pwc-archive/papers-with-abstracts huggingface dataset:
Online Object Tracking with Proposal Selection
Authors: Yang Hua, Karteek Alahari, Cordelia Schmid
Date: 2015-09-30 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, Object Tracking, Visual Object Tracking

Tracking-by-detection approaches are some of the most successful object
trackers in recent years. Their success is largely determined by the detector
model they learn initially and then update over time. However, under
challenging conditions where an object can undergo transformations, e.g.,
severe rotation, these methods are found to be lacking. In this paper, we
address this problem by formulating it as a proposal selection task and making
two contributions. The first one is introducing novel proposals estimated from
the geometric transformations undergone by the object, and building a rich
candidate set for predicting the object location. The second one is devising a
novel selection strategy using multiple cues, i.e., detection score and
edgeness score computed from state-of-the-art object edges and motion
boundaries. We extensively evaluate our approach on the visual object tracking
2014 challenge and online tracking benchmark datasets, and show the best
performance.
Polish to English Statistical Machine Translation
Authors: Krzysztof Wołk
Date: 2015-09-30 00:00:00
Tasks: Machine Translation, Translation

This research explores the effects of various training settings on a Polish
to English Statistical Machine Translation system for spoken language. Various
elements of the TED, Europarl, and OPUS parallel text corpora were used as the
basis for training of language models, for development, tuning and testing of
the translation system. The BLEU, NIST, METEOR and TER metrics were used to
evaluate the effects of the data preparations on the translation results.
Polish - English Speech Statistical Machine Translation Systems for the IWSLT 2013
Authors: Krzysztof Wołk, Krzysztof Marasek
Date: 2015-09-30 00:00:00
Tasks: Machine Translation, Translation

This research explores the effects of various training settings from Polish
to English Statistical Machine Translation system for spoken language. Various
elements of the TED parallel text corpora for the IWSLT 2013 evaluation
campaign were used as the basis for training of language models, and for
development, tuning and testing of the translation system. The BLEU, NIST,
METEOR and TER metrics were used to evaluate the effects of data preparations
on translation results. Our experiments included systems, which use stems and
morphological information on Polish words. We also conducted a deep analysis of
provided Polish data as preparatory work for the automatic data correction and
cleaning phase.
A Sentence Meaning Based Alignment Method for Parallel Text Corpora Preparation
Authors: Krzysztof Wołk, Krzysztof Marasek
Date: 2015-09-30 00:00:00
Tasks: Machine Translation, Position, Sentence, Translation

Text alignment is crucial to the accuracy of Machine Translation (MT)
systems, some NLP tools or any other text processing tasks requiring bilingual
data. This research proposes a language independent sentence alignment approach
based on Polish (not position-sensitive language) to English experiments. This
alignment approach was developed on the TED Talks corpus, but can be used for
any text domain or language pair. The proposed approach implements various
heuristics for sentence recognition. Some of them value synonyms and semantic
text structure analysis as a part of additional information. Minimization of
data loss was ensured. The solution is compared to other sentence alignment
implementations. Also an improvement in MT system score with text processed
with described tool is shown.
Real-Time Statistical Speech Translation
Authors: Krzysztof Wołk, Krzysztof Marasek
Date: 2015-09-30 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Part-Of-Speech Tagging, speech-recognition, Speech Recognition, Translation

This research investigates the Statistical Machine Translation approaches to
translate speech in real time automatically. Such systems can be used in a
pipeline with speech recognition and synthesis software in order to produce a
real-time voice communication system between foreigners. We obtained three main
data sets from spoken proceedings that represent three different types of human
speech. TED, Europarl, and OPUS parallel text corpora were used as the basis
for training of language models, for developmental tuning and testing of the
translation system. We also conducted experiments involving part of speech
tagging, compound splitting, linear language model interpolation, TrueCasing
and morphosyntactic analysis. We evaluated the effects of variety of data
preparations on the translation results using the BLEU, NIST, METEOR and TER
metrics and tried to give answer which metric is most suitable for PL-EN
language pair.
Moving Object Detection in Video Using Saliency Map and Subspace Learning
Authors: Yanwei Pang, Li Ye, Xuelong. Li, Jing Pan
Date: 2015-09-30 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection

