pwc-archive/papers-with-abstracts huggingface dataset:
A location-aware embedding technique for accurate landmark recognition
Authors: Federico Magliani, Navid Mahmoudian Bidgoli, Andrea Prati
Date: 2017-04-19 00:00:00
Tasks: Landmark Recognition

The current state of the research in landmark recognition highlights the good
accuracy which can be achieved by embedding techniques, such as Fisher vector
and VLAD. All these techniques do not exploit spatial information, i.e.
consider all the features and the corresponding descriptors without embedding
their location in the image. This paper presents a new variant of the
well-known VLAD (Vector of Locally Aggregated Descriptors) embedding technique
which accounts, at a certain degree, for the location of features. The driving
motivation comes from the observation that, usually, the most interesting part
of an image (e.g., the landmark to be recognized) is almost at the center of
the image, while the features at the borders are irrelevant features which do
no depend on the landmark. The proposed variant, called locVLAD (location-aware
VLAD), computes the mean of the two global descriptors: the VLAD executed on
the entire original image, and the one computed on a cropped image which
removes a certain percentage of the image borders. This simple variant shows an
accuracy greater than the existing state-of-the-art approach. Experiments are
conducted on two public datasets (ZuBuD and Holidays) which are used both for
training and testing. Morever a more balanced version of ZuBuD is proposed.
Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks
Authors: Julie Dequaire, Dushyant Rao, Peter Ondruska, Dominic Wang, Ingmar Posner
Date: 2016-09-29 00:00:00

This paper presents an end-to-end approach for tracking static and dynamic
objects for an autonomous vehicle driving through crowded urban environments.
Unlike traditional approaches to tracking, this method is learned end-to-end,
and is able to directly predict a full unoccluded occupancy grid map from raw
laser input data. Inspired by the recently presented DeepTracking approach
[Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the
temporal evolution of the state of the environment, and propose to use Spatial
Transformer modules to exploit estimates of the egomotion of the vehicle. Our
results demonstrate the ability to track a range of objects, including cars,
buses, pedestrians, and cyclists through occlusion, from both moving and
stationary platforms, using a single learned model. Experimental results
demonstrate that the model can also predict the future states of objects from
current inputs, with greater accuracy than previous work.
The Famine of Forte: Few Search Problems Greatly Favor Your Algorithm
Authors: George D. Montanez
Date: 2016-09-28 00:00:00
Tasks: BIG-bench Machine Learning

Casting machine learning as a type of search, we demonstrate that the
proportion of problems that are favorable for a fixed algorithm is strictly
bounded, such that no single algorithm can perform well over a large fraction
of them. Our results explain why we must either continue to develop new
learning methods year after year or move towards highly parameterized models
that are both flexible and sensitive to their hyperparameters. We further give
an upper bound on the expected performance for a search algorithm as a function
of the mutual information between the target and the information resource
(e.g., training dataset), proving the importance of certain types of dependence
for machine learning. Lastly, we show that the expected per-query probability
of success for an algorithm is mathematically equivalent to a single-query
probability of success under a distribution (called a search strategy), and
prove that the proportion of favorable strategies is also strictly bounded.
Thus, whether one holds fixed the search algorithm and considers all possible
problems or one fixes the search problem and looks at all possible search
strategies, favorable matches are exceedingly rare. The forte (strength) of any
algorithm is quantifiably restricted.
Adversarial Multi-task Learning for Text Classification
Authors: Pengfei Liu, Xipeng Qiu, Xuanjing Huang
Date: 2017-04-19 00:00:00
Proceeding: ACL 2017 7
Tasks: Classification, General Classification, Multi-Task Learning, text-classification, Text Classification

