pwc-archive/papers-with-abstracts huggingface dataset:
Deformable Parts Correlation Filters for Robust Visual Tracking
Authors: Alan Lukežič, Luka Čehovin, Matej Kristan
Date: 2016-05-12 00:00:00
Tasks: Object, Object Localization, Visual Tracking

Deformable parts models show a great potential in tracking by principally
addressing non-rigid object deformations and self occlusions, but according to
recent benchmarks, they often lag behind the holistic approaches. The reason is
that potentially large number of degrees of freedom have to be estimated for
object localization and simplifications of the constellation topology are often
assumed to make the inference tractable. We present a new formulation of the
constellation model with correlation filters that treats the geometric and
visual constraints within a single convex cost function and derive a highly
efficient optimization for MAP inference of a fully-connected constellation. We
propose a tracker that models the object at two levels of detail. The coarse
level corresponds a root correlation filter and a novel color model for
approximate object localization, while the mid-level representation is composed
of the new deformable constellation of correlation filters that refine the
object location. The resulting tracker is rigorously analyzed on a highly
challenging OTB, VOT2014 and VOT2015 benchmarks, exhibits a state-of-the-art
performance and runs in real-time.
Movie Description
Authors: Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, Bernt Schiele
Date: 2016-05-12 00:00:00
Tasks: Benchmarking

Audio Description (AD) provides linguistic descriptions of movies and allows
visually impaired people to follow a movie along with their peers. Such
descriptions are by design mainly visual and thus naturally form an interesting
data source for computer vision and computational linguistics. In this work we
propose a novel dataset which contains transcribed ADs, which are temporally
aligned to full length movies. In addition we also collected and aligned movie
scripts used in prior work and compare the two sources of descriptions. In
total the Large Scale Movie Description Challenge (LSMDC) contains a parallel
corpus of 118,114 sentences and video clips from 202 movies. First we
characterize the dataset by benchmarking different approaches for generating
video descriptions. Comparing ADs to scripts, we find that ADs are indeed more
visual and describe precisely what is shown rather than what should happen
according to the scripts created prior to movie production. Furthermore, we
present and compare the results of several teams who participated in a
challenge organized in the context of the workshop "Describing and
Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at
ICCV 2015.
Robust and Efficient Relative Pose with a Multi-camera System for Autonomous Vehicle in Highly Dynamic Environments
Authors: Liu Liu, Hongdong Li, Yuchao Dai
Date: 2016-05-12 00:00:00
Tasks: Motion Estimation

This paper studies the relative pose problem for autonomous vehicle driving
in highly dynamic and possibly cluttered environments. This is a challenging
scenario due to the existence of multiple, large, and independently moving
objects in the environment, which often leads to excessive portion of outliers
and results in erroneous motion estimation. Existing algorithms cannot cope
with such situations well. This paper proposes a new algorithm for relative
pose using a multi-camera system with multiple non-overlapping individual
cameras. The method works robustly even when the numbers of outliers are
overwhelming. By exploiting specific prior knowledge of driving scene we have
developed an efficient 4-point algorithm for multi-camera relative pose, which
admits analytic solutions by solving a polynomial root-finding equation, and
runs extremely fast (at about 0.5$u$s per root). When the solver is used in
combination with RANSAC, we are able to quickly prune unpromising hypotheses,
significantly improve the chance of finding inliers. Experiments on synthetic
data have validated the performance of the proposed algorithm. Tests on real
data further confirm the method's practical relevance.
Going Deeper into First-Person Activity Recognition
Authors: Minghuang Ma, Haoqi Fan, Kris M. Kitani
Date: 2016-05-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Recognition, Activity Recognition, Object, Temporal Action Localization

