pwc-archive/papers-with-abstracts huggingface dataset:
Towards ultra-high resolution 3D reconstruction of a whole rat brain from 3D-PLI data
Authors: Sharib Ali, Martin Schober, Philipp Schlöme, Katrin Amunts, Markus Axer, Karl Rohr
Date: 2018-07-29 00:00:00
Tasks: 3D Reconstruction

3D reconstruction of the fiber connectivity of the rat brain at microscopic
scale enables gaining detailed insight about the complex structural
organization of the brain. We introduce a new method for registration and 3D
reconstruction of high- and ultra-high resolution (64 $\mu$m and 1.3 $\mu$m
pixel size) histological images of a Wistar rat brain acquired by 3D polarized
light imaging (3D-PLI). Our method exploits multi-scale and multi-modal 3D-PLI
data up to cellular resolution. We propose a new feature transform-based
similarity measure and a weighted regularization scheme for accurate and robust
non-rigid registration. To transform the 1.3 $\mu$m ultra-high resolution data
to the reference blockface images a feature-based registration method followed
by a non-rigid registration is proposed. Our approach has been successfully
applied to 278 histological sections of a rat brain and the performance has
been quantitatively evaluated using manually placed landmarks by an expert.
ReenactGAN: Learning to Reenact Faces via Boundary Transfer
Authors: Wayne Wu, Yunxuan Zhang, Cheng Li, Chen Qian, Chen Change Loy
Date: 2018-07-29 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Face Reenactment, GPU, Talking Face Generation, Talking Head Generation

We present a novel learning-based framework for face reenactment. The
proposed method, known as ReenactGAN, is capable of transferring facial
movements and expressions from monocular video input of an arbitrary person to
a target person. Instead of performing a direct transfer in the pixel space,
which could result in structural artifacts, we first map the source face onto a
boundary latent space. A transformer is subsequently used to adapt the boundary
of source face to the boundary of target face. Finally, a target-specific
decoder is used to generate the reenacted target face. Thanks to the effective
and reliable boundary-based transfer, our method can perform photo-realistic
face reenactment. In addition, ReenactGAN is appealing in that the whole
reenactment process is purely feed-forward, and thus the reenactment process
can run in real-time (30 FPS on one GTX 1080 GPU). Dataset and model will be
publicly available at
https://wywu.github.io/projects/ReenactGAN/ReenactGAN.html
Semi-supervised Transfer Learning for Image Rain Removal
Authors: Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, Ying Wu
Date: 2018-07-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deep Learning, Rain Removal, Single Image Deraining, Transfer Learning

Single image rain removal is a typical inverse problem in computer vision.
The deep learning technique has been verified to be effective for this task and
achieved state-of-the-art performance. However, previous deep learning methods
need to pre-collect a large set of image pairs with/without synthesized rain
for training, which tends to make the neural network be biased toward learning
the specific patterns of the synthesized rain, while be less able to generalize
to real test samples whose rain types differ from those in the training data.
To this issue, this paper firstly proposes a semi-supervised learning paradigm
toward this task. Different from traditional deep learning methods which only
use supervised image pairs with/without synthesized rain, we further put real
rainy images, without need of their clean ones, into the network training
process. This is realized by elaborately formulating the residual between an
input rainy image and its expected network output (clear image without rain) as
a specific parametrized rain streaks distribution. The network is therefore
trained to adapt real unsupervised diverse rain types through transferring from
the supervised synthesized rain, and thus both the short-of-training-sample and
bias-to-supervised-sample issues can be evidently alleviated. Experiments on
synthetic and real data verify the superiority of our model compared to the
state-of-the-arts.
Visual Analogies between Atari Games for Studying Transfer Learning in RL
Authors: Doron Sobol, Lior Wolf, Yaniv Taigman
Date: 2018-07-29 00:00:00
Tasks: Atari Games, Transfer Learning, Visual Analogies

In this work, we ask the following question: Can visual analogies, learned in
an unsupervised way, be used in order to transfer knowledge between pairs of
games and even play one game using an agent trained for another game? We
attempt to answer this research question by creating visual analogies between a
pair of games: a source game and a target game. For example, given a video
frame in the target game, we map it to an analogous state in the source game
and then attempt to play using a trained policy learned for the source game. We
demonstrate convincing visual mapping between four pairs of games (eight
mappings), which are used to evaluate three transfer learning approaches.
Joint Representation and Truncated Inference Learning for Correlation Filter based Tracking
Authors: Yingjie Yao, Xiaohe Wu, Lei Zhang, Shiguang Shan, WangMeng Zuo
Date: 2018-07-29 00:00:00
Proceeding: ECCV 2018 9

