pwc-archive/papers-with-abstracts huggingface dataset:
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling
Authors: Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
Date: 2015-05-22 00:00:00
Tasks: Computational Efficiency, Organ Segmentation, Pancreas Segmentation, Segmentation, Specificity, Superpixels

Robust automated organ segmentation is a prerequisite for computer-aided
diagnosis (CAD), quantitative imaging analysis and surgical assistance. For
high-variability organs such as the pancreas, previous approaches report
undesirably low accuracies. We present a bottom-up approach for pancreas
segmentation in abdominal CT scans that is based on a hierarchy of information
propagation by classifying image patches at different resolutions; and
cascading superpixels. There are four stages: 1) decomposing CT slice images as
a set of disjoint boundary-preserving superpixels; 2) computing pancreas class
probability maps via dense patch labeling; 3) classifying superpixels by
pooling both intensity and probability features to form empirical statistics in
cascaded random forest frameworks; and 4) simple connectivity based
post-processing. The dense image patch labeling are conducted by: efficient
random forest classifier on image histogram, location and texture features; and
more expensive (but with better specificity) deep convolutional neural network
classification on larger image windows (with more spatial contexts). Evaluation
of the approach is performed on a database of 80 manually segmented CT volumes
in six-fold cross-validation (CV). Our achieved results are comparable, or
better than the state-of-the-art methods (evaluated by
"leave-one-patient-out"), with Dice 70.7% and Jaccard 57.9%. The computational
efficiency has been drastically improved in the order of 6~8 minutes, comparing
with others of ~10 hours per case. Finally, we implement a multi-atlas label
fusion (MALF) approach for pancreas segmentation using the same datasets. Under
six-fold CV, our bottom-up segmentation method significantly outperforms its
MALF counterpart: (70.7 +/- 13.0%) versus (52.5 +/- 20.8%) in Dice. Deep CNN
patch labeling confidences offer more numerical stability, reflected by smaller
standard deviations.
Distributed Multi-Task Learning with Shared Representation
Authors: Jialei Wang, Mladen Kolar, Nathan Srebro
Date: 2016-03-07 00:00:00
Tasks: Multi-Task Learning

We study the problem of distributed multi-task learning with shared
representation, where each machine aims to learn a separate, but related, task
in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix
has low rank. We consider a setting where each task is handled by a different
machine, with samples for the task available locally on the machine, and study
communication-efficient methods for exploiting the shared structure.
Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
Authors: Dimitrios Tzionas, Luca Ballan, Abhilash Srikantha, Pablo Aponte, Marc Pollefeys, Juergen Gall
Date: 2015-06-06 00:00:00

Hand motion capture is a popular research field, recently gaining more
attention due to the ubiquity of RGB-D sensors. However, even most recent
approaches focus on the case of a single isolated hand. In this work, we focus
on hands that interact with other hands or objects and present a framework that
successfully captures motion in such interaction scenarios for both rigid and
articulated objects. Our framework combines a generative model with
discriminatively trained salient points to achieve a low tracking error and
with collision detection and physics simulation to achieve physically plausible
estimates even in case of occlusions and missing visual data. Since all
components are unified in a single objective function which is almost
everywhere differentiable, it can be optimized with standard optimization
techniques. Our approach works for monocular RGB-D sequences as well as setups
with multiple synchronized RGB cameras. For a qualitative and quantitative
evaluation, we captured 29 sequences with a large variety of interactions and
up to 150 degrees of freedom.
Communicating Semantics: Reference by Description
Authors: Ramanathan V. Guha, Vineet Gupta
Date: 2015-11-19 00:00:00
Tasks: Descriptive

Messages often refer to entities such as people, places and events. Correct
identification of the intended reference is an essential part of communication.
Lack of shared unique names often complicates entity reference. Shared
knowledge can be used to construct uniquely identifying descriptive references
for entities with ambiguous names. We introduce a mathematical model for
`Reference by Description', derive results on the conditions under which, with
high probability, programs can construct unambiguous references to most
entities in the domain of discourse and provide empirical validation of these
results.
Learning a Discriminative Null Space for Person Re-identification
Authors: Li Zhang, Tao Xiang, Shaogang Gong
Date: 2016-03-07 00:00:00
Proceeding: CVPR 2016 6
Tasks: Dimensionality Reduction, Metric Learning, Person Re-Identification

