pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Eye Movement Detection Using Convolutional Neural Networks
Authors: Sabrina Hoppe, Andreas Bulling
Date: 2016-09-08 00:00:00

Common computational methods for automated eye movement detection - i.e. the
task of detecting different types of eye movement in a continuous stream of
gaze data - are limited in that they either involve thresholding on
hand-crafted signal features, require individual detectors each only detecting
a single movement, or require pre-segmented data. We propose a novel approach
for eye movement detection that only involves learning a single detector
end-to-end, i.e. directly from the continuous gaze data stream and
simultaneously for different eye movements without any manual feature crafting
or segmentation. Our method is based on convolutional neural networks (CNN)
that recently demonstrated superior performance in a variety of tasks in
computer vision, signal processing, and machine learning. We further introduce
a novel multi-participant dataset that contains scripted and free-viewing
sequences of ground-truth annotated saccades, fixations, and smooth pursuits.
We show that our CNN-based method outperforms state-of-the-art baselines by a
large margin on this challenging dataset, thereby underlining the significant
potential of this approach for holistic, robust, and accurate eye movement
protocol analysis.
A Notation for Markov Decision Processes
Authors: Philip S. Thomas, Billy Okal
Date: 2015-12-30 00:00:00

This paper specifies a notation for Markov decision processes.
Dense Motion Estimation for Smoke
Authors: Da Chen, Wenbin Li, Peter Hall
Date: 2016-09-07 00:00:00
Tasks: Motion Estimation

Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.
Making a Case for Learning Motion Representations with Phase
Authors: S. L. Pintea, J. C. van Gemert
Date: 2016-09-06 00:00:00
Tasks: Action Recognition, motion prediction, Optical Flow Estimation, Representation Learning, Temporal Action Localization

This work advocates Eulerian motion representation learning over the current
standard Lagrangian optical flow model. Eulerian motion is well captured by
using phase, as obtained by decomposing the image through a complex-steerable
pyramid. We discuss the gain of Eulerian motion in a set of practical use
cases: (i) action recognition, (ii) motion prediction in static images, (iii)
motion transfer in static images and, (iv) motion transfer in video. For each
task we motivate the phase-based direction and provide a possible approach.
Improved Optimistic Mirror Descent for Sparsity and Curvature
Authors: Parameswaran Kamalaruban
Date: 2016-09-08 00:00:00

Online Convex Optimization plays a key role in large scale machine learning.
Early approaches to this problem were conservative, in which the main focus was
protection against the worst case scenario. But recently several algorithms
have been developed for tightening the regret bounds in easy data instances
such as sparsity, predictable sequences, and curved losses. In this work we
unify some of these existing techniques to obtain new update rules for the
cases when these easy instances occur together. First we analyse an adaptive
and optimistic update rule which achieves tighter regret bound when the loss
sequence is sparse and predictable. Then we explain an update rule that
dynamically adapts to the curvature of the loss function and utilizes the
predictable nature of the loss sequence as well. Finally we extend these
results to composite losses.
Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep Learning
Authors: Mohammad H. Jafari, Ebrahim Nasr-Esfahani, Nader Karimi, S. M. Reza Soroushmehr, Shadrokh Samavi, Kayvan Najarian
Date: 2016-09-08 00:00:00
Tasks: Segmentation

