pwc-archive/papers-with-abstracts huggingface dataset:
Self-expressive Dictionary Learning for Dynamic 3D Reconstruction
Authors: Enliang Zheng, Dinghuang Ji, Enrique Dunn, Jan-Michael Frahm
Date: 2016-05-22 00:00:00
Tasks: 3D Reconstruction, compressed sensing, Dictionary Learning

We target the problem of sparse 3D reconstruction of dynamic objects observed
by multiple unsynchronized video cameras with unknown temporal overlap. To this
end, we develop a framework to recover the unknown structure without sequencing
information across video sequences. Our proposed compressed sensing framework
poses the estimation of 3D structure as the problem of dictionary learning,
where the dictionary is defined as an aggregation of the temporally varying 3D
structures. Given the smooth motion of dynamic objects, we observe any element
in the dictionary can be well approximated by a sparse linear combination of
other elements in the same dictionary (i. e. self-expression). Moreover, the
sparse coefficients describing a locally linear 3D structural interpolation
reveal the local sequencing information. Our formulation optimizes a biconvex
cost function that leverages a compressed sensing formulation and enforces both
structural dependency coherence across video streams, as well as motion
smoothness across estimates from common video sources. We further analyze the
reconstructability of our approach under different capture scenarios, and its
comparison and relation to existing methods. Experimental results on large
amounts of synthetic data as well as real imagery demonstrate the effectiveness
of our approach.
Heuristics for Planning, Plan Recognition and Parsing
Authors: Miquel Ramirez, Hector Geffner
Date: 2016-05-19 00:00:00

In a recent paper, we have shown that Plan Recognition over STRIPS can be
formulated and solved using Classical Planning heuristics and algorithms. In
this work, we show that this formulation subsumes the standard formulation of
Plan Recognition over libraries through a compilation of libraries into STRIPS
theories. The libraries correspond to AND/OR graphs that may be cyclic and
where children of AND nodes may be partially ordered. These libraries include
Context-Free Grammars as a special case, where the Plan Recognition problem
becomes a parsing with missing tokens problem. Plan Recognition over the
standard libraries become Planning problems that can be easily solved by any
modern planner, while recognition over more complex libraries, including
Context-Free Grammars (CFGs), illustrate limitations of current Planning
heuristics and suggest improvements that may be relevant in other Planning
problems too.
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression
Authors: David Rim, Sina Honari, Md. Kamrul Hasan, Chris Pal
Date: 2015-12-27 00:00:00
Tasks: Emotion Recognition, Point Tracking

We present techniques for improving performance driven facial animation,
emotion recognition, and facial key-point or landmark prediction using learned
identity invariant representations. Established approaches to these problems
can work well if sufficient examples and labels for a particular identity are
available and factors of variation are highly controlled. However, labeled
examples of facial expressions, emotions and key-points for new individuals are
difficult and costly to obtain. In this paper we improve the ability of
techniques to generalize to new and unseen individuals by explicitly modeling
previously seen variations related to identity and expression. We use a
weakly-supervised approach in which identity labels are used to learn the
different factors of variation linked to identity separately from factors
related to expression. We show how probabilistic modeling of these sources of
variation allows one to learn identity-invariant representations for
expressions which can then be used to identity-normalize various procedures for
facial expression analysis and animation control. We also show how to extend
the widely used techniques of active appearance models and constrained local
models through replacing the underlying point distribution models which are
typically constructed using principal component analysis with
identity-expression factorized representations. We present a wide variety of
experiments in which we consistently improve performance on emotion
recognition, markerless performance-driven facial animation and facial
key-point tracking.
Smart broadcasting: Do you want to be seen?
Authors: Mohammad Reza Karimi, Erfan Tavakoli, Mehrdad Farajtabar, Le Song, Manuel Gomez-Rodriguez
Date: 2016-05-22 00:00:00
Tasks: Point Processes

Many users in online social networks are constantly trying to gain attention
from their followers by broadcasting posts to them. These broadcasters are
likely to gain greater attention if their posts can remain visible for a longer
period of time among their followers' most recent feeds. Then when to post? In
this paper, we study the problem of smart broadcasting using the framework of
temporal point processes, where we model users feeds and posts as discrete
events occurring in continuous time. Based on such continuous-time model, then
choosing a broadcasting strategy for a user becomes a problem of designing the
conditional intensity of her posting events. We derive a novel formula which
links this conditional intensity with the visibility of the user in her
followers' feeds. Furthermore, by exploiting this formula, we develop an
efficient convex optimization framework for the when-to-post problem. Our
method can find broadcasting strategies that reach a desired visibility level
with provable guarantees. We experimented with data gathered from Twitter, and
show that our framework can consistently make broadcasters' post more visible
than alternatives.
Dynamic Capacity Networks
Authors: Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville
Date: 2015-11-24 00:00:00

