pwc-archive/papers-with-abstracts huggingface dataset:
Simple Distances for Trajectories via Landmarks
Authors: Jeff M. Phillips, Pingfan Tang
Date: 2018-04-30 00:00:00
Tasks: Clustering

We develop a new class of distances for objects including lines, hyperplanes, and trajectories, based on the distance to a set of landmarks. These distances easily and interpretably map objects to a Euclidean space, are simple to compute, and perform well in data analysis tasks. For trajectories, they match and in some cases significantly out-perform all state-of-the-art other metrics, can effortlessly be used in k-means clustering, and directly plugged into approximate nearest neighbor approaches which immediately out-perform the best recent advances in trajectory similarity search by several orders of magnitude. These distances do not require a geometry distorting dual (common in the line or halfspace case) or complicated alignment (common in trajectory case). We show reasonable and often simple conditions under which these distances are metrics.
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
Authors: Max Grusky, Mor Naaman, Yoav Artzi
Date: 2018-04-30 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, Diversity

We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications. Extracted from search and social media metadata between 1998 and 2017, these high-quality summaries demonstrate high diversity of summarization styles. In particular, the summaries combine abstractive and extractive strategies, borrowing words and phrases from articles at varying rates. We analyze the extraction strategies used in NEWSROOM summaries against other datasets to quantify the diversity and difficulty of our new data, and train existing methods on the data to evaluate its utility and challenges.
4D Temporally Coherent Light-field Video
Authors: Armin Mustafa, Marco Volino, Jean-yves Guillemaut, Adrian Hilton
Date: 2018-04-30 00:00:00
Tasks: Scene Flow Estimation

Light-field video has recently been used in virtual and augmented reality
applications to increase realism and immersion. However, existing light-field
methods are generally limited to static scenes due to the requirement to
acquire a dense scene representation. The large amount of data and the absence
of methods to infer temporal coherence pose major challenges in storage,
compression and editing compared to conventional video. In this paper, we
propose the first method to extract a spatio-temporally coherent light-field
video representation. A novel method to obtain Epipolar Plane Images (EPIs)
from a spare light-field camera array is proposed. EPIs are used to constrain
scene flow estimation to obtain 4D temporally coherent representations of
dynamic light-fields. Temporal coherence is achieved on a variety of
light-field datasets. Evaluation of the proposed light-field scene flow against
existing multi-view dense correspondence approaches demonstrates a significant
improvement in accuracy of temporal coherence.
Gaussian Process Behaviour in Wide Deep Neural Networks
Authors: Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
Date: 2018-04-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Gaussian Processes

Whilst deep neural networks have shown great empirical success, there is
still much work to be done to understand their theoretical properties. In this
paper, we study the relationship between random, wide, fully connected,
feedforward networks with more than one hidden layer and Gaussian processes
with a recursive kernel definition. We show that, under broad conditions, as we
make the architecture increasingly wide, the implied random function converges
in distribution to a Gaussian process, formalising and extending existing
results by Neal (1996) to deep networks. To evaluate convergence rates
empirically, we use maximum mean discrepancy. We then compare finite Bayesian
deep networks from the literature to Gaussian processes in terms of the key
predictive quantities of interest, finding that in some cases the agreement can
be very close. We discuss the desirability of Gaussian process behaviour and
review non-Gaussian alternative models from the literature.
Interpreting weight maps in terms of cognitive or clinical neuroscience: nonsense?
Authors: Jessica Schrouff, Janaina Mourao-Miranda
Date: 2018-04-30 00:00:00

Since machine learning models have been applied to neuroimaging data,
researchers have drawn conclusions from the derived weight maps. In particular,
weight maps of classifiers between two conditions are often described as a
proxy for the underlying signal differences between the conditions. Recent
studies have however suggested that such weight maps could not reliably recover
the source of the neural signals and even led to false positives (FP). In this
work, we used semi-simulated data from ElectroCorticoGraphy (ECoG) to
investigate how the signal-to-noise ratio and sparsity of the neural signal
affect the similarity between signal and weights. We show that not all cases
produce FP and that it is unlikely for FP features to have a high weight in
most cases.
On the Feasibility of Real-Time 3D Hand Tracking using Edge GPGPU Acceleration
Authors: Ammar Qammaz, Sokol Kosta, Nikolaos Kyriazis, Antonis Argyros
Date: 2018-04-30 00:00:00
Tasks: CPU

