pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Single-View Instance Recognition
Authors: David Held, Sebastian Thrun, Silvio Savarese
Date: 2015-07-29 00:00:00
Tasks: Deep Learning

Deep learning methods have typically been trained on large datasets in which
many training examples are available. However, many real-world product datasets
have only a small number of images available for each product. We explore the
use of deep learning methods for recognizing object instances when we have only
a single training example per class. We show that feedforward neural networks
outperform state-of-the-art methods for recognizing objects from novel
viewpoints even when trained from just a single image per object. To further
improve our performance on this task, we propose to take advantage of a
supplementary dataset in which we observe a separate set of objects from
multiple viewpoints. We introduce a new approach for training deep learning
methods for instance recognition with limited training data, in which we use an
auxiliary multi-view dataset to train our network to be robust to viewpoint
changes. We find that this approach leads to a more robust classifier for
recognizing objects from novel viewpoints, outperforming previous
state-of-the-art approaches including keypoint-matching, template-based
techniques, and sparse coding.
Context-aware learning for generative models
Authors: Serafeim Perdikis, Robert Leeb, Ricardo Chavarriaga, José del R. Millán
Date: 2015-07-29 00:00:00
Tasks: General Classification

This work studies the class of algorithms for learning with side-information that emerge by extending generative models with embedded context-related variables. Using finite mixture models (FMM) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over the regular unsupervised case and can approach the performances of supervised learning, despite the absence of any explicit ground truth data labeling. By direct application of the missing information principle (MIP), the algorithms' performances are proven to range between the conventional supervised and unsupervised MLE extremities proportionally to the information content of the contextual assistance provided. The acquired benefits regard higher estimation precision, smaller standard errors, faster convergence rates and improved classification accuracy or regression fitness shown in various scenarios, while also highlighting important properties and differences among the outlined situations. Applicability is showcased with three real-world unsupervised classification scenarios employing Gaussian Mixture Models. Importantly, we exemplify the natural extension of this methodology to any type of generative model by deriving an equivalent context-aware algorithm for variational autoencoders (VAs), thus broadening the spectrum of applicability to unsupervised deep learning with artificial neural networks. The latter is contrasted with a neural-symbolic algorithm exploiting side-information.
Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian
Authors: Yongxin Yang, Timothy Hospedales
Date: 2015-07-28 00:00:00
Tasks: Domain Adaptation, regression, Unsupervised Domain Adaptation

Most visual recognition methods implicitly assume the data distribution
remains unchanged from training to testing. However, in practice domain shift
often exists, where real-world factors such as lighting and sensor type change
between train and test, and classifiers do not generalise from source to target
domains. It is impractical to train separate models for all possible situations
because collecting and labelling the data is expensive. Domain adaptation
algorithms aim to ameliorate domain shift, allowing a model trained on a source
to perform well on a different target domain. However, even for the setting of
unsupervised domain adaptation, where the target domain is unlabelled,
collecting data for every possible target domain is still costly. In this
paper, we propose a new domain adaptation method that has no need to access
either data or labels of the target domain when it can be described by a
parametrised vector and there exits several related source domains within the
same parametric space. It greatly reduces the burden of data collection and
annotation, and our experiments show some promising results.
The SYSU System for the Interspeech 2015 Automatic Speaker Verification Spoofing and Countermeasures Challenge
Authors: Shitao Weng, Shushan Chen, Lei Yu, Xuewei Wu, Weicheng Cai, Zhi Liu, Ming Li
Date: 2015-07-24 00:00:00
Tasks: Speaker Verification, Speech Synthesis, Voice Conversion

