pwc-archive/papers-with-abstracts huggingface dataset:
A Framework for the Volumetric Integration of Depth Images
Authors: Victor Adrian Prisacariu, Olaf Kähler, Ming Ming Cheng, Carl Yuheng Ren, Julien Valentin, Philip H. S. Torr, Ian D. Reid, David W. Murray
Date: 2014-10-03 00:00:00
Tasks: 3D Reconstruction, GPU

Volumetric models have become a popular representation for 3D scenes in
recent years. One of the breakthroughs leading to their popularity was
KinectFusion, where the focus is on 3D reconstruction using RGB-D sensors.
However, monocular SLAM has since also been tackled with very similar
approaches. Representing the reconstruction volumetrically as a truncated
signed distance function leads to most of the simplicity and efficiency that
can be achieved with GPU implementations of these systems. However, this
representation is also memory-intensive and limits the applicability to small
scale reconstructions. Several avenues have been explored for overcoming this
limitation. With the aim of summarizing them and providing for a fast and
flexible 3D reconstruction pipeline, we propose a new, unifying framework
called InfiniTAM. The core idea is that individual steps like camera tracking,
scene representation and integration of new data can easily be replaced and
adapted to the needs of the user. Along with the framework we also provide a
set of components for scalable reconstruction: two implementations of camera
trackers, based on RGB data and on depth data, two representations of the 3D
volumetric data, a dense volume and one based on hashes of subblocks, and an
optional module for swapping subblocks in and out of the typically limited GPU
memory.
Foreground-Background Segmentation Based on Codebook and Edge Detector
Authors: Mikaël A. Mousse, Eugène C. Ezin, Cina Motamed
Date: 2014-10-23 00:00:00
Tasks: Edge Detection, Moving Object Detection, Object, object-detection, Object Detection, Segmentation

Background modeling techniques are used for moving object detection in video.
Many algorithms exist in the field of object detection with different purposes.
In this paper, we propose an improvement of moving object detection based on
codebook segmentation. We associate the original codebook algorithm with an
edge detection algorithm. Our goal is to prove the efficiency of using an edge
detection algorithm with a background modeling algorithm. Throughout our study,
we compared the quality of the moving object detection when codebook
segmentation algorithm is associated with some standard edge detectors. In each
case, we use frame-based metrics for the evaluation of the detection. The
different results are presented and analyzed.
Density-Based Region Search with Arbitrary Shape for Object Localization
Authors: Ji Zhao, Deyu Meng, Jiayi Ma
Date: 2014-10-23 00:00:00
Tasks: Object Localization, Weakly-Supervised Object Localization

Region search is widely used for object localization. Typically, the region
search methods project the score of a classifier into an image plane, and then
search the region with the maximal score. The recently proposed region search
methods, such as efficient subwindow search and efficient region search, %which
localize objects from the score distribution on an image are much more
efficient than sliding window search. However, for some classifiers and tasks,
the projected scores are nearly all positive, and hence maximizing the score of
a region results in localizing nearly the entire images as objects, which is
meaningless.
  In this paper, we observe that the large scores are mainly concentrated on or
around objects. Based on this observation, we propose a method, named level set
maximum-weight connected subgraph (LS-MWCS), which localizes objects with
arbitrary shapes by searching regions with the densest score rather than the
maximal score. The region density can be controlled by a parameter flexibly.
And we prove an important property of the proposed LS-MWCS, which guarantees
that the region with the densest score can be searched. Moreover, the LS-MWCS
can be efficiently optimized by belief propagation. The method is evaluated on
the problem of weakly-supervised object localization, and the quantitative
results demonstrate the superiorities of our LS-MWCS compared to other
state-of-the-art methods.
Enhanced Multiobjective Evolutionary Algorithm based on Decomposition for Solving the Unit Commitment Problem
Authors: Anupam Trivedi, Kunal Pal, Chiranjib Saha, Dipti Srinivasan
Date: 2014-10-16 00:00:00
Tasks: Benchmarking

