pwc-archive/papers-with-abstracts huggingface dataset:
Retrieving sinusoids from nonuniformly sampled data using recursive formulation
Authors: Ivan Maric
Date: 2016-12-14 00:00:00

A heuristic procedure based on novel recursive formulation of sinusoid (RFS)
and on regression with predictive least-squares (LS) enables to decompose both
uniformly and nonuniformly sampled 1-d signals into a sparse set of sinusoids
(SSS). An optimal SSS is found by Levenberg-Marquardt (LM) optimization of RFS
parameters of near-optimal sinusoids combined with common criteria for the
estimation of the number of sinusoids embedded in noise. The procedure
estimates both the cardinality and the parameters of SSS. The proposed
algorithm enables to identify the RFS parameters of a sinusoid from a data
sequence containing only a fraction of its cycle. In extreme cases when the
frequency of a sinusoid approaches zero the algorithm is able to detect a
linear trend in data. Also, an irregular sampling pattern enables the algorithm
to correctly reconstruct the under-sampled sinusoid. Parsimonious nature of the
obtaining models opens the possibilities of using the proposed method in
machine learning and in expert and intelligent systems needing analysis and
simple representation of 1-d signals. The properties of the proposed algorithm
are evaluated on examples of irregularly sampled artificial signals in noise
and are compared with high accuracy frequency estimation algorithms based on
linear prediction (LP) approach, particularly with respect to Cramer-Rao Bound
(CRB).
Unsupervised Construction of Human Body Models Using Principles of Organic Computing
Authors: Thomas Walther, Rolf P. Würtz
Date: 2017-04-12 00:00:00

Unsupervised learning of a generalizable model of the visual appearance of
humans from video data is of major importance for computing systems interacting
naturally with their users and others. We propose a step towards automatic
behavior understanding by integrating principles of Organic Computing into the
posture estimation cycle, thereby relegating the need for human intervention
while simultaneously raising the level of system autonomy. The system extracts
coherent motion from moving upper bodies and autonomously decides about limbs
and their possible spatial relationships. The models from many videos are
integrated into meta-models, which show good generalization to different
individuals, backgrounds, and attire. These models allow robust interpretation
of single video frames without temporal continuity and posture mimicking by an
android robot.
Beliefs in Markov Trees - From Local Computations to Local Valuation
Authors: Mieczysław A. Kłopotek
Date: 2017-04-12 00:00:00

This paper is devoted to expressiveness of hypergraphs for which uncertainty
propagation by local computations via Shenoy/Shafer method applies. It is
demonstrated that for this propagation method for a given joint belief
distribution no valuation of hyperedges of a hypergraph may provide with
simpler hypergraph structure than valuation of hyperedges by conditional
distributions. This has vital implication that methods recovering belief
networks from data have no better alternative for finding the simplest
hypergraph structure for belief propagation. A method for recovery
tree-structured belief networks has been developed and specialized for
Dempster-Shafer belief functions
Investigation on the use of Hidden-Markov Models in automatic transcription of music
Authors: D. Cazau, G. Nuel
Date: 2017-04-12 00:00:00
Tasks: Music Transcription, Segmentation, Time Series, Time Series Analysis

Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data,
and have been widely used in two main tasks of Automatic Music Transcription
(AMT): note segmentation, i.e. identifying the played notes after a multi-pitch
estimation, and sequential post-processing, i.e. correcting note segmentation
using training data. In this paper, we employ the multi-pitch estimation method
called Probabilistic Latent Component Analysis (PLCA), and develop AMT systems
by integrating different HMM-based modules in this framework. For note
segmentation, we use two different twostate on/o? HMMs, including a
higher-order one for duration modeling. For sequential post-processing, we
focused on a musicological modeling of polyphonic harmonic transitions, using a
first- and second-order HMMs whose states are defined through candidate note
mixtures. These different PLCA plus HMM systems have been evaluated
comparatively on two different instrument repertoires, namely the piano (using
the MAPS database) and the marovany zither. Our results show that the use of
HMMs could bring noticeable improvements to transcription results, depending on
the instrument repertoire.
Object proposal generation applying the distance dependent Chinese restaurant process
Authors: Mikko Lauri, Simone Frintrop
Date: 2017-04-12 00:00:00
Tasks: Bayesian Inference, Object, Object Discovery, Object Proposal Generation

