pwc-archive/papers-with-abstracts huggingface dataset:
Collective behavior recognition using compact descriptors
Authors: Gustavo Fuhr, Claudio Rosito Jung
Date: 2018-09-27 00:00:00

This paper presents a novel hierarchical approach for collective behavior
recognition based solely on ground-plane trajectories. In the first layer of
our classifier, we introduce a novel feature called Personal Interaction
Descriptor (PID), which combines the spatial distribution of a pair of
pedestrians within a temporal window with a pyramidal representation of the
relative speed to detect pairwise interactions. These interactions are then
combined with higher level features related to the mean speed and shape formed
by the pedestrians in the scene, generating a Collective Behavior Descriptor
(CBD) that is used to identify collective behaviors in a second stage. In both
layers, Random Forests were used as classifiers, since they allow features of
different natures to be combined seamlessly. Our experimental results indicate
that the proposed method achieves results on par with state of the art
techniques with a better balance of class errors. Moreover, we show that our
method can generalize well across different camera setups through cross-dataset
experiments.
On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA
Authors: Dan Garber
Date: 2018-09-27 00:00:00

In this paper we focus on the problem of Online Principal Component Analysis
in the regret minimization framework. For this problem, all existing regret
minimization algorithms for the fully-adversarial setting are based on a
positive semidefinite convex relaxation, and hence require quadratic memory and
SVD computation (either thin of full) on each iteration, which amounts to at
least quadratic runtime per iteration. This is in stark contrast to a
corresponding stochastic i.i.d. variant of the problem, which was studied
extensively lately, and admits very efficient gradient ascent algorithms that
work directly on the natural non-convex formulation of the problem, and hence
require only linear memory and linear runtime per iteration. This raises the
question: can non-convex online gradient ascent algorithms be shown to minimize
regret in online adversarial settings? In this paper we take a step forward
towards answering this question. We introduce an
\textit{adversarially-perturbed spiked-covariance model} in which, each data
point is assumed to follow a fixed stochastic distribution with a non-zero
spectral gap in the covariance matrix, but is then perturbed with some
adversarial vector. This model is a natural extension of a well studied
standard stochastic setting that allows for non-stationary (adversarial)
patterns to arise in the data and hence, might serve as a significantly better
approximation for real-world data-streams. We show that in an interesting
regime of parameters, when the non-convex online gradient ascent algorithm is
initialized with a "warm-start" vector, it provably minimizes the regret with
high probability. We further discuss the possibility of computing such a
"warm-start" vector, and also the use of regularization to obtain fast regret
rates. Our theoretical findings are supported by empirical experiments on both
synthetic and real-world data.
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Authors: Fabian Isensee, Jens Petersen, Andre Klein, David Zimmerer, Paul F. Jaeger, Simon Kohl, Jakob Wasserthal, Gregor Koehler, Tobias Norajitra, Sebastian Wirkert, Klaus H. Maier-Hein
Date: 2018-09-27 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The U-Net was presented in 2015. With its straight-forward and successful
architecture it quickly evolved to a commonly used benchmark in medical image
segmentation. The adaptation of the U-Net to novel problems, however, comprises
several degrees of freedom regarding the exact architecture, preprocessing,
training and inference. These choices are not independent of each other and
substantially impact the overall performance. The present paper introduces the
nnU-Net ('no-new-Net'), which refers to a robust and self-adapting framework on
the basis of 2D and 3D vanilla U-Nets. We argue the strong case for taking away
superfluous bells and whistles of many proposed network designs and instead
focus on the remaining aspects that make out the performance and
generalizability of a method. We evaluate the nnU-Net in the context of the
Medical Segmentation Decathlon challenge, which measures segmentation
performance in ten disciplines comprising distinct entities, image modalities,
image geometries and dataset sizes, with no manual adjustments between datasets
allowed. At the time of manuscript submission, nnU-Net achieves the highest
mean dice scores across all classes and seven phase 1 tasks (except class 1 in
BrainTumour) in the online leaderboard of the challenge.
No New-Net
Authors: Fabian Isensee, Philipp Kickingereder, Wolfgang Wick, Martin Bendszus, Klaus H. Maier-Hein
Date: 2018-09-27 00:00:00