The unit commitment (UC) problem is a nonlinear, high-dimensional, highly
constrained, mixed-integer power system optimization problem and is generally
solved in the literature considering minimizing the system operation cost as
the only objective. However, due to increasing environmental concerns, the
recent attention has shifted to incorporating emission in the problem
formulation. In this paper, a multi-objective evolutionary algorithm based on
decomposition (MOEA/D) is proposed to solve the UC problem as a multi-objective
optimization problem considering minimizing cost and emission as the multiple
objec- tives. Since, UC problem is a mixed-integer optimization problem
consisting of binary UC variables and continuous power dispatch variables, a
novel hybridization strategy is proposed within the framework of MOEA/D such
that genetic algorithm (GA) evolves the binary variables while differential
evolution (DE) evolves the continuous variables. Further, a novel non-uniform
weight vector distribution strategy is proposed and a parallel island model
based on combination of MOEA/D with uniform and non-uniform weight vector
distribution strategy is implemented to enhance the performance of the
presented algorithm. Extensive case studies are presented on different test
systems and the effectiveness of the proposed hybridization strategy, the
non-uniform weight vector distribution strategy and parallel island model is
verified through stringent simulated results. Further, exhaustive benchmarking
against the algorithms proposed in the literature is presented to demonstrate
the superiority of the proposed algorithm in obtaining significantly better
converged and uniformly distributed trade-off solutions.
Initialization of multilayer forecasting artifical neural networks
Authors: Vladimir V. Bochkarev, Yulia S. Maslennikova
Date: 2014-10-23 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

In this paper, a new method was developed for initialising artificial neural
networks predicting dynamics of time series. Initial weighting coefficients
were determined for neurons analogously to the case of a linear prediction
filter. Moreover, to improve the accuracy of the initialization method for a
multilayer neural network, some variants of decomposition of the transformation
matrix corresponding to the linear prediction filter were suggested. The
efficiency of the proposed neural network prediction method by forecasting
solutions of the Lorentz chaotic system is shown in this paper.
On Lower and Upper Bounds in Smooth Strongly Convex Optimization - A Unified Approach via Linear Iterative Methods
Authors: Yossi Arjevani
Date: 2014-10-23 00:00:00
Tasks: valid

In this thesis we develop a novel framework to study smooth and strongly
convex optimization algorithms, both deterministic and stochastic. Focusing on
quadratic functions we are able to examine optimization algorithms as a
recursive application of linear operators. This, in turn, reveals a powerful
connection between a class of optimization algorithms and the analytic theory
of polynomials whereby new lower and upper bounds are derived. In particular,
we present a new and natural derivation of Nesterov's well-known Accelerated
Gradient Descent method by employing simple 'economic' polynomials. This rather
natural interpretation of AGD contrasts with earlier ones which lacked a
simple, yet solid, motivation. Lastly, whereas existing lower bounds are only
valid when the dimensionality scales with the number of iterations, our lower
bound holds in the natural regime where the dimensionality is fixed.
Attribute Efficient Linear Regression with Data-Dependent Sampling
Authors: Doron Kukliansky, Ohad Shamir
Date: 2014-10-23 00:00:00
Tasks: Attribute, regression

In this paper we analyze a budgeted learning setting, in which the learner
can only choose and observe a small subset of the attributes of each training
example. We develop efficient algorithms for ridge and lasso linear regression,
which utilize the geometry of the data by a novel data-dependent sampling
scheme. When the learner has prior knowledge on the second moments of the
attributes, the optimal sampling probabilities can be calculated precisely, and
result in data-dependent improvements factors for the excess risk over the
state-of-the-art that may be as large as $O(\sqrt{d})$, where $d$ is the
problem's dimension. Moreover, under reasonable assumptions our algorithms can
use less attributes than full-information algorithms, which is the main concern
in budgeted learning settings. To the best of our knowledge, these are the
first algorithms able to do so in our setting. Where no such prior knowledge is
available, we develop a simple estimation technique that given a sufficient
amount of training examples, achieves similar improvements. We complement our
theoretical analysis with experiments on several data sets which support our
claims.
Subspace Alignment For Domain Adaptation
Authors: Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars
Date: 2014-09-18 00:00:00
Tasks: Domain Adaptation