In application domains such as robotics, it is useful to represent the
uncertainty related to the robot's belief about the state of its environment.
Algorithms that only yield a single "best guess" as a result are not
sufficient. In this paper, we propose object proposal generation based on
non-parametric Bayesian inference that allows quantification of the likelihood
of the proposals. We apply Markov chain Monte Carlo to draw samples of image
segmentations via the distance dependent Chinese restaurant process. Our method
achieves state-of-the-art performance on an indoor object discovery data set,
while additionally providing a likelihood term for each proposal. We show that
the likelihood term can effectively be used to rank proposals according to
their quality.
Trainable Referring Expression Generation using Overspecification Preferences
Authors: Thiago castro Ferreira, Ivandre Paraboni
Date: 2017-04-12 00:00:00
Tasks: Referring Expression, Referring expression generation

Referring expression generation (REG) models that use speaker-dependent
information require a considerable amount of training data produced by every
individual speaker, or may otherwise perform poorly. In this work we present a
simple REG experiment that allows the use of larger training data sets by
grouping speakers according to their overspecification preferences. Intrinsic
evaluation shows that this method generally outperforms the personalised method
found in previous work.
Detection, Recognition and Tracking of Moving Objects from Real-time Video via SP Theory of Intelligence and Species Inspired PSO
Authors: Kumar S. Ray, Sayandip Dutta, Anit Chakraborty
Date: 2017-04-12 00:00:00

In this paper, we address the basic problem of recognizing moving objects in
video images using SP Theory of Intelligence. The concept of SP Theory of
Intelligence which is a framework of artificial intelligence, was first
introduced by Gerard J Wolff, where S stands for Simplicity and P stands for
Power. Using the concept of multiple alignment, we detect and recognize object
of our interest in video frames with multilevel hierarchical parts and
subparts, based on polythetic categories. We track the recognized objects using
the species based Particle Swarm Optimization (PSO). First, we extract the
multiple alignment of our object of interest from training images. In order to
recognize accurately and handle occlusion, we use the polythetic concepts on
raw data line to omit the redundant noise via searching for best alignment
representing the features from the extracted alignments. We recognize the
domain of interest from the video scenes in form of wide variety of multiple
alignments to handle scene variability. Unsupervised learning is done in the SP
model following the DONSVIC principle and natural structures are discovered via
information compression and pattern analysis. After successful recognition of
objects, we use species based PSO algorithm as the alignments of our object of
interest is analogues to observation likelihood and fitness ability of species.
Subsequently, we analyze the competition and repulsion among species with
annealed Gaussian based PSO. We have tested our algorithms on David, Walking2,
FaceOcc1, Jogging and Dudek, obtaining very satisfactory and competitive
results.
3D Menagerie: Modeling the 3D shape and pose of animals
Authors: Silvia Zuffi, Angjoo Kanazawa, David Jacobs, Michael J. Black
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7

There has been significant work on learning realistic, articulated, 3D models
of the human body. In contrast, there are few such models of animals, despite
many applications. The main challenge is that animals are much less cooperative
than humans. The best human body models are learned from thousands of 3D scans
of people in specific poses, which is infeasible with live animals.
Consequently, we learn our model from a small set of 3D scans of toy figurines
in arbitrary poses. We employ a novel part-based shape model to compute an
initial registration to the scans. We then normalize their pose, learn a
statistical shape model, and refine the registrations and the model together.
In this way, we accurately align animal scans from different quadruped families
with very different shapes and poses. With the registration to a common
template we learn a shape space representing animals including lions, cats,
dogs, horses, cows and hippos. Animal shapes can be sampled from the model,
posed, animated, and fit to data. We demonstrate generalization by fitting it
to images of real animals including species not seen in training.
Learning a Deep Embedding Model for Zero-Shot Learning
Authors: Li Zhang, Tao Xiang, Shaogang Gong
Date: 2016-11-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Captioning, Sentence, Zero-Shot Action Recognition, Zero-Shot Learning

