pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Environment Mapping for Augmented Reality Applications on Mobile Devices
Authors: Rafael Monroy, Matis Hudon, Aljosa Smolic
Date: 2018-09-21 00:00:00

Augmented Reality is a topic of foremost interest nowadays. Its main goal is
to seamlessly blend virtual content in real-world scenes. Due to the lack of
computational power in mobile devices, rendering a virtual object with
high-quality, coherent appearance and in real-time, remains an area of active
research. In this work, we present a novel pipeline that allows for coupled
environment acquisition and virtual object rendering on a mobile device
equipped with a depth sensor. While keeping human interaction to a minimum, our
system can scan a real scene and project it onto a two-dimensional environment
map containing RGB+Depth data. Furthermore, we define a set of criteria that
allows for an adaptive update of the environment map to account for dynamic
changes in the scene. Then, under the assumption of diffuse surfaces and
distant illumination, our method exploits an analytic expression for the
irradiance in terms of spherical harmonic coefficients, which leads to a very
efficient rendering algorithm. We show that all the processes in our pipeline
can be executed while maintaining an average frame rate of 31Hz on a mobile
device.
Analysing object detectors from the perspective of co-occurring object categories
Authors: Csaba Nemes, Sandor Jordan
Date: 2018-09-21 00:00:00
Tasks: Object

The accuracy of state-of-the-art Faster R-CNN and YOLO object detectors are
evaluated and compared on a special masked MS COCO dataset to measure how much
their predictions rely on contextual information encoded at object category
level. Category level representation of context is motivated by the fact that
it could be an adequate way to transfer knowledge between visual and non-visual
domains. According to our measurements, current detectors usually do not build
strong dependency on contextual information at category level, however, when
they does, they does it in a similar way, suggesting that contextual dependence
of object categories is an independent property that is relevant to be
transferred.
Learning of Tree-Structured Gaussian Graphical Models on Distributed Data under Communication Constraints
Authors: Mostafa Tavassolipour, Seyed Abolfazl Motahari, Mohammad-Taghi Manzuri Shalmani
Date: 2018-09-21 00:00:00

In this paper, learning of tree-structured Gaussian graphical models from
distributed data is addressed. In our model, samples are stored in a set of
distributed machines where each machine has access to only a subset of
features. A central machine is then responsible for learning the structure
based on received messages from the other nodes. We present a set of
communication efficient strategies, which are theoretically proved to convey
sufficient information for reliable learning of the structure. In particular,
our analyses show that even if each machine sends only the signs of its local
data samples to the central node, the tree structure can still be recovered
with high accuracy. Our simulation results on both synthetic and real-world
datasets show that our strategies achieve a desired accuracy in inferring the
underlying structure, while spending a small budget on communication.
From 2D to 3D Geodesic-based Garment Matching
Authors: Meysam Madadi, Egils Avots, Sergio Escalera, Jordi Gonzalez, Xavier Baro, Gholamreza Anbarjafari
Date: 2018-09-21 00:00:00

A new approach for 2D to 3D garment retexturing is proposed based on Gaussian
mixture models and thin plate splines (TPS). An automatically segmented garment
of an individual is matched to a new source garment and rendered, resulting in
augmented images in which the target garment has been retextured by using the
texture of the source garment. We divide the problem into garment boundary
matching based on Gaussian mixture models and then interpolate inner points
using surface topology extracted through geodesic paths, which leads to a more
realistic result than standard approaches. We evaluated and compared our system
quantitatively by mean square error (MSE) and qualitatively using the mean
opinion score (MOS), showing the benefits of the proposed methodology on our
gathered dataset.
Conducting Feasibility Studies for Knowledge Based Systems
Authors: John Kingston
Date: 2018-09-21 00:00:00

This paper describes how to carry out a feasibility study for a potential
knowledge based system application. It discusses factors to be considered under
three headings: the business case, the technical feasibility, and stakeholder
issues. It concludes with a case study of a feasibility study for a KBS to
guide surgeons in diagnosis and treatment of thyroid conditions.
Compressed Sensing with Adversarial Sparse Noise via L1 Regression
Authors: Sushrut Karmalkar, Eric Price
Date: 2018-09-21 00:00:00
Tasks: compressed sensing, regression