This paper presents the case study of a non-intrusive porting of a monolithic
C++ library for real-time 3D hand tracking, to the domain of edge-based
computation. Towards a proof of concept, the case study considers a pair of
workstations, a computationally powerful and a computationally weak one. By
wrapping the C++ library in Java container and by capitalizing on a Java-based
offloading infrastructure that supports both CPU and GPGPU computations, we are
able to establish automatically the required server-client workflow that best
addresses the resource allocation problem in the effort to execute from the
weak workstation. As a result, the weak workstation can perform well at the
task, despite lacking the sufficient hardware to do the required computations
locally. This is achieved by offloading computations which rely on GPGPU, to
the powerful workstation, across the network that connects them. We show the
edge-based computation challenges associated with the information flow of the
ported algorithm, demonstrate how we cope with them, and identify what needs to
be improved for achieving even better performance.
BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes
Authors: Enrico Santus, Chris Biemann, Emmanuele Chersoni
Date: 2018-04-30 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Relation Extraction

This paper describes BomJi, a supervised system for capturing discriminative
attributes in word pairs (e.g. yellow as discriminative for banana over
watermelon). The system relies on an XGB classifier trained on carefully
engineered graph-, pattern- and word embedding based features. It participated
in the SemEval- 2018 Task 10 on Capturing Discriminative Attributes, achieving
an F1 score of 0:73 and ranking 2nd out of 26 participant systems.
Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Authors: Mike Voets, Kajsa Møllersen, Lars Ailo Bongo
Date: 2018-03-12 00:00:00
Tasks: Diabetic Retinopathy Detection, Medical Image Analysis, Medical Image Segmentation, Mitosis Detection

Replication studies are essential for validation of new methods, and are
crucial to maintain the high standards of scientific publications, and to use
the results in practice. We have attempted to replicate the main method in
'Development and validation of a deep learning algorithm for detection of
diabetic retinopathy in retinal fundus photographs' published in JAMA 2016;
316(22). We re-implemented the method since the source code is not available,
and we used publicly available data sets. The original study used non-public
fundus images from EyePACS and three hospitals in India for training. We used a
different EyePACS data set from Kaggle. The original study used the benchmark
data set Messidor-2 to evaluate the algorithm's performance. We used the same
data set. In the original study, ophthalmologists re-graded all images for
diabetic retinopathy, macular edema, and image gradability. There was one
diabetic retinopathy grade per image for our data sets, and we assessed image
gradability ourselves. Hyper-parameter settings were not described in the
original study. But some of these were later published. We were not able to
replicate the original study. Our algorithm's area under the receiver operating
curve (AUC) of 0.94 on the Kaggle EyePACS test set and 0.80 on Messidor-2 did
not come close to the reported AUC of 0.99 in the original study. This may be
caused by the use of a single grade per image, different data, or different not
described hyper-parameter settings. This study shows the challenges of
replicating deep learning, and the need for more replication studies to
validate deep learning methods, especially for medical image analysis.
  Our source code and instructions are available at:
https://github.com/mikevoets/jama16-retina-replication
Dilated Temporal Relational Adversarial Network for Generic Video Summarization
Authors: Yu-jia Zhang, Michael Kampffmeyer, Xiaodan Liang, Dingwen Zhang, Min Tan, Eric P. Xing
Date: 2018-04-30 00:00:00
Tasks: Generative Adversarial Network, Video Summarization, Video Understanding

The large amount of videos popping up every day, make it more and more critical that key information within videos can be extracted and understood in a very short time. Video summarization, the task of finding the smallest subset of frames, which still conveys the whole story of a given video, is thus of great significance to improve efficiency of video understanding. We propose a novel Dilated Temporal Relational Generative Adversarial Network (DTR-GAN) to achieve frame-level video summarization. Given a video, it selects the set of key frames, which contain the most meaningful and compact information. Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner. A new dilated temporal relation (DTR) unit is introduced to enhance temporal representation capturing. The generator uses this unit to effectively exploit global multi-scale temporal context to select key frames and to complement the commonly used Bi-LSTM. To ensure that summaries capture enough key video representation from a global perspective rather than a trivial randomly shorten sequence, we present a discriminator that learns to enforce both the information completeness and compactness of summaries via a three-player loss. The loss includes the generated summary loss, the random summary loss, and the real summary (ground-truth) loss, which play important roles for better regularizing the learned model to obtain useful summaries. Comprehensive experiments on three public datasets show the effectiveness of the proposed approach.
Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model
Authors: Tobias Geimer, Paul Keall, Katharina Breininger, Vincent Caillet, Michelle Dunbar, Christoph Bert, Andreas Maier
Date: 2018-04-30 00:00:00

