pwc-archive/papers-with-abstracts huggingface dataset:
Generalized Higher-Order Tensor Decomposition via Parallel ADMM
Authors: Fanhua Shang, Yuanyuan Liu, James Cheng
Date: 2014-07-05 00:00:00
Tasks: Computational Efficiency, Tensor Decomposition

Higher-order tensors are becoming prevalent in many scientific areas such as
computer vision, social network analysis, data mining and neuroscience.
Traditional tensor decomposition approaches face three major challenges: model
selecting, gross corruptions and computational efficiency. To address these
problems, we first propose a parallel trace norm regularized tensor
decomposition method, and formulate it as a convex optimization problem. This
method does not require the rank of each mode to be specified beforehand, and
can automatically determine the number of factors in each mode through our
optimization scheme. By considering the low-rank structure of the observed
tensor, we analyze the equivalent relationship of the trace norm between a
low-rank tensor and its core tensor. Then, we cast a non-convex tensor
decomposition model into a weighted combination of multiple much smaller-scale
matrix trace norm minimization. Finally, we develop two parallel alternating
direction methods of multipliers (ADMM) to solve our problems. Experimental
results verify that our regularized formulation is effective, and our methods
are robust to noise or outliers.
Homophilic Clustering by Locally Asymmetric Geometry
Authors: Deli Zhao, Xiaoou Tang
Date: 2014-07-05 00:00:00
Tasks: Clustering

Clustering is indispensable for data analysis in many scientific disciplines.
Detecting clusters from heavy noise remains challenging, particularly for
high-dimensional sparse data. Based on graph-theoretic framework, the present
paper proposes a novel algorithm to address this issue. The locally asymmetric
geometries of neighborhoods between data points result in a directed similarity
graph to model the structural connectivity of data points. Performing
similarity propagation on this directed graph simply by its adjacency matrix
powers leads to an interesting discovery, in the sense that if the in-degrees
are ordered by the corresponding sorted out-degrees, they will be
self-organized to be homophilic layers according to the different distributions
of cluster densities, which is dubbed the Homophilic In-degree figure (the HI
figure). With the HI figure, we can easily single out all cores of clusters,
identify the boundary between cluster and noise, and visualize the intrinsic
structures of clusters. Based on the in-degree homophily, we also develop a
simple efficient algorithm of linear space complexity to cluster noisy data.
Extensive experiments on toy and real-world scientific data validate the
effectiveness of our algorithms.
Inverse Graphics with Probabilistic CAD Models
Authors: Tejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli, Joshua B. Tenenbaum
Date: 2014-07-04 00:00:00
Tasks: 3D Human Pose Estimation, Object, Pose Estimation

Recently, multiple formulations of vision problems as probabilistic
inversions of generative models based on computer graphics have been proposed.
However, applications to 3D perception from natural images have focused on
low-dimensional latent scenes, due to challenges in both modeling and
inference. Accounting for the enormous variability in 3D object shape and 2D
appearance via realistic generative models seems intractable, as does inverting
even simple versions of the many-to-many computations that link 3D scenes to 2D
images. This paper proposes and evaluates an approach that addresses key
aspects of both these challenges. We show that it is possible to solve
challenging, real-world 3D vision problems by approximate inference in
generative models for images based on rendering the outputs of probabilistic
CAD (PCAD) programs. Our PCAD object geometry priors generate deformable 3D
meshes corresponding to plausible objects and apply affine transformations to
place them in a scene. Image likelihoods are based on similarity in a feature
space based on standard mid-level image representations from the vision
literature. Our inference algorithm integrates single-site and locally blocked
Metropolis-Hastings proposals, Hamiltonian Monte Carlo and discriminative
data-driven proposals learned from training data generated from our models. We
apply this approach to 3D human pose estimation and object shape reconstruction
from single images, achieving quantitative and qualitative performance
improvements over state-of-the-art baselines.
Calibration of Multiple Fish-Eye Cameras Using a Wand
Authors: Qiang Fu, Quan Quan, Kai-Yuan Cai
Date: 2014-07-04 00:00:00
Tasks: Camera Auto-Calibration, Camera Calibration