In this paper we demonstrate the effectiveness of a well trained U-Net in the
context of the BraTS 2018 challenge. This endeavour is particularly interesting
given that researchers are currently besting each other with architectural
modifications that are intended to improve the segmentation performance. We
instead focus on the training process arguing that a well trained U-Net is hard
to beat. Our baseline U-Net, which has only minor modifications and is trained
with a large patch size and a Dice loss function indeed achieved competitive
Dice scores on the BraTS2018 validation data. By incorporating additional
measures such as region based training, additional training data, a simple
postprocessing technique and a combination of loss functions, we obtain Dice
scores of 77.88, 87.81 and 80.62, and Hausdorff Distances (95th percentile) of
2.90, 6.03 and 5.08 for the enhancing tumor, whole tumor and tumor core,
respectively on the test data. This setup achieved rank two in BraTS2018, with
more than 60 teams participating in the challenge.
Budgeted Multi-Objective Optimization with a Focus on the Central Part of the Pareto Front -- Extended Version
Authors: David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, Vincent Herbert
Date: 2018-09-27 00:00:00

Optimizing nonlinear systems involving expensive computer experiments with regard to conflicting objectives is a common challenge. When the number of experiments is severely restricted and/or when the number of objectives increases, uncovering the whole set of Pareto optimal solutions is out of reach, even for surrogate-based approaches: the proposed solutions are sub-optimal or do not cover the front well. As non-compromising optimal solutions have usually little point in applications, this work restricts the search to solutions that are close to the Pareto front center. The article starts by characterizing this center, which is defined for any type of front. Next, a Bayesian multi-objective optimization method for directing the search towards it is proposed. Targeting a subset of the Pareto front allows an improved optimality of the solutions and a better coverage of this zone, which is our main concern. A criterion for detecting convergence to the center is described. If the criterion is triggered, a widened central part of the Pareto front is targeted such that sufficiently accurate convergence to it is forecasted within the remaining budget. Numerical experiments show how the resulting algorithm, C-EHI, better locates the central part of the Pareto front when compared to state-of-the-art Bayesian algorithms.
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems
Authors: Dan Garber, Atara Kaplan
Date: 2018-09-27 00:00:00
Tasks: Stochastic Optimization

Composite convex optimization problems which include both a nonsmooth term
and a low-rank promoting term have important applications in machine learning
and signal processing, such as when one wishes to recover an unknown matrix
that is simultaneously low-rank and sparse. However, such problems are highly
challenging to solve in large-scale: the low-rank promoting term prohibits
efficient implementations of proximal methods for composite optimization and
even simple subgradient methods. On the other hand, methods which are tailored
for low-rank optimization, such as conditional gradient-type methods, which are
often applied to a smooth approximation of the nonsmooth objective, are slow
since their runtime scales with both the large Lipshitz parameter of the
smoothed gradient vector and with $1/\epsilon$. In this paper we develop
efficient algorithms for \textit{stochastic} optimization of a strongly-convex
objective which includes both a nonsmooth term and a low-rank promoting term.
In particular, to the best of our knowledge, we present the first algorithm
that enjoys all following critical properties for large-scale problems: i)
(nearly) optimal sample complexity, ii) each iteration requires only a single
\textit{low-rank} SVD computation, and iii) overall number of thin-SVD
computations scales only with $\log{1/\epsilon}$ (as opposed to
$\textrm{poly}(1/\epsilon)$ in previous methods). We also give an algorithm for
the closely-related finite-sum setting. At the heart of our results lie a novel
combination of a variance-reduction technique and the use of a
\textit{weak-proximal oracle} which is key to obtaining all above three
properties simultaneously.
Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding
Authors: Syeda Mariam Ahmed, Yan Zhi Tan, Chee Meng Chew, Abdullah Al Mamun, Fook Seng Wong
Date: 2018-09-27 00:00:00
Tasks: Edge Detection, Semantic Segmentation

In this paper, we propose novel edge and corner detection algorithms for
unorganized point clouds. Our edge detection method evaluates symmetry in a
local neighborhood and uses an adaptive density based threshold to
differentiate 3D edge points. We extend this algorithm to propose a novel
corner detector that clusters curvature vectors and uses their geometrical
statistics to classify a point as corner. We perform rigorous evaluation of the
algorithms on RGB-D semantic segmentation and 3D washer models from the
ShapeNet dataset and report higher precision and recall scores. Finally, we
also demonstrate how our edge and corner detectors can be used as a novel
approach towards automatic weld seam detection for robotic welding. We propose
to generate weld seams directly from a point cloud as opposed to using 3D
models for offline planning of welding paths. For this application, we show a
comparison between Harris 3D and our proposed approach on a panel workpiece.
Learning to Train a Binary Neural Network
Authors: Joseph Bethge, Haojin Yang, Christian Bartz, Christoph Meinel
Date: 2018-09-27 00:00:00