Zero-shot learning (ZSL) models rely on learning a joint embedding space where both textual/semantic description of object classes and visual representation of object images can be projected to for nearest neighbour search. Despite the success of deep neural networks that learn an end-to-end model between text and images in other vision problems such as image captioning, very few deep ZSL model exists and they show little advantage over ZSL models that utilise deep feature representations but do not learn an end-to-end embedding. In this paper we argue that the key to make deep ZSL models succeed is to choose the right embedding space. Instead of embedding into a semantic space or an intermediate space, we propose to use the visual space as the embedding space. This is because that in this space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective. This model design also provides a natural mechanism for multiple semantic modalities (e.g., attributes and sentence descriptions) to be fused and optimised jointly in an end-to-end manner. Extensive experiments on four benchmarks show that our model significantly outperforms the existing models. Code is available at https://github.com/lzrobots/DeepEmbeddingModel_ZSL
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
Authors: Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Išgum
Date: 2017-04-12 00:00:00
Tasks: Segmentation

We propose an automatic method using dilated convolutional neural networks
(CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR
(CMR) of patients with congenital heart disease (CHD).
  Ten training and ten test CMR scans cropped to an ROI around the heart were
provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive
field of 131x131 voxels was trained for myocardium and blood pool segmentation
in axial, sagittal and coronal image slices. Performance was evaluated within
the HVSMR challenge.
  Automatic segmentation of the test scans resulted in Dice indices of
0.80$\pm$0.06 and 0.93$\pm$0.02, average distances to boundaries of
0.96$\pm$0.31 and 0.89$\pm$0.24 mm, and Hausdorff distances of 6.13$\pm$3.76
and 7.07$\pm$3.01 mm for the myocardium and blood pool, respectively.
Segmentation took 41.5$\pm$14.7 s per scan.
  In conclusion, dilated CNNs trained on a small set of CMR images of CHD
patients showing large anatomical variability provide accurate myocardium and
blood pool segmentations.
Stigmergy-based modeling to discover urban activity patterns from positioning data
Authors: Antonio L. Alfeo, Mario G. C. A. Cimino, Sara Egidi, Bruno Lepri, Alex Pentland, Gigliola Vaglini
Date: 2017-04-12 00:00:00
Tasks: Clustering, Position

Positioning data offer a remarkable source of information to analyze crowds
urban dynamics. However, discovering urban activity patterns from the emergent
behavior of crowds involves complex system modeling. An alternative approach is
to adopt computational techniques belonging to the emergent paradigm, which
enables self-organization of data and allows adaptive analysis. Specifically,
our approach is based on stigmergy. By using stigmergy each sample position is
associated with a digital pheromone deposit, which progressively evaporates and
aggregates with other deposits according to their spatiotemporal proximity.
Based on this principle, we exploit positioning data to identify high density
areas (hotspots) and characterize their activity over time. This
characterization allows the comparison of dynamics occurring in different days,
providing a similarity measure exploitable by clustering techniques. Thus, we
cluster days according to their activity behavior, discovering unexpected urban
activity patterns. As a case study, we analyze taxi traces in New York City
during 2015.
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
Authors: Hongsong Wang, Liang Wang
Date: 2017-04-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Action Recognition, Action Recognition, Data Augmentation, Skeleton Based Action Recognition, Temporal Action Localization