Moving object detection is a key to intelligent video analysis. On the one
hand, what moves is not only interesting objects but also noise and cluttered
background. On the other hand, moving objects without rich texture are prone
not to be detected. So there are undesirable false alarms and missed alarms in
many algorithms of moving object detection. To reduce the false alarms and
missed alarms, in this paper, we propose to incorporate a saliency map into an
incremental subspace analysis framework where the saliency map makes estimated
background has less chance than foreground (i.e., moving objects) to contain
salient objects. The proposed objective function systematically takes account
into the properties of sparsity, low-rank, connectivity, and saliency. An
alternative minimization algorithm is proposed to seek the optimal solutions.
Experimental results on the Perception Test Images Sequences demonstrate that
the proposed method is effective in reducing false alarms and missed alarms.
Enhanced Bilingual Evaluation Understudy
Authors: Krzysztof Wołk, Krzysztof Marasek
Date: 2015-09-30 00:00:00
Tasks: Machine Translation, Translation

Our research extends the Bilingual Evaluation Understudy (BLEU) evaluation
technique for statistical machine translation to make it more adjustable and
robust. We intend to adapt it to resemble human evaluation more. We perform
experiments to evaluate the performance of our technique against the primary
existing evaluation methods. We describe and show the improvements it makes
over existing methods as well as correlation to them. When human translators
translate a text, they often use synonyms, different word orders or style, and
other similar variations. We propose an SMT evaluation technique that enhances
the BLEU metric to consider variations such as those.
Distributed Weighted Parameter Averaging for SVM Training on Big Data
Authors: Ayan Das, Sourangshu Bhattacharya
Date: 2015-09-30 00:00:00

Two popular approaches for distributed training of SVMs on big data are
parameter averaging and ADMM. Parameter averaging is efficient but suffers from
loss of accuracy with increase in number of partitions, while ADMM in the
feature space is accurate but suffers from slow convergence. In this paper, we
report a hybrid approach called weighted parameter averaging (WPA), which
optimizes the regularized hinge loss with respect to weights on parameters. The
problem is shown to be same as solving SVM in a projected space. We also
demonstrate an $O(\frac{1}{N})$ stability bound on final hypothesis given by
WPA, using novel proof techniques. Experimental results on a variety of toy and
real world datasets show that our approach is significantly more accurate than
parameter averaging for high number of partitions. It is also seen the proposed
method enjoys much faster convergence compared to ADMM in features space.
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring
Authors: Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
Date: 2015-09-30 00:00:00
Proceeding: NeurIPS 2015 12

Partial monitoring is a general model for sequential learning with limited
feedback formalized as a game between two players. In this game, the learner
chooses an action and at the same time the opponent chooses an outcome, then
the learner suffers a loss and receives a feedback signal. The goal of the
learner is to minimize the total loss. In this paper, we study partial
monitoring with finite actions and stochastic outcomes. We derive a logarithmic
distribution-dependent regret lower bound that defines the hardness of the
problem. Inspired by the DMED algorithm (Honda and Takemura, 2010) for the
multi-armed bandit problem, we propose PM-DMED, an algorithm that minimizes the
distribution-dependent regret. PM-DMED significantly outperforms
state-of-the-art algorithms in numerical experiments. To show the optimality of
PM-DMED with respect to the regret bound, we slightly modify the algorithm by
introducing a hinge function (PM-DMED-Hinge). Then, we derive an asymptotically
optimal regret upper bound of PM-DMED-Hinge that matches the lower bound.
Learning without Recall: A Case for Log-Linear Learning
Authors: Mohammad Amin Rahimian, Ali Jadbabaie
Date: 2015-09-30 00:00:00

We analyze a model of learning and belief formation in networks in which
agents follow Bayes rule yet they do not recall their history of past
observations and cannot reason about how other agents' beliefs are formed. They
do so by making rational inferences about their observations which include a
sequence of independent and identically distributed private signals as well as
the beliefs of their neighboring agents at each time. Fully rational agents
would successively apply Bayes rule to the entire history of observations. This
leads to forebodingly complex inferences due to lack of knowledge about the
global network structure that causes those observations. To address these
complexities, we consider a Learning without Recall model, which in addition to
providing a tractable framework for analyzing the behavior of rational agents
in social networks, can also provide a behavioral foundation for the variety of
non-Bayesian update rules in the literature. We present the implications of
various choices for time-varying priors of such agents and how this choice
affects learning and its rate.
Symbol Emergence in Robotics: A Survey
Authors: Tadahiro Taniguchi, Takayuki Nagai, Tomoaki Nakamura, Naoto Iwahashi, Tetsuya OGATA, Hideki Asoh
Date: 2015-09-29 00:00:00
Tasks: multimodal interaction, Survey

Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.
Light Field Reconstruction Using Shearlet Transform
Authors: Suren Vagharshakyan, Robert Bregovic, Atanas Gotchev
Date: 2015-09-29 00:00:00