In this paper, we introduce a new domain adaptation (DA) algorithm where the
source and target domains are represented by subspaces spanned by eigenvectors.
Our method seeks a domain invariant feature space by learning a mapping
function which aligns the source subspace with the target one. We show that the
solution of the corresponding optimization problem can be obtained in a simple
closed form, leading to an extremely fast algorithm. We present two approaches
to determine the only hyper-parameter in our method corresponding to the size
of the subspaces. In the first approach we tune the size of subspaces using a
theoretical bound on the stability of the obtained result. In the second
approach, we use maximum likelihood estimation to determine the subspace size,
which is particularly useful for high dimensional data. Apart from PCA, we
propose a subspace creation method that outperform partial least squares (PLS)
and linear discriminant analysis (LDA) in domain adaptation. We test our method
on various datasets and show that, despite its intrinsic simplicity, it
outperforms state of the art DA methods.
A General Stochastic Algorithmic Framework for Minimizing Expensive Black Box Objective Functions Based on Surrogate Models and Sensitivity Analysis
Authors: Yi-Lun Wang, Christine A. Shoemaker
Date: 2014-10-23 00:00:00
Tasks: global-optimization, Sensitivity

We are focusing on bound constrained global optimization problems, whose
objective functions are computationally expensive black-box functions and have
multiple local minima. The recently popular Metric Stochastic Response Surface
(MSRS) algorithm proposed by \cite{Regis2007SRBF} based on adaptive or
sequential learning based on response surfaces is revisited and further
extended for better performance in case of higher dimensional problems.
Specifically, we propose a new way to generate the candidate points which the
next function evaluation point is picked from according to the metric criteria,
based on a new definition of distance, and prove the global convergence of the
corresponding. Correspondingly, a more adaptive implementation of MSRS, named
"SO-SA", is presented. "SO-SA" is is more likely to perturb those most
sensitive coordinates when generating the candidate points, instead of
perturbing all coordinates simultaneously.
  Numerical experiments on both synthetic problems and real problems
demonstrate the advantages of our new algorithm, compared with many state of
the art alternatives.}
Capturing spatial interdependence in image features: the counting grid, an epitomic representation for bags of features
Authors: Alessandro Perina, Nebojsa Jojic
Date: 2014-10-23 00:00:00
Tasks: Scene Recognition

In recent scene recognition research images or large image regions are often
represented as disorganized "bags" of features which can then be analyzed using
models originally developed to capture co-variation of word counts in text.
However, image feature counts are likely to be constrained in different ways
than word counts in text. For example, as a camera pans upwards from a building
entrance over its first few floors and then further up into the sky Fig. 1,
some feature counts in the image drop while others rise -- only to drop again
giving way to features found more often at higher elevations. The space of all
possible feature count combinations is constrained both by the properties of
the larger scene and the size and the location of the window into it. To
capture such variation, in this paper we propose the use of the counting grid
model. This generative model is based on a grid of feature counts, considerably
larger than any of the modeled images, and considerably smaller than the real
estate needed to tile the images next to each other tightly. Each modeled image
is assumed to have a representative window in the grid in which the feature
counts mimic the feature distribution in the image. We provide a learning
procedure that jointly maps all images in the training set to the counting grid
and estimates the appropriate local counts in it. Experimentally, we
demonstrate that the resulting representation captures the space of feature
count combinations more accurately than the traditional models, not only when
the input images come from a panning camera, but even when modeling images of
different scenes from the same category.
Online Group Feature Selection
Authors: Wang Jing, Zhao Zhong-Qiu, Hu Xuegang, Cheung Yiu-ming, Wang Meng, Wu Xindong
Date: 2014-04-18 00:00:00
Tasks: feature selection