Recently, skeleton based action recognition gains more popularity due to
cost-effective depth sensors coupled with real-time skeleton estimation
algorithms. Traditional approaches based on handcrafted features are limited to
represent the complexity of motion patterns. Recent methods that use Recurrent
Neural Networks (RNN) to handle raw skeletons only focus on the contextual
dependency in the temporal domain and neglect the spatial configurations of
articulated skeletons. In this paper, we propose a novel two-stream RNN
architecture to model both temporal dynamics and spatial configurations for
skeleton based action recognition. We explore two different structures for the
temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed
according to human body kinematics. We also propose two effective methods to
model the spatial structure by converting the spatial graph into a sequence of
joints. To improve generalization of our model, we further exploit 3D
transformation based data augmentation techniques including rotation and
scaling transformation to transform the 3D coordinates of skeletons during
training. Experiments on 3D action recognition benchmark datasets show that our
method brings a considerable improvement for a variety of actions, i.e.,
generic actions, interaction activities and gestures.
Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization
Authors: Davis M. Vigneault, Weidi Xie, David A. Bluemke, J. Alison Noble
Date: 2017-04-12 00:00:00

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an
area of interest for quantification of regional cardiac function from balanced,
steady state free precession (SSFP) cine sequences. However, currently
available techniques lack full automation, limiting reproducibility. We propose
a fully automated technique whereby a CMR image sequence is first segmented
with a deep, fully convolutional neural network (CNN) architecture, and
quadratic basis splines are fitted simultaneously across all cardiac frames
using least squares optimization. Experiments are performed using data from 42
patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control
subjects. In terms of segmentation, we compared state-of-the-art CNN
frameworks, U-Net and dilated convolution architectures, with and without
temporal context, using cross validation with three folds. Performance relative
to expert manual segmentation was similar across all networks: pixel accuracy
was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU
across foreground classes only was ~85%. Endocardial left ventricular
circumferential strain calculated from the proposed pipeline was significantly
different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in
agreement with the current clinical literature.
Detecting Visual Relationships with Deep Relational Networks
Authors: Bo Dai, Yuqi Zhang, Dahua Lin
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diversity, General Classification

Relationships among objects play a crucial role in image understanding.
Despite the great success of deep learning techniques in recognizing individual
objects, reasoning about the relationships among objects remains a challenging
task. Previous methods often treat this as a classification problem,
considering each type of relationship (e.g. "ride") or each distinct visual
phrase (e.g. "person-ride-horse") as a category. Such approaches are faced with
significant difficulties caused by the high diversity of visual appearance for
each kind of relationships or the large number of distinct visual phrases. We
propose an integrated framework to tackle this problem. At the heart of this
framework is the Deep Relational Network, a novel formulation designed
specifically for exploiting the statistical dependencies between objects and
their relationships. On two large datasets, the proposed method achieves
substantial improvement over state-of-the-art.
Preferential Bayesian Optimization
Authors: Javier Gonzalez, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence
Date: 2017-04-12 00:00:00
Tasks: Bayesian Optimization, Recommendation Systems

Bayesian optimization (BO) has emerged during the last few years as an
effective approach to optimizing black-box functions where direct queries of
the objective are expensive. In this paper we consider the case where direct
access to the function is not possible, but information about user preferences
is. Such scenarios arise in problems where human preferences are modeled, such
as A/B tests or recommender systems. We present a new framework for this
scenario that we call Preferential Bayesian Optimization (PBO) which allows us
to find the optimum of a latent function that can only be queried through
pairwise comparisons, the so-called duels. PBO extends the applicability of
standard BO ideas and generalizes previous discrete dueling approaches by
modeling the probability of the winner of each duel by means of a Gaussian
process model with a Bernoulli likelihood. The latent preference function is
used to define a family of acquisition functions that extend usual policies
used in BO. We illustrate the benefits of PBO in a variety of experiments,
showing that PBO needs drastically fewer comparisons for finding the optimum.
According to our experiments, the way of modeling correlations in PBO is key in
obtaining this advantage.
Pixel Objectness
Authors: Suyog Dutt Jain, Bo Xiong, Kristen Grauman
Date: 2017-01-19 00:00:00
Tasks: Foreground Segmentation, Image Retargeting, Image Retrieval, Object, Object Discovery, Retrieval, Structured Prediction

