pwc-archive/papers-with-abstracts huggingface dataset:
Extraction of Airways using Graph Neural Networks
Authors: Raghavendra Selvan, Thomas Kipf, Max Welling, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne
Date: 2018-04-12 00:00:00
Tasks: Decoder

We present extraction of tree structures, such as airways, from image data as
a graph refinement task. To this end, we propose a graph auto-encoder model
that uses an encoder based on graph neural networks (GNNs) to learn embeddings
from input node features and a decoder to predict connections between nodes.
Performance of the GNN model is compared with mean-field networks in their
ability to extract airways from 3D chest CT scans.
Variational Composite Autoencoders
Authors: Jiangchao Yao, Ivor Tsang, Ya zhang
Date: 2018-04-12 00:00:00
Tasks: Decoder

Learning in the latent variable model is challenging in the presence of the
complex data structure or the intractable latent variable. Previous variational
autoencoders can be low effective due to the straightforward encoder-decoder
structure. In this paper, we propose a variational composite autoencoder to
sidestep this issue by amortizing on top of the hierarchical latent variable
model. The experimental results confirm the advantages of our model.
Regularized Greedy Column Subset Selection
Authors: Bruno Ordozgoiti, Alberto Mozo, Jesús García López de Lacalle
Date: 2018-04-12 00:00:00
Tasks: Combinatorial Optimization, feature selection

The Column Subset Selection Problem provides a natural framework for
unsupervised feature selection. Despite being a hard combinatorial optimization
problem, there exist efficient algorithms that provide good approximations. The
drawback of the problem formulation is that it incorporates no form of
regularization, and is therefore very sensitive to noise when presented with
scarce data. In this paper we propose a regularized formulation of this
problem, and derive a correct greedy algorithm that is similar in efficiency to
existing greedy methods for the unregularized problem. We study its adequacy
for feature selection and propose suitable formulations. Additionally, we
derive a lower bound for the error of the proposed problems. Through various
numerical experiments on real and synthetic data, we demonstrate the
significantly increased robustness and stability of our method, as well as the
improved conditioning of its output, all while remaining efficient for
practical use.
Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification
Authors: Julio C. S. Jacques Junior, Xavier Baró, Sergio Escalera
Date: 2018-04-12 00:00:00
Tasks: Person Re-Identification

Person re-identification has received special attention by the human analysis
community in the last few years. To address the challenges in this field, many
researchers have proposed different strategies, which basically exploit either
cross-view invariant features or cross-view robust metrics. In this work, we
propose to exploit a post-ranking approach and combine different feature
representations through ranking aggregation. Spatial information, which
potentially benefits the person matching, is represented using a 2D body model,
from which color and texture information are extracted and combined. We also
consider background/foreground information, automatically extracted via Deep
Decompositional Network, and the usage of Convolutional Neural Network (CNN)
features. To describe the matching between images we use the polynomial feature
map, also taking into account local and global information. The Discriminant
Context Information Analysis based post-ranking approach is used to improve
initial ranking lists. Finally, the Stuart ranking aggregation method is
employed to combine complementary ranking lists obtained from different feature
representations. Experimental results demonstrated that we improve the
state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on
top-1 rank recognition rate, respectively, as well as obtaining competitive
results on CUHK01 dataset.
Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector
Authors: Huy H. Nguyen, Ngoc-Dung T. Tieu, Hoang-Quoc Nguyen-Son, Junichi Yamagishi, Isao Echizen
Date: 2018-04-12 00:00:00

Making computer-generated (CG) images more difficult to detect is an
interesting problem in computer graphics and security. While most approaches
focus on the image rendering phase, this paper presents a method based on
increasing the naturalness of CG facial images from the perspective of spoofing
detectors. The proposed method is implemented using a convolutional neural
network (CNN) comprising two autoencoders and a transformer and is trained
using a black-box discriminator without gradient information. Over 50% of the
transformed CG images were not detected by three state-of-the-art spoofing
detectors. This capability raises an alarm regarding the reliability of facial
authentication systems, which are becoming widely used in daily life.
Unsupervised Discovery of Object Landmarks as Structural Representations
Authors: Yuting Zhang, Yijie Guo, Yixin Jin, Yijun Luo, Zhiyuan He, Honglak Lee
Date: 2018-04-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, Unsupervised Facial Landmark Detection, Unsupervised Human Pose Estimation, Unsupervised Keypoint Estimation