We present a simple and effective algorithm for the problem of \emph{sparse
robust linear regression}. In this problem, one would like to estimate a sparse
vector $w^* \in \mathbb{R}^n$ from linear measurements corrupted by sparse
noise that can arbitrarily change an adversarially chosen $\eta$ fraction of
measured responses $y$, as well as introduce bounded norm noise to the
responses. For Gaussian measurements, we show that a simple algorithm based on
L1 regression can successfully estimate $w^*$ for any $\eta < \eta_0 \approx
0.239$, and that this threshold is tight for the algorithm. The number of
measurements required by the algorithm is $O(k \log \frac{n}{k})$ for
$k$-sparse estimation, which is within constant factors of the number needed
without any sparse noise. Of the three properties we show---the ability to
estimate sparse, as well as dense, $w^*$; the tolerance of a large constant
fraction of outliers; and tolerance of adversarial rather than distributional
(e.g., Gaussian) dense noise---to the best of our knowledge, no previous result
achieved more than two.
Stochasticity from function -- why the Bayesian brain may need no noise
Authors: Dominik Dold, Ilja Bytschok, Akos F. Kungl, Andreas Baumbach, Oliver Breitwieser, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici
Date: 2018-09-21 00:00:00
Tasks: Bayesian Inference

An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing. Since the precise statistical properties of neural activity are important in this context, many models assume an ad-hoc source of well-behaved, explicit noise, either on the input or on the output side of single neuron dynamics, most often assuming an independent Poisson process in either case. However, these assumptions are somewhat problematic: neighboring neurons tend to share receptive fields, rendering both their input and their output correlated; at the same time, neurons are known to behave largely deterministically, as a function of their membrane potential and conductance. We suggest that spiking neural networks may, in fact, have no need for noise to perform sampling-based Bayesian inference. We study analytically the effect of auto- and cross-correlations in functionally Bayesian spiking networks and demonstrate how their effect translates to synaptic interaction strengths, rendering them controllable through synaptic plasticity. This allows even small ensembles of interconnected deterministic spiking networks to simultaneously and co-dependently shape their output activity through learning, enabling them to perform complex Bayesian computation without any need for noise, which we demonstrate in silico, both in classical simulation and in neuromorphic emulation. These results close a gap between the abstract models and the biology of functionally Bayesian spiking networks, effectively reducing the architectural constraints imposed on physical neural substrates required to perform probabilistic computing, be they biological or artificial.
Non-Line-of-Sight Reconstruction using Efficient Transient Rendering
Authors: Julian Iseringhausen, Matthias B. Hullin
Date: 2018-09-21 00:00:00

Being able to see beyond the direct line of sight is an intriguing prospective and could benefit a wide variety of important applications. Recent work has demonstrated that time-resolved measurements of indirect diffuse light contain valuable information for reconstructing shape and reflectance properties of objects located around a corner. In this paper, we introduce a novel reconstruction scheme that, by design, produces solutions that are consistent with state-of-the-art physically-based rendering. Our method combines an efficient forward model (a custom renderer for time-resolved three-bounce indirect light transport) with an optimization framework to reconstruct object geometry in an analysis-by-synthesis sense. We evaluate our algorithm on a variety of synthetic and experimental input data, and show that it gracefully handles uncooperative scenes with high levels of noise or non-diffuse material reflectance.
Understanding Convolutional Neural Networks for Text Classification
Authors: Alon Jacovi, Oren Sar Shalom, Yoav Goldberg
Date: 2018-09-21 00:00:00
Proceeding: WS 2018 11
Tasks: Classification, General Classification, text-classification, Text Classification

We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery. We aim to understand the method by which the networks process and classify text. We examine common hypotheses to this problem: that filters, accompanied by global max-pooling, serve as ngram detectors. We show that filters may capture several different semantic classes of ngrams by using different activation patterns, and that global max-pooling induces behavior which separates important ngrams from the rest. Finally, we show practical use cases derived from our findings in the form of model interpretability (explaining a trained model by deriving a concrete identity for each filter, bridging the gap between visualization tools in vision tasks and NLP) and prediction interpretability (explaining predictions). Code implementation is available online at github.com/sayaendo/interpreting-cnn-for-text.
Answering the "why" in Answer Set Programming - A Survey of Explanation Approaches
Authors: Jorge Fandinno, Claudia Schulz
Date: 2018-09-21 00:00:00
Tasks: Decision Making

Artificial Intelligence (AI) approaches to problem-solving and
decision-making are becoming more and more complex, leading to a decrease in
the understandability of solutions. The European Union's new General Data
Protection Regulation tries to tackle this problem by stipulating a "right to
explanation" for decisions made by AI systems. One of the AI paradigms that may
be affected by this new regulation is Answer Set Programming (ASP). Thanks to
the emergence of efficient solvers, ASP has recently been used for
problem-solving in a variety of domains, including medicine, cryptography, and
biology. To ensure the successful application of ASP as a problem-solving
paradigm in the future, explanations of ASP solutions are crucial. In this
survey, we give an overview of approaches that provide an answer to the
question of why an answer set is a solution to a given problem, notably
off-line justifications, causal graphs, argumentative explanations and why-not
provenance, and highlight their similarities and differences. Moreover, we
review methods explaining why a set of literals is not an answer set or why no
solution exists at all.
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements
Authors: Silvia Makowski, Lena Jäger, Ahmed Abdelwahab, Niels Landwehr, Tobias Scheffer
Date: 2018-09-21 00:00:00
Tasks: Reading Comprehension

