pwc-archive/papers-with-abstracts huggingface dataset:
Non-Adaptive Policies for 20 Questions Target Localization
Authors: Ehsan Variani, Kamel Lahouel, Avner Bar-Hen, Bruno Jedynak
Date: 2015-04-22 00:00:00

The problem of target localization with noise is addressed. The target is a
sample from a continuous random variable with known distribution and the goal
is to locate it with minimum mean squared error distortion. The localization
scheme or policy proceeds by queries, or questions, weather or not the target
belongs to some subset as it is addressed in the 20-question framework. These
subsets are not constrained to be intervals and the answers to the queries are
noisy. While this situation is well studied for adaptive querying, this paper
is focused on the non adaptive querying policies based on dyadic questions. The
asymptotic minimum achievable distortion under such policies is derived.
Furthermore, a policy named the Aurelian1 is exhibited which achieves
asymptotically this distortion.
Monotonous (Semi-)Nonnegative Matrix Factorization
Authors: Nirav Bhatt, Arun Ayyar
Date: 2015-05-01 00:00:00

Nonnegative matrix factorization (NMF) factorizes a non-negative matrix into
product of two non-negative matrices, namely a signal matrix and a mixing
matrix. NMF suffers from the scale and ordering ambiguities. Often, the source
signals can be monotonous in nature. For example, in source separation problem,
the source signals can be monotonously increasing or decreasing while the
mixing matrix can have nonnegative entries. NMF methods may not be effective
for such cases as it suffers from the ordering ambiguity. This paper proposes
an approach to incorporate notion of monotonicity in NMF, labeled as monotonous
NMF. An algorithm based on alternating least-squares is proposed for recovering
monotonous signals from a data matrix. Further, the assumption on mixing matrix
is relaxed to extend monotonous NMF for data matrix with real numbers as
entries. The approach is illustrated using synthetic noisy data. The results
obtained by monotonous NMF are compared with standard NMF algorithms in the
literature, and it is shown that monotonous NMF estimates source signals well
in comparison to standard NMF algorithms when the underlying sources signals
are monotonous.
Automatic Observer Script for StarCraft: Brood War Bot Games (technical report)
Authors: Björn Persson Mattsson, Tomáš Vajda, Michal Čertický
Date: 2015-05-01 00:00:00
Tasks: Starcraft

This short report describes an automated BWAPI-based script developed for
live streams of a StarCraft Brood War bot tournament, SSCAIT. The script
controls the in-game camera in order to follow the relevant events and improve
the viewer experience. We enumerate its novel features and provide a few
implementation notes.
Grounded Discovery of Coordinate Term Relationships between Software Entities
Authors: Dana Movshovitz-Attias, William W. Cohen
Date: 2015-05-01 00:00:00

We present an approach for the detection of coordinate-term relationships
between entities from the software domain, that refer to Java classes. Usually,
relations are found by examining corpus statistics associated with text
entities. In some technical domains, however, we have access to additional
information about the real-world objects named by the entities, suggesting that
coupling information about the "grounded" entities with corpus statistics might
lead to improved methods for relation discovery. To this end, we develop a
similarity measure for Java classes using distributional information about how
they are used in software, which we combine with corpus statistics on the
distribution of contexts in which the classes appear in text. Using our
approach, cross-validation accuracy on this dataset can be improved
dramatically, from around 60% to 88%. Human labeling results show that our
classifier has an F1 score of 86% over the top 1000 predicted pairs.
Joint Object and Part Segmentation using Deep Learned Potentials
Authors: Peng Wang, Xiaohui Shen, Zhe Lin, Scott Cohen, Brian Price, Alan Yuille
Date: 2015-05-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, Segmentation, Semantic Segmentation