Most existing person re-identification (re-id) methods focus on learning the
optimal distance metrics across camera views. Typically a person's appearance
is represented using features of thousands of dimensions, whilst only hundreds
of training samples are available due to the difficulties in collecting matched
training images. With the number of training samples much smaller than the
feature dimension, the existing methods thus face the classic small sample size
(SSS) problem and have to resort to dimensionality reduction techniques and/or
matrix regularisation, which lead to loss of discriminative power. In this
work, we propose to overcome the SSS problem in re-id distance metric learning
by matching people in a discriminative null space of the training data. In this
null space, images of the same person are collapsed into a single point thus
minimising the within-class scatter to the extreme and maximising the relative
between-class separation simultaneously. Importantly, it has a fixed dimension,
a closed-form solution and is very efficient to compute. Extensive experiments
carried out on five person re-identification benchmarks including VIPeR,
PRID2011, CUHK01, CUHK03 and Market1501 show that such a simple approach beats
the state-of-the-art alternatives, often by a big margin.
Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval
Authors: Yubao Sun, Renlong Hang, Qingshan Liu, Fuping Zhu, Hucheng Pei
Date: 2016-02-22 00:00:00
Tasks: regression, Retrieval

In this paper, we propose a novel data-driven regression model for aerosol
optical depth (AOD) retrieval. First, we adopt a low rank representation (LRR)
model to learn a powerful representation of the spectral response. Then, graph
regularization is incorporated into the LRR model to capture the local
structure information and the nonlinear property of the remote-sensing data.
Since it is easy to acquire the rich satellite-retrieval results, we use them
as a baseline to construct the graph. Finally, the learned feature
representation is feeded into support vector machine (SVM) to retrieve AOD.
Experiments are conducted on two widely used data sets acquired by different
sensors, and the experimental results show that the proposed method can achieve
superior performance compared to the physical models and other state-of-the-art
empirical models.
Freshman or Fresher? Quantifying the Geographic Variation of Internet Language
Authors: Vivek Kulkarni, Bryan Perozzi, Steven Skiena
Date: 2015-10-22 00:00:00

We present a new computational technique to detect and analyze statistically
significant geographic variation in language. Our meta-analysis approach
captures statistical properties of word usage across geographical regions and
uses statistical methods to identify significant changes specific to regions.
While previous approaches have primarily focused on lexical variation between
regions, our method identifies words that demonstrate semantic and syntactic
variation as well.
  We extend recently developed techniques for neural language models to learn
word representations which capture differing semantics across geographical
regions. In order to quantify this variation and ensure robust detection of
true regional differences, we formulate a null model to determine whether
observed changes are statistically significant. Our method is the first such
approach to explicitly account for random variation due to chance while
detecting regional variation in word meaning.
  To validate our model, we study and analyze two different massive online data
sets: millions of tweets from Twitter spanning not only four different
countries but also fifty states, as well as millions of phrases contained in
the Google Book Ngrams. Our analysis reveals interesting facets of language
change at multiple scales of geographic resolution -- from neighboring states
to distant continents.
  Finally, using our model, we propose a measure of semantic distance between
languages. Our analysis of British and American English over a period of 100
years reveals that semantic variation between these dialects is shrinking.
Visual object tracking performance measures revisited
Authors: Luka Čehovin, Aleš Leonardis, Matej Kristan
Date: 2015-02-20 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking, Visual Tracking

The problem of visual tracking evaluation is sporting a large variety of
performance measures, and largely suffers from lack of consensus about which
measures should be used in experiments. This makes the cross-paper tracker
comparison difficult. Furthermore, as some measures may be less effective than
others, the tracking results may be skewed or biased towards particular
tracking aspects. In this paper we revisit the popular performance measures and
tracker performance visualizations and analyze them theoretically and
experimentally. We show that several measures are equivalent from the point of
information they provide for tracker comparison and, crucially, that some are
more brittle than the others. Based on our analysis we narrow down the set of
potential measures to only two complementary ones, describing accuracy and
robustness, thus pushing towards homogenization of the tracker evaluation
methodology. These two measures can be intuitively interpreted and visualized
and have been employed by the recent Visual Object Tracking (VOT) challenges as
the foundation for the evaluation methodology.
Learning Deep Generative Models with Doubly Stochastic MCMC
Authors: Chao Du, Jun Zhu, Bo Zhang
Date: 2015-06-15 00:00:00
Tasks: Bayesian Inference, Density Estimation, Imputation

