pwc-archive/papers-with-abstracts huggingface dataset:
A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery
Authors: Arturo Marban, Vignesh Srinivasan, Wojciech Samek, Josep Fernández, Alicia Casals
Date: 2018-05-22 00:00:00

Providing force feedback as relevant information in current Robot-Assisted
Minimally Invasive Surgery systems constitutes a technological challenge due to
the constraints imposed by the surgical environment. In this context,
Sensorless Force Estimation techniques represent a potential solution, enabling
to sense the interaction forces between the surgical instruments and
soft-tissues. Specifically, if visual feedback is available for observing
soft-tissues' deformation, this feedback can be used to estimate the forces
applied to these tissues. To this end, a force estimation model, based on
Convolutional Neural Networks and Long-Short Term Memory networks, is proposed
in this work. This model is designed to process both, the spatiotemporal
information present in video sequences and the temporal structure of tool data
(the surgical tool-tip trajectory and its grasping status). A series of
analyses are carried out to reveal the advantages of the proposal and the
challenges that remain for real applications. This research work focuses on two
surgical task scenarios, referred to as pushing and pulling tissue. For these
two scenarios, different input data modalities and their effect on the force
estimation quality are investigated. These input data modalities are tool data,
video sequences and a combination of both. The results suggest that the force
estimation quality is better when both, the tool data and video sequences, are
processed by the neural network model. Moreover, this study reveals the need
for a loss function, designed to promote the modeling of smooth and sharp
details found in force signals. Finally, the results show that the modeling of
forces due to pulling tasks is more challenging than for the simplest pushing
actions.
Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements
Authors: Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä
Date: 2018-05-22 00:00:00
Tasks: Deblurring

Many computer vision and image processing applications rely on local
features. It is well-known that motion blur decreases the performance of
traditional feature detectors and descriptors. We propose an inertial-based
deblurring method for improving the robustness of existing feature detectors
and descriptors against the motion blur. Unlike most deblurring algorithms, the
method can handle spatially-variant blur and rolling shutter distortion.
Furthermore, it is capable of running in real-time contrary to state-of-the-art
algorithms. The limitations of inertial-based blur estimation are taken into
account by validating the blur estimates using image data. The evaluation shows
that when the method is used with traditional feature detector and descriptor,
it increases the number of detected keypoints, provides higher repeatability
and improves the localization accuracy. We also demonstrate that such features
will lead to more accurate and complete reconstructions when used in the
application of 3D visual reconstruction.
Visual Object Tracking: The Initialisation Problem
Authors: George De Ath, Richard Everson
Date: 2018-05-03 00:00:00
Tasks: Image Matting, Missing Labels, Object, Object Tracking, Visual Object Tracking

Model initialisation is an important component of object tracking. Tracking
algorithms are generally provided with the first frame of a sequence and a
bounding box (BB) indicating the location of the object. This BB may contain a
large number of background pixels in addition to the object and can lead to
parts-based tracking algorithms initialising their object models in background
regions of the BB. In this paper, we tackle this as a missing labels problem,
marking pixels sufficiently away from the BB as belonging to the background and
learning the labels of the unknown pixels. Three techniques, One-Class SVM
(OC-SVM), Sampled-Based Background Model (SBBM) (a novel background model based
on pixel samples), and Learning Based Digital Matting (LBDM), are adapted to
the problem. These are evaluated with leave-one-video-out cross-validation on
the VOT2016 tracking benchmark. Our evaluation shows both OC-SVMs and SBBM are
capable of providing a good level of segmentation accuracy but are too
parameter-dependent to be used in real-world scenarios. We show that LBDM
achieves significantly increased performance with parameters selected by cross
validation and we show that it is robust to parameter variation.
Semi-Supervised Learning on Graphs Based on Local Label Distributions
Authors: Evgeniy Faerman, Felix Borutta, Julian Busch, Matthias Schubert
Date: 2018-02-15 00:00:00
Tasks: Attribute, General Classification, Node Classification

Most approaches that tackle the problem of node classification consider nodes
to be similar, if they have shared neighbors or are close to each other in the
graph. Recent methods for attributed graphs additionally take attributes of
neighboring nodes into account. We argue that the class labels of the neighbors
bear important information and considering them helps to improve classification
quality. Two nodes which are similar based on class labels in their
neighborhood do not need to be close-by in the graph and may even belong to
different connected components. In this work, we propose a novel approach for
the semi-supervised node classification. Precisely, we propose a new node
embedding which is based on the class labels in the local neighborhood of a
node. We show that this is a different setting from attribute-based embeddings
and thus, we propose a new method to learn label-based node embeddings which
can mirror a variety of relations between the class labels of neighboring
nodes. Our experimental evaluation demonstrates that our new methods can
significantly improve the prediction quality on real world data sets.
Fully Understanding the Hashing Trick
Authors: Casper Benjamin Freksen, Lior Kamma, Kasper Green Larsen
Date: 2018-05-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Open-Ended Question Answering