Many existing speaker verification systems are reported to be vulnerable
against different spoofing attacks, for example speaker-adapted speech
synthesis, voice conversion, play back, etc. In order to detect these spoofed
speech signals as a countermeasure, we propose a score level fusion approach
with several different i-vector subsystems. We show that the acoustic level
Mel-frequency cepstral coefficients (MFCC) features, the phase level modified
group delay cepstral coefficients (MGDCC) and the phonetic level phoneme
posterior probability (PPP) tandem features are effective for the
countermeasure. Furthermore, feature level fusion of these features before
i-vector modeling also enhance the performance. A polynomial kernel support
vector machine is adopted as the supervised classifier. In order to enhance the
generalizability of the countermeasure, we also adopted the cosine similarity
and PLDA scoring as one-class classifications methods. By combining the
proposed i-vector subsystems with the OpenSMILE baseline which covers the
acoustic and prosodic information further improves the final performance. The
proposed fusion system achieves 0.29% and 3.26% EER on the development and test
set of the database provided by the INTERSPEECH 2015 automatic speaker
verification spoofing and countermeasures challenge.
Layered Interpretation of Street View Images
Authors: Ming-Yu Liu, Shuoxin Lin, Srikumar Ramalingam, Oncel Tuzel
Date: 2015-06-15 00:00:00
Tasks: Autonomous Driving, GPU, Scene Labeling, Scene Segmentation

We propose a layered street view model to encode both depth and semantic
information on street view images for autonomous driving. Recently, stixels,
stix-mantics, and tiered scene labeling methods have been proposed to model
street view images. We propose a 4-layer street view model, a compact
representation over the recently proposed stix-mantics model. Our layers encode
semantic classes like ground, pedestrians, vehicles, buildings, and sky in
addition to the depths. The only input to our algorithm is a pair of stereo
images. We use a deep neural network to extract the appearance features for
semantic classes. We use a simple and an efficient inference algorithm to
jointly estimate both semantic classes and layered depth values. Our method
outperforms other competing approaches in Daimler urban scene segmentation
dataset. Our algorithm is massively parallelizable, allowing a GPU
implementation with a processing speed about 9 fps.
IT-Dendrogram: A New Member of the In-Tree (IT) Clustering Family
Authors: Teng Qiu, Yong-Jie Li
Date: 2015-07-29 00:00:00
Tasks: Clustering

Previously, we proposed a physically-inspired method to construct data points
into an effective in-tree (IT) structure, in which the underlying cluster
structure in the dataset is well revealed. Although there are some edges in the
IT structure requiring to be removed, such undesired edges are generally
distinguishable from other edges and thus are easy to be determined. For
instance, when the IT structures for the 2-dimensional (2D) datasets are
graphically presented, those undesired edges can be easily spotted and
interactively determined. However, in practice, there are many datasets that do
not lie in the 2D Euclidean space, thus their IT structures cannot be
graphically presented. But if we can effectively map those IT structures into a
visualized space in which the salient features of those undesired edges are
preserved, then the undesired edges in the IT structures can still be visually
determined in a visualization environment. Previously, this purpose was reached
by our method called IT-map. The outstanding advantage of IT-map is that
clusters can still be found even with the so-called crowding problem in the
embedding.
  In this paper, we propose another method, called IT-Dendrogram, to achieve
the same goal through an effective combination of the IT structure and the
single link hierarchical clustering (SLHC) method. Like IT-map, IT-Dendrogram
can also effectively represent the IT structures in a visualization
environment, whereas using another form, called the Dendrogram. IT-Dendrogram
can serve as another visualization method to determine the undesired edges in
the IT structures and thus benefit the IT-based clustering analysis. This was
demonstrated on several datasets with different shapes, dimensions, and
attributes. Unlike IT-map, IT-Dendrogram can always avoid the crowding problem,
which could help users make more reliable cluster analysis in certain problems.
Learning Representations for Outlier Detection on a Budget
Authors: Barbora Micenková, Brian McWilliams, Ira Assent
Date: 2015-07-29 00:00:00
Tasks: Fraud Detection, Outlier Detection