In this article we develop an image based rendering technique based on light
field reconstruction from a limited set of perspective views acquired by
cameras. Our approach utilizes sparse representation of epipolar-plane images
in a directionally sensitive transform domain, obtained by an adapted discrete
shearlet transform. The used iterative thresholding algorithm provides
high-quality reconstruction results for relatively big disparities between
neighboring views. The generated densely sampled light field of a given 3D
scene is thus suitable for all applications which requires light field
reconstruction. The proposed algorithm is compared favorably against state of
the art depth image based rendering techniques.
Polish -English Statistical Machine Translation of Medical Texts
Authors: Krzysztof Wołk, Krzysztof Marasek
Date: 2015-09-29 00:00:00
Tasks: Machine Translation, POS, POS Tagging, Translation

This new research explores the effects of various training methods on a
Polish to English Statistical Machine Translation system for medical texts.
Various elements of the EMEA parallel text corpora from the OPUS project were
used as the basis for training of phrase tables and language models and for
development, tuning and testing of the translation system. The BLEU, NIST,
METEOR, RIBES and TER metrics have been used to evaluate the effects of various
system and data preparations on translation results. Our experiments included
systems that used POS tagging, factored phrase models, hierarchical models,
syntactic taggers, and many different alignment methods. We also conducted a
deep analysis of Polish data as preparatory work for automatic data correction
such as true casing and punctuation normalization phase.
Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval
Authors: Gaurav Sharma, Bernt Schiele
Date: 2015-09-29 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Retrieval, Metric Learning, Retrieval

We propose a novel algorithm for the task of supervised discriminative
distance learning by nonlinearly embedding vectors into a low dimensional
Euclidean space. We work in the challenging setting where supervision is with
constraints on similar and dissimilar pairs while training. The proposed method
is derived by an approximate kernelization of a linear Mahalanobis-like
distance metric learning algorithm and can also be seen as a kernel neural
network. The number of model parameters and test time evaluation complexity of
the proposed method are O(dD) where D is the dimensionality of the input
features and d is the dimension of the projection space - this is in contrast
to the usual kernelization methods as, unlike them, the complexity does not
scale linearly with the number of training examples. We propose a stochastic
gradient based learning algorithm which makes the method scalable (w.r.t. the
number of training examples), while being nonlinear. We train the method with
up to half a million training pairs of 4096 dimensional CNN features. We give
empirical comparisons with relevant baselines on seven challenging datasets for
the task of low dimensional semantic category based image retrieval.
A Semi-Supervised Method for Predicting Cancer Survival Using Incomplete Clinical Data
Authors: Hamid Reza Hassanzadeh, John H. Phan, May D. Wang
Date: 2015-09-29 00:00:00
Tasks: General Classification, Prediction

Prediction of survival for cancer patients is an open area of research.
However, many of these studies focus on datasets with a large number of
patients. We present a novel method that is specifically designed to address
the challenge of data scarcity, which is often the case for cancer datasets.
Our method is able to use unlabeled data to improve classification by adopting
a semi-supervised training approach to learn an ensemble classifier. The
results of applying our method to three cancer datasets show the promise of
semi-supervised learning for prediction of cancer survival.
Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs
Authors: Krzysztof Wołk, Krzysztof Marasek
Date: 2015-09-29 00:00:00
Tasks: Articles, Machine Translation, Retrieval, Sentence, Translation

Parallel sentences are a relatively scarce but extremely useful resource for
many applications including cross-lingual retrieval and statistical machine
translation. This research explores our methodology for mining such data from
previously obtained comparable corpora. The task is highly practical since
non-parallel multilingual data exist in far greater quantities than parallel
corpora, but parallel sentences are a much more useful resource. Here we
propose a web crawling method for building subject-aligned comparable corpora
from Wikipedia articles. We also introduce a method for extracting truly
parallel sentences that are filtered out from noisy or just comparable sentence
pairs. We describe our implementation of a specialized tool for this task as
well as training and adaption of a machine translation system that supplies our
filter with additional information about the similarity of comparable sentence
pairs.
Polish - English Speech Statistical Machine Translation Systems for the IWSLT 2014
Authors: Krzysztof Wołk, Krzysztof Marasek
Date: 2015-09-29 00:00:00
Tasks: LEMMA, Machine Translation, Translation