Feature hashing, also known as {\em the hashing trick}, introduced by
Weinberger et al. (2009), is one of the key techniques used in scaling-up
machine learning algorithms. Loosely speaking, feature hashing uses a random
sparse projection matrix $A : \mathbb{R}^n \to \mathbb{R}^m$ (where $m \ll n$)
in order to reduce the dimension of the data from $n$ to $m$ while
approximately preserving the Euclidean norm. Every column of $A$ contains
exactly one non-zero entry, equals to either $-1$ or $1$.
  Weinberger et al. showed tail bounds on $\|Ax\|_2^2$. Specifically they
showed that for every $\varepsilon, \delta$, if $\|x\|_{\infty} / \|x\|_2$ is
sufficiently small, and $m$ is sufficiently large, then $$\Pr[ \; |
\;\|Ax\|_2^2 - \|x\|_2^2\; | < \varepsilon \|x\|_2^2 \;] \ge 1 - \delta \;.$$
These bounds were later extended by Dasgupta \etal (2010) and most recently
refined by Dahlgaard et al. (2017), however, the true nature of the performance
of this key technique, and specifically the correct tradeoff between the
pivotal parameters $\|x\|_{\infty} / \|x\|_2, m, \varepsilon, \delta$ remained
an open question.
  We settle this question by giving tight asymptotic bounds on the exact
tradeoff between the central parameters, thus providing a complete
understanding of the performance of feature hashing. We complement the
asymptotic bound with empirical data, which shows that the constants "hiding"
in the asymptotic notation are, in fact, very close to $1$, thus further
illustrating the tightness of the presented bounds in practice.
First-Hitting Times Under Additive Drift
Authors: Timo Kötzing, Martin S. Krejca
Date: 2018-05-22 00:00:00

For the last ten years, almost every theoretical result concerning the
expected run time of a randomized search heuristic used drift theory, making it
the arguably most important tool in this domain. Its success is due to its ease
of use and its powerful result: drift theory allows the user to derive bounds
on the expected first-hitting time of a random process by bounding expected
local changes of the process -- the drift. This is usually far easier than
bounding the expected first-hitting time directly.
  Due to the widespread use of drift theory, it is of utmost importance to have
the best drift theorems possible. We improve the fundamental additive,
multiplicative, and variable drift theorems by stating them in a form as
general as possible and providing examples of why the restrictions we keep are
still necessary. Our additive drift theorem for upper bounds only requires the
process to be nonnegative, that is, we remove unnecessary restrictions like a
finite, discrete, or bounded search space. As corollaries, the same is true for
our upper bounds in the case of variable and multiplicative drift.
Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach
Authors: Nora Al-Twairesh, Hend Al-Khalifa, Abdulmalik Al-Salman, Yousef Al-Ohali
Date: 2018-05-22 00:00:00
Tasks: Feature Engineering, General Classification, Sentiment Analysis

Sentiment Analysis in Arabic is a challenging task due to the rich morphology
of the language. Moreover, the task is further complicated when applied to
Twitter data that is known to be highly informal and noisy. In this paper, we
develop a hybrid method for sentiment analysis for Arabic tweets for a specific
Arabic dialect which is the Saudi Dialect. Several features were engineered and
evaluated using a feature backward selection method. Then a hybrid method that
combines a corpus-based and lexicon-based method was developed for several
classification models (two-way, three-way, four-way). The best F1-score for
each of these models was (69.9,61.63,55.07) respectively.
Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning from Unlabeled Videos and 3D-Shape Retrieval
Authors: Nenad Markuš, Igor S. Pandžić, Jörgen Ahlberg
Date: 2016-03-30 00:00:00
Tasks: 3D Shape Classification, 3D Shape Retrieval, Retrieval

Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs. However, data of this kind is not always available since detailed keypoint correspondences can be hard to establish. On the other hand, we can often obtain labels for pairs of keypoint bags. For example, keypoint bags extracted from two images of the same object under different views form a matching pair, and keypoint bags extracted from images of different objects form a non-matching pair. On average, matching pairs should contain more corresponding keypoints than non-matching pairs. We describe an end-to-end differentiable architecture that enables the learning of local keypoint descriptors from such weakly-labeled data. Additionally, we discuss how to improve the method by incorporating the procedure of mining hard negatives. We also show how can our approach be used to learn convolutional features from unlabeled video signals and 3D models. Our implementation is available at https://github.com/nenadmarkus/wlrn
Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial Iterations
Authors: Raphaël Berthier, Francis Bach, Pierre Gaillard
Date: 2018-05-22 00:00:00
Tasks: Denoising