We study the problem of inferring readers' identities and estimating their
level of text comprehension from observations of their eye movements during
reading. We develop a generative model of individual gaze patterns (scanpaths)
that makes use of lexical features of the fixated words. Using this generative
model, we derive a Fisher-score representation of eye-movement sequences. We
study whether a Fisher-SVM with this Fisher kernel and several reference
methods are able to identify readers and estimate their level of text
comprehension based on eye-tracking data. While none of the methods are able to
estimate text comprehension accurately, we find that the SVM with Fisher kernel
excels at identifying readers.
On-field player workload exposure and knee injury risk monitoring via deep learning
Authors: William R. Johnson, Ajmal Mian, David G. Lloyd, Jacqueline A. Alderson
Date: 2018-09-21 00:00:00
Tasks: Sports Analytics

In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload exposure and knee injury risk. Traditionally, this analysis has relied on captive laboratory force plates and associated downstream biomechanical modeling, and many researchers have approached the problem of portability by extrapolating models built on linear statistics. An alternative approach would be to capitalize on recent advances in deep learning. In this study, using the pre-trained CaffeNet convolutional neural network (CNN) model, multivariate regression of marker-based motion capture to 3D KJM for three sports-related movement types were compared. The strongest overall mean correlation to source modeling of 0.8895 was achieved over the initial 33 % of stance phase for sidestepping. The accuracy of these mean predictions of the three critical KJM associated with anterior cruciate ligament (ACL) injury demonstrate the feasibility of on-field knee injury assessment using deep learning in lieu of laboratory embedded force plates. This multidisciplinary research approach significantly advances machine representation of real-world physical models with practical application for both community and professional level athletes.
Multi-Dimensional, Multilayer, Nonlinear and Dynamic HITS
Authors: Francesca Arrigo, Francesco Tudisco
Date: 2018-09-21 00:00:00

We introduce a ranking model for temporal multi-dimensional weighted and
directed networks based on the Perron eigenvector of a multi-homogeneous
order-preserving map. The model extends to the temporal multilayer setting the
HITS algorithm and defines five centrality vectors: two for the nodes, two for
the layers, and one for the temporal stamps. Nonlinearity is introduced in the
standard HITS model in order to guarantee existence and uniqueness of these
centrality vectors for any network, without any requirement on its connectivity
structure. We introduce a globally convergent power iteration like algorithm
for the computation of the centrality vectors. Numerical experiments on
real-world networks are performed in order to assess the effectiveness of the
proposed model and showcase the performance of the accompanying algorithm.
Perfect match: Improved cross-modal embeddings for audio-visual synchronisation
Authors: Soo-Whan Chung, Joon Son Chung, Hong-Goo Kang
Date: 2018-09-21 00:00:00
Tasks: Binary Classification, Cross-Modal Retrieval, Retrieval, speech-recognition, Speech Recognition, Video Synchronization, Visual Speech Recognition

This paper proposes a new strategy for learning powerful cross-modal
embeddings for audio-to-video synchronization. Here, we set up the problem as
one of cross-modal retrieval, where the objective is to find the most relevant
audio segment given a short video clip. The method builds on the recent
advances in learning representations from cross-modal self-supervision.
  The main contributions of this paper are as follows: (1) we propose a new
learning strategy where the embeddings are learnt via a multi-way matching
problem, as opposed to a binary classification (matching or non-matching)
problem as proposed by recent papers; (2) we demonstrate that performance of
this method far exceeds the existing baselines on the synchronization task; (3)
we use the learnt embeddings for visual speech recognition in self-supervision,
and show that the performance matches the representations learnt end-to-end in
a fully-supervised manner.
Multimodal Dual Attention Memory for Video Story Question Answering
Authors: Kyung-Min Kim, Seong-Ho Choi, Jin-Hwa Kim, Byoung-Tak Zhang
Date: 2018-09-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Question Answering

We propose a video story question-answering (QA) architecture, Multimodal
Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism
with late fusion. MDAM uses self-attention to learn the latent concepts in
scene frames and captions. Given a question, MDAM uses the second attention
over these latent concepts. Multimodal fusion is performed after the dual
attention processes (late fusion). Using this processing pipeline, MDAM learns
to infer a high-level vision-language joint representation from an abstraction
of the full video content. We evaluate MDAM on PororoQA and MovieQA datasets
which have large-scale QA annotations on cartoon videos and movies,
respectively. For both datasets, MDAM achieves new state-of-the-art results
with significant margins compared to the runner-up models. We confirm the best
performance of the dual attention mechanism combined with late fusion by
ablation studies. We also perform qualitative analysis by visualizing the
inference mechanisms of MDAM.
SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection
Authors: Meijun Sun, Ziqi Zhou, QinGhua Hu, Zheng Wang, Jianmin Jiang
Date: 2018-09-21 00:00:00
Tasks: Saliency Detection, Video Saliency Detection