Online feature selection with dynamic features has become an active research
area in recent years. However, in some real-world applications such as image
analysis and email spam filtering, features may arrive by groups. Existing
online feature selection methods evaluate features individually, while existing
group feature selection methods cannot handle online processing. Motivated by
this, we formulate the online group feature selection problem, and propose a
novel selection approach for this problem. Our proposed approach consists of
two stages: online intra-group selection and online inter-group selection. In
the intra-group selection, we use spectral analysis to select discriminative
features in each group when it arrives. In the inter-group selection, we use
Lasso to select a globally optimal subset of features. This 2-stage procedure
continues until there are no more features to come or some predefined stopping
conditions are met. Extensive experiments conducted on benchmark and real-world
data sets demonstrate that our proposed approach outperforms other
state-of-the-art online feature selection methods.
Low-Rank Modeling and Its Applications in Image Analysis
Authors: Xiaowei Zhou, Can Yang, Hongyu Zhao, Weichuan Yu
Date: 2014-01-15 00:00:00
Tasks: Collaborative Filtering, Matrix Completion

Low-rank modeling generally refers to a class of methods that solve problems
by representing variables of interest as low-rank matrices. It has achieved
great success in various fields including computer vision, data mining, signal
processing and bioinformatics. Recently, much progress has been made in
theories, algorithms and applications of low-rank modeling, such as exact
low-rank matrix recovery via convex programming and matrix completion applied
to collaborative filtering. These advances have brought more and more
attentions to this topic. In this paper, we review the recent advance of
low-rank modeling, the state-of-the-art algorithms, and related applications in
image analysis. We first give an overview to the concept of low-rank modeling
and challenging problems in this area. Then, we summarize the models and
algorithms for low-rank matrix recovery and illustrate their advantages and
limitations with numerical experiments. Next, we introduce a few applications
of low-rank modeling in the context of image analysis. Finally, we conclude
this paper with some discussions.
Motion Estimation via Robust Decomposition with Constrained Rank
Authors: German Ros, Jose Alvarez, Julio Guerrero
Date: 2014-10-22 00:00:00
Tasks: Motion Estimation, Outlier Detection, Visual Odometry

In this work, we address the problem of outlier detection for robust motion
estimation by using modern sparse-low-rank decompositions, i.e., Robust
PCA-like methods, to impose global rank constraints. Robust decompositions have
shown to be good at splitting a corrupted matrix into an uncorrupted low-rank
matrix and a sparse matrix, containing outliers. However, this process only
works when matrices have relatively low rank with respect to their ambient
space, a property not met in motion estimation problems. As a solution, we
propose to exploit the partial information present in the decomposition to
decide which matches are outliers. We provide evidences showing that even when
it is not possible to recover an uncorrupted low-rank matrix, the resulting
information can be exploited for outlier detection. To this end we propose the
Robust Decomposition with Constrained Rank (RD-CR), a proximal gradient based
method that enforces the rank constraints inherent to motion estimation. We
also present a general framework to perform robust estimation for stereo Visual
Odometry, based on our RD-CR and a simple but effective compressed optimization
method that achieves high performance. Our evaluation on synthetic data and on
the KITTI dataset demonstrates the applicability of our approach in complex
scenarios and it yields state-of-the-art performance.
Learning without Concentration
Authors: Shahar Mendelson
Date: 2014-01-01 00:00:00

We obtain sharp bounds on the performance of Empirical Risk Minimization
performed in a convex class and with respect to the squared loss, without
assuming that class members and the target are bounded functions or have
rapidly decaying tails.
  Rather than resorting to a concentration-based argument, the method used here
relies on a `small-ball' assumption and thus holds for classes consisting of
heavy-tailed functions and for heavy-tailed targets.
  The resulting estimates scale correctly with the `noise level' of the
problem, and when applied to the classical, bounded scenario, always improve
the known bounds.
Rich feature hierarchies for accurate object detection and semantic segmentation
Authors: Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
Date: 2013-11-11 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object Detection, Semantic Segmentation