We introduce the Dynamic Capacity Network (DCN), a neural network that can
adaptively assign its capacity across different portions of the input data.
This is achieved by combining modules of two types: low-capacity sub-networks
and high-capacity sub-networks. The low-capacity sub-networks are applied
across most of the input, but also provide a guide to select a few portions of
the input on which to apply the high-capacity sub-networks. The selection is
made using a novel gradient-based attention mechanism, that efficiently
identifies input regions for which the DCN's output is most sensitive and to
which we should devote more capacity. We focus our empirical evaluation on the
Cluttered MNIST and SVHN image datasets. Our findings indicate that DCNs are
able to drastically reduce the number of computations, compared to traditional
convolutional neural networks, while maintaining similar or even better
performance.
Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint
Authors: Nikolay Savinov, Christian Haene, Lubor Ladicky, Marc Pollefeys
Date: 2016-04-11 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Reconstruction

We propose an approach for dense semantic 3D reconstruction which uses a data term that is defined as potentials over viewing rays, combined with continuous surface area penalization. Our formulation is a convex relaxation which we augment with a crucial non-convex constraint that ensures exact handling of visibility. To tackle the non-convex minimization problem, we propose a majorize-minimize type strategy which converges to a critical point. We demonstrate the benefits of using the non-convex constraint experimentally. For the geometry-only case, we set a new state of the art on two datasets of the commonly used Middlebury multi-view stereo benchmark. Moreover, our general-purpose formulation directly reconstructs thin objects, which are usually treated with specialized algorithms. A qualitative evaluation on the dense semantic 3D reconstruction task shows that we improve significantly over previous methods.
Automated Resolution Selection for Image Segmentation
Authors: Fares Al-Qunaieer, Hamid. R. Tizhoosh, Shahryar Rahnamayan
Date: 2016-05-22 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

It is well-known in image processing that computational cost increases
rapidly with the number and dimensions of the images to be processed. Several
fields, such as medical imaging, routinely use numerous very large images,
which might also be 3D and/or captured at several frequency bands, all adding
to the computational expense. Multiresolution analysis is a method of
increasing the efficiency of the segmentation process. One multiresolution
approach is the coarse-to-fine segmentation strategy, whereby the segmentation
starts at a coarse resolution and is then fine-tuned during subsequent steps.
The starting resolution for segmentation is generally selected arbitrarily with
no clear selection criteria. The research reported in this paper showed that
starting from different resolutions for image segmentation results in different
accuracies and computational times, even for images of the same category
(depicting similar scenes or objects). An automated method for resolution
selection for an input image would thus be beneficial. This paper introduces a
framework for the automated selection of the best resolution for image
segmentation. We propose a measure for defining the best resolution based on
user/system criteria, offering a trade-off between accuracy and computation
time. A learning approach is then introduced for the selection of the
resolution, whereby extracted image features are mapped to the previously
determined best resolution. In the learning process, class (i.e., resolution)
distribution is generally imbalanced, making effective learning from the data
difficult. Experiments conducted with three datasets using two different
segmentation algorithms show that the resolutions selected through learning
enable much faster segmentation than the original ones, while retaining at
least the original accuracy.
Minimizing the Maximal Loss: How and Why?
Authors: Shai Shalev-Shwartz, Yonatan Wexler
Date: 2016-02-04 00:00:00

A commonly used learning rule is to approximately minimize the \emph{average}
loss over the training set. Other learning algorithms, such as AdaBoost and
hard-SVM, aim at minimizing the \emph{maximal} loss over the training set. The
average loss is more popular, particularly in deep learning, due to three main
reasons. First, it can be conveniently minimized using online algorithms, that
process few examples at each iteration. Second, it is often argued that there
is no sense to minimize the loss on the training set too much, as it will not
be reflected in the generalization loss. Last, the maximal loss is not robust
to outliers. In this paper we describe and analyze an algorithm that can
convert any online algorithm to a minimizer of the maximal loss. We prove that
in some situations better accuracy on the training set is crucial to obtain
good performance on unseen examples. Last, we propose robust versions of the
approach that can handle outliers.
Textual Paralanguage and its Implications for Marketing Communications
Authors: Andrea Webb Luangrath, Joann Peck, Victor A. Barger
Date: 2016-05-22 00:00:00
Tasks: Marketing