Data-driven saliency detection has attracted strong interest as a result of
applying convolutional neural networks to the detection of eye fixations.
Although a number of imagebased salient object and fixation detection models
have been proposed, video fixation detection still requires more exploration.
Different from image analysis, motion and temporal information is a crucial
factor affecting human attention when viewing video sequences. Although
existing models based on local contrast and low-level features have been
extensively researched, they failed to simultaneously consider interframe
motion and temporal information across neighboring video frames, leading to
unsatisfactory performance when handling complex scenes. To this end, we
propose a novel and efficient video eye fixation detection model to improve the
saliency detection performance. By simulating the memory mechanism and visual
attention mechanism of human beings when watching a video, we propose a
step-gained fully convolutional network by combining the memory information on
the time axis with the motion information on the space axis while storing the
saliency information of the current frame. The model is obtained through
hierarchical training, which ensures the accuracy of the detection. Extensive
experiments in comparison with 11 state-of-the-art methods are carried out, and
the results show that our proposed model outperforms all 11 methods across a
number of publicly available datasets.
Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy
Authors: Da Chen, Jiong Zhang, Laurent D. Cohen
Date: 2018-09-21 00:00:00

The minimal path method has proven to be particularly useful and efficient in
tubular structure segmentation applications. In this paper, we propose a new
minimal path model associated with a dynamic Riemannian metric embedded with an
appearance feature coherence penalty and an adaptive anisotropy enhancement
term. The features that characterize the appearance and anisotropy properties
of a tubular structure are extracted through the associated orientation score.
The proposed dynamic Riemannian metric is updated in the course of the geodesic
distance computation carried out by the efficient single-pass fast marching
method. Compared to state-of-the-art minimal path models, the proposed minimal
path model is able to extract the desired tubular structures from a complicated
vessel tree structure. In addition, we propose an efficient prior path-based
method to search for vessel radius value at each centerline position of the
target. Finally, we perform the numerical experiments on both synthetic and
real images. The quantitive validation is carried out on retinal vessel images.
The results indicate that the proposed model indeed achieves a promising
performance.
On Variational Methods for Motion Compensated Inpainting
Authors: Francois Lauze, Mads Nielsen
Date: 2018-09-21 00:00:00

We develop in this paper a generic Bayesian framework for the joint
estimation of motion and recovery of missing data in a damaged video sequence.
Using standard maximum a posteriori to variational formulation rationale, we
derive generic minimum energy formulations for the estimation of a
reconstructed sequence as well as motion recovery. We instantiate these energy
formulations and from their Euler-Lagrange Equations, we propose a full
multiresolution algorithms in order to compute good local minimizers for our
energies and discuss their numerical implementations, focusing on the missing
data recovery part, i.e. inpainting. Experimental results for synthetic as well
as real sequences are presented. Image sequences and extra material is
available at http://image.diku.dk/francois/seqinp.php.
Paraphrase Detection on Noisy Subtitles in Six Languages
Authors: Eetu Sjöblom, Mathias Creutz, Mikko Aulamo
Date: 2018-09-21 00:00:00
Proceeding: WS 2018 11
Tasks: Sentence, Sentence Embedding, Sentence-Embedding

We perform automatic paraphrase detection on subtitle data from the
Opusparcus corpus comprising six European languages: German, English, Finnish,
French, Russian, and Swedish. We train two types of supervised sentence
embedding models: a word-averaging (WA) model and a gated recurrent averaging
network (GRAN) model. We find out that GRAN outperforms WA and is more robust
to noisy training data. Better results are obtained with more and noisier data
than less and cleaner data. Additionally, we experiment on other datasets,
without reaching the same level of performance, because of domain mismatch
between training and test data.
Real-Time Stereo Vision on FPGAs with SceneScan
Authors: Konstantin Schauwecker
Date: 2018-09-21 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

We present a flexible FPGA stereo vision implementation that is capable of
processing up to 100 frames per second or image resolutions up to 3.4
megapixels, while consuming only 8 W of power. The implementation uses a
variation of the Semi-Global Matching (SGM) algorithm, which provides superior
results compared to many simpler approaches. The stereo matching results are
improved significantly through a post-processing chain that operates on the
computed cost cube and the disparity map. With this implementation we have
created two stand-alone hardware systems for stereo vision, called SceneScan
and SceneScan Pro. Both systems have been developed to market maturity and are
available from Nerian Vision GmbH.