Neural network models have shown their promising opportunities for multi-task
learning, which focus on learning the shared layers to extract the common and
task-invariant features. However, in most existing approaches, the extracted
shared features are prone to be contaminated by task-specific features or the
noise brought by other tasks. In this paper, we propose an adversarial
multi-task learning framework, alleviating the shared and private latent
feature spaces from interfering with each other. We conduct extensive
experiments on 16 different text classification tasks, which demonstrates the
benefits of our approach. Besides, we show that the shared knowledge learned by
our proposed model can be regarded as off-the-shelf knowledge and easily
transferred to new tasks. The datasets of all 16 tasks are publicly available
at \url{http://nlp.fudan.edu.cn/data/}
Pessimistic Uplift Modeling
Authors: Atef Shaar, Talel Abdessalem, Olivier Segard
Date: 2016-03-31 00:00:00

Uplift modeling is a machine learning technique that aims to model treatment
effects heterogeneity. It has been used in business and health sectors to
predict the effect of a specific action on a given individual. Despite its
advantages, uplift models show high sensitivity to noise and disturbance, which
leads to unreliable results. In this paper we show different approaches to
address the problem of uplift modeling, we demonstrate how disturbance in data
can affect uplift measurement. We propose a new approach, we call it
Pessimistic Uplift Modeling, that minimizes disturbance effects. We compared
our approach with the existing uplift methods, on simulated and real data-sets.
The experiments show that our approach outperforms the existing approaches,
especially in the case of high noise data environment.
The True Destination of EGO is Multi-local Optimization
Authors: Simon Wessing, Mike Preuss
Date: 2017-04-19 00:00:00
Tasks: global-optimization

Efficient global optimization is a popular algorithm for the optimization of
expensive multimodal black-box functions. One important reason for its
popularity is its theoretical foundation of global convergence. However, as the
budgets in expensive optimization are very small, the asymptotic properties
only play a minor role and the algorithm sometimes comes off badly in
experimental comparisons. Many alternative variants have therefore been
proposed over the years. In this work, we show experimentally that the
algorithm instead has its strength in a setting where multiple optima are to be
identified.
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring
Authors: U. M. Khan, Z. Kabir, S. A. Hassan, S. H. Ahmed
Date: 2017-04-19 00:00:00
Tasks: Activity Recognition, Deep Learning, Human Activity Recognition, Time Series, Time Series Analysis

This paper presents an end-to-end deep learning framework using passive WiFi
sensing to classify and estimate human respiration activity. A passive radar
test-bed is used with two channels where the first channel provides the
reference WiFi signal, whereas the other channel provides a surveillance signal
that contains reflections from the human target. Adaptive filtering is
performed to make the surveillance signal source-data invariant by eliminating
the echoes of the direct transmitted signal. We propose a novel convolutional
neural network to classify the complex time series data and determine if it
corresponds to a breathing activity, followed by a random forest estimator to
determine breathing rate. We collect an extensive dataset to train the learning
models and develop reference benchmarks for the future studies in the field.
Based on the results, we conclude that deep learning techniques coupled with
passive radars offer great potential for end-to-end human activity recognition.
Detailed, accurate, human shape estimation from clothed 3D scan sequences
Authors: Chao Zhang, Sergi Pujades, Michael Black, Gerard Pons-Moll
Date: 2017-03-13 00:00:00
Proceeding: CVPR 2017 7
Tasks: Pose Estimation, Virtual Try-on

We address the problem of estimating human pose and body shape from 3D scans
over time. Reliable estimation of 3D body shape is necessary for many
applications including virtual try-on, health monitoring, and avatar creation
for virtual reality. Scanning bodies in minimal clothing, however, presents a
practical barrier to these applications. We address this problem by estimating
body shape under clothing from a sequence of 3D scans. Previous methods that
have exploited body models produce smooth shapes lacking personalized details.
We contribute a new approach to recover a personalized shape of the person. The
estimated shape deviates from a parametric model to fit the 3D scans. We
demonstrate the method using high quality 4D data as well as sequences of
visual hulls extracted from multi-view images. We also make available BUFF, a
new 4D dataset that enables quantitative evaluation
(http://buff.is.tue.mpg.de). Our method outperforms the state of the art in
both pose estimation and shape estimation, qualitatively and quantitatively.
Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network
Authors: Majd Zreik, Tim Leiner, Bob D. de Vos, Robbert W. van Hamersvelt, Max A. Viergever, Ivana Isgum
Date: 2017-04-19 00:00:00
Tasks: General Classification, LV Segmentation, Segmentation