The problem of detecting a small number of outliers in a large dataset is an
important task in many fields from fraud detection to high-energy physics. Two
approaches have emerged to tackle this problem: unsupervised and supervised.
Supervised approaches require a sufficient amount of labeled data and are
challenged by novel types of outliers and inherent class imbalance, whereas
unsupervised methods do not take advantage of available labeled training
examples and often exhibit poorer predictive performance. We propose BORE (a
Bagged Outlier Representation Ensemble) which uses unsupervised outlier scoring
functions (OSFs) as features in a supervised learning framework. BORE is able
to adapt to arbitrary OSF feature representations, to the imbalance in labeled
data as well as to prediction-time constraints on computational cost. We
demonstrate the good performance of BORE compared to a variety of competing
methods in the non-budgeted and the budgeted outlier detection problem on 12
real-world datasets.
Cross-pose Face Recognition by Canonical Correlation Analysis
Authors: Annan Li, Shiguang Shan, Xilin Chen, Bingpeng Ma, Shuicheng Yan, Wen Gao
Date: 2015-07-29 00:00:00
Tasks: Face Recognition

The pose problem is one of the bottlenecks in automatic face recognition. We
argue that one of the diffculties in this problem is the severe misalignment in
face images or feature vectors with different poses. In this paper, we propose
that this problem can be statistically solved or at least mitigated by
maximizing the intra-subject across-pose correlations via canonical correlation
analysis (CCA). In our method, based on the data set with coupled face images
of the same identities and across two different poses, CCA learns
simultaneously two linear transforms, each for one pose. In the transformed
subspace, the intra-subject correlations between the different poses are
maximized, which implies pose-invariance or pose-robustness is achieved. The
experimental results show that our approach could considerably improve the
recognition performance. And if further enhanced with holistic+local feature
representation, the performance could be comparable to the state-of-the-art.
STC Anti-spoofing Systems for the ASVspoof 2015 Challenge
Authors: Sergey Novoselov, Alexandr Kozlov, Galina Lavrentyeva, Konstantin Simonchik, Vadim Shchemelinin
Date: 2015-07-29 00:00:00
Tasks: Speaker Verification

This paper presents the Speech Technology Center (STC) systems submitted to
Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof)
Challenge 2015. In this work we investigate different acoustic feature spaces
to determine reliable and robust countermeasures against spoofing attacks. In
addition to the commonly used front-end MFCC features we explored features
derived from phase spectrum and features based on applying the multiresolution
wavelet transform. Similar to state-of-the-art ASV systems, we used the
standard TV-JFA approach for probability modelling in spoofing detection
systems. Experiments performed on the development and evaluation datasets of
the Challenge demonstrate that the use of phase-related and wavelet-based
features provides a substantial input into the efficiency of the resulting STC
systems. In our research we also focused on the comparison of the linear (SVM)
and nonlinear (DBN) classifiers.
Preprint Extending Touch-less Interaction on Vision Based Wearable Device
Authors: Zhihan Lv, Liangbing Feng, Shengzhong Feng, Hai-Bo Li
Date: 2015-04-04 00:00:00

This is the preprint version of our paper on IEEE Virtual Reality Conference
2015. A touch-less interaction technology on vision based wearable device is
designed and evaluated. Users interact with the application with dynamic
hands/feet gestures in front of the camera. Several proof-of-concept prototypes
with eleven dynamic gestures are developed based on the touch-less interaction.
At last, a comparing user study evaluation is proposed to demonstrate the
usability of the touch-less approach, as well as the impact on user's emotion,
running on a wearable framework or Google Glass.
Collaborative Representation Classification Ensemble for Face Recognition
Authors: Xiaochao Qu, Suah Kim, Run Cui, Hyoung Joong Kim
Date: 2015-07-29 00:00:00
Tasks: Classification, Face Recognition, General Classification