Consider a network of agents connected by communication links, where each agent holds a real value. The gossip problem consists in estimating the average of the values diffused in the network in a distributed manner. We develop a method solving the gossip problem that depends only on the spectral dimension of the network, that is, in the communication network set-up, the dimension of the space in which the agents live. This contrasts with previous work that required the spectral gap of the network as a parameter, or suffered from slow mixing. Our method shows an important improvement over existing algorithms in the non-asymptotic regime, i.e., when the values are far from being fully mixed in the network. Our approach stems from a polynomial-based point of view on gossip algorithms, as well as an approximation of the spectral measure of the graphs with a Jacobi measure. We show the power of the approach with simulations on various graphs, and with performance guarantees on graphs of known spectral dimension, such as grids and random percolation bonds. An extension of this work to distributed Laplacian solvers is discussed. As a side result, we also use the polynomial-based point of view to show the convergence of the message passing algorithm for gossip of Moallemi \& Van Roy on regular graphs. The explicit computation of the rate of the convergence shows that message passing has a slow rate of convergence on graphs with small spectral gap.
Recognizing Objects In-the-wild: Where Do We Stand?
Authors: Mohammad Reza Loghmani, Barbara Caputo, Markus Vincze
Date: 2017-09-18 00:00:00
Tasks: Object, Object Recognition

The ability to recognize objects is an essential skill for a robotic system
acting in human-populated environments. Despite decades of effort from the
robotic and vision research communities, robots are still missing good visual
perceptual systems, preventing the use of autonomous agents for real-world
applications. The progress is slowed down by the lack of a testbed able to
accurately represent the world perceived by the robot in-the-wild. In order to
fill this gap, we introduce a large-scale, multi-view object dataset collected
with an RGB-D camera mounted on a mobile robot. The dataset embeds the
challenges faced by a robot in a real-life application and provides a useful
tool for validating object recognition algorithms. Besides describing the
characteristics of the dataset, the paper evaluates the performance of a
collection of well-established deep convolutional networks on the new dataset
and analyzes the transferability of deep representations from Web images to
robotic data. Despite the promising results obtained with such representations,
the experiments demonstrate that object classification with real-life robotic
data is far from being solved. Finally, we provide a comparative study to
analyze and highlight the open challenges in robot vision, explaining the
discrepancies in the performance.
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Authors: Shuai Li, Wanqing Li, Chris Cook, Ce Zhu, Yanbo Gao
Date: 2018-03-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Language Modelling, Sequential Image Classification, Skeleton Based Action Recognition

Recurrent neural networks (RNNs) have been widely used for processing
sequential data. However, RNNs are commonly difficult to train due to the
well-known gradient vanishing and exploding problems and hard to learn
long-term patterns. Long short-term memory (LSTM) and gated recurrent unit
(GRU) were developed to address these problems, but the use of hyperbolic
tangent and the sigmoid action functions results in gradient decay over layers.
Consequently, construction of an efficiently trainable deep network is
challenging. In addition, all the neurons in an RNN layer are entangled
together and their behaviour is hard to interpret. To address these problems, a
new type of RNN, referred to as independently recurrent neural network
(IndRNN), is proposed in this paper, where neurons in the same layer are
independent of each other and they are connected across layers. We have shown
that an IndRNN can be easily regulated to prevent the gradient exploding and
vanishing problems while allowing the network to learn long-term dependencies.
Moreover, an IndRNN can work with non-saturated activation functions such as
relu (rectified linear unit) and be still trained robustly. Multiple IndRNNs
can be stacked to construct a network that is deeper than the existing RNNs.
Experimental results have shown that the proposed IndRNN is able to process
very long sequences (over 5000 time steps), can be used to construct very deep
networks (21 layers used in the experiment) and still be trained robustly.
Better performances have been achieved on various tasks by using IndRNNs
compared with the traditional RNN and LSTM. The code is available at
https://github.com/Sunnydreamrain/IndRNN_Theano_Lasagne.
OK Google, What Is Your Ontology? Or: Exploring Freebase Classification to Understand Google's Knowledge Graph
Authors: Niel Chah
Date: 2018-05-10 00:00:00
Tasks: General Classification