Segmenting semantic objects from images and parsing them into their
respective semantic parts are fundamental steps towards detailed object
understanding in computer vision. In this paper, we propose a joint solution
that tackles semantic object and part segmentation simultaneously, in which
higher object-level context is provided to guide part segmentation, and more
detailed part-level localization is utilized to refine object segmentation.
Specifically, we first introduce the concept of semantic compositional parts
(SCP) in which similar semantic parts are grouped and shared among different
objects. A two-channel fully convolutional network (FCN) is then trained to
provide the SCP and object potentials at each pixel. At the same time, a
compact set of segments can also be obtained from the SCP predictions of the
network. Given the potentials and the generated segments, in order to explore
long-range context, we finally construct an efficient fully connected
conditional random field (FCRF) to jointly predict the final object and part
labels. Extensive evaluation on three different datasets shows that our
approach can mutually enhance the performance of object and part segmentation,
and outperforms the current state-of-the-art on both tasks.
Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph
Authors: Aleksandar Zlateski, H. Sebastian Seung
Date: 2015-05-01 00:00:00
Tasks: Clustering, Image Segmentation, Semantic Segmentation

We present a method for hierarchical image segmentation that defines a
disaffinity graph on the image, over-segments it into watershed basins, defines
a new graph on the basins, and then merges basins with a modified,
size-dependent version of single linkage clustering. The quasilinear runtime of
the method makes it suitable for segmenting large images. We illustrate the
method on the challenging problem of segmenting 3D electron microscopic brain
images.
Probabilistic Depth Image Registration incorporating Nonvisual Information
Authors: Manuel Wüthrich, Peter Pastor, Ludovic Righetti, Aude Billard, Stefan Schaal
Date: 2015-04-29 00:00:00
Tasks: Image Registration, Object

In this paper, we derive a probabilistic registration algorithm for object
modeling and tracking. In many robotics applications, such as manipulation
tasks, nonvisual information about the movement of the object is available,
which we will combine with the visual information. Furthermore we do not only
consider observations of the object, but we also take space into account which
has been observed to not be part of the object. Furthermore we are computing a
posterior distribution over the relative alignment and not a point estimate as
typically done in for example Iterative Closest Point (ICP). To our knowledge
no existing algorithm meets these three conditions and we thus derive a novel
registration algorithm in a Bayesian framework. Experimental results suggest
that the proposed methods perform favorably in comparison to PCL
implementations of feature mapping and ICP, especially if nonvisual information
is available.
Volumetric Bias in Segmentation and Reconstruction: Secrets and Solutions
Authors: Yuri Boykov, Hossam Isack, Carl Olsson, Ismail Ben Ayed
Date: 2015-05-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Segmentation

Many standard optimization methods for segmentation and reconstruction
compute ML model estimates for appearance or geometry of segments, e.g.
Zhu-Yuille 1996, Torr 1998, Chan-Vese 2001, GrabCut 2004, Delong et al. 2012.
We observe that the standard likelihood term in these formulations corresponds
to a generalized probabilistic K-means energy. In learning it is well known
that this energy has a strong bias to clusters of equal size, which can be
expressed as a penalty for KL divergence from a uniform distribution of
cardinalities. However, this volumetric bias has been mostly ignored in
computer vision. We demonstrate significant artifacts in standard segmentation
and reconstruction methods due to this bias. Moreover, we propose binary and
multi-label optimization techniques that either (a) remove this bias or (b)
replace it by a KL divergence term for any given target volume distribution.
Our general ideas apply to many continuous or discrete energy formulations in
segmentation, stereo, and other reconstruction problems.
Topic Extraction and Bundling of Related Scientific Articles
Authors: Shameem A Puthiya Parambath
Date: 2012-12-21 00:00:00
Tasks: Articles, Clustering, Information Retrieval, Retrieval