Convolutional neural networks have achieved astonishing results in different
application areas. Various methods which allow us to use these models on mobile
and embedded devices have been proposed. Especially binary neural networks seem
to be a promising approach for these devices with low computational power.
However, understanding binary neural networks and training accurate models for
practical applications remains a challenge. In our work, we focus on increasing
our understanding of the training process and making it accessible to everyone.
We publish our code and models based on BMXNet for everyone to use. Within this
framework, we systematically evaluated different network architectures and
hyperparameters to provide useful insights on how to train a binary neural
network. Further, we present how we improved accuracy by increasing the number
of connections in the network.
Sample Efficient Adaptive Text-to-Speech
Authors: Yutian Chen, Yannis Assael, Brendan Shillingford, David Budden, Scott Reed, Heiga Zen, Quan Wang, Luis C. Cobo, Andrew Trask, Ben Laurie, Caglar Gulcehre, Aäron van den Oord, Oriol Vinyals, Nando de Freitas
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Meta-Learning, text-to-speech, Text to Speech, Voice Similarity

We present a meta-learning approach for adaptive text-to-speech (TTS) with
few data. During training, we learn a multi-speaker model using a shared
conditional WaveNet core and independent learned embeddings for each speaker.
The aim of training is not to produce a neural network with fixed weights,
which is then deployed as a TTS system. Instead, the aim is to produce a
network that requires few data at deployment time to rapidly adapt to new
speakers. We introduce and benchmark three strategies: (i) learning the speaker
embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire
architecture with stochastic gradient descent, and (iii) predicting the speaker
embedding with a trained neural network encoder. The experiments show that
these approaches are successful at adapting the multi-speaker neural network to
new speakers, obtaining state-of-the-art results in both sample naturalness and
voice similarity with merely a few minutes of audio data from new speakers.
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields
Authors: Reuben A. Farrugia, C. Guillemot
Date: 2018-09-27 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution

Plenoptic cameras offer a cost effective solution to capture light fields by
multiplexing multiple views on a single image sensor. However, the high angular
resolution is achieved at the expense of reducing the spatial resolution of
each view by orders of magnitude compared to the raw sensor image. While light
field super-resolution is still at an early stage, the field of single image
super-resolution (SISR) has recently known significant advances with the use of
deep learning techniques. This paper describes a simple framework allowing us
to leverage state-of-the-art SISR techniques into light fields, while taking
into account specific light field geometrical constraints. The idea is to first
compute a representation compacting most of the light field energy into as few
components as possible. This is achieved by aligning the light field using
optical flows and then by decomposing the aligned light field using singular
value decomposition (SVD). The principal basis captures the information that is
coherent across all the views, while the other basis contain the high angular
frequencies. Super-resolving this principal basis using an SISR method allows
us to super-resolve all the information that is coherent across the entire
light field. This framework allows the proposed light field super-resolution
method to inherit the benefits of the SISR method used. Experimental results
show that the proposed method is competitive, and most of the time superior, to
recent light field super-resolution methods in terms of both PSNR and SSIM
quality metrics, with a lower complexity.
A Way to Facilitate Decision Making in a Mixed Group of Manned and Unmanned Aerial Vehicles
Authors: Dmitry Maximov, Yury Legovich, Vladimir Goncharenko
Date: 2018-09-27 00:00:00
Tasks: Decision Making

A mixed group of manned and unmanned aerial vehicles is considered as a
distributed system. A lattice of tasks which may be fulfilled by the system
matches to it. An external multiplication operation is defined at the lattice,
which defines correspondingly linear logic operations. Linear implication and
tensor product are used to choose a system reconfiguration variant, i.e., to
determine a new task executor choice. The task lattice structure (i.e., the
system purpose) and the operation definitions largely define the choice. Thus,
the choice is mainly the system purpose consequence. Such a method of the
behavior variant choice facilitates the decision making by the pilot
controlling the group. The suggested approach is illustrated using an example
of a mixed group control at forest fire compression.
Compressing the Input for CNNs with the First-Order Scattering Transform
Authors: Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko
Date: 2018-09-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, Translation

We study the first-order scattering transform as a candidate for reducing the
signal processed by a convolutional neural network (CNN). We show theoretical
and empirical evidence that in the case of natural images and sufficiently
small translation invariance, this transform preserves most of the signal
information needed for classification while substantially reducing the spatial
resolution and total signal size. We demonstrate that cascading a CNN with this
representation performs on par with ImageNet classification models, commonly
used in downstream tasks, such as the ResNet-50. We subsequently apply our
trained hybrid ImageNet model as a base model on a detection system, which has
typically larger image inputs. On Pascal VOC and COCO detection tasks we
demonstrate improvements in the inference speed and training memory consumption
compared to models trained directly on the input image.
Wafer Quality Inspection using Memristive LSTM, ANN, DNN and HTM
Authors: Kazybek Adam, Kamilya Smagulova, Olga Krestinskaya, Alex Pappachen James
Date: 2018-09-27 00:00:00
Tasks: General Classification