Fish-eye cameras are becoming increasingly popular in computer vision, but
their use for 3D measurement is limited partly due to the lack of an accurate,
efficient and user-friendly calibration procedure. For such a purpose, we
propose a method to calibrate the intrinsic and extrinsic parameters (including
radial distortion parameters) of two/multiple fish-eye cameras simultaneously
by using a wand under general motions. Thanks to the generic camera model used,
the proposed calibration method is also suitable for two/multiple conventional
cameras and mixed cameras (e.g. two conventional cameras and a fish-eye
camera). Simulation and real experiments demonstrate the effectiveness of the
proposed method. Moreover, we develop the camera calibration toolbox, which is
available online.
Weakly Supervised Action Labeling in Videos Under Ordering Constraints
Authors: Piotr Bojanowski, Rémi Lajugie, Francis Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid, Josef Sivic
Date: 2014-07-04 00:00:00

We are given a set of video clips, each one annotated with an {\em ordered}
list of actions, such as "walk" then "sit" then "answer phone" extracted from,
for example, the associated text script. We seek to temporally localize the
individual actions in each clip as well as to learn a discriminative classifier
for each action. We formulate the problem as a weakly supervised temporal
assignment with ordering constraints. Each video clip is divided into small
time intervals and each time interval of each video clip is assigned one action
label, while respecting the order in which the action labels appear in the
given annotations. We show that the action label assignment can be determined
together with learning a classifier for each action in a discriminative manner.
We evaluate the proposed model on a new and challenging dataset of 937 video
clips with a total of 787720 frames containing sequences of 16 different
actions from 69 Hollywood movies.
Improving Performance of Self-Organising Maps with Distance Metric Learning Method
Authors: Piotr Płoński, Krzysztof Zaremba
Date: 2014-07-04 00:00:00
Tasks: General Classification, Metric Learning

Self-Organising Maps (SOM) are Artificial Neural Networks used in Pattern
Recognition tasks. Their major advantage over other architectures is human
readability of a model. However, they often gain poorer accuracy. Mostly used
metric in SOM is the Euclidean distance, which is not the best approach to some
problems. In this paper, we study an impact of the metric change on the SOM's
performance in classification problems. In order to change the metric of the
SOM we applied a distance metric learning method, so-called 'Large Margin
Nearest Neighbour'. It computes the Mahalanobis matrix, which assures small
distance between nearest neighbour points from the same class and separation of
points belonging to different classes by large margin. Results are presented on
several real data sets, containing for example recognition of written digits,
spoken letters or faces.
Identifying Higher-order Combinations of Binary Features
Authors: Felipe Llinares, Mahito Sugiyama, Karsten M. Borgwardt
Date: 2014-07-04 00:00:00

Finding statistically significant interactions between binary variables is
computationally and statistically challenging in high-dimensional settings, due
to the combinatorial explosion in the number of hypotheses. Terada et al.
recently showed how to elegantly address this multiple testing problem by
excluding non-testable hypotheses. Still, it remains unclear how their approach
scales to large datasets.
  We here proposed strategies to speed up the approach by Terada et al. and
evaluate them thoroughly in 11 real-world benchmark datasets. We observe that
one approach, incremental search with early stopping, is orders of magnitude
faster than the current state-of-the-art approach.
Recognition of Isolated Words using Zernike and MFCC features for Audio Visual Speech Recognition
Authors: Prashant Bordea, Amarsinh Varpeb, Ramesh Manzac, Pravin Yannawara
Date: 2014-07-04 00:00:00
Tasks: Audio-Visual Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Visual Speech Recognition