Accurate delineation of the left ventricle (LV) is an important step in
evaluation of cardiac function. In this paper, we present an automatic method
for segmentation of the LV in cardiac CT angiography (CCTA) scans. Segmentation
is performed in two stages. First, a bounding box around the LV is detected
using a combination of three convolutional neural networks (CNNs).
Subsequently, to obtain the segmentation of the LV, voxel classification is
performed within the defined bounding box using a CNN. The study included CCTA
scans of sixty patients, fifty scans were used to train the CNNs for the LV
localization, five scans were used to train LV segmentation and the remaining
five scans were used for testing the method. Automatic segmentation resulted in
the average Dice coefficient of 0.85 and mean absolute surface distance of 1.1
mm. The results demonstrate that automatic segmentation of the LV in CCTA scans
using voxel classification with convolutional neural networks is feasible.
A multi-method simulation of a high-frequency bus line using AnyLogic
Authors: Thierry van der Spek
Date: 2017-04-19 00:00:00

In this work a mixed agent-based and discrete event simulation model is
developed for a high frequency bus route in the Netherlands. With this model,
different passenger growth scenarios can be easily evaluated. This simulation
model helps policy makers to predict changes that have to be made to bus routes
and planned travel times before problems occur. The model is validated using
several performance indicators, showing that under some model assumptions, it
can realistically simulate real-life situations. The simulation's workings are
illustrated by two use cases.
ArtGAN: Artwork Synthesis with Conditional Categorical GANs
Authors: Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, Kiyoshi Tanaka
Date: 2017-02-11 00:00:00
Tasks: Art Analysis, Conditional Image Generation, Image Generation

This paper proposes an extension to the Generative Adversarial Networks
(GANs), namely as ARTGAN to synthetically generate more challenging and complex
images such as artwork that have abstract characteristics. This is in contrast
to most of the current solutions that focused on generating natural images such
as room interiors, birds, flowers and faces. The key innovation of our work is
to allow back-propagation of the loss function w.r.t. the labels (randomly
assigned to each generated images) to the generator from the discriminator.
With the feedback from the label information, the generator is able to learn
faster and achieve better generated image quality. Empirically, we show that
the proposed ARTGAN is capable to create realistic artwork, as well as generate
compelling real world images that globally look natural with clear shape on
CIFAR-10.
Design of low-cost, compact and weather-proof whole sky imagers for high-dynamic-range captures
Authors: Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, Stefan Winkler
Date: 2017-04-19 00:00:00

Ground-based whole sky imagers are popular for monitoring cloud formations,
which is necessary for various applications. We present two new Wide Angle
High-Resolution Sky Imaging System (WAHRSIS) models, which were designed
especially to withstand the hot and humid climate of Singapore. The first uses
a fully sealed casing, whose interior temperature is regulated using a Peltier
cooler. The second features a double roof design with ventilation grids on the
sides, allowing the outside air to flow through the device. Measurements of
temperature inside these two devices show their ability to operate in Singapore
weather conditions. Unlike our original WAHRSIS model, neither uses a
mechanical sun blocker to prevent the direct sunlight from reaching the camera;
instead they rely on high-dynamic-range imaging (HDRI) techniques to reduce the
glare from the sun.
On the Complexity of Robust PCA and $\ell_1$-norm Low-Rank Matrix Approximation
Authors: Nicolas Gillis, Stephen A. Vavasis
Date: 2015-09-30 00:00:00