We propose an end-to-end learning framework for generating foreground object
segmentations. Given a single novel image, our approach produces pixel-level
masks for all "object-like" regions---even for object categories never seen
during training. We formulate the task as a structured prediction problem of
assigning foreground/background labels to all pixels, implemented using a deep
fully convolutional network. Key to our idea is training with a mix of
image-level object category examples together with relatively few images with
boundary-level annotations. Our method substantially improves the
state-of-the-art on foreground segmentation for ImageNet and MIT Object
Discovery datasets. Furthermore, on over 1 million images, we show that it
generalizes well to segment object categories unseen in the foreground maps
used for training. Finally, we demonstrate how our approach benefits image
retrieval and image retargeting, both of which flourish when given our
high-quality foreground maps.
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Authors: Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, Jason Yosinski
Date: 2016-11-30 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Captioning, Image Inpainting

Generating high-resolution, photo-realistic images has been a long-standing
goal in machine learning. Recently, Nguyen et al. (2016) showed one interesting
way to synthesize novel images by performing gradient ascent in the latent
space of a generator network to maximize the activations of one or multiple
neurons in a separate classifier network. In this paper we extend this method
by introducing an additional prior on the latent code, improving both sample
quality and sample diversity, leading to a state-of-the-art generative model
that produces high quality images at higher resolutions (227x227) than previous
generative models, and does so for all 1000 ImageNet categories. In addition,
we provide a unified probabilistic interpretation of related activation
maximization methods and call the general class of models "Plug and Play
Generative Networks". PPGNs are composed of 1) a generator network G that is
capable of drawing a wide range of image types and 2) a replaceable "condition"
network C that tells the generator what to draw. We demonstrate the generation
of images conditioned on a class (when C is an ImageNet or MIT Places
classification network) and also conditioned on a caption (when C is an image
captioning network). Our method also improves the state of the art of
Multifaceted Feature Visualization, which generates the set of synthetic inputs
that activate a neuron in order to better understand how deep neural networks
operate. Finally, we show that our model performs reasonably well at the task
of image inpainting. While image models are used in this paper, the approach is
modality-agnostic and can be applied to many types of data.
Joint Semi-supervised RSS Dimensionality Reduction and Fingerprint Based Algorithm for Indoor Localization
Authors: Caifa Zhou, Lin Ma, Xuezhi Tan
Date: 2017-04-12 00:00:00
Tasks: Dimensionality Reduction, Indoor Localization

With the recent development in mobile computing devices and as the ubiquitous
deployment of access points(APs) of Wireless Local Area Networks(WLANs), WLAN
based indoor localization systems(WILSs) are of mounting concentration and are
becoming more and more prevalent for they do not require additional
infrastructure. As to the localization methods in WILSs, for the approaches
used to localization in satellite based global position systems are difficult
to achieve in indoor environments, fingerprint based localization
algorithms(FLAs) are predominant in the RSS based schemes. However, the
performance of FLAs has close relationship with the number of APs and the
number of reference points(RPs) in WILSs, especially as the redundant
deployment of APs and RPs in the system. There are two fatal problems, curse of
dimensionality (CoD) and asymmetric matching(AM), caused by increasing number
of APs and breaking down APs during online stage. In this paper, a
semi-supervised RSS dimensionality reduction algorithm is proposed to solve
these two dilemmas at the same time and there are numerous analyses about the
theoretical realization of the proposed method. Another significant innovation
of this paper is jointing the fingerprint based algorithm with CM-SDE algorithm
to improve the localization accuracy of indoor localization.