Automatic Speech Recognition (ASR) by machine is an attractive research topic
in signal processing domain and has attracted many researchers to contribute in
this area. In recent year, there have been many advances in automatic speech
reading system with the inclusion of audio and visual speech features to
recognize words under noisy conditions. The objective of audio-visual speech
recognition system is to improve recognition accuracy. In this paper we
computed visual features using Zernike moments and audio feature using Mel
Frequency Cepstral Coefficients (MFCC) on vVISWa (Visual Vocabulary of
Independent Standard Words) dataset which contains collection of isolated set
of city names of 10 speakers. The visual features were normalized and dimension
of features set was reduced by Principal Component Analysis (PCA) in order to
recognize the isolated word utterance on PCA space.The performance of
recognition of isolated words based on visual only and audio only features
results in 63.88 and 100 respectively.
Optimizing Ranking Measures for Compact Binary Code Learning
Authors: Guosheng Lin, Chunhua Shen, Jianxin Wu
Date: 2014-07-04 00:00:00
Tasks: Image Retrieval, Information Retrieval, Retrieval

Hashing has proven a valuable tool for large-scale information retrieval.
Despite much success, existing hashing methods optimize over simple objectives
such as the reconstruction error or graph Laplacian related loss functions,
instead of the performance evaluation criteria of interest---multivariate
performance measures such as the AUC and NDCG. Here we present a general
framework (termed StructHash) that allows one to directly optimize multivariate
performance measures. The resulting optimization problem can involve
exponentially or infinitely many variables and constraints, which is more
challenging than standard structured output learning. To solve the StructHash
optimization problem, we use a combination of column generation and
cutting-plane techniques. We demonstrate the generality of StructHash by
applying it to ranking prediction and image retrieval, and show that it
outperforms a few state-of-the-art hashing methods.
A framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data
Authors: Ognjen Arandjelovic
Date: 2014-06-28 00:00:00
Tasks: Face Verification

In this paper we address the problem of matching patterns in the so-called
verification setting in which a novel, query pattern is verified against a
single training pattern: the decision sought is whether the two match (i.e.
belong to the same class) or not. Unlike previous work which has universally
focused on the development of more discriminative distance functions between
patterns, here we consider the equally important and pervasive task of
selecting a distance threshold which fits a particular operational requirement
- specifically, the target false positive rate (FPR). First, we argue on
theoretical grounds that a data-driven approach is inherently ill-conditioned
when the desired FPR is low, because by the very nature of the challenge only a
small portion of training data affects or is affected by the desired threshold.
This leads us to propose a general, statistical model-based method instead. Our
approach is based on the interpretation of an inter-pattern distance as
implicitly defining a pattern embedding which approximately distributes
patterns according to an isotropic multi-variate normal distribution in some
space. This interpretation is then used to show that the distribution of
training inter-pattern distances is the non-central chi2 distribution,
differently parameterized for each class. Thus, to make the class-specific
threshold choice we propose a novel analysis-by-synthesis iterative algorithm
which estimates the three free parameters of the model (for each class) using
task-specific constraints. The validity of the premises of our work and the
effectiveness of the proposed method are demonstrated by applying the method to
the task of set-based face verification on a large database of pseudo-random
head motion videos.
Expanding the Family of Grassmannian Kernels: An Embedding Perspective
Authors: Mehrtash T. Harandi, Mathieu Salzmann, Sadeep Jayasumana, Richard Hartley, Hongdong Li
Date: 2014-07-04 00:00:00
Tasks: Clustering

Modeling videos and image-sets as linear subspaces has proven beneficial for
many visual recognition tasks. However, it also incurs challenges arising from
the fact that linear subspaces do not obey Euclidean geometry, but lie on a
special type of Riemannian manifolds known as Grassmannian. To leverage the
techniques developed for Euclidean spaces (e.g, support vector machines) with
subspaces, several recent studies have proposed to embed the Grassmannian into
a Hilbert space by making use of a positive definite kernel. Unfortunately,
only two Grassmannian kernels are known, none of which -as we will show- is
universal, which limits their ability to approximate a target function
arbitrarily well. Here, we introduce several positive definite Grassmannian
kernels, including universal ones, and demonstrate their superiority over
previously-known kernels in various tasks, such as classification, clustering,
sparse coding and hashing.
Robust Optimization using Machine Learning for Uncertainty Sets
Authors: Theja Tulabandhula, Cynthia Rudin
Date: 2014-07-04 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Learning Theory