We bring together ideas from recent work on feature design for egocentric
action recognition under one framework by exploring the use of deep
convolutional neural networks (CNN). Recent work has shown that features such
as hand appearance, object attributes, local hand motion and camera ego-motion
are important for characterizing first-person actions. To integrate these ideas
under one framework, we propose a twin stream network architecture, where one
stream analyzes appearance information and the other stream analyzes motion
information. Our appearance stream encodes prior knowledge of the egocentric
paradigm by explicitly training the network to segment hands and localize
objects. By visualizing certain neuron activation of our network, we show that
our proposed architecture naturally learns features that capture object
attributes and hand-object configurations. Our extensive experiments on
benchmark egocentric action datasets show that our deep architecture enables
recognition rates that significantly outperform state-of-the-art techniques --
an average $6.6\%$ increase in accuracy over all datasets. Furthermore, by
learning to recognize objects, actions and activities jointly, the performance
of individual recognition tasks also increase by $30\%$ (actions) and $14\%$
(objects). We also include the results of extensive ablative analysis to
highlight the importance of network design decisions..
Real-Time Web Scale Event Summarization Using Sequential Decision Making
Authors: Chris Kedzie, Fernando Diaz, Kathleen McKeown
Date: 2016-05-12 00:00:00
Tasks: Decision Making, Sequential Decision Making

We present a system based on sequential decision making for the online
summarization of massive document streams, such as those found on the web.
Given an event of interest (e.g. "Boston marathon bombing"), our system is able
to filter the stream for relevance and produce a series of short text updates
describing the event as it unfolds over time. Unlike previous work, our
approach is able to jointly model the relevance, comprehensiveness, novelty,
and timeliness required by time-sensitive queries. We demonstrate a 28.3%
improvement in summary F1 and a 43.8% improvement in time-sensitive F1 metrics.
Item Popularity Prediction in E-commerce Using Image Quality Feature Vectors
Authors: Stephen Zakrewsky, Kamelia Aryafar, Ali Shokoufandeh
Date: 2016-05-12 00:00:00

Online retail is a visual experience- Shoppers often use images as first
order information to decide if an item matches their personal style. Image
characteristics such as color, simplicity, scene composition, texture, style,
aesthetics and overall quality play a crucial role in making a purchase
decision, clicking on or liking a product listing. In this paper we use a set
of image features that indicate quality to predict product listing popularity
on a major e-commerce website, Etsy. We first define listing popularity through
search clicks, favoriting and purchase activity. Next, we infer listing quality
from the pixel-level information of listed images as quality features. We then
compare our findings to text-only models for popularity prediction. Our initial
results indicate that a combined image and text modeling of product listings
outperforms text-only models in popularity prediction.
Distributed Compressive Sensing: A Deep Learning Approach
Authors: Hamid Palangi, Rabab Ward, Li Deng
Date: 2015-08-20 00:00:00
Tasks: compressed sensing, Compressive Sensing, Decoder, Deep Learning

Various studies that address the compressed sensing problem with Multiple
Measurement Vectors (MMVs) have been recently carried. These studies assume the
vectors of the different channels to be jointly sparse. In this paper, we relax
this condition. Instead we assume that these sparse vectors depend on each
other but that this dependency is unknown. We capture this dependency by
computing the conditional probability of each entry in each vector being
non-zero, given the "residuals" of all previous vectors. To estimate these
probabilities, we propose the use of the Long Short-Term Memory (LSTM)[1], a
data driven model for sequence modelling that is deep in time. To calculate the
model parameters, we minimize a cross entropy cost function. To reconstruct the
sparse vectors at the decoder, we propose a greedy solver that uses the above
model to estimate the conditional probabilities. By performing extensive
experiments on two real world datasets, we show that the proposed method
significantly outperforms the general MMV solver (the Simultaneous Orthogonal
Matching Pursuit (SOMP)) and a number of the model-based Bayesian methods. The
proposed method does not add any complexity to the general compressive sensing
encoder. The trained model is used just at the decoder. As the proposed method
is a data driven method, it is only applicable when training data is available.
In many applications however, training data is indeed available, e.g. in
recorded images and videos.
A Survey of Semantic Segmentation
Authors: Martin Thoma
Date: 2016-02-21 00:00:00
Tasks: Segmentation, Semantic Segmentation, Survey