This research explores effects of various training settings between Polish
and English Statistical Machine Translation systems for spoken language.
Various elements of the TED parallel text corpora for the IWSLT 2014 evaluation
campaign were used as the basis for training of language models, and for
development, tuning and testing of the translation system as well as Wikipedia
based comparable corpora prepared by us. The BLEU, NIST, METEOR and TER metrics
were used to evaluate the effects of data preparations on translation results.
Our experiments included systems, which use lemma and morphological information
on Polish words. We also conducted a deep analysis of provided Polish data as
preparatory work for the automatic data correction and cleaning phase.
On-the-Fly Learning in a Perpetual Learning Machine
Authors: Andrew J. R. Simpson
Date: 2015-09-03 00:00:00
Tasks: BIG-bench Machine Learning

Despite the promise of brain-inspired machine learning, deep neural networks
(DNN) have frustratingly failed to bridge the deceptively large gap between
learning and memory. Here, we introduce a Perpetual Learning Machine; a new
type of DNN that is capable of brain-like dynamic 'on the fly' learning because
it exists in a self-supervised state of Perpetual Stochastic Gradient Descent.
Thus, we provide the means to unify learning and memory within a machine
learning framework. We also explore the elegant duality of abstraction and
synthesis: the Yin and Yang of deep learning.
Automatically Segmenting Oral History Transcripts
Authors: Ryan Shaw
Date: 2015-09-29 00:00:00
Tasks: Segmentation

Dividing oral histories into topically coherent segments can make them more
accessible online. People regularly make judgments about where coherent
segments can be extracted from oral histories. But making these judgments can
be taxing, so automated assistance is potentially attractive to speed the task
of extracting segments from open-ended interviews. When different people are
asked to extract coherent segments from the same oral histories, they often do
not agree about precisely where such segments begin and end. This low agreement
makes the evaluation of algorithmic segmenters challenging, but there is reason
to believe that for segmenting oral history transcripts, some approaches are
more promising than others. The BayesSeg algorithm performs slightly better
than TextTiling, while TextTiling does not perform significantly better than a
uniform segmentation. BayesSeg might be used to suggest boundaries to someone
segmenting oral histories, but this segmentation task needs to be better
defined.
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
Authors: Adriana Gonzalez, Véronique Delouille, Laurent Jacques
Date: 2014-12-19 00:00:00

Context: Characterization of instrumental effects in astronomical imaging is
important in order to extract accurate physical information from the
observations. The measured image in a real optical instrument is usually
represented by the convolution of an ideal image with a Point Spread Function
(PSF). Additionally, the image acquisition process is also contaminated by
other sources of noise (read-out, photon-counting). The problem of estimating
both the PSF and a denoised image is called blind deconvolution and is
ill-posed.
  Aims: We propose a blind deconvolution scheme that relies on image
regularization. Contrarily to most methods presented in the literature, our
method does not assume a parametric model of the PSF and can thus be applied to
any telescope.
  Methods: Our scheme uses a wavelet analysis prior model on the image and weak
assumptions on the PSF. We use observations from a celestial transit, where the
occulting body can be assumed to be a black disk. These constraints allow us to
retain meaningful solutions for the filter and the image, eliminating trivial,
translated and interchanged solutions. Under an additive Gaussian noise
assumption, they also enforce noise canceling and avoid reconstruction
artifacts by promoting the whiteness of the residual between the blurred
observations and the cleaned data.
  Results: Our method is applied to synthetic and experimental data. The PSF is
estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for
SDO/AIA using the 2012 Venus transit. Results show that the proposed
non-parametric blind deconvolution method is able to estimate the core of the
PSF with a similar quality to parametric methods proposed in the literature. We
also show that, if these parametric estimations are incorporated in the
acquisition model, the resulting PSF outperforms both the parametric and
non-parametric methods.
How to Formulate and Solve Statistical Recognition and Learning Problems
Authors: Michail Schlesinger, Evgeniy Vodolazskiy
Date: 2015-09-29 00:00:00
Tasks: Two-sample testing

We formulate problems of statistical recognition and learning in a common
framework of complex hypothesis testing. Based on arguments from multi-criteria
optimization, we identify strategies that are improper for solving these
problems and derive a common form of the remaining strategies. We show that
some widely used approaches to recognition and learning are improper in this
sense. We then propose a generalized formulation of the recognition and
learning problem which embraces the whole range of sizes of the learning
sample, including the zero size. Learning becomes a special case of recognition
without learning. We define the concept of closest to optimal strategy, being a
solution to the formulated problem, and describe a technique for finding such a
strategy. On several illustrative cases, the strategy is shown to be superior
to the widely used learning methods based on maximal likelihood estimation.