Collaborative Representation Classification (CRC) for face recognition
attracts a lot attention recently due to its good recognition performance and
fast speed. Compared to Sparse Representation Classification (SRC), CRC
achieves a comparable recognition performance with 10-1000 times faster speed.
In this paper, we propose to ensemble several CRC models to promote the
recognition rate, where each CRC model uses different and divergent randomly
generated biologically-inspired features as the face representation. The
proposed ensemble algorithm calculates an ensemble weight for each CRC model
that guided by the underlying classification rule of CRC. The obtained weights
reflect the confidences of those CRC models where the more confident CRC models
have larger weights. The proposed weighted ensemble method proves to be very
effective and improves the performance of each CRC model significantly.
Extensive experiments are conducted to show the superior performance of the
proposed method.
Adapted sampling for 3D X-ray computed tomography
Authors: Anthony Cazasnoves, Fanny Buyens, Sylvie Sevestre
Date: 2015-07-29 00:00:00

In this paper, we introduce a method to build an adapted mesh representation
of a 3D object for X-Ray tomography reconstruction. Using this representation,
we provide means to reduce the computational cost of reconstruction by way of
iterative algorithms. The adapted sampling of the reconstruction space is
directly obtained from the projection dataset and prior to any reconstruction.
It is built following two stages : firstly, 2D structural information is
extracted from the projection images and is secondly merged in 3D to obtain a
3D pointcloud sampling the interfaces of the object. A relevant mesh is then
built from this cloud by way of tetrahedralization. Critical parameters
selections have been automatized through a statistical framework, thus avoiding
dependence on users expertise. Applying this approach on geometrical shapes and
on a 3D Shepp-Logan phantom, we show the relevance of such a sampling -
obtained in a few seconds - and the drastic decrease in cells number to be
estimated during reconstruction when compared to the usual regular voxel
lattice. A first iterative reconstruction of the Shepp-Logan using this kind of
sampling shows the relevant advantages in terms of low dose or sparse
acquisition sampling contexts. The method can also prove useful for other
applications such as finite element method computations.
Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks
Authors: Xiao Lin, Devi Parikh
Date: 2015-02-21 00:00:00
Proceeding: CVPR 2015 6
Tasks: Common Sense Reasoning

Artificial agents today can answer factual questions. But they fall short on
questions that require common sense reasoning. Perhaps this is because most
existing common sense databases rely on text to learn and represent knowledge.
But much of common sense knowledge is unwritten - partly because it tends not
to be interesting enough to talk about, and partly because some common sense is
unnatural to articulate in text. While unwritten, it is not unseen. In this
paper we leverage semantic common sense knowledge learned from images - i.e.
visual common sense - in two textual tasks: fill-in-the-blank and visual
paraphrasing. We propose to "imagine" the scene behind the text, and leverage
visual cues from the "imagined" scenes in addition to textual cues while
answering these questions. We imagine the scenes as a visual abstraction. Our
approach outperforms a strong text-only baseline on these tasks. Our proposed
tasks can serve as benchmarks to quantitatively evaluate progress in solving
tasks that go "beyond recognition". Our code and datasets are publicly
available.
SAT-based Analysis of Large Real-world Feature Models is Easy
Authors: Jia Hui Liang, Vijay Ganesh, Venkatesh Raman, Krzysztof Czarnecki
Date: 2015-06-17 00:00:00

Modern conflict-driven clause-learning (CDCL) Boolean SAT solvers provide
efficient automatic analysis of real-world feature models (FM) of systems
ranging from cars to operating systems. It is well-known that solver-based
analysis of real-world FMs scale very well even though SAT instances obtained
from such FMs are large, and the corresponding analysis problems are known to
be NP-complete. To better understand why SAT solvers are so effective, we
systematically studied many syntactic and semantic characteristics of a
representative set of large real-world FMs. We discovered that a key reason why
large real-world FMs are easy-to-analyze is that the vast majority of the
variables in these models are unrestricted, i.e., the models are satisfiable
for both true and false assignments to such variables under the current partial
assignment. Given this discovery and our understanding of CDCL SAT solvers, we
show that solvers can easily find satisfying assignments for such models
without too many backtracks relative to the model size, explaining why solvers
scale so well. Further analysis showed that the presence of unrestricted
variables in these real-world models can be attributed to their high-degree of
variability. Additionally, we experimented with a series of well-known
non-backtracking simplifications that are particularly effective in solving
FMs. The remaining variables/clauses after simplifications, called the core,
are so few that they are easily solved even with backtracking, further
strengthening our conclusions.
Unification of field theory and maximum entropy methods for learning probability densities
Authors: Justin B. Kinney
Date: 2014-11-19 00:00:00
Tasks: Density Estimation