This survey gives an overview over different techniques used for pixel-level
semantic segmentation. Metrics and datasets for the evaluation of segmentation
algorithms and traditional approaches for segmentation such as unsupervised
methods, Decision Forests and SVMs are described and pointers to the relevant
papers are given. Recently published approaches with convolutional neural
networks are mentioned and typical problematic situations for segmentation
algorithms are examined. A taxonomy of segmentation algorithms is given.
Modified Weibull distribution for Biomedical signals denoising
Authors: A. M. Adam, R. M. Farouk, B. S . El-Desouky
Date: 2016-05-11 00:00:00
Tasks: Denoising

A wide range of signs are acquired from the human body called Biomedical signs or biosignals, they can be at the cell level, organ level, or sub-atomic level. Electroencephalogramis the electrical activity from the cerebrum, the electrocardiogram is the electrical activity from the heart, electrical action from the muscle sound signals referred to as electromyogram, the electroretinogram from the eye, and so on. Studying these signals can be so helpful for doctors, it can help them examine and predict and cure many diseases.
Asymptotic sequential Rademacher complexity of a finite function class
Authors: Dmitry B. Rokhlin
Date: 2016-05-11 00:00:00

For a finite function class we describe the large sample limit of the
sequential Rademacher complexity in terms of the viscosity solution of a
$G$-heat equation. In the language of Peng's sublinear expectation theory, the
same quantity equals to the expected value of the largest order statistics of a
multidimensional $G$-normal random variable. We illustrate this result by
deriving upper and lower bounds for the asymptotic sequential Rademacher
complexity.
COCO: Performance Assessment
Authors: Nikolaus Hansen, Anne Auger, Dimo Brockhoff, Dejan Tušar, Tea Tušar
Date: 2016-05-11 00:00:00
Tasks: Benchmarking

We present an any-time performance assessment for benchmarking numerical
optimization algorithms in a black-box scenario, applied within the COCO
benchmarking platform. The performance assessment is based on runtimes measured
in number of objective function evaluations to reach one or several quality
indicator target values. We argue that runtime is the only available measure
with a generic, meaningful, and quantitative interpretation. We discuss the
choice of the target values, runlength-based targets, and the aggregation of
results by using simulated restarts, averages, and empirical distribution
functions.
Linear Shape Deformation Models with Local Support Using Graph-based Structured Matrix Factorisation
Authors: Florian Bernard, Peter Gemmar, Frank Hertel, Jorge Goncalves, Johan Thunberg
Date: 2015-10-28 00:00:00
Proceeding: CVPR 2016 6

Representing 3D shape deformations by linear models in high-dimensional space
has many applications in computer vision and medical imaging, such as
shape-based interpolation or segmentation. Commonly, using Principal Components
Analysis a low-dimensional (affine) subspace of the high-dimensional shape
space is determined. However, the resulting factors (the most dominant
eigenvectors of the covariance matrix) have global support, i.e. changing the
coefficient of a single factor deforms the entire shape. In this paper, a
method to obtain deformation factors with local support is presented. The
benefits of such models include better flexibility and interpretability as well
as the possibility of interactively deforming shapes locally. For that, based
on a well-grounded theoretical motivation, we formulate a matrix factorisation
problem employing sparsity and graph-based regularisation terms. We demonstrate
that for brain shapes our method outperforms the state of the art in local
support models with respect to generalisation ability and sparse shape
reconstruction, whereas for human body shapes our method gives more realistic
deformations.
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks
Authors: Uri Shaham, Roy Lederman
Date: 2015-12-29 00:00:00
Tasks: Representation Learning, Semantic Similarity, Semantic Textual Similarity

We consider the statistical problem of learning common source of variability
in data which are synchronously captured by multiple sensors, and demonstrate
that Siamese neural networks can be naturally applied to this problem. This
approach is useful in particular in exploratory, data-driven applications,
where neither a model nor label information is available. In recent years, many
researchers have successfully applied Siamese neural networks to obtain an
embedding of data which corresponds to a "semantic similarity". We present an
interpretation of this "semantic similarity" as learning of equivalence
classes. We discuss properties of the embedding obtained by Siamese networks
and provide empirical results that demonstrate the ability of Siamese networks
to learn common variability.
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms
Authors: Yossi Arjevani, Ohad Shamir
Date: 2016-05-11 00:00:00