Automatic classification of scientific articles based on common
characteristics is an interesting problem with many applications in digital
library and information retrieval systems. Properly organized articles can be
useful for automatic generation of taxonomies in scientific writings, textual
summarization, efficient information retrieval etc. Generating article bundles
from a large number of input articles, based on the associated features of the
articles is tedious and computationally expensive task. In this report we
propose an automatic two-step approach for topic extraction and bundling of
related articles from a set of scientific articles in real-time. For topic
extraction, we make use of Latent Dirichlet Allocation (LDA) topic modeling
techniques and for bundling, we make use of hierarchical agglomerative
clustering techniques.
  We run experiments to validate our bundling semantics and compare it with
existing models in use. We make use of an online crowdsourcing marketplace
provided by Amazon called Amazon Mechanical Turk to carry out experiments. We
explain our experimental setup and empirical results in detail and show that
our method is advantageous over existing ones.
Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes
Authors: Heike Benninghoff, Harald Garcke
Date: 2015-05-01 00:00:00
Tasks: Image Restoration, Image Segmentation, Segmentation, Semantic Segmentation

In this article, a new method for segmentation and restoration of images on
two-dimensional surfaces is given. Active contour models for image segmentation
are extended to images on surfaces. The evolving curves on the surfaces are
mathematically described using a parametric approach. For image restoration, a
diffusion equation with Neumann boundary conditions is solved in a
postprocessing step in the individual regions. Numerical schemes are presented
which allow to efficiently compute segmentations and denoised versions of
images on surfaces. Also topology changes of the evolving curves are detected
and performed using a fast sub-routine. Finally, several experiments are
presented where the developed methods are applied on different artificial and
real images defined on different surfaces.
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
Authors: Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, Roberto Cipolla
Date: 2015-05-01 00:00:00
Tasks: Scene Understanding, Segmentation, Semantic Segmentation

We are interested in automatic scene understanding from geometric cues. To
this end, we aim to bring semantic segmentation in the loop of real-time
reconstruction. Our semantic segmentation is built on a deep autoencoder stack
trained exclusively on synthetic depth data generated from our novel 3D scene
library, SynthCam3D. Importantly, our network is able to segment real world
scenes without any noise modelling. We present encouraging preliminary results.
A Modification of the Halpern-Pearl Definition of Causality
Authors: Joseph Y. Halpern
Date: 2015-05-01 00:00:00

The original Halpern-Pearl definition of causality [Halpern and Pearl, 2001]
was updated in the journal version of the paper [Halpern and Pearl, 2005] to
deal with some problems pointed out by Hopkins and Pearl [2003]. Here the
definition is modified yet again, in a way that (a) leads to a simpler
definition, (b) handles the problems pointed out by Hopkins and Pearl, and many
others, (c) gives reasonable answers (that agree with those of the original and
updated definition) in the standard problematic examples of causality, and (d)
has lower complexity than either the original or updated definitions.
Embedding Semantic Relations into Word Representations
Authors: Danushka Bollegala, Takanori Maehara, Ken-ichi Kawarabayashi
Date: 2015-05-01 00:00:00
Tasks: Relation Classification

Learning representations for semantic relations is important for various
tasks such as analogy detection, relational search, and relation
classification. Although there have been several proposals for learning
representations for individual words, learning word representations that
explicitly capture the semantic relations between words remains under
developed. We propose an unsupervised method for learning vector
representations for words such that the learnt representations are sensitive to
the semantic relations that exist between two words. First, we extract lexical
patterns from the co-occurrence contexts of two words in a corpus to represent
the semantic relations that exist between those two words. Second, we represent
a lexical pattern as the weighted sum of the representations of the words that
co-occur with that lexical pattern. Third, we train a binary classifier to
detect relationally similar vs. non-similar lexical pattern pairs. The proposed
method is unsupervised in the sense that the lexical pattern pairs we use as
train data are automatically sampled from a corpus, without requiring any
manual intervention. Our proposed method statistically significantly
outperforms the current state-of-the-art word representations on three
benchmark datasets for proportional analogy detection, demonstrating its
ability to accurately capture the semantic relations among words.
Thompson Sampling for Budgeted Multi-armed Bandits
Authors: Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, Tie-Yan Liu
Date: 2015-05-01 00:00:00
Tasks: Multi-Armed Bandits, Thompson Sampling