The low-rank matrix approximation problem with respect to the component-wise
$\ell_1$-norm ($\ell_1$-LRA), which is closely related to robust principal
component analysis (PCA), has become a very popular tool in data mining and
machine learning. Robust PCA aims at recovering a low-rank matrix that was
perturbed with sparse noise, with applications for example in
foreground-background video separation. Although $\ell_1$-LRA is strongly
believed to be NP-hard, there is, to the best of our knowledge, no formal proof
of this fact. In this paper, we prove that $\ell_1$-LRA is NP-hard, already in
the rank-one case, using a reduction from MAX CUT. Our derivations draw
interesting connections between $\ell_1$-LRA and several other well-known
problems, namely, robust PCA, $\ell_0$-LRA, binary matrix factorization, a
particular densest bipartite subgraph problem, the computation of the cut norm
of $\{-1,+1\}$ matrices, and the discrete basis problem, which we all prove to
be NP-hard.
How can one sample images with sampling rates close to the theoretical minimum?
Authors: Leonid Yaroslavsky
Date: 2016-01-03 00:00:00

A problem is addressed of minimization of the number of measurements needed
for digital image acquisition and reconstruction with a given accuracy. A
sampling theory based method of image sampling and reconstruction is suggested
that allows to draw near the minimal rate of image sampling defined by the
sampling theory. Presented and discussed are also results of experimental
verification of the method and its possible applicability extensions.
Unsupervised object segmentation in video by efficient selection of highly probable positive features
Authors: Emanuela Haller, Marius Leordeanu
Date: 2017-04-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Semantic Segmentation, Unsupervised Object Segmentation

We address an essential problem in computer vision, that of unsupervised
object segmentation in video, where a main object of interest in a video
sequence should be automatically separated from its background. An efficient
solution to this task would enable large-scale video interpretation at a high
semantic level in the absence of the costly manually labeled ground truth. We
propose an efficient unsupervised method for generating foreground object
soft-segmentation masks based on automatic selection and learning from highly
probable positive features. We show that such features can be selected
efficiently by taking into consideration the spatio-temporal, appearance and
motion consistency of the object during the whole observed sequence. We also
emphasize the role of the contrasting properties between the foreground object
and its background. Our model is created in two stages: we start from pixel
level analysis, on top of which we add a regression model trained on a
descriptor that considers information over groups of pixels and is both
discriminative and invariant to many changes that the object undergoes
throughout the video. We also present theoretical properties of our
unsupervised learning method, that under some mild constraints is guaranteed to
learn a correct discriminative classifier even in the unsupervised case. Our
method achieves competitive and even state of the art results on the
challenging Youtube-Objects and SegTrack datasets, while being at least one
order of magnitude faster than the competition. We believe that the competitive
performance of our method in practice, along with its theoretical properties,
constitute an important step towards solving unsupervised discovery in video.
CNN based music emotion classification
Authors: Xin Liu, Qingcai Chen, Xiang-Ping Wu, Yan Liu, Yang Liu
Date: 2017-04-19 00:00:00
Tasks: Classification, Emotion Classification, Emotion Recognition, General Classification, Music Emotion Recognition

Music emotion recognition (MER) is usually regarded as a multi-label tagging
task, and each segment of music can inspire specific emotion tags. Most
researchers extract acoustic features from music and explore the relations
between these features and their corresponding emotion tags. Considering the
inconsistency of emotions inspired by the same music segment for human beings,
seeking for the key acoustic features that really affect on emotions is really
a challenging task. In this paper, we propose a novel MER method by using deep
convolutional neural network (CNN) on the music spectrograms that contains both
the original time and frequency domain information. By the proposed method, no
additional effort on extracting specific features required, which is left to
the training procedure of the CNN model. Experiments are conducted on the
standard CAL500 and CAL500exp dataset. Results show that, for both datasets,
the proposed method outperforms state-of-the-art methods.
Expert Gate: Lifelong Learning with a Network of Experts
Authors: Rahaf Aljundi, Punarjay Chakravarty, Tinne Tuytelaars
Date: 2016-11-18 00:00:00
Proceeding: CVPR 2017 7
Tasks: image-classification, Image Classification, Lifelong learning, Video Prediction