Melanoma is amongst most aggressive types of cancer. However, it is highly
curable if detected in its early stages. Prescreening of suspicious moles and
lesions for malignancy is of great importance. Detection can be done by images
captured by standard cameras, which are more preferable due to low cost and
availability. One important step in computerized evaluation of skin lesions is
accurate detection of lesion region, i.e. segmentation of an image into two
regions as lesion and normal skin. Accurate segmentation can be challenging due
to burdens such as illumination variation and low contrast between lesion and
healthy skin. In this paper, a method based on deep neural networks is proposed
for accurate extraction of a lesion region. The input image is preprocessed and
then its patches are fed to a convolutional neural network (CNN). Local texture
and global structure of the patches are processed in order to assign pixels to
lesion or normal classes. A method for effective selection of training patches
is used for more accurate detection of a lesion border. The output segmentation
mask is refined by some post processing operations. The experimental results of
qualitative and quantitative evaluations demonstrate that our method can
outperform other state-of-the-art algorithms exist in the literature.
Ear-to-ear Capture of Facial Intrinsics
Authors: Alassane Seck, William A. P. Smith, Arnaud Dessein, Bernard Tiddeman, Hannah Dee, Abhishek Dutta
Date: 2016-09-08 00:00:00
Tasks: Face Model

We present a practical approach to capturing ear-to-ear face models
comprising both 3D meshes and intrinsic textures (i.e. diffuse and specular
albedo). Our approach is a hybrid of geometric and photometric methods and
requires no geometric calibration. Photometric measurements made in a
lightstage are used to estimate view dependent high resolution normal maps. We
overcome the problem of having a single photometric viewpoint by capturing in
multiple poses. We use uncalibrated multiview stereo to estimate a coarse base
mesh to which the photometric views are registered. We propose a novel approach
to robustly stitching surface normal and intrinsic texture data into a
seamless, complete and highly detailed face model. The resulting relightable
models provide photorealistic renderings in any view.
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
Authors: James Townsend, Niklas Koep, Sebastian Weichwald
Date: 2016-03-10 00:00:00
Tasks: Riemannian optimization

Optimization on manifolds is a class of methods for optimization of an
objective function, subject to constraints which are smooth, in the sense that
the set of points which satisfy the constraints admits the structure of a
differentiable manifold. While many optimization problems are of the described
form, technicalities of differential geometry and the laborious calculation of
derivatives pose a significant barrier for experimenting with these methods.
  We introduce Pymanopt (available at https://pymanopt.github.io), a toolbox
for optimization on manifolds, implemented in Python, that---similarly to the
Manopt Matlab toolbox---implements several manifold geometries and optimization
algorithms. Moreover, we lower the barriers to users further by using automated
differentiation for calculating derivative information, saving users time and
saving them from potential calculation and implementation errors.
Ms. Pac-Man Versus Ghost Team CIG 2016 Competition
Authors: Piers R. Williams, Diego Perez-Liebana, Simon M. Lucas
Date: 2016-09-08 00:00:00

This paper introduces the revival of the popular Ms. Pac-Man Versus Ghost
Team competition. We present an updated game engine with Partial Observability
constraints, a new Multi-Agent Systems approach to developing Ghost agents and
several sample controllers to ease the development of entries. A restricted
communication protocol is provided for the Ghosts, providing a more challenging
environment than before. The competition will debut at the IEEE Computational
Intelligence and Games Conference 2016. Some preliminary results showing the
effects of Partial Observability and the benefits of simple communication are
also presented.
Random matrices meet machine learning: a large dimensional analysis of LS-SVM
Authors: Zhenyu Liao, Romain Couillet
Date: 2016-09-07 00:00:00
Tasks: BIG-bench Machine Learning

This article proposes a performance analysis of kernel least squares support
vector machines (LS-SVMs) based on a random matrix approach, in the regime
where both the dimension of data $p$ and their number $n$ grow large at the
same rate. Under a two-class Gaussian mixture model for the input data, we
prove that the LS-SVM decision function is asymptotically normal with means and
covariances shown to depend explicitly on the derivatives of the kernel
function. This provides improved understanding along with new insights into the
internal workings of SVM-type methods for large datasets.
Learning Action Concept Trees and Semantic Alignment Networks from Image-Description Data
Authors: Jiyang Gao, Ram Nevatia
Date: 2016-09-08 00:00:00
Tasks: Action Classification, Classification, General Classification, Image Description, Sentence