Our goal is to build robust optimization problems for making decisions based
on complex data from the past. In robust optimization (RO) generally, the goal
is to create a policy for decision-making that is robust to our uncertainty
about the future. In particular, we want our policy to best handle the the
worst possible situation that could arise, out of an uncertainty set of
possible situations. Classically, the uncertainty set is simply chosen by the
user, or it might be estimated in overly simplistic ways with strong
assumptions; whereas in this work, we learn the uncertainty set from data
collected in the past. The past data are drawn randomly from an (unknown)
possibly complicated high-dimensional distribution. We propose a new
uncertainty set design and show how tools from statistical learning theory can
be employed to provide probabilistic guarantees on the robustness of the
policy.
Online Submodular Maximization under a Matroid Constraint with Application to Learning Assignments
Authors: Daniel Golovin, Andreas Krause, Matthew Streeter
Date: 2014-07-03 00:00:00

Which ads should we display in sponsored search in order to maximize our
revenue? How should we dynamically rank information sources to maximize the
value of the ranking? These applications exhibit strong diminishing returns:
Redundancy decreases the marginal utility of each ad or information source. We
show that these and other problems can be formalized as repeatedly selecting an
assignment of items to positions to maximize a sequence of monotone submodular
functions that arrive one by one. We present an efficient algorithm for this
general problem and analyze it in the no-regret model. Our algorithm possesses
strong theoretical guarantees, such as a performance ratio that converges to
the optimal constant of 1 - 1/e. We empirically evaluate our algorithm on two
real-world online optimization problems on the web: ad allocation with
submodular utilities, and dynamically ranking blogs to detect information
cascades. Finally, we present a second algorithm that handles the more general
case in which the feasible sets are given by a matroid constraint, while still
maintaining a 1 - 1/e asymptotic performance ratio.
A Map of Update Constraints in Inductive Inference
Authors: Timo Kötzing, Raphaela Palenta
Date: 2014-04-29 00:00:00

We investigate how different learning restrictions reduce learning power and
how the different restrictions relate to one another. We give a complete map
for nine different restrictions both for the cases of complete information
learning and set-driven learning. This completes the picture for these
well-studied \emph{delayable} learning restrictions. A further insight is
gained by different characterizations of \emph{conservative} learning in terms
of variants of \emph{cautious} learning.
  Our analyses greatly benefit from general theorems we give, for example
showing that learners with exclusively delayable restrictions can always be
assumed total.
n-Valued Refined Neutrosophic Logic and Its Applications to Physics
Authors: Florentin Smarandache
Date: 2014-07-03 00:00:00

In this paper we present a short history of logics: from particular cases of
2-symbol or numerical valued logic to the general case of n-symbol or numerical
valued logic. We show generalizations of 2-valued Boolean logic to fuzzy logic,
also from the Kleene and Lukasiewicz 3-symbol valued logics or Belnap 4-symbol
valued logic to the most general n-symbol or numerical valued refined
neutrosophic logic. Two classes of neutrosophic norm (n-norm) and neutrosophic
conorm (n-conorm) are defined. Examples of applications of neutrosophic logic
to physics are listed in the last section. Similar generalizations can be done
for n-Valued Refined Neutrosophic Set, and respectively n- Valued Refined
Neutrosopjhic Probability.
Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach
Authors: M. T Gopalakrishna, M. Ravishankar, D. R. Rameshbabu
Date: 2014-07-03 00:00:00
Tasks: Object, Object Recognition

Object recognition in the video sequence or images is one of the sub-field of
computer vision. Moving object recognition from a video sequence is an
appealing topic with applications in various areas such as airport safety,
intrusion surveillance, video monitoring, intelligent highway, etc. Moving
object recognition is the most challenging task in intelligent video
surveillance system. In this regard, many techniques have been proposed based
on different methods. Despite of its importance, moving object recognition in
complex environments is still far from being completely solved for low
resolution videos, foggy videos, and also dim video sequences. All in all,
these make it necessary to develop exceedingly robust techniques. This paper
introduces multiple moving object recognition in the video sequence based on
LoG Gabor-PCA approach and Angle based distance Similarity measures techniques
used to recognize the object as a human, vehicle etc. Number of experiments are
conducted for indoor and outdoor video sequences of standard datasets and also
our own collection of video sequences comprising of partial night vision video
sequences. Experimental results show that our proposed approach achieves an
excellent recognition rate. Results obtained are satisfactory and competent.
Solving QVIs for Image Restoration with Adaptive Constraint Sets
Authors: Frank Lenzen, Jan Lellmann, Florian Becker, Christoph Schnörr
Date: 2014-07-03 00:00:00
Tasks: Image Restoration