Data-driven respiratory signal extraction from rotational X-ray scans is a
challenge as angular effects overlap with respiration-induced change in the
scene. In this paper, we use the linearity of the X-ray transform to propose a
bilinear model based on a prior 4D scan to separate angular and respiratory
variation. The bilinear estimation process is supported by a B-spline
interpolation using prior knowledge about the trajectory angle. Consequently,
extraction of respiratory features simplifies to a linear problem. Though the
need for a prior 4D CT seems steep, our proposed use-case of driving a
respiratory motion model in radiation therapy usually meets this requirement.
We evaluate on DRRs of 5 patient 4D CTs in a leave-one-phase-out manner and
achieve a mean estimation error of 3.01 % in the gray values for unseen viewing
angles. We further demonstrate suitability of the extracted weights to drive a
motion model for treatments with a continuously rotating gantry.
Mobile Multi-View Object Image Search
Authors: Fatih Calisir, Muhammet Bastan, Ozgur Ulusoy, Ugur Gudukbay
Date: 2015-07-31 00:00:00
Tasks: Image Retrieval, Object, Retrieval

High user interaction capability of mobile devices can help improve the
accuracy of mobile visual search systems. At query time, it is possible to
capture multiple views of an object from different viewing angles and at
different scales with the mobile device camera to obtain richer information
about the object compared to a single view and hence return more accurate
results. Motivated by this, we developed a mobile multi-view object image
search system, using a client-server architecture. Multi-view images of objects
acquired by the mobile clients are processed and local features are sent to the
server, which combines the query image representations with early/late fusion
methods based on bag-of-visual-words and sends back the query results. We
performed a comprehensive analysis of early and late fusion approaches using
various similarity functions, on an existing single view and a new multi-view
object image database. The experimental results show that multi-view search
provides significantly better retrieval accuracy compared to single view
search.
An Anti-fraud System for Car Insurance Claim Based on Visual Evidence
Authors: Pei Li, Bingyu Shen, Weishan Dong
Date: 2018-04-30 00:00:00
Tasks: Scene Understanding

Automatically scene understanding using machine learning algorithms has been
widely applied to different industries to reduce the cost of manual labor.
Nowadays, insurance companies launch express vehicle insurance claim and
settlement by allowing customers uploading pictures taken by mobile devices.
This kind of insurance claim is treated as small claim and can be processed
either manually or automatically in a quick fashion. However, due to the
increasing amount of claims every day, system or people are likely to be fooled
by repeated claims for identical case leading to big lost to insurance
companies.Thus, an anti-fraud checking before processing the claim is
necessary. We create the first data set of car damage images collected from
internet and local parking lots. In addition, we proposed an approach to
generate robust deep features by locating the damages accurately and
efficiently in the images. The state-of-the-art real-time object detector YOLO
\cite{redmon2016you}is modified to train and discover damage region as an
important part of the pipeline. Both local and global deep features are
extracted using VGG model\cite{Simonyan14c}, which are fused later for more
robust system performance. Experiments show our approach is effective in
preventing fraud claims as well as meet the requirement to speed up the
insurance claim prepossessing.
Sketch-a-Classifier: Sketch-based Photo Classifier Generation
Authors: Conghui Hu, Da Li, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales
Date: 2018-04-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Transfer Learning, Zero-Shot Learning

Contemporary deep learning techniques have made image recognition a
reasonably reliable technology. However training effective photo classifiers
typically takes numerous examples which limits image recognition's scalability
and applicability to scenarios where images may not be available. This has
motivated investigation into zero-shot learning, which addresses the issue via
knowledge transfer from other modalities such as text. In this paper we
investigate an alternative approach of synthesizing image classifiers: almost
directly from a user's imagination, via free-hand sketch. This approach doesn't
require the category to be nameable or describable via attributes as per
zero-shot learning. We achieve this via training a {model regression} network
to map from {free-hand sketch} space to the space of photo classifiers. It
turns out that this mapping can be learned in a category-agnostic way, allowing
photo classifiers for new categories to be synthesized by user with no need for
annotated training photos. {We also demonstrate that this modality of
classifier generation can also be used to enhance the granularity of an
existing photo classifier, or as a complement to name-based zero-shot learning.
Learning Explicit Deep Representations from Deep Kernel Networks
Authors: Mingyuan Jiu, Hichem Sahbi
Date: 2018-04-30 00:00:00

Deep kernel learning aims at designing nonlinear combinations of multiple
standard elementary kernels by training deep networks. This scheme has proven
to be effective, but intractable when handling large-scale datasets especially
when the depth of the trained networks increases; indeed, the complexity of
evaluating these networks scales quadratically w.r.t. the size of training data
and linearly w.r.t. the depth of the trained networks. In this paper, we
address the issue of efficient computation in Deep Kernel Networks (DKNs) by
designing effective maps in the underlying Reproducing Kernel Hilbert Spaces.
Given a pretrained DKN, our method builds its associated Deep Map Network (DMN)
whose inner product approximates the original network while being far more
efficient. The design principle of our method is greedy and achieved
layer-wise, by finding maps that approximate DKNs at different (input,
intermediate and output) layers. This design also considers an extra
fine-tuning step based on unsupervised learning, that further enhances the
generalization ability of the trained DMNs. When plugged into SVMs, these DMNs
turn out to be as accurate as the underlying DKNs while being at least an order
of magnitude faster on large-scale datasets, as shown through extensive
experiments on the challenging ImageCLEF and COREL5k benchmarks.
Towards Accurate Markerless Human Shape and Pose Estimation over Time
Authors: Yinghao Huang, Federica Bogo, Christoph Lassner, Angjoo Kanazawa, Peter V. Gehler, Ijaz Akhter, Michael J. Black
Date: 2017-07-24 00:00:00
Tasks: Pose Estimation