We present doubly stochastic gradient MCMC, a simple and generic method for
(approximate) Bayesian inference of deep generative models (DGMs) in a
collapsed continuous parameter space. At each MCMC sampling step, the algorithm
randomly draws a mini-batch of data samples to estimate the gradient of
log-posterior and further estimates the intractable expectation over hidden
variables via a neural adaptive importance sampler, where the proposal
distribution is parameterized by a deep neural network and learnt jointly. We
demonstrate the effectiveness on learning various DGMs in a wide range of
tasks, including density estimation, data generation and missing data
imputation. Our method outperforms many state-of-the-art competitors.
Learning Shared Representations in Multi-task Reinforcement Learning
Authors: Diana Borsa, Thore Graepel, John Shawe-Taylor
Date: 2016-03-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We investigate a paradigm in multi-task reinforcement learning (MT-RL) in
which an agent is placed in an environment and needs to learn to perform a
series of tasks, within this space. Since the environment does not change,
there is potentially a lot of common ground amongst tasks and learning to solve
them individually seems extremely wasteful. In this paper, we explicitly model
and learn this shared structure as it arises in the state-action value space.
We will show how one can jointly learn optimal value-functions by modifying the
popular Value-Iteration and Policy-Iteration procedures to accommodate this
shared representation assumption and leverage the power of multi-task
supervised learning. Finally, we demonstrate that the proposed model and
training procedures, are able to infer good value functions, even under low
samples regimes. In addition to data efficiency, we will show in our analysis,
that learning abstractions of the state space jointly across tasks leads to
more robust, transferable representations with the potential for better
generalization. this shared representation assumption and leverage the power of
multi-task supervised learning. Finally, we demonstrate that the proposed model
and training procedures, are able to infer good value functions, even under low
samples regimes. In addition to data efficiency, we will show in our analysis,
that learning abstractions of the state space jointly across tasks leads to
more robust, transferable representations with the potential for better
generalization.
Unscented Bayesian Optimization for Safe Robot Grasping
Authors: José Nogueira, Ruben Martinez-Cantin, Alexandre Bernardino, Lorenzo Jamone
Date: 2016-03-07 00:00:00
Tasks: Bayesian Optimization

We address the robot grasp optimization problem of unknown objects
considering uncertainty in the input space. Grasping unknown objects can be
achieved by using a trial and error exploration strategy. Bayesian optimization
is a sample efficient optimization algorithm that is especially suitable for
this setups as it actively reduces the number of trials for learning about the
function to optimize. In fact, this active object exploration is the same
strategy that infants do to learn optimal grasps. One problem that arises while
learning grasping policies is that some configurations of grasp parameters may
be very sensitive to error in the relative pose between the object and robot
end-effector. We call these configurations unsafe because small errors during
grasp execution may turn good grasps into bad grasps. Therefore, to reduce the
risk of grasp failure, grasps should be planned in safe areas. We propose a new
algorithm, Unscented Bayesian optimization that is able to perform sample
efficient optimization while taking into consideration input noise to find safe
optima. The contribution of Unscented Bayesian optimization is twofold as if
provides a new decision process that drives exploration to safe regions and a
new selection procedure that chooses the optimal in terms of its safety without
extra analysis or computational cost. Both contributions are rooted on the
strong theory behind the unscented transformation, a popular nonlinear
approximation method. We show its advantages with respect to the classical
Bayesian optimization both in synthetic problems and in realistic robot grasp
simulations. The results highlights that our method achieves optimal and robust
grasping policies after few trials while the selected grasps remain in safe
regions.
Adaptive Visualisation System for Construction Building Information Models Using Saliency
Authors: Hugo Martin, Sylvain Chevallier, Eric Monacelli
Date: 2016-03-07 00:00:00

Building Information Modeling (BIM) is a recent construction process based on
a 3D model, containing every component related to the building achievement.
Architects, structure engineers, method engineers, and others participant to
the building process work on this model through the design-to-construction
cycle. The high complexity and the large amount of information included in
these models raise several issues, delaying its wide adoption in the industrial
world. One of the most important is the visualization: professionals have
difficulties to find out the relevant information for their job. Actual
solutions suffer from two limitations: the BIM models information are processed
manually and insignificant information are simply hidden, leading to
inconsistencies in the building model. This paper describes a system relying on
an ontological representation of the building information to label
automatically the building elements. Depending on the user's department, the
visualization is modified according to these labels by automatically adjusting
the colors and image properties based on a saliency model. The proposed
saliency model incorporates several adaptations to fit the specificities of
architectural images.
Differentially Private Policy Evaluation
Authors: Borja Balle, Maziar Gomrokchi, Doina Precup
Date: 2016-03-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present the first differentially private algorithms for reinforcement
learning, which apply to the task of evaluating a fixed policy. We establish
two approaches for achieving differential privacy, provide a theoretical
analysis of the privacy and utility of the two algorithms, and show promising
results on simple empirical examples.
Position paper: Towards an observer-oriented theory of shape comparison
Authors: Patrizio Frosini
Date: 2016-03-07 00:00:00
Tasks: Position

In this position paper we suggest a possible metric approach to shape
comparison that is based on a mathematical formalization of the concept of
observer, seen as a collection of suitable operators acting on a metric space
of functions. These functions represent the set of data that are accessible to
the observer, while the operators describe the way the observer elaborates the
data and enclose the invariance that he/she associates with them. We expose
this model and illustrate some theoretical reasons that justify its possible
use for shape comparison.
From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators
Authors: Paul Upchurch, Noah Snavely, Kavita Bala
Date: 2016-03-07 00:00:00