Thompson sampling is one of the earliest randomized algorithms for
multi-armed bandits (MAB). In this paper, we extend the Thompson sampling to
Budgeted MAB, where there is random cost for pulling an arm and the total cost
is constrained by a budget. We start with the case of Bernoulli bandits, in
which the random rewards (costs) of an arm are independently sampled from a
Bernoulli distribution. To implement the Thompson sampling algorithm in this
case, at each round, we sample two numbers from the posterior distributions of
the reward and cost for each arm, obtain their ratio, select the arm with the
maximum ratio, and then update the posterior distributions. We prove that the
distribution-dependent regret bound of this algorithm is $O(\ln B)$, where $B$
denotes the budget. By introducing a Bernoulli trial, we further extend this
algorithm to the setting that the rewards (costs) are drawn from general
distributions, and prove that its regret bound remains almost the same. Our
simulation results demonstrate the effectiveness of the proposed algorithm.
Quality Control in Crowdsourced Object Segmentation
Authors: Ferran Cabezas, Axel Carlier, Amaia Salvador, Xavier Giró-i-Nieto, Vincent Charvillat
Date: 2015-05-01 00:00:00
Tasks: Interactive Segmentation, Object, Segmentation, Semantic Segmentation

This paper explores processing techniques to deal with noisy data in
crowdsourced object segmentation tasks. We use the data collected with
"Click'n'Cut", an online interactive segmentation tool, and we perform several
experiments towards improving the segmentation results. First, we introduce
different superpixel-based techniques to filter users' traces, and assess their
impact on the segmentation result. Second, we present different criteria to
detect and discard the traces from potential bad users, resulting in a
remarkable increase in performance. Finally, we show a novel superpixel-based
segmentation algorithm which does not require any prior filtering and is based
on weighting each user's contribution according to his/her level of expertise.
Word Representations via Gaussian Embedding
Authors: Luke Vilnis, Andrew McCallum
Date: 2014-12-20 00:00:00

Current work in lexical distributed representations maps each word to a point
vector in low-dimensional space. Mapping instead to a density provides many
interesting advantages, including better capturing uncertainty about a
representation and its relationships, expressing asymmetries more naturally
than dot product or cosine similarity, and enabling more expressive
parameterization of decision boundaries. This paper advocates for density-based
distributed embeddings and presents a method for learning representations in
the space of Gaussian distributions. We compare performance on various word
embedding benchmarks, investigate the ability of these embeddings to model
entailment and other asymmetric relationships, and explore novel properties of
the representation.
Compositional Distributional Semantics with Compact Closed Categories and Frobenius Algebras
Authors: Dimitri Kartsaklis
Date: 2015-05-01 00:00:00

This thesis contributes to ongoing research related to the categorical
compositional model for natural language of Coecke, Sadrzadeh and Clark in
three ways: Firstly, I propose a concrete instantiation of the abstract
framework based on Frobenius algebras (joint work with Sadrzadeh). The theory
improves shortcomings of previous proposals, extends the coverage of the
language, and is supported by experimental work that improves existing results.
The proposed framework describes a new class of compositional models that find
intuitive interpretations for a number of linguistic phenomena. Secondly, I
propose and evaluate in practice a new compositional methodology which
explicitly deals with the different levels of lexical ambiguity (joint work
with Pulman). A concrete algorithm is presented, based on the separation of
vector disambiguation from composition in an explicit prior step. Extensive
experimental work shows that the proposed methodology indeed results in more
accurate composite representations for the framework of Coecke et al. in
particular and every other class of compositional models in general. As a last
contribution, I formalize the explicit treatment of lexical ambiguity in the
context of the categorical framework by resorting to categorical quantum
mechanics (joint work with Coecke). In the proposed extension, the concept of a
distributional vector is replaced with that of a density matrix, which
compactly represents a probability distribution over the potential different
meanings of the specific word. Composition takes the form of quantum
measurements, leading to interesting analogies between quantum physics and
linguistics.
The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs
Authors: Hongping Cai, Qi Wu, Tadeo Corradi, Peter Hall
Date: 2015-05-01 00:00:00
Tasks: Deep Learning, Domain Adaptation