Action classification in still images has been a popular research topic in
computer vision. Labelling large scale datasets for action classification
requires tremendous manual work, which is hard to scale up. Besides, the action
categories in such datasets are pre-defined and vocabularies are fixed. However
humans may describe the same action with different phrases, which leads to the
difficulty of vocabulary expansion for traditional fully-supervised methods. We
observe that large amounts of images with sentence descriptions are readily
available on the Internet. The sentence descriptions can be regarded as weak
labels for the images, which contain rich information and could be used to
learn flexible expressions of action categories. We propose a method to learn
an Action Concept Tree (ACT) and an Action Semantic Alignment (ASA) model for
classification from image-description data via a two-stage learning process. A
new dataset for the task of learning actions from descriptions is built.
Experimental results show that our method outperforms several baseline methods
significantly.
How useful is photo-realistic rendering for visual learning?
Authors: Yair Movshovitz-Attias, Takeo Kanade, Yaser Sheikh
Date: 2016-03-26 00:00:00
Tasks: Domain Adaptation, Viewpoint Estimation

Data seems cheap to get, and in many ways it is, but the process of creating
a high quality labeled dataset from a mass of data is time-consuming and
expensive.
  With the advent of rich 3D repositories, photo-realistic rendering systems
offer the opportunity to provide nearly limitless data. Yet, their primary
value for visual learning may be the quality of the data they can provide
rather than the quantity. Rendering engines offer the promise of perfect labels
in addition to the data: what the precise camera pose is; what the precise
lighting location, temperature, and distribution is; what the geometry of the
object is.
  In this work we focus on semi-automating dataset creation through use of
synthetic data and apply this method to an important task -- object viewpoint
estimation. Using state-of-the-art rendering software we generate a large
labeled dataset of cars rendered densely in viewpoint space. We investigate the
effect of rendering parameters on estimation performance and show realism is
important. We show that generalizing from synthetic data is not harder than the
domain adaptation required between two real-image datasets and that combining
synthetic images with a small amount of real data improves estimation accuracy.
3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information
Authors: Sungheon Park, Jihye Hwang, Nojun Kwak
Date: 2016-08-10 00:00:00
Tasks: 2D Human Pose Estimation, 2D Pose Estimation, 3D Human Pose Estimation, Pose Estimation

While there has been a success in 2D human pose estimation with convolutional
neural networks (CNNs), 3D human pose estimation has not been thoroughly
studied. In this paper, we tackle the 3D human pose estimation task with
end-to-end learning using CNNs. Relative 3D positions between one joint and the
other joints are learned via CNNs. The proposed method improves the performance
of CNN with two novel ideas. First, we added 2D pose information to estimate a
3D pose from an image by concatenating 2D pose estimation result with the
features from an image. Second, we have found that more accurate 3D poses are
obtained by combining information on relative positions with respect to
multiple joints, instead of just one root joint. Experimental results show that
the proposed method achieves comparable performance to the state-of-the-art
methods on Human 3.6m dataset.
Determination of Pedestrian Flow Performance Based on Video Tracking and Microscopic Simulations
Authors: Kardi Teknomo, Yasushi Takeyama, Hajime Inamura
Date: 2016-09-08 00:00:00