Correlation filter (CF) based trackers generally include two modules, i.e.,
feature representation and on-line model adaptation. In existing off-line deep
learning models for CF trackers, the model adaptation usually is either
abandoned or has closed-form solution to make it feasible to learn deep
representation in an end-to-end manner. However, such solutions fail to exploit
the advances in CF models, and cannot achieve competitive accuracy in
comparison with the state-of-the-art CF trackers. In this paper, we investigate
the joint learning of deep representation and model adaptation, where an
updater network is introduced for better tracking on future frame by taking
current frame representation, tracking result, and last CF tracker as input. By
modeling the representor as convolutional neural network (CNN), we truncate the
alternating direction method of multipliers (ADMM) and interpret it as a deep
network of updater, resulting in our model for learning representation and
truncated inference (RTINet). Experiments demonstrate that our RTINet tracker
achieves favorable tracking accuracy against the state-of-the-art trackers and
its rapid version can run at a real-time speed of 24 fps. The code and
pre-trained models will be publicly available at
https://github.com/tourmaline612/RTINet.
Clause Vivification by Unit Propagation in CDCL SAT Solvers
Authors: Chu-min Li, Fan Xiao, Mao Luo, Felip Manyà, Zhipeng Lü, Yu Li
Date: 2018-07-29 00:00:00

Original and learnt clauses in Conflict-Driven Clause Learning (CDCL) SAT
solvers often contain redundant literals. This may have a negative impact on
performance because redundant literals may deteriorate both the effectiveness
of Boolean constraint propagation and the quality of subsequent learnt clauses.
To overcome this drawback, we propose a clause vivification approach that
eliminates redundant literals by applying unit propagation. The proposed clause
vivification is activated before the SAT solver triggers some selected
restarts, and only affects a subset of original and learnt clauses, which are
considered to be more relevant according to metrics like the literal block
distance (LBD). Moreover, we conducted an empirical investigation with
instances coming from the hard combinatorial and application categories of
recent SAT competitions. The results show that a remarkable number of
additional instances are solved when the proposed approach is incorporated into
five of the best performing CDCL SAT solvers (Glucose, TC_Glucose, COMiniSatPS,
MapleCOMSPS and MapleCOMSPS_LRB). More importantly, the empirical investigation
includes an in-depth analysis of the effectiveness of clause vivification. It
is worth mentioning that one of the SAT solvers described here was ranked first
in the main track of SAT Competition 2017 thanks to the incorporation of the
proposed clause vivification. That solver was further improved in this paper
and won the bronze medal in the main track of SAT Competition 2018.
NMT-based Cross-lingual Document Embeddings
Authors: Wei Li, Brian Mak
Date: 2018-07-29 00:00:00
Tasks: Cross-Lingual Document Classification, Document Classification, Document Embedding, General Classification, Machine Translation, NMT, Translation

This paper investigates a cross-lingual document embedding method that improves the current Neural machine Translation framework based Document Vector (NTDV or simply NV). NV is developed with a self-attention mechanism under the neural machine translation (NMT) framework. In NV, each pair of parallel documents in different languages are projected to the same shared layer in the model. However, the pair of NV embeddings are not guaranteed to be similar. This paper further adds a distance constraint to the training objective function of NV so that the two embeddings of a parallel document are required to be as close as possible. The new method will be called constrained NV (cNV). In a cross-lingual document classification task, the new cNV performs as well as NV and outperforms other published studies that require forward-pass decoding. Compared with the previous NV, cNV does not need a translator during testing, and so the method is lighter and more flexible.
Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification
Authors: Fu Xiong, Yang Xiao, Zhiguo Cao, Kaicheng Gong, Zhiwen Fang, Joey Tianyi Zhou
Date: 2018-07-29 00:00:00
Tasks: Open-Ended Question Answering, Person Re-Identification