The automated wafer inspection and quality control is a complex and
time-consuming task, which can speed up using neuromorphic memristive
architectures, as a separate inspection device or integrating directly into
sensors. This paper presents the performance analysis and comparison of
different neuromorphic architectures for patterned wafer quality inspection and
classification. The application of non-volatile memristive devices in these
architectures ensures low power consumption, small on-chip area scalability. We
demonstrate that Long-Short Term Memory (LSTM) outperforms other architectures
for the same number of training iterations, and has relatively low on-chip area
and power consumption.
Generating Ontologies from Templates: A Rule-Based Approach for Capturing Regularity
Authors: Henrik Forssell, Christian Kindermann, Daniel P. Lupp, Uli Sattler, Evgenij Thorstensen
Date: 2018-09-27 00:00:00
Tasks: Negation

We present a second-order language that can be used to succinctly specify
ontologies in a consistent and transparent manner. This language is based on
ontology templates (OTTR), a framework for capturing recurring patterns of
axioms in ontological modelling. The language and our results are independent
of any specific DL. We define the language and its semantics, including the
case of negation-as-failure, investigate reasoning over ontologies specified
using our language, and show results about the decidability of useful reasoning
tasks about the language itself. We also state and discuss some open problems
that we believe to be of interest.
CNN Based Posture-Free Hand Detection
Authors: Richard Adiguna, Yustinus Eko Soelistio
Date: 2018-09-27 00:00:00
Tasks: Hand Detection, Translation

Although many studies suggest high performance hand detection methods, those
methods are likely to be overfitting. Fortunately, the Convolution Neural
Network (CNN) based approach provides a better way that is less sensitive to
translation and hand poses. However the CNN approach is complex and can
increase computational time, which at the end reduce its effectiveness on a
system where the speed is essential.In this study we propose a shallow CNN
network which is fast, and insensitive to translation and hand poses. It is
tested on two different domains of hand datasets, and performs in relatively
comparable performance and faster than the other state-of-the-art hand
CNN-based hand detection method. Our evaluation shows that the proposed shallow
CNN network performs at 93.9% accuracy and reaches much faster speed than its
competitors.
Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI
Authors: Jörg Sander, Bob D. de Vos, Jelmer M. Wolterink, Ivana Išgum
Date: 2018-09-27 00:00:00
Tasks: Deep Learning, Image Segmentation, Segmentation, Semantic Segmentation

Current state-of-the-art deep learning segmentation methods have not yet made
a broad entrance into the clinical setting in spite of high demand for such
automatic methods. One important reason is the lack of reliability caused by
models that fail unnoticed and often locally produce anatomically implausible
results that medical experts would not make. This paper presents an automatic
image segmentation method based on (Bayesian) dilated convolutional networks
(DCNN) that generate segmentation masks and spatial uncertainty maps for the
input image at hand. The method was trained and evaluated using segmentation of
the left ventricle (LV) cavity, right ventricle (RV) endocardium and myocardium
(Myo) at end-diastole (ED) and end-systole (ES) in 100 cardiac 2D MR scans from
the MICCAI 2017 Challenge (ACDC). Combining segmentations and uncertainty maps
and employing a human-in-the-loop setting, we provide evidence that image areas
indicated as highly uncertain regarding the obtained segmentation almost
entirely cover regions of incorrect segmentations. The fused information can be
harnessed to increase segmentation performance. Our results reveal that we can
obtain valuable spatial uncertainty maps with low computational effort using
DCNNs.
Deformable Object Tracking with Gated Fusion
Authors: Wenxi Liu, Yibing Song, Dengsheng Chen, Shengfeng He, Yuanlong Yu, Tao Yan, Gerhard P. Hancke, Rynson W. H. Lau
Date: 2018-09-27 00:00:00
Tasks: Object, Object Tracking

The tracking-by-detection framework receives growing attentions through the
integration with the Convolutional Neural Networks (CNNs). Existing
tracking-by-detection based methods, however, fail to track objects with severe
appearance variations. This is because the traditional convolutional operation
is performed on fixed grids, and thus may not be able to find the correct
response while the object is changing pose or under varying environmental
conditions. In this paper, we propose a deformable convolution layer to enrich
the target appearance representations in the tracking-by-detection framework.
We aim to capture the target appearance variations via deformable convolution,
which adaptively enhances its original features. In addition, we also propose a
gated fusion scheme to control how the variations captured by the deformable
convolution affect the original appearance. The enriched feature representation
through deformable convolution facilitates the discrimination of the CNN
classifier on the target object and background. Extensive experiments on the
standard benchmarks show that the proposed tracker performs favorably against
state-of-the-art methods.