One of the objectives of understanding pedestrian behavior is to predict the
effect of proposed changes in the design or evaluation of pedestrian
facilities. We want to know the impact to the user of the facilities, as the
design of the facilities change. That impact was traditionally evaluated by
level of service standards. Another design criterion to measure the impact of
design change is measured by the pedestrian flow performance index. This paper
describes the determination of pedestrian flow performance based video tracking
or any microscopic pedestrian simulation models. Most of pedestrian researches
have been done on a macroscopic level, which is an aggregation of all
pedestrian movement in pedestrian areas into flow, average speed and area
module. Macroscopic level, however, does not consider the interaction between
pedestrians. It is also not well suited for prediction of pedestrian flow
performance in pedestrian areas or in buildings with some obstruction, that
reduces the effective width of the walkways. On the other hand, the microscopic
level has a more general usage and considers detail in the design. More
efficient pedestrian flow can even be reached with less space. Those results
have rejected the linearity assumption of space and flow in the macroscopic
level.
Automated Segmentation of Retinal Layers from Optical Coherent Tomography Images Using Geodesic Distance
Authors: Jinming Duan, Christopher Tench, Irene Gottlob, Frank Proudlock, Li Bai
Date: 2016-09-07 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Optical coherence tomography (OCT) is a non-invasive imaging technique that
can produce images of the eye at the microscopic level. OCT image segmentation
to localise retinal layer boundaries is a fundamental procedure for diagnosing
and monitoring the progression of retinal and optical nerve disorders. In this
paper, we introduce a novel and accurate geodesic distance method (GDM) for OCT
segmentation of both healthy and pathological images in either two- or
three-dimensional spaces. The method uses a weighted geodesic distance by an
exponential function, taking into account both horizontal and vertical
intensity variations. The weighted geodesic distance is efficiently calculated
from an Eikonal equation via the fast sweeping method. The segmentation is then
realised by solving an ordinary differential equation with the geodesic
distance. The results of the GDM are compared with manually segmented retinal
layer boundaries/surfaces. Extensive experiments demonstrate that the proposed
GDM is robust to complex retinal structures with large curvatures and
irregularities and it outperforms the parametric active contour algorithm as
well as the graph theoretic based approaches for delineating the retinal layers
in both healthy and pathological images.
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
Authors: Weihao Gao, Sewoong Oh, Pramod Viswanath
Date: 2016-09-07 00:00:00
Proceeding: NeurIPS 2016 12

Estimators of information theoretic measures such as entropy and mutual
information are a basic workhorse for many downstream applications in modern
data science. State of the art approaches have been either geometric (nearest
neighbor (NN) based) or kernel based (with a globally chosen bandwidth). In
this paper, we combine both these approaches to design new estimators of
entropy and mutual information that outperform state of the art methods. Our
estimator uses local bandwidth choices of $k$-NN distances with a finite $k$,
independent of the sample size. Such a local and data dependent choice improves
performance in practice, but the bandwidth is vanishing at a fast rate, leading
to a non-vanishing bias. We show that the asymptotic bias of the proposed
estimator is universal; it is independent of the underlying distribution.
Hence, it can be pre-computed and subtracted from the estimate. As a byproduct,
we obtain a unified way of obtaining both kernel and NN estimators. The
corresponding theoretical contribution relating the asymptotic geometry of
nearest neighbors to order statistics is of independent mathematical interest.
Predicting Branch Visits and Credit Card Up-selling using Temporal Banking Data
Authors: Sandra Mitrović, Gaurav Singh
Date: 2016-07-20 00:00:00
Tasks: BIG-bench Machine Learning, Task 2

There is an abundance of temporal and non-temporal data in banking (and other
industries), but such temporal activity data can not be used directly with
classical machine learning models. In this work, we perform extensive feature
extraction from the temporal user activity data in an attempt to predict user
visits to different branches and credit card up-selling utilizing user
information and the corresponding activity data, as part of \emph{ECML/PKDD
Discovery Challenge 2016 on Bank Card Usage Analysis}. Our solution ranked
\nth{4} for \emph{Task 1} and achieved an AUC of \textbf{$0.7056$} for
\emph{Task 2} on public leaderboard.
Constrained $H^1$-regularization schemes for diffeomorphic image registration
Authors: Andreas Mang, George Biros
Date: 2015-03-02 00:00:00
Tasks: Image Registration