Object detection performance, as measured on the canonical PASCAL VOC
dataset, has plateaued in the last few years. The best-performing methods are
complex ensemble systems that typically combine multiple low-level image
features with high-level context. In this paper, we propose a simple and
scalable detection algorithm that improves mean average precision (mAP) by more
than 30% relative to the previous best result on VOC 2012---achieving a mAP of
53.3%. Our approach combines two key insights: (1) one can apply high-capacity
convolutional neural networks (CNNs) to bottom-up region proposals in order to
localize and segment objects and (2) when labeled training data is scarce,
supervised pre-training for an auxiliary task, followed by domain-specific
fine-tuning, yields a significant performance boost. Since we combine region
proposals with CNNs, we call our method R-CNN: Regions with CNN features. We
also compare R-CNN to OverFeat, a recently proposed sliding-window detector
based on a similar CNN architecture. We find that R-CNN outperforms OverFeat by
a large margin on the 200-class ILSVRC2013 detection dataset. Source code for
the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.
Online Energy Price Matrix Factorization for Power Grid Topology Tracking
Authors: Vassilis Kekatos, Georgios B. Giannakis, Ross Baldick
Date: 2014-10-22 00:00:00

Grid security and open markets are two major smart grid goals. Transparency
of market data facilitates a competitive and efficient energy environment, yet
it may also reveal critical physical system information. Recovering the grid
topology based solely on publicly available market data is explored here.
Real-time energy prices are calculated as the Lagrange multipliers of
network-constrained economic dispatch; that is, via a linear program (LP)
typically solved every 5 minutes. Granted the grid Laplacian is a parameter of
this LP, one could infer such a topology-revealing matrix upon observing
successive LP dual outcomes. The matrix of spatio-temporal prices is first
shown to factor as the product of the inverse Laplacian times a sparse matrix.
Leveraging results from sparse matrix decompositions, topology recovery schemes
with complementary strengths are subsequently formulated. Solvers scalable to
high-dimensional and streaming market data are devised. Numerical validation
using real load data on the IEEE 30-bus grid provide useful input for current
and future market designs.
Cosine Similarity Measure According to a Convex Cost Function
Authors: Osman Gunay, Cem Emre Akbas, A. Enis Cetin
Date: 2014-10-22 00:00:00

In this paper, we describe a new vector similarity measure associated with a
convex cost function. Given two vectors, we determine the surface normals of
the convex function at the vectors. The angle between the two surface normals
is the similarity measure. Convex cost function can be the negative entropy
function, total variation (TV) function and filtered variation function. The
convex cost function need not be differentiable everywhere. In general, we need
to compute the gradient of the cost function to compute the surface normals. If
the gradient does not exist at a given vector, it is possible to use the
subgradients and the normal producing the smallest angle between the two
vectors is used to compute the similarity measure.
Bayesian matrix completion: prior specification
Authors: Pierre Alquier, Vincent Cottet, Nicolas Chopin, Judith Rousseau
Date: 2014-06-05 00:00:00
Tasks: Matrix Completion, Recommendation Systems

Low-rank matrix estimation from incomplete measurements recently received
increased attention due to the emergence of several challenging applications,
such as recommender systems; see in particular the famous Netflix challenge.
While the behaviour of algorithms based on nuclear norm minimization is now
well understood, an as yet unexplored avenue of research is the behaviour of
Bayesian algorithms in this context. In this paper, we briefly review the
priors used in the Bayesian literature for matrix completion. A standard
approach is to assign an inverse gamma prior to the singular values of a
certain singular value decomposition of the matrix of interest; this prior is
conjugate. However, we show that two other types of priors (again for the
singular values) may be conjugate for this model: a gamma prior, and a discrete
prior. Conjugacy is very convenient, as it makes it possible to implement
either Gibbs sampling or Variational Bayes. Interestingly enough, the maximum a
posteriori for these different priors is related to the nuclear norm
minimization problems. We also compare all these priors on simulated datasets,
and on the classical MovieLens and Netflix datasets.