Existing marker-less motion capture methods often assume known backgrounds,
static cameras, and sequence specific motion priors, which narrows its
application scenarios. Here we propose a fully automatic method that given
multi-view video, estimates 3D human motion and body shape. We take recent
SMPLify \cite{bogo2016keep} as the base method, and extend it in several ways.
First we fit the body to 2D features detected in multi-view images. Second, we
use a CNN method to segment the person in each image and fit the 3D body model
to the contours to further improves accuracy. Third we utilize a generic and
robust DCT temporal prior to handle the left and right side swapping issue
sometimes introduced by the 2D pose estimator. Validation on standard
benchmarks shows our results are comparable to the state of the art and also
provide a realistic 3D shape avatar. We also demonstrate accurate results on
HumanEva and on challenging dance sequences from YouTube in monocular case.
Q-Map: Clinical Concept Mining from Clinical Documents
Authors: Sheikh Shams Azam, Manoj Raju, Venkatesh Pagidimarri, Vamsi Kasivajjala
Date: 2018-04-30 00:00:00
Tasks: Information Retrieval, Retrieval, Survival Analysis

Over the past decade, there has been a steep rise in the data-driven analysis
in major areas of medicine, such as clinical decision support system, survival
analysis, patient similarity analysis, image analytics etc. Most of the data in
the field are well-structured and available in numerical or categorical formats
which can be used for experiments directly. But on the opposite end of the
spectrum, there exists a wide expanse of data that is intractable for direct
analysis owing to its unstructured nature which can be found in the form of
discharge summaries, clinical notes, procedural notes which are in human
written narrative format and neither have any relational model nor any standard
grammatical structure. An important step in the utilization of these texts for
such studies is to transform and process the data to retrieve structured
information from the haystack of irrelevant data using information retrieval
and data mining techniques. To address this problem, the authors present Q-Map
in this paper, which is a simple yet robust system that can sift through
massive datasets with unregulated formats to retrieve structured information
aggressively and efficiently. It is backed by an effective mining technique
which is based on a string matching algorithm that is indexed on curated
knowledge sources, that is both fast and configurable. The authors also briefly
examine its comparative performance with MetaMap, one of the most reputed tools
for medical concepts retrieval and present the advantages the former displays
over the latter.
Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings
Authors: Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Nicolas Thome, Matthieu Cord
Date: 2018-04-30 00:00:00
Tasks: BIG-bench Machine Learning, Cross-Modal Retrieval, Retrieval

Designing powerful tools that support cooking activities has rapidly gained
popularity due to the massive amounts of available data, as well as recent
advances in machine learning that are capable of analyzing them. In this paper,
we propose a cross-modal retrieval model aligning visual and textual data (like
pictures of dishes and their recipes) in a shared representation space. We
describe an effective learning scheme, capable of tackling large-scale
problems, and validate it on the Recipe1M dataset containing nearly 1 million
picture-recipe pairs. We show the effectiveness of our approach regarding
previous state-of-the-art models and present qualitative results over
computational cooking use cases.
Evolution of Visual Odometry Techniques
Authors: Shashi Poddar, Rahul Kottath, Vinod Karar
Date: 2018-04-30 00:00:00
Tasks: Motion Estimation, Pose Estimation, Visual Odometry

With rapid advancements in the area of mobile robotics and industrial
automation, a growing need has arisen towards accurate navigation and
localization of moving objects. Camera based motion estimation is one such
technique which is gaining huge popularity owing to its simplicity and use of
limited resources in generating motion path. In this paper, an attempt is made
to introduce this topic for beginners covering different aspects of vision
based motion estimation task. The evolution of VO schemes over last few decades
is discussed under two broad categories, that is, geometric and non-geometric
approaches. The geometric approaches are further detailed under three different
classes, that is, feature-based, appearance-based, and a hybrid of feature and
appearance based schemes. The non-geometric approach is one of the recent
paradigm shift from conventional pose estimation technique and is thus
discussed in a separate section. Towards the end, a list of different datasets
for visual odometry and allied research areas are provided for a ready
reference.