In this paper we introduce a model of lifelong learning, based on a Network
of Experts. New tasks / experts are learned and added to the model
sequentially, building on what was learned before. To ensure scalability of
this process,data from previous tasks cannot be stored and hence is not
available when learning a new task. A critical issue in such context, not
addressed in the literature so far, relates to the decision which expert to
deploy at test time. We introduce a set of gating autoencoders that learn a
representation for the task at hand, and, at test time, automatically forward
the test sample to the relevant expert. This also brings memory efficiency as
only one expert network has to be loaded into memory at any given time.
Further, the autoencoders inherently capture the relatedness of one task to
another, based on which the most relevant prior model to be used for training a
new expert, with finetuning or learning without-forgetting, can be selected. We
evaluate our method on image classification and video prediction problems.
Effects of the optimisation of the margin distribution on generalisation in deep architectures
Authors: Lech Szymanski, Brendan McCane, Wei Gao, Zhi-Hua Zhou
Date: 2017-04-19 00:00:00
Tasks: Deep Learning

Despite being so vital to success of Support Vector Machines, the principle
of separating margin maximisation is not used in deep learning. We show that
minimisation of margin variance and not maximisation of the margin is more
suitable for improving generalisation in deep architectures. We propose the
Halfway loss function that minimises the Normalised Margin Variance (NMV) at
the output of a deep learning models and evaluate its performance against the
Softmax Cross-Entropy loss on the MNIST, smallNORB and CIFAR-10 datasets.
On the convergence of the sparse possibilistic c-means algorithm
Authors: Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
Date: 2015-08-05 00:00:00

In this paper, a convergence proof for the recently proposed sparse
possibilistic c-means (SPCM) algorithm is provided, utilizing the celebrated
Zangwill convergence theorem. It is shown that the iterative sequence generated
by SPCM converges to a stationary point or there exists a subsequence of it
that converges to a stationary point of the cost function of the algorithm.
Skeleton Boxes: Solving skeleton based action detection with a single deep convolutional neural network
Authors: Bo Li, Huahui Chen, Yu-cheng Chen, Yuchao Dai, Mingyi He
Date: 2017-04-19 00:00:00
Tasks: Action Detection, Action Recognition, object-detection, Object Detection, Temporal Action Localization

Action recognition from well-segmented 3D skeleton video has been intensively
studied. However, due to the difficulty in representing the 3D skeleton video
and the lack of training data, action detection from streaming 3D skeleton
video still lags far behind its recognition counterpart and image based object
detection. In this paper, we propose a novel approach for this problem, which
leverages both effective skeleton video encoding and deep regression based
object detection from images. Our framework consists of two parts:
skeleton-based video image mapping, which encodes a skeleton video to a color
image in a temporal preserving way, and an end-to-end trainable fast skeleton
action detector (Skeleton Boxes) based on image detection. Experimental results
on the latest and largest PKU-MMD benchmark dataset demonstrate that our method
outperforms the state-of-the-art methods with a large margin. We believe our
idea would inspire and benefit future research in this important area.
Embedding Knowledge Graphs Based on Transitivity and Antisymmetry of Rules
Authors: Mengya Wang, Hankui Zhuo, Huiling Zhu
Date: 2017-02-24 00:00:00
Tasks: Knowledge Graph Completion, Knowledge Graphs, Relation, Representation Learning

Representation learning of knowledge graphs encodes entities and relation
types into a continuous low-dimensional vector space, learns embeddings of
entities and relation types. Most existing methods only concentrate on
knowledge triples, ignoring logic rules which contain rich background
knowledge. Although there has been some work aiming at leveraging both
knowledge triples and logic rules, they ignore the transitivity and
antisymmetry of logic rules. In this paper, we propose a novel approach to
learn knowledge representations with entities and ordered relations in
knowledges and logic rules. The key idea is to integrate knowledge triples and
logic rules, and approximately order the relation types in logic rules to
utilize the transitivity and antisymmetry of logic rules. All entries of the
embeddings of relation types are constrained to be non-negative. We translate
the general constrained optimization problem into an unconstrained optimization
problem to solve the non-negative matrix factorization. Experimental results
show that our model significantly outperforms other baselines on knowledge
graph completion task. It indicates that our model is capable of capturing the
transitivity and antisymmetry information, which is significant when learning
embeddings of knowledge graphs.