Both face-to-face communication and communication in online environments
convey information beyond the actual verbal message. In a traditional
face-to-face conversation, paralanguage, or the ancillary meaning- and
emotion-laden aspects of speech that are not actual verbal prose, gives
contextual information that allows interactors to more appropriately understand
the message being conveyed. In this paper, we conceptualize textual
paralanguage (TPL), which we define as written manifestations of nonverbal
audible, tactile, and visual elements that supplement or replace written
language and that can be expressed through words, symbols, images, punctuation,
demarcations, or any combination of these elements. We develop a typology of
textual paralanguage using data from Twitter, Facebook, and Instagram. We
present a conceptual framework of antecedents and consequences of brands' use
of textual paralanguage. Implications for theory and practice are discussed.
openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit
Authors: Maximilian Schmitt, Björn W. Schuller
Date: 2016-05-22 00:00:00
Tasks: Document Classification, Emotion Recognition, General Classification, Sentiment Analysis

We introduce openXBOW, an open-source toolkit for the generation of
bag-of-words (BoW) representations from multimodal input. In the BoW principle,
word histograms were first used as features in document classification, but the
idea was and can easily be adapted to, e.g., acoustic or visual low-level
descriptors, introducing a prior step of vector quantisation. The openXBOW
toolkit supports arbitrary numeric input features and text input and
concatenates computed subbags to a final bag. It provides a variety of
extensions and options. To our knowledge, openXBOW is the first publicly
available toolkit for the generation of crossmodal bags-of-words. The
capabilities of the tool are exemplified in two sample scenarios:
time-continuous speech-based emotion recognition and sentiment analysis in
tweets where improved results over other feature representation forms were
observed.
Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources
Authors: Huynh Van Luong, Jürgen Seiler, André Kaup, Søren Forchhammer
Date: 2016-05-22 00:00:00
Tasks: Compressive Sensing

This work considers reconstructing a target signal in a context of
distributed sparse sources. We propose an efficient reconstruction algorithm
with the aid of other given sources as multiple side information (SI). The
proposed algorithm takes advantage of compressive sensing (CS) with SI and
adaptive weights by solving a proposed weighted $n$-$\ell_{1}$ minimization.
The proposed algorithm computes the adaptive weights in two levels, first each
individual intra-SI and then inter-SI weights are iteratively updated at every
reconstructed iteration. This two-level optimization leads the proposed
reconstruction algorithm with multiple SI using adaptive weights (RAMSIA) to
robustly exploit the multiple SIs with different qualities. We experimentally
perform our algorithm on generated sparse signals and also correlated feature
histograms as multiview sparse sources from a multiview image database. The
results show that RAMSIA significantly outperforms both classical CS and CS
with single SI, and RAMSIA with higher number of SIs gained more than the one
with smaller number of SIs.
Automatic Construction of Discourse Corpora for Dialogue Translation
Authors: Long-Yue Wang, Xiaojun Zhang, Zhaopeng Tu, Andy Way, Qun Liu
Date: 2016-05-22 00:00:00
Proceeding: LREC 2016 5
Tasks: Information Retrieval, Language Modeling, Language Modelling, Machine Translation, Retrieval, Translation

In this paper, a novel approach is proposed to automatically construct
parallel discourse corpus for dialogue machine translation. Firstly, the
parallel subtitle data and its corresponding monolingual movie script data are
crawled and collected from Internet. Then tags such as speaker and discourse
boundary from the script data are projected to its subtitle data via an
information retrieval approach in order to map monolingual discourse to
bilingual texts. We not only evaluate the mapping results, but also integrate
speaker information into the translation. Experiments show our proposed method
can achieve 81.79% and 98.64% accuracy on speaker and dialogue boundary
annotation, and speaker-based language model adaptation can obtain around 0.5
BLEU points improvement in translation qualities. Finally, we publicly release
around 100K parallel discourse data with manual speaker and dialogue boundary
annotation.
3D Face Tracking and Texture Fusion in the Wild
Authors: Patrik Huber, Philipp Kopp, Matthias Rätsch, William Christmas, Josef Kittler
Date: 2016-05-22 00:00:00
Tasks: 3D Face Reconstruction, Face Model, Face Reconstruction

We present a fully automatic approach to real-time 3D face reconstruction
from monocular in-the-wild videos. With the use of a cascaded-regressor based
face tracking and a 3D Morphable Face Model shape fitting, we obtain a
semi-dense 3D face shape. We further use the texture information from multiple
frames to build a holistic 3D face representation from the video frames. Our
system is able to capture facial expressions and does not require any
person-specific training. We demonstrate the robustness of our approach on the
challenging 300 Videos in the Wild (300-VW) dataset. Our real-time fitting
framework is available as an open source library at http://4dface.org.
Nonstationary Distance Metric Learning
Authors: Kristjan Greenewald, Stephen Kelley, Alfred Hero
Date: 2016-03-11 00:00:00
Tasks: Clustering, Metric Learning, Retrieval