We consider a broad class of first-order optimization algorithms which are
\emph{oblivious}, in the sense that their step sizes are scheduled regardless
of the function under consideration, except for limited side-information such
as smoothness or strong convexity parameters. With the knowledge of these two
parameters, we show that any such algorithm attains an iteration complexity
lower bound of $\Omega(\sqrt{L/\epsilon})$ for $L$-smooth convex functions, and
$\tilde{\Omega}(\sqrt{L/\mu}\ln(1/\epsilon))$ for $L$-smooth $\mu$-strongly
convex functions. These lower bounds are stronger than those in the traditional
oracle model, as they hold independently of the dimension. To attain these, we
abandon the oracle model in favor of a structure-based approach which builds
upon a framework recently proposed in (Arjevani et al., 2015). We further show
that without knowing the strong convexity parameter, it is impossible to attain
an iteration complexity better than
$\tilde{\Omega}\left((L/\mu)\ln(1/\epsilon)\right)$. This result is then used
to formalize an observation regarding $L$-smooth convex functions, namely, that
the iteration complexity of algorithms employing time-invariant step sizes must
be at least $\Omega(L/\epsilon)$.
The Yahoo Query Treebank, V. 1.0
Authors: Yuval Pinter, Roi Reichart, Idan Szpektor
Date: 2016-05-10 00:00:00

A description and annotation guidelines for the Yahoo Webscope release of
Query Treebank, Version 1.0, May 2016.
Characterizing Quantifier Fuzzification Mechanisms: a behavioral guide for practical applications
Authors: F. Diaz-Hermida, M. Pereira-Fariña, Juan. C. Vidal, A. Ramos-Soto
Date: 2016-05-11 00:00:00

Important advances have been made in the fuzzy quantification field.
Nevertheless, some problems remain when we face the decision of selecting the
most convenient model for a specific application. In the literature, several
desirable adequacy properties have been proposed, but theoretical limits impede
quantification models from simultaneously fulfilling every adequacy property
that has been defined. Besides, the complexity of model definitions and
adequacy properties makes very difficult for real users to understand the
particularities of the different models that have been presented. In this work
we will present several criteria conceived to help in the process of selecting
the most adequate Quantifier Fuzzification Mechanisms for specific practical
applications. In addition, some of the best known well-behaved models will be
compared against this list of criteria. Based on this analysis, some guidance
to choose fuzzy quantification models for practical applications will be
provided.
When Do Luxury Cars Hit the Road? Findings by A Big Data Approach
Authors: Yang Feng, Jiebo Luo
Date: 2016-05-10 00:00:00
Tasks: Diversity, Marketing

In this paper, we focus on studying the appearing time of different kinds of
cars on the road. This information will enable us to infer the life style of
the car owners. The results can further be used to guide marketing towards car
owners. Conventionally, this kind of study is carried out by sending out
questionnaires, which is limited in scale and diversity. To solve this problem,
we propose a fully automatic method to carry out this study. Our study is based
on publicly available surveillance camera data. To make the results reliable,
we only use the high resolution cameras (i.e. resolution greater than $1280
\times 720$). Images from the public cameras are downloaded every minute. After
obtaining 50,000 images, we apply faster R-CNN (region-based convoluntional
neural network) to detect the cars in the downloaded images and a fine-tuned
VGG16 model is used to recognize the car makes. Based on the recognition
results, we present a data-driven analysis on the relationship between car
makes and their appearing times, with implications on lifestyles.
Stochastic Shortest Path with Energy Constraints in POMDPs
Authors: Tomáš Brázdil, Krishnendu Chatterjee, Martin Chmelík, Anchit Gupta, Petr Novotný
Date: 2016-02-24 00:00:00