This paper reconstructs the Freebase data dumps to understand the underlying
ontology behind Google's semantic search feature. The Freebase knowledge base
was a major Semantic Web and linked data technology that was acquired by Google
in 2010 to support the Google Knowledge Graph, the backend for Google search
results that include structured answers to queries instead of a series of links
to external resources. After its shutdown in 2016, Freebase is contained in a
data dump of 1.9 billion Resource Description Format (RDF) triples. A
recomposition of the Freebase ontology will be analyzed in relation to concepts
and insights from the literature on classification by Bowker and Star. This
paper will explore how the Freebase ontology is shaped by many of the forces
that also shape classification systems through a deep dive into the ontology
and a small correlational study. These findings will provide a glimpse into the
proprietary blackbox Knowledge Graph and what is meant by Google's mission to
"organize the world's information and make it universally accessible and
useful".
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Authors: Wenjia Bai, Matthew Sinclair, Giacomo Tarroni, Ozan Oktay, Martin Rajchl, Ghislain Vaillant, Aaron M. Lee, Nay Aung, Elena Lukaschuk, Mihir M. Sanghvi, Filip Zemrak, Kenneth Fung, Jose Miguel Paiva, Valentina Carapella, Young Jin Kim, Hideaki Suzuki, Bernhard Kainz, Paul M. Matthews, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Ben Glocker, Daniel Rueckert
Date: 2017-10-25 00:00:00

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging
modality for assessing cardiovascular diseases (CVDs), the leading cause of
death globally. CMR enables accurate quantification of the cardiac chamber
volume, ejection fraction and myocardial mass, providing information for
diagnosis and monitoring of CVDs. However, for years, clinicians have been
relying on manual approaches for CMR image analysis, which is time consuming
and prone to subjective errors. It is a major clinical challenge to
automatically derive quantitative and clinically relevant information from CMR
images. Deep neural networks have shown a great potential in image pattern
recognition and segmentation for a variety of tasks. Here we demonstrate an
automated analysis method for CMR images, which is based on a fully
convolutional network (FCN). The network is trained and evaluated on a
large-scale dataset from the UK Biobank, consisting of 4,875 subjects with
93,500 pixelwise annotated images. The performance of the method has been
evaluated using a number of technical metrics, including the Dice metric, mean
contour distance and Hausdorff distance, as well as clinically relevant
measures, including left ventricle (LV) end-diastolic volume (LVEDV) and
end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic
volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a
large-scale annotated dataset, the proposed automated method achieves a high
performance on par with human experts in segmenting the LV and RV on short-axis
CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR
images.
Part-based Tracking by Sampling
Authors: George De Ath, Richard M. Everson
Date: 2018-05-22 00:00:00
Tasks: Object

We propose a novel part-based method for tracking an arbitrary object in challenging video sequences. The colour distribution of tracked image patches on the target object are represented by pairs of RGB samples and counts of how many pixels in the patch are similar to them. Patches are placed by segmenting the object in the given bounding box and placing patches in homogeneous regions of the object. These are located in subsequent image frames by applying non-shearing affine transformations to the patches' previous locations, locally optimising the best of these, and evaluating their quality using a modified Bhattacharyya distance. In experiments carried out on VOT2018 and OTB100 benchmarks, the tracker achieves higher performance than all other part-based trackers. An ablation study is used to reveal the effectiveness of each tracking component, with largest performance gains found when using the patch placement scheme.
Optimal approximation of piecewise smooth functions using deep ReLU neural networks
Authors: Philipp Petersen, Felix Voigtlaender
Date: 2017-09-15 00:00:00

We study the necessary and sufficient complexity of ReLU neural networks---in
terms of depth and number of weights---which is required for approximating
classifier functions in $L^2$. As a model class, we consider the set
$\mathcal{E}^\beta (\mathbb R^d)$ of possibly discontinuous piecewise $C^\beta$
functions $f : [-1/2, 1/2]^d \to \mathbb R$, where the different smooth regions
of $f$ are separated by $C^\beta$ hypersurfaces. For dimension $d \geq 2$,
regularity $\beta > 0$, and accuracy $\varepsilon > 0$, we construct artificial
neural networks with ReLU activation function that approximate functions from
$\mathcal{E}^\beta(\mathbb R^d)$ up to $L^2$ error of $\varepsilon$. The
constructed networks have a fixed number of layers, depending only on $d$ and
$\beta$, and they have $O(\varepsilon^{-2(d-1)/\beta})$ many nonzero weights,
which we prove to be optimal. In addition to the optimality in terms of the
number of weights, we show that in order to achieve the optimal approximation
rate, one needs ReLU networks of a certain depth. Precisely, for piecewise
$C^\beta(\mathbb R^d)$ functions, this minimal depth is given---up to a
multiplicative constant---by $\beta/d$. Up to a log factor, our constructed
networks match this bound. This partly explains the benefits of depth for ReLU
networks by showing that deep networks are necessary to achieve efficient
approximation of (piecewise) smooth functions. Finally, we analyze
approximation in high-dimensional spaces where the function $f$ to be
approximated can be factorized into a smooth dimension reducing feature map
$\tau$ and classifier function $g$---defined on a low-dimensional feature
space---as $f = g \circ \tau$. We show that in this case the approximation rate
depends only on the dimension of the feature space and not the input dimension.