Deep neural networks can model images with rich latent representations, but
they cannot naturally conceptualize structures of object categories in a
human-perceptible way. This paper addresses the problem of learning object
structures in an image modeling process without supervision. We propose an
autoencoding formulation to discover landmarks as explicit structural
representations. The encoding module outputs landmark coordinates, whose
validity is ensured by constraints that reflect the necessary properties for
landmarks. The decoding module takes the landmarks as a part of the learnable
input representations in an end-to-end differentiable framework. Our discovered
landmarks are semantically meaningful and more predictive of manually annotated
landmarks than those discovered by previous methods. The coordinates of our
landmarks are also complementary features to pretrained deep-neural-network
representations in recognizing visual attributes. In addition, the proposed
method naturally creates an unsupervised, perceptible interface to manipulate
object shapes and decode images with controllable structures. The project
webpage is at http://ytzhang.net/projects/lmdis-rep
Protecting Sensory Data against Sensitive Inferences
Authors: Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi
Date: 2018-02-21 00:00:00
Tasks: Activity Recognition, Attribute, Gender Classification, Privacy Preserving

There is growing concern about how personal data are used when users grant
applications direct access to the sensors of their mobile devices. In fact,
high resolution temporal data generated by motion sensors reflect directly the
activities of a user and indirectly physical and demographic attributes. In
this paper, we propose a feature learning architecture for mobile devices that
provides flexible and negotiable privacy-preserving sensor data transmission by
appropriately transforming raw sensor data. The objective is to move from the
current binary setting of granting or not permission to an application, toward
a model that allows users to grant each application permission over a limited
range of inferences according to the provided services. The internal structure
of each component of the proposed architecture can be flexibly changed and the
trade-off between privacy and utility can be negotiated between the constraints
of the user and the underlying application. We validated the proposed
architecture in an activity recognition application using two real-world
datasets, with the objective of recognizing an activity without disclosing
gender as an example of private information. Results show that the proposed
framework maintains the usefulness of the transformed data for activity
recognition, with an average loss of only around three percentage points, while
reducing the possibility of gender classification to around 50\%, the target
random guess, from more than 90\% when using raw sensor data. We also present
and distribute MotionSense, a new dataset for activity and attribute
recognition collected from motion sensors.
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
Authors: Jinhui Tang, Xiangbo Shu, Zechao Li, Yu-Gang Jiang, Qi Tian
Date: 2018-04-12 00:00:00
Tasks: Graph Learning, TAG

Image retagging aims to improve tag quality of social images by refining
their original tags or assigning new high-quality tags. Recent approaches
simultaneously explore visual, user and tag information to improve the
performance of image retagging by constructing and exploring an image-tag-user
graph. However, such methods will become computationally infeasible with the
rapidly increasing number of images, tags and users. It has been proven that
Anchor Graph Regularization (AGR) can significantly accelerate large-scale
graph learning model by exploring only a small number of anchor points.
Inspired by this, we propose a novel Social anchor-Unit GrAph Regularized
Tensor Completion (SUGAR-TC) method to effectively refine the tags of social
images, which is insensitive to the scale of the applied data. First, we
construct an anchor-unit graph across multiple domains (e.g., image and user
domains) rather than traditional anchor graph in a single domain. Second, a
tensor completion based on SUGAR is implemented on the original image-tag-user
tensor to refine the tags of the anchor images. Third, we efficiently assign
tags to non-anchor images by leveraging the relationship between the non-anchor
images and the anchor units. Experimental results on a real-world social image
database well demonstrate the effectiveness of SUGAR-TC, outperforming several
related methods.
Multi-Label Wireless Interference Identification with Convolutional Neural Networks
Authors: Sergej Grunau, Dimitri Block, Uwe Meier
Date: 2018-04-12 00:00:00
Tasks: General Classification, Management

The steadily growing use of license-free frequency bands require reliable
coexistence management and therefore proper wireless interference
identification (WII). In this work, we propose a WII approach based upon a deep
convolutional neural network (CNN) which classifies multiple IEEE 802.15.1,
IEEE 802.11 b/g and IEEE 802.15.4 interfering signals in the presence of a
utilized signal. The generated multi-label dataset contains frequency- and
time-limited sensing snapshots with the bandwidth of 10 MHz and duration of
12.8 $\mu$s, respectively. Each snapshot combines one utilized signal with up
to multiple interfering signals. The approach shows promising results for
same-technology interference with a classification accuracy of approximately
100 % for IEEE 802.15.1 and IEEE 802.15.4 signals. For IEEE 802.11 b/g signals
the accuracy increases for cross-technology interference with at least 90 %.
MGGAN: Solving Mode Collapse using Manifold Guided Training
Authors: Duhyeon Bang, Hyunjung Shim
Date: 2018-04-12 00:00:00
Tasks: Generative Adversarial Network