We propose a new neural network architecture for solving single-image
analogies - the generation of an entire set of stylistically similar images
from just a single input image. Solving this problem requires separating image
style from content. Our network is a modified variational autoencoder (VAE)
that supports supervised training of single-image analogies and in-network
evaluation of outputs with a structured similarity objective that captures
pixel covariances. On the challenging task of generating a 62-letter font from
a single example letter we produce images with 22.4% lower dissimilarity to the
ground truth than state-of-the-art.
A matter of words: NLP for quality evaluation of Wikipedia medical articles
Authors: Vittoria Cozza, Marinella Petrocchi, Angelo Spognardi
Date: 2016-03-07 00:00:00
Tasks: Articles, Informativeness

Automatic quality evaluation of Web information is a task with many fields of
applications and of great relevance, especially in critical domains like the
medical one. We move from the intuition that the quality of content of medical
Web documents is affected by features related with the specific domain. First,
the usage of a specific vocabulary (Domain Informativeness); then, the adoption
of specific codes (like those used in the infoboxes of Wikipedia articles) and
the type of document (e.g., historical and technical ones). In this paper, we
propose to leverage specific domain features to improve the results of the
evaluation of Wikipedia medical articles. In particular, we evaluate the
articles adopting an "actionable" model, whose features are related to the
content of the articles, so that the model can also directly suggest strategies
for improving a given article quality. We rely on Natural Language Processing
(NLP) and dictionaries-based techniques in order to extract the bio-medical
concepts in a text. We prove the effectiveness of our approach by classifying
the medical articles of the Wikipedia Medicine Portal, which have been
previously manually labeled by the Wiki Project team. The results of our
experiments confirm that, by considering domain-oriented features, it is
possible to obtain sensible improvements with respect to existing solutions,
mainly for those articles that other approaches have less correctly classified.
Other than being interesting by their own, the results call for further
research in the area of domain specific features suitable for Web data quality
assessment.
Deep convolutional neural networks for pedestrian detection
Authors: Denis Tomè, Federico Monti, Luca Baroffio, Luca Bondi, Marco Tagliasacchi, Stefano Tubaro
Date: 2015-10-13 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, Pedestrian Detection, Self-Driving Cars

Pedestrian detection is a popular research topic due to its paramount
importance for a number of applications, especially in the fields of
automotive, surveillance and robotics. Despite the significant improvements,
pedestrian detection is still an open challenge that calls for more and more
accurate algorithms. In the last few years, deep learning and in particular
convolutional neural networks emerged as the state of the art in terms of
accuracy for a number of computer vision tasks such as image classification,
object detection and segmentation, often outperforming the previous gold
standards by a large margin. In this paper, we propose a pedestrian detection
system based on deep learning, adapting a general-purpose convolutional network
to the task at hand. By thoroughly analyzing and optimizing each step of the
detection pipeline we propose an architecture that outperforms traditional
methods, achieving a task accuracy close to that of state-of-the-art
approaches, while requiring a low computational time. Finally, we tested the
system on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a
forerunner computational brain of future self-driving cars.
Brains and pseudorandom generators
Authors: Vašek Chvátal, Mark Goldsmith, Nan Yang
Date: 2013-11-26 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In a pioneering classic, Warren McCulloch and Walter Pitts proposed a model
of the central nervous system; motivated by EEG recordings of normal brain
activity, Chv\' atal and Goldsmith asked whether or not this model can be
engineered to provide pseudorandom number generators. We supply evidence
suggesting that the answer is negative.
Deep Contrast Learning for Salient Object Detection
Authors: Guanbin Li, Yizhou Yu
Date: 2016-03-07 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Salient object detection has recently witnessed substantial progress due to
powerful features extracted using deep convolutional neural networks (CNNs).
However, existing CNN-based methods operate at the patch level instead of the
pixel level. Resulting saliency maps are typically blurry, especially near the
boundary of salient objects. Furthermore, image patches are treated as
independent samples even when they are overlapping, giving rise to significant
redundancy in computation and storage. In this CVPR 2016 paper, we propose an
end-to-end deep contrast network to overcome the aforementioned limitations.
Our deep network consists of two complementary components, a pixel-level fully
convolutional stream and a segment-wise spatial pooling stream. The first
stream directly produces a saliency map with pixel-level accuracy from an input
image. The second stream extracts segment-wise features very efficiently, and
better models saliency discontinuities along object boundaries. Finally, a
fully connected CRF model can be optionally incorporated to improve spatial
coherence and contour localization in the fused result from these two streams.
Experimental results demonstrate that our deep model significantly improves the
state of the art.