We consider a class of quasi-variational inequalities (QVIs) for adaptive
image restoration, where the adaptivity is described via solution-dependent
constraint sets. In previous work we studied both theoretical and numerical
issues. While we were able to show the existence of solutions for a relatively
broad class of problems, we encountered problems concerning uniqueness of the
solution as well as convergence of existing algorithms for solving QVIs. In
particular, it seemed that with increasing image size the growing condition
number of the involved differential operator poses severe problems. In the
present paper we prove uniqueness for a larger class of problems and in
particular independent of the image size. Moreover, we provide a numerical
algorithm with proved convergence. Experimental results support our theoretical
findings.
Reducing Offline Evaluation Bias in Recommendation Systems
Authors: Arnaud De Myttenaere, Bénédicte Le Grand, Boris Golden, Fabrice Rossi
Date: 2014-07-03 00:00:00
Tasks: Recommendation Systems

Recommendation systems have been integrated into the majority of large online
systems. They tailor those systems to individual users by filtering and ranking
information according to user profiles. This adaptation process influences the
way users interact with the system and, as a consequence, increases the
difficulty of evaluating a recommendation algorithm with historical data (via
offline evaluation). This paper analyses this evaluation bias and proposes a
simple item weighting solution that reduces its impact. The efficiency of the
proposed solution is evaluated on real world data extracted from Viadeo
professional social network.
$ N^4 $-Fields: Neural Network Nearest Neighbor Fields for Image Transforms
Authors: Yaroslav Ganin, Victor Lempitsky
Date: 2014-06-25 00:00:00
Tasks: Edge Detection, Semantic Segmentation

We propose a new architecture for difficult image processing operations, such
as natural edge detection or thin object segmentation. The architecture is
based on a simple combination of convolutional neural networks with the nearest
neighbor search.
  We focus our attention on the situations when the desired image
transformation is too hard for a neural network to learn explicitly. We show
that in such situations, the use of the nearest neighbor search on top of the
network output allows to improve the results considerably and to account for
the underfitting effect during the neural network training. The approach is
validated on three challenging benchmarks, where the performance of the
proposed architecture matches or exceeds the state-of-the-art.
Decision-theoretic approaches to non-knowledge in economics
Authors: Ekaterina Svetlova, Henk van Elst
Date: 2014-07-03 00:00:00

We review two strands of conceptual approaches to the formal representation
of a decision maker's non-knowledge at the initial stage of a static
one-person, one-shot decision problem in economic theory. One focuses on
representations of non-knowledge in terms of probability measures over sets of
mutually exclusive and exhaustive consequence-relevant states of Nature, the
other deals with unawareness of potentially important events by means of sets
of states that are less complete than the full set of consequence-relevant
states of Nature. We supplement our review with a brief discussion of
unresolved matters in both approaches.
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
Authors: Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel
Date: 2014-07-03 00:00:00
Tasks: Pedestrian Detection

We propose a simple yet effective approach to the problem of pedestrian
detection which outperforms the current state-of-the-art. Our new features are
built on the basis of low-level visual features and spatial pooling.
Incorporating spatial pooling improves the translational invariance and thus
the robustness of the detection process. We then directly optimise the partial
area under the ROC curve (\pAUC) measure, which concentrates detection
performance in the range of most practical importance. The combination of these
factors leads to a pedestrian detector which outperforms all competitors on all
of the standard benchmark datasets. We advance state-of-the-art results by
lowering the average miss rate from $13\%$ to $11\%$ on the INRIA benchmark,
$41\%$ to $37\%$ on the ETH benchmark, $51\%$ to $42\%$ on the TUD-Brussels
benchmark and $36\%$ to $29\%$ on the Caltech-USA benchmark.