Mode collapse is a critical problem in training generative adversarial
networks. To alleviate mode collapse, several recent studies introduce new
objective functions, network architectures or alternative training schemes.
However, their achievement is often the result of sacrificing the image
quality. In this paper, we propose a new algorithm, namely a manifold guided
generative adversarial network (MGGAN), which leverages a guidance network on
existing GAN architecture to induce generator learning all modes of data
distribution. Based on extensive evaluations, we show that our algorithm
resolves mode collapse without losing image quality. In particular, we
demonstrate that our algorithm is easily extendable to various existing GANs.
Experimental analysis justifies that the proposed algorithm is an effective and
efficient tool for training GANs.
Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
Authors: Nikolas Lessmann, Bram van Ginneken, Pim A. de Jong, Ivana Išgum
Date: 2018-04-12 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

Precise segmentation and anatomical identification of the vertebrae provides
the basis for automatic analysis of the spine, such as detection of vertebral
compression fractures or other abnormalities. Most dedicated spine CT and MR
scans as well as scans of the chest, abdomen or neck cover only part of the
spine. Segmentation and identification should therefore not rely on the
visibility of certain vertebrae or a certain number of vertebrae. We propose an
iterative instance segmentation approach that uses a fully convolutional neural
network to segment and label vertebrae one after the other, independently of
the number of visible vertebrae. This instance-by-instance segmentation is
enabled by combining the network with a memory component that retains
information about already segmented vertebrae. The network iteratively analyzes
image patches, using information from both image and memory to search for the
next vertebra. To efficiently traverse the image, we include the prior
knowledge that the vertebrae are always located next to each other, which is
used to follow the vertebral column. This method was evaluated with five
diverse datasets, including multiple modalities (CT and MR), various fields of
view and coverages of different sections of the spine, and a particularly
challenging set of low-dose chest CT scans. The proposed iterative segmentation
method compares favorably with state-of-the-art methods and is fast, flexible
and generalizable.
Blood Vessel Geometry Synthesis using Generative Adversarial Networks
Authors: Jelmer M. Wolterink, Tim Leiner, Ivana Isgum
Date: 2018-04-12 00:00:00
Tasks: Anatomy, Attribute, Generative Adversarial Network, Medical Image Analysis

Computationally synthesized blood vessels can be used for training and
evaluation of medical image analysis applications. We propose a deep generative
model to synthesize blood vessel geometries, with an application to coronary
arteries in cardiac CT angiography (CCTA).
  In the proposed method, a Wasserstein generative adversarial network (GAN)
consisting of a generator and a discriminator network is trained. While the
generator tries to synthesize realistic blood vessel geometries, the
discriminator tries to distinguish synthesized geometries from those of real
blood vessels. Both real and synthesized blood vessel geometries are
parametrized as 1D signals based on the central vessel axis. The generator can
optionally be provided with an attribute vector to synthesize vessels with
particular characteristics.
  The GAN was optimized using a reference database with parametrizations of
4,412 real coronary artery geometries extracted from CCTA scans. After
training, plausible coronary artery geometries could be synthesized based on
random vectors sampled from a latent space. A qualitative analysis showed
strong similarities between real and synthesized coronary arteries. A detailed
analysis of the latent space showed that the diversity present in coronary
artery anatomy was accurately captured by the generator.
  Results show that Wasserstein generative adversarial networks can be used to
synthesize blood vessel geometries.
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification
Authors: Alon Rozental, Daniel Fleischer
Date: 2018-04-12 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Classification, Ordinal Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

This paper describes the participation of Amobee in the shared sentiment
analysis task at SemEval 2018. We participated in all the English sub-tasks and
the Spanish valence tasks. Our system consists of three parts: training
task-specific word embeddings, training a model consisting of
gated-recurrent-units (GRU) with a convolution neural network (CNN) attention
mechanism and training stacking-based ensembles for each of the sub-tasks. Our
algorithm reached 3rd and 1st places in the valence ordinal classification
sub-tasks in English and Spanish, respectively.
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Authors: Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico Gorbach, Andreas Krause, Joachim M. Buhmann
Date: 2018-04-12 00:00:00
Tasks: regression, Time Series, Time Series Analysis

Parameter identification and comparison of dynamical systems is a challenging
task in many fields. Bayesian approaches based on Gaussian process regression
over time-series data have been successfully applied to infer the parameters of
a dynamical system without explicitly solving it. While the benefits in
computational cost are well established, a rigorous mathematical framework has
been missing. We offer a novel interpretation which leads to a better
understanding and improvements in state-of-the-art performance in terms of
accuracy for nonlinear dynamical systems.
Image Correction via Deep Reciprocating HDR Transformation
Authors: Xin Yang, Ke Xu, Yibing Song, Qiang Zhang, Xiaopeng Wei, Rynson Lau
Date: 2018-04-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: HDR Reconstruction, Tone Mapping