The cross-depiction problem is that of recognising visual objects regardless
of whether they are photographed, painted, drawn, etc. It is a potentially
significant yet under-researched problem. Emulating the remarkable human
ability to recognise objects in an astonishingly wide variety of depictive
forms is likely to advance both the foundations and the applications of
Computer Vision.
  In this paper we benchmark classification, domain adaptation, and deep
learning methods; demonstrating that none perform consistently well in the
cross-depiction problem. Given the current interest in deep learning, the fact
such methods exhibit the same behaviour as all but one other method: they show
a significant fall in performance over inhomogeneous databases compared to
their peak performance, which is always over data comprising photographs only.
Rather, we find the methods that have strong models of spatial relations
between parts tend to be more robust and therefore conclude that such
information is important in modelling object classes regardless of appearance
details.
A Cooperative Framework for Fireworks Algorithm
Authors: Shaoqiu Zheng, Junzhi Li, Andreas Janecek, Ying Tan
Date: 2015-05-01 00:00:00

This paper presents a cooperative framework for fireworks algorithm (CoFFWA).
A detailed analysis of existing fireworks algorithm (FWA) and its recently
developed variants has revealed that (i) the selection strategy lead to the
contribution of the firework with the best fitness (core firework) for the
optimization overwhelms the contributions of the rest of fireworks (non-core
fireworks) in the explosion operator, (ii) the Gaussian mutation operator is
not as effective as it is designed to be. To overcome these limitations, the
CoFFWA is proposed, which can greatly enhance the exploitation ability of
non-core fireworks by using independent selection operator and increase the
exploration capacity by crowdness-avoiding cooperative strategy among the
fireworks. Experimental results on the CEC2013 benchmark functions suggest that
CoFFWA outperforms the state-of-the-art FWA variants, artificial bee colony,
differential evolution, the standard particle swarm optimization (SPSO) in 2007
and the most recent SPSO in 2011 in term of convergence performance.
Overlapping and Non-overlapping Camera Layouts for Robot Pose Estimation
Authors: Mohammad Ehab Ragab
Date: 2015-04-30 00:00:00
Tasks: Pose Estimation, Robot Pose Estimation

We study the use of overlapping and non-overlapping camera layouts in
estimating the ego-motion of a moving robot. To estimate the location and
orientation of the robot, we investigate using four cameras as non-overlapping
individuals, and as two stereo pairs. The pros and cons of the two approaches
are elucidated. The cameras work independently and can have larger field of
view in the non-overlapping layout. However, a scale factor ambiguity should be
dealt with. On the other hand, stereo systems provide more accuracy but require
establishing feature correspondence with more computational demand. For both
approaches, the extended Kalman filter is used as a real-time recursive
estimator. The approaches studied are verified with synthetic and real
experiments alike.
A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching
Authors: Quynh Nguyen, Antoine Gautier, Matthias Hein
Date: 2015-04-29 00:00:00
Proceeding: CVPR 2015 6
Tasks: Graph Matching, Hypergraph Matching, Second-order methods

The estimation of correspondences between two images resp. point sets is a
core problem in computer vision. One way to formulate the problem is graph
matching leading to the quadratic assignment problem which is NP-hard. Several
so called second order methods have been proposed to solve this problem. In
recent years hypergraph matching leading to a third order problem became
popular as it allows for better integration of geometric information. For most
of these third order algorithms no theoretical guarantees are known. In this
paper we propose a general framework for tensor block coordinate ascent methods
for hypergraph matching. We propose two algorithms which both come along with
the guarantee of monotonic ascent in the matching score on the set of discrete
assignment matrices. In the experiments we show that our new algorithms
outperform previous work both in terms of achieving better matching scores and
matching accuracy. This holds in particular for very challenging settings where
one has a high number of outliers and other forms of noise.