The need to estimate smooth probability distributions (a.k.a. probability
densities) from finite sampled data is ubiquitous in science. Many approaches
to this problem have been described, but none is yet regarded as providing a
definitive solution. Maximum entropy estimation and Bayesian field theory are
two such approaches. Both have origins in statistical physics, but the
relationship between them has remained unclear. Here I unify these two methods
by showing that every maximum entropy density estimate can be recovered in the
infinite smoothness limit of an appropriate Bayesian field theory. I also show
that Bayesian field theory estimation can be performed without imposing any
boundary conditions on candidate densities, and that the infinite smoothness
limit of these theories recovers the most common types of maximum entropy
estimates. Bayesian field theory is thus seen to provide a natural test of the
validity of the maximum entropy null hypothesis. Bayesian field theory also
returns a lower entropy density estimate when the maximum entropy hypothesis is
falsified. The computations necessary for this approach can be performed
rapidly for one-dimensional data, and software for doing this is provided.
Based on these results, I argue that Bayesian field theory is poised to provide
a definitive solution to the density estimation problem in one dimension.
On the Performance of ConvNet Features for Place Recognition
Authors: Niko Sünderhauf, Feras Dayoub, Sareh Shirazi, Ben Upcroft, Michael Milford
Date: 2015-01-17 00:00:00
Tasks: Visual Navigation

After the incredible success of deep learning in the computer vision domain,
there has been much interest in applying Convolutional Network (ConvNet)
features in robotic fields such as visual navigation and SLAM. Unfortunately,
there are fundamental differences and challenges involved. Computer vision
datasets are very different in character to robotic camera data, real-time
performance is essential, and performance priorities can be different. This
paper comprehensively evaluates and compares the utility of three
state-of-the-art ConvNets on the problems of particular relevance to navigation
for robots; viewpoint-invariance and condition-invariance, and for the first
time enables real-time place recognition performance using ConvNets with large
maps by integrating a variety of existing (locality-sensitive hashing) and
novel (semantic search space partitioning) optimization techniques. We present
extensive experiments on four real world datasets cultivated to evaluate each
of the specific challenges in place recognition. The results demonstrate that
speed-ups of two orders of magnitude can be achieved with minimal accuracy
degradation, enabling real-time performance. We confirm that networks trained
for semantic place categorization also perform better at (specific) place
recognition when faced with severe appearance changes and provide a reference
for which networks and layers are optimal for different aspects of the place
recognition problem.
Identifying missing dictionary entries with frequency-conserving context models
Authors: Jake Ryland Williams, Eric M. Clark, James P. Bagrow, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2015-03-07 00:00:00

In an effort to better understand meaning from natural language texts, we
explore methods aimed at organizing lexical objects into contexts. A number of
these methods for organization fall into a family defined by word ordering.
Unlike demographic or spatial partitions of data, these collocation models are
of special importance for their universal applicability. While we are
interested here in text and have framed our treatment appropriately, our work
is potentially applicable to other areas of research (e.g., speech, genomics,
and mobility patterns) where one has ordered categorical data, (e.g., sounds,
genes, and locations). Our approach focuses on the phrase (whether word or
larger) as the primary meaning-bearing lexical unit and object of study. To do
so, we employ our previously developed framework for generating word-conserving
phrase-frequency data. Upon training our model with the Wiktionary---an
extensive, online, collaborative, and open-source dictionary that contains over
100,000 phrasal-definitions---we develop highly effective filters for the
identification of meaningful, missing phrase-entries. With our predictions we
then engage the editorial community of the Wiktionary and propose short lists
of potential missing entries for definition, developing a breakthrough, lexical
extraction technique, and expanding our knowledge of the defined English
lexicon of phrases.