Image correction aims to adjust an input image into a visually pleasing one.
Existing approaches are proposed mainly from the perspective of image pixel
manipulation. They are not effective to recover the details in the under/over
exposed regions. In this paper, we revisit the image formation procedure and
notice that the missing details in these regions exist in the corresponding
high dynamic range (HDR) data. These details are well perceived by the human
eyes but diminished in the low dynamic range (LDR) domain because of the tone
mapping process. Therefore, we formulate the image correction task as an HDR
transformation process and propose a novel approach called Deep Reciprocating
HDR Transformation (DRHT). Given an input LDR image, we first reconstruct the
missing details in the HDR domain. We then perform tone mapping on the
predicted HDR data to generate the output LDR image with the recovered details.
To this end, we propose a united framework consisting of two CNNs for HDR
reconstruction and tone mapping. They are integrated end-to-end for joint
training and prediction. Experiments on the standard benchmarks demonstrate
that the proposed method performs favorably against state-of-the-art image
correction methods.
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
Authors: Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J. Cree
Date: 2018-04-12 00:00:00

We investigate the effect of explicitly enforcing the Lipschitz continuity of neural networks with respect to their inputs. To this end, we provide a simple technique for computing an upper bound to the Lipschitz constant---for multiple $p$-norms---of a feed forward neural network composed of commonly used layer types. Our technique is then used to formulate training a neural network with a bounded Lipschitz constant as a constrained optimisation problem that can be solved using projected stochastic gradient methods. Our evaluation study shows that the performance of the resulting models exceeds that of models trained with other common regularisers. We also provide evidence that the hyperparameters are intuitive to tune, demonstrate how the choice of norm for computing the Lipschitz constant impacts the resulting model, and show that the performance gains provided by our method are particularly noticeable when only a small amount of training data is available.
BigSR: an empirical study of real-time expressive RDF stream reasoning on modern Big Data platforms
Authors: Xiangnan Ren, Olivier Curé, Hubert Naacke, Guohui Xiao
Date: 2018-04-12 00:00:00
Tasks: Distributed Computing

The trade-off between language expressiveness and system scalability (E&S) is
a well-known problem in RDF stream reasoning. Higher expressiveness supports
more complex reasoning logic, however, it may also hinder system scalability.
Current research mainly focuses on logical frameworks suitable for stream
reasoning as well as the implementation and the evaluation of prototype
systems. These systems are normally developed in a centralized setting which
suffer from inherent limited scalability, while an in-depth study of applying
distributed solutions to cover E&S is still missing. In this paper, we aim to
explore the feasibility of applying modern distributed computing frameworks to
meet E&S all together. To do so, we first propose BigSR, a technical
demonstrator that supports a positive fragment of the LARS framework. For the
sake of generality and to cover a wide variety of use cases, BigSR relies on
the two main execution models adopted by major distributed execution
frameworks: Bulk Synchronous Processing (BSP) and Record-at-A-Time (RAT).
Accordingly, we implement BigSR on top of Apache Spark Streaming (BSP model)
and Apache Flink (RAT model). In order to conclude on the impacts of BSP and
RAT on E&S, we analyze the ability of the two models to support distributed
stream reasoning and identify several types of use cases characterized by their
levels of support. This classification allows for quantifying the E&S trade-off
by assessing the scalability of each type of use case \wrt its level of
expressiveness. Then, we conduct a series of experiments with 15 queries from 4
different datasets. Our experiments show that BigSR over both BSP and RAT
generally scales up to high throughput beyond million-triples per second (with
or without recursion), and RAT attains sub-millisecond delay for stateless
query operators.
Evaluation of the visual odometry methods for semi-dense real-time
Authors: Haidara Gaoussou, Peng Dewei
Date: 2018-04-10 00:00:00
Tasks: Visual Odometry

Recent decades have witnessed a significant increase in the use of visual
odometry(VO) in the computer vision area. It has also been used in varieties of
robotic applications, for example on the Mars Exploration Rovers. This paper,
firstly, discusses two popular existing visual odometry approaches, namely
LSD-SLAM and ORB-SLAM2 to improve the performance metrics of visual SLAM
systems using Umeyama Method. We carefully evaluate the methods referred to
above on three different well-known KITTI datasets, EuRoC MAV dataset, and TUM
RGB-D dataset to obtain the best results and graphically compare the results to
evaluation metrics from different visual odometry approaches. Secondly, we
propose an approach running in real-time with a stereo camera, which combines
an existing feature-based (indirect) method and an existing feature-less
(direct) method matching with accurate semidense direct image alignment and
reconstructing an accurate 3D environment directly on pixels that have image
gradient. Keywords VO, performance metrics, Umeyama Method, feature-based
method, feature-less method & semi-dense real-time.