieve a performance of at most $H(\pi)+r$, and
show that $\Omega(rn^{1/r})$ questions are required to achieve such a
guarantee.
  Our second main result gives a set $\mathcal{Q}$ of $1.25^{n+o(n)}$ questions
such that for every distribution $\pi$, Bob can implement an optimal strategy
for $\pi$ using only questions from $\mathcal{Q}$. We also show that
$1.25^{n-o(n)}$ questions are needed, for infinitely many $n$. If we allow a
small slack of $r$ over the optimal strategy, then roughly $(rn)^{\Theta(1/r)}$
questions are necessary and sufficient.
Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks
Authors: Liner Yang, Meishan Zhang, Yang Liu, Nan Yu, Maosong Sun, Guohong Fu
Date: 2017-04-25 00:00:00
Tasks: Dependency Parsing, Part-Of-Speech Tagging, POS, POS Tagging

While part-of-speech (POS) tagging and dependency parsing are observed to be
closely related, existing work on joint modeling with manually crafted feature
templates suffers from the feature sparsity and incompleteness problems. In
this paper, we propose an approach to joint POS tagging and dependency parsing
using transition-based neural networks. Three neural network based classifiers
are designed to resolve shift/reduce, tagging, and labeling conflicts.
Experiments show that our approach significantly outperforms previous methods
for joint POS tagging and dependency parsing across a variety of natural
languages.
Learning molecular energies using localized graph kernels
Authors: G. Ferré, T. Haut, K. Barros
Date: 2016-12-01 00:00:00
Tasks: BIG-bench Machine Learning, Translation

Recent machine learning methods make it possible to model potential energy of
atomic configurations with chemical-level accuracy (as calculated from
ab-initio calculations) and at speeds suitable for molecular dynam- ics
simulation. Best performance is achieved when the known physical constraints
are encoded in the machine learning models. For example, the atomic energy is
invariant under global translations and rotations, it is also invariant to
permutations of same-species atoms. Although simple to state, these symmetries
are complicated to encode into machine learning algorithms. In this paper, we
present a machine learning approach based on graph theory that naturally
incorporates translation, rotation, and permutation symmetries. Specifically,
we use a random walk graph kernel to measure the similarity of two adjacency
matrices, each of which represents a local atomic environment. This Graph
Approximated Energy (GRAPE) approach is flexible and admits many possible
extensions. We benchmark a simple version of GRAPE by predicting atomization
energies on a standard dataset of organic molecules.
Sparse Ternary Codes for similarity search have higher coding gain than dense binary codes
Authors: Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov, Taras Holotyak
Date: 2017-01-26 00:00:00
Tasks: Binarization

This paper addresses the problem of Approximate Nearest Neighbor (ANN) search
in pattern recognition where feature vectors in a database are encoded as
compact codes in order to speed-up the similarity search in large-scale
databases. Considering the ANN problem from an information-theoretic
perspective, we interpret it as an encoding, which maps the original feature
vectors to a less entropic sparse representation while requiring them to be as
informative as possible. We then define the coding gain for ANN search using
information-theoretic measures. We next show that the classical approach to
this problem, which consists of binarization of the projected vectors is
sub-optimal. Instead, a properly designed ternary encoding achieves higher
coding gains and lower complexity.
Skeleton-based Action Recognition with Convolutional Neural Networks
Authors: Chao Li, Qiaoyong Zhong, Di Xie, ShiLiang Pu
Date: 2017-04-25 00:00:00
Tasks: Action Classification, Action Detection, Action Recognition, General Classification, Skeleton Based Action Recognition, Temporal Action Localization

Current state-of-the-art approaches to skeleton-based action recognition are
mostly based on recurrent neural networks (RNN). In this paper, we propose a
novel convolutional neural networks (CNN) based framework for both action
classification and detection. Raw skeleton coordinates as well as skeleton
motion are fed directly into CNN for label prediction. A novel skeleton
transformer module is designed to rearrange and select important skeleton
joints automatically. With a simple 7-layer network, we obtain 89.3% accuracy
on validation set of the NTU RGB+D dataset. For action detection in untrimmed
videos, we develop a window proposal network to extract temporal segment
proposals, which are further classified within the same network. On the recent
PKU-MMD dataset, we achieve 93.7% mAP, surpassing the baseline by a large
margin.
Towards a quality metric for dense light fields
Authors: Vamsi Kiran Adhikarla, Marek Vinkler, Denis Sumin, Rafał K. Mantiuk, Karol Myszkowski, Hans-Peter Seidel, Piotr Didyk
Date: 2017-04-25 00:00:00
Proceeding: CVPR 2017 7

Light fields become a popular representation of three dimensional scenes, and
there is interest in their processing, resampling, and compression. As those
operations often result in loss of quality, there is a need to quantify it. In
this work, we collect a new dataset of dense reference and distorted light
fields as well as the corresponding quality scores which are scaled in
perceptual units. The scores were acquired in a subjective experiment using an
interactive light-field viewing setup. The dataset contains typical artifacts
that occur in light-field processing chain due to light-field reconstruction,
multi-view compression, and limitations of automultiscopic displays. We test a
number of existing objective quality metrics to determine how well they can
predict the quality of light fields. We find that the existing image quality
metrics provide good measures of light-field quality, but require dense
reference light- fields for optimal performance. For more complex tasks of
comparing two distorted light fields, their performance drops significantly,
which reveals the need for new, light-field-specific metrics.
3D Object Proposals using Stereo Imagery for Accurate Object Class Detection
Authors: Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Huimin Ma, Sanja Fidler, Raquel Urtasun
Date: 2016-08-27 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Object, object-detection, Object Detection

The goal of this paper is to perform 3D object detection in the context of
autonomous driving. Our method first aims at generating a set of high-quality
3D object proposals by exploiting stereo imagery. We formulate the problem as
minimizing an energy function that encodes object size priors, placement of
objects on the ground plane as well as several depth informed features that
reason about free space, point cloud densities and distance to the ground. We
then exploit a CNN on top of these proposals to perform object detection. In
particular, we employ a convolutional neural net (CNN) that exploits context
and depth information to jointly regress to 3D bounding box coordinates and
object pose. Our experiments show significant performance gains over existing
RGB and RGB-D object proposal methods on the challenging KITTI benchmark. When
combined with the CNN, our approach outperforms all existing results in object
detection and orientation estimation tasks for all three KITTI object classes.
Furthermore, we experiment also with the setting where LIDAR information is
available, and show that using both LIDAR and stereo leads to the best result.