We propose regularization schemes for deformable registration and efficient
algorithms for their numerical approximation. We treat image registration as a
variational optimal control problem. The deformation map is parametrized by its
velocity. Tikhonov regularization ensures well-posedness. Our scheme augments
standard smoothness regularization operators based on $H^1$- and
$H^2$-seminorms with a constraint on the divergence of the velocity field,
which resembles variational formulations for Stokes incompressible flows. In
our formulation, we invert for a stationary velocity field and a mass source
map. This allows us to explicitly control the compressibility of the
deformation map and by that the determinant of the deformation gradient. We
also introduce a new regularization scheme that allows us to control shear. We
use a globalized, preconditioned, matrix-free, reduced space
(Gauss--)Newton--Krylov scheme for numerical optimization. We exploit variable
elimination techniques to reduce the number of unknowns of our system; we only
iterate on the reduced space of the velocity field. Our current implementation
is limited to the two-dimensional case. The numerical experiments demonstrate
that we can control the determinant of the deformation gradient without
compromising registration quality. This additional control allows us to avoid
oversmoothing of the deformation map. We also demonstrate that we can promote
or penalize shear while controlling the determinant of the deformation
gradient.
UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory
Authors: Iasonas Kokkinos
Date: 2016-09-07 00:00:00
Tasks: Boundary Detection, GPU, Human Part Segmentation, object-detection, Object Detection, Region Proposal, Saliency Prediction, Semantic Segmentation

In this work we introduce a convolutional neural network (CNN) that jointly
handles low-, mid-, and high-level vision tasks in a unified architecture that
is trained end-to-end. Such a universal network can act like a `swiss knife'
for vision tasks; we call this architecture an UberNet to indicate its
overarching nature.
  We address two main technical challenges that emerge when broadening up the
range of tasks handled by a single CNN: (i) training a deep architecture while
relying on diverse training sets and (ii) training many (potentially unlimited)
tasks with a limited memory budget. Properly addressing these two problems
allows us to train accurate predictors for a host of tasks, without
compromising accuracy.
  Through these advances we train in an end-to-end manner a CNN that
simultaneously addresses (a) boundary detection (b) normal estimation (c)
saliency estimation (d) semantic segmentation (e) human part segmentation (f)
semantic boundary detection, (g) region proposal generation and object
detection. We obtain competitive performance while jointly addressing all of
these tasks in 0.7 seconds per frame on a single GPU. A demonstration of this
system can be found at http://cvn.ecp.fr/ubernet/.
Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs
Authors: Andrew J. Reagan, Brian Tivnan, Jake Ryland Williams, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2015-12-02 00:00:00
Tasks: Benchmarking, Sentiment Analysis

The emergence and global adoption of social media has rendered possible the
real-time estimation of population-scale sentiment, bearing profound
implications for our understanding of human behavior. Given the growing
assortment of sentiment measuring instruments, comparisons between them are
evidently required. Here, we perform detailed tests of 6 dictionary-based
methods applied to 4 different corpora, and briefly examine a further 20
methods. We show that a dictionary-based method will only perform both reliably
and meaningfully if (1) the dictionary covers a sufficiently large enough
portion of a given text's lexicon when weighted by word usage frequency; and
(2) words are scored on a continuous scale.
Asymptotic Convergence in Online Learning with Unbounded Delays
Authors: Scott Garrabrant, Nate Soares, Jessica Taylor
Date: 2016-04-18 00:00:00

We study the problem of predicting the results of computations that are too
expensive to run, via the observation of the results of smaller computations.
We model this as an online learning problem with delayed feedback, where the
length of the delay is unbounded, which we study mainly in a stochastic
setting. We show that in this setting, consistency is not possible in general,
and that optimal forecasters might not have average regret going to zero.
However, it is still possible to give algorithms that converge asymptotically
to Bayes-optimal predictions, by evaluating forecasters on specific sparse
independent subsequences of their predictions. We give an algorithm that does
this, which converges asymptotically on good behavior, and give very weak
bounds on how long it takes to converge. We then relate our results back to the
problem of predicting large computations in a deterministic setting.