Recent work in distance metric learning has focused on learning
transformations of data that best align with provided sets of pairwise
similarity and dissimilarity constraints. The learned transformations lead to
improved retrieval, classification, and clustering algorithms due to the better
adapted distance or similarity measures. Here, we introduce the problem of
learning these transformations when the underlying constraint generation
process is nonstationary. This nonstationarity can be due to changes in either
the ground-truth clustering used to generate constraints or changes to the
feature subspaces in which the class structure is apparent. We propose and
evaluate COMID-SADL, an adaptive, online approach for learning and tracking
optimal metrics as they change over time that is highly robust to a variety of
nonstationary behaviors in the changing metric. We demonstrate COMID-SADL on
both real and synthetic data sets and show significant performance improvements
relative to previously proposed batch and online distance metric learning
algorithms.
A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines
Authors: Wenshuo Wang, Junqiang Xi
Date: 2016-05-22 00:00:00
Tasks: Clustering

A rapid pattern-recognition approach to characterize driver's
curve-negotiating behavior is proposed. To shorten the recognition time and
improve the recognition of driving styles, a k-means clustering-based support
vector machine ( kMC-SVM) method is developed and used for classifying drivers
into two types: aggressive and moderate. First, vehicle speed and throttle
opening are treated as the feature parameters to reflect the driving styles.
Second, to discriminate driver curve-negotiating behaviors and reduce the
number of support vectors, the k-means clustering method is used to extract and
gather the two types of driving data and shorten the recognition time. Then,
based on the clustering results, a support vector machine approach is utilized
to generate the hyperplane for judging and predicting to which types the human
driver are subject. Lastly, to verify the validity of the kMC-SVM method, a
cross-validation experiment is designed and conducted. The research results
show that the $ k $MC-SVM is an effective method to classify driving styles
with a short time, compared with SVM method.
Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problem
Authors: Peng Guo, Wenming Cheng, Yi Wang
Date: 2016-05-22 00:00:00

This paper considers the two-stage capacitated facility location problem
(TSCFLP) in which products manufactured in plants are delivered to customers
via storage depots. Customer demands are satisfied subject to limited plant
production and limited depot storage capacity. The objective is to determine
the locations of plants and depots in order to minimize the total cost
including the fixed cost and transportation cost. A hybrid evolutionary
algorithm (HEA) with genetic operations and local search is proposed. To avoid
the expensive calculation of fitness of population in terms of computational
time, the HEA uses extreme machine learning to approximate the fitness of most
of the individuals. Moreover, two heuristics based on the characteristic of the
problem is incorporated to generate a good initial population.
  Computational experiments are performed on two sets of test instances from
the recent literature. The performance of the proposed algorithm is evaluated
and analyzed. Compared with the state-of-the-art genetic algorithm, the
proposed algorithm can find the optimal or near-optimal solutions in a
reasonable computational time.
Factored Temporal Sigmoid Belief Networks for Sequence Learning
Authors: Jiaming Song, Zhe Gan, Lawrence Carin
Date: 2016-05-22 00:00:00
Tasks: General Classification

Deep conditional generative models are developed to simultaneously learn the
temporal dependencies of multiple sequences. The model is designed by
introducing a three-way weight tensor to capture the multiplicative
interactions between side information and sequences. The proposed model builds
on the Temporal Sigmoid Belief Network (TSBN), a sequential stack of Sigmoid
Belief Networks (SBNs). The transition matrices are further factored to reduce
the number of parameters and improve generalization. When side information is
not available, a general framework for semi-supervised learning based on the
proposed model is constituted, allowing robust sequence classification.
Experimental results show that the proposed approach achieves state-of-the-art
predictive and classification performance on sequential data, and has the
capacity to synthesize sequences, with controlled style transitioning and
blending.
Evolutionary Demographic Algorithms
Authors: Marco AR Erra, Pedro MM Mitra, Agostinho C. Rosa
Date: 2016-05-22 00:00:00
Tasks: Diversity

Most of the problems in genetic algorithms are very complex and demand a
large amount of resources that current technology can not offer. Our purpose
was to develop a Java-JINI distributed library that implements Genetic
Algorithms with sub-populations (coarse grain) and a graphical interface in
order to configure and follow the evolution of the search. The sub-populations
are simulated/evaluated in personal computers connected trough a network,
keeping in mind different models of sub-populations, migration policies and
network topologies. We show that this model delays the convergence of the
population keeping a higher level of genetic diversity and allows a much
greater number of evaluations since they are distributed among several
computers compared with the traditional Genetic Algorithms.