We consider partially observable Markov decision processes (POMDPs) with a
set of target states and positive integer costs associated with every
transition. The traditional optimization objective (stochastic shortest path)
asks to minimize the expected total cost until the target set is reached. We
extend the traditional framework of POMDPs to model energy consumption, which
represents a hard constraint. The energy levels may increase and decrease with
transitions, and the hard constraint requires that the energy level must remain
positive in all steps till the target is reached. First, we present a novel
algorithm for solving POMDPs with energy levels, developing on existing POMDP
solvers and using RTDP as its main method. Our second contribution is related
to policy representation. For larger POMDP instances the policies computed by
existing solvers are too large to be understandable. We present an automated
procedure based on machine learning techniques that automatically extracts
important decisions of the policy allowing us to compute succinct human
readable policies. Finally, we show experimentally that our algorithm performs
well and computes succinct policies on a number of POMDP instances from the
literature that were naturally enhanced with energy levels.
On-the-fly Network Pruning for Object Detection
Authors: Marc Masana, Joost Van de Weijer, Andrew D. Bagdanov
Date: 2016-05-11 00:00:00
Tasks: Network Pruning, Object, object-detection, Object Detection

Object detection with deep neural networks is often performed by passing a
few thousand candidate bounding boxes through a deep neural network for each
image. These bounding boxes are highly correlated since they originate from the
same image. In this paper we investigate how to exploit feature occurrence at
the image scale to prune the neural network which is subsequently applied to
all bounding boxes. We show that removing units which have near-zero activation
in the image allows us to significantly reduce the number of parameters in the
network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that
up to 40% of units in some fully-connected layers can be entirely eliminated
with little change in the detection result.
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning
Authors: Christoph Dann, Emma Brunskill
Date: 2015-10-29 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, there has been significant progress in understanding reinforcement
learning in discounted infinite-horizon Markov decision processes (MDPs) by
deriving tight sample complexity bounds. However, in many real-world
applications, an interactive learning agent operates for a fixed or bounded
period of time, for example tutoring students for exams or handling customer
service requests. Such scenarios can often be better treated as episodic
fixed-horizon MDPs, for which only looser bounds on the sample complexity
exist. A natural notion of sample complexity in this setting is the number of
episodes required to guarantee a certain performance with high probability (PAC
guarantee). In this paper, we derive an upper PAC bound $\tilde
O(\frac{|\mathcal S|^2 |\mathcal A| H^2}{\epsilon^2} \ln\frac 1 \delta)$ and a
lower PAC bound $\tilde \Omega(\frac{|\mathcal S| |\mathcal A| H^2}{\epsilon^2}
\ln \frac 1 {\delta + c})$ that match up to log-terms and an additional linear
dependency on the number of states $|\mathcal S|$. The lower bound is the first
of its kind for this setting. Our upper bound leverages Bernstein's inequality
to improve on previous bounds for episodic finite-horizon MDPs which have a
time-horizon dependency of at least $H^3$.
Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition
Authors: Vivek Sharma, Luc van Gool
Date: 2016-05-11 00:00:00
Tasks: Classification, Face Recognition, General Classification

In this paper, we proposed a novel pipeline for image-level classification in
the hyperspectral images. By doing this, we show that the discriminative
spectral information at image-level features lead to significantly improved
performance in a face recognition task. We also explored the potential of
traditional feature descriptors in the hyperspectral images. From our
evaluations, we observe that SIFT features outperform the state-of-the-art
hyperspectral face recognition methods, and also the other descriptors. With
the increasing deployment of hyperspectral sensors in a multitude of
applications, we believe that our approach can effectively exploit the spectral
information in hyperspectral images, thus beneficial to more accurate
classification.
Concept based Attention
Authors: Jie You, Xin Yang, Matthias Hub
Date: 2016-05-11 00:00:00

Attention endows animals an ability to concentrate on the most relevant
information among a deluge of distractors at any given time, either through
volitionally 'top-down' biasing, or driven by automatically 'bottom-up'
saliency of stimuli, in favour of advantageous competition in neural
modulations for information processing. Nevertheless, instead of being limited
to perceive simple features, human and other advanced animals adaptively learn
the world into categories and abstract concepts from experiences, imparting the
world meanings. This thesis suggests that the high-level cognitive ability of
human is more likely driven by attention basing on abstract perceptions, which
is defined as concept based attention (CbA).