Person re-identification is indeed a challenging visual recognition task due
to the critical issues of human pose variation, human body occlusion, camera
view variation, etc. To address this, most of the state-of-the-art approaches
are proposed based on deep convolutional neural network (CNN), being leveraged
by its strong feature learning power and classification boundary fitting
capacity. Although the vital role towards person re-identification, how to
build effective CNN baseline model has not been well studied yet. To answer
this open question, we propose 3 good practices in this paper from the
perspectives of adjusting CNN architecture and training procedure. In
particular, they are adding batch normalization after the global pooling layer,
executing identity categorization directly using only one fully-connected, and
using Adam as optimizer. The extensive experiments on 3 widely-used benchmark
datasets demonstrate that, our propositions essentially facilitate the CNN
baseline model to achieve the state-of-the-art performance without any other
high-level domain knowledge or low-level technical trick.
Efficient Uncertainty Estimation for Semantic Segmentation in Videos
Authors: Po-Yu Huang, Wan-Ting Hsu, Chun-Yueh Chiu, Ting-Fan Wu, Min Sun
Date: 2018-07-29 00:00:00
Proceeding: ECCV 2018 9

Uncertainty estimation in deep learning becomes more important recently. A
deep learning model can't be applied in real applications if we don't know
whether the model is certain about the decision or not. Some literature
proposes the Bayesian neural network which can estimate the uncertainty by
Monte Carlo Dropout (MC dropout). However, MC dropout needs to forward the
model $N$ times which results in $N$ times slower. For real-time applications
such as a self-driving car system, which needs to obtain the prediction and the
uncertainty as fast as possible, so that MC dropout becomes impractical. In
this work, we propose the region-based temporal aggregation (RTA) method which
leverages the temporal information in videos to simulate the sampling
procedure. Our RTA method with Tiramisu backbone is 10x faster than the MC
dropout with Tiramisu backbone ($N=5$). Furthermore, the uncertainty estimation
obtained by our RTA method is comparable to MC dropout's uncertainty estimation
on pixel-level and frame-level metrics.
Texture Mixing by Interpolating Deep Statistics via Gaussian Models
Authors: Zi-Ming Wang, Gui-Song Xia, Yi-Peng Zhang
Date: 2018-07-29 00:00:00
Tasks: Style Transfer, Texture Synthesis

Recently, enthusiastic studies have devoted to texture synthesis using deep
neural networks, because these networks excel at handling complex patterns in
images. In these models, second-order statistics, such as Gram matrix, are used
to describe textures. Despite the fact that these model have achieved promising
results, the structure of their parametric space is still unclear,
consequently, it is difficult to use them to mix textures. This paper addresses
the texture mixing problem by using a Gaussian scheme to interpolate deep
statistics computed from deep neural networks. More precisely, we first reveal
that the statistics used in existing deep models can be unified using a
stationary Gaussian scheme. We then present a novel algorithm to mix these
statistics by interpolating between Gaussian models using optimal transport. We
further apply our scheme to Neural Style Transfer, where we can create mixed
styles. The experiments demonstrate that our method can achieve
state-of-the-art results. Because all the computations are implemented in
closed forms, our mixing algorithm adds only negligible time to the original
texture synthesis procedure.
PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction
Authors: Wei Dong, Qiuyuan Wang, Xin Wang, Hongbin Zha
Date: 2018-07-29 00:00:00
Proceeding: ECCV 2018 9

We propose a novel 3D spatial representation for data fusion and scene
reconstruction. Probabilistic Signed Distance Function (Probabilistic SDF,
PSDF) is proposed to depict uncertainties in the 3D space. It is modeled by a
joint distribution describing SDF value and its inlier probability, reflecting
input data quality and surface geometry. A hybrid data structure involving
voxel, surfel, and mesh is designed to fully exploit the advantages of various
prevalent 3D representations. Connected by PSDF, these components reasonably
cooperate in a consistent frame- work. Given sequential depth measurements,
PSDF can be incrementally refined with less ad hoc parametric Bayesian
updating. Supported by PSDF and the efficient 3D data representation,
high-quality surfaces can be extracted on-the-fly, and in return contribute to
reliable data fu- sion using the geometry information. Experiments demonstrate
that our system reconstructs scenes with higher model quality and lower
redundancy, and runs faster than existing online mesh generation systems.
Consistent polynomial-time unseeded graph matching for Lipschitz graphons
Authors: Yuan Zhang
Date: 2018-07-29 00:00:00
Tasks: Graph Matching, Graphon Estimation

We propose a consistent polynomial-time method for the unseeded node matching
problem for networks with smooth underlying structures. Despite widely
conjectured by the research community that the structured graph matching
problem to be significantly easier than its worst case counterpart, well-known
to be NP-hard, the statistical version of the problem has stood a challenge
that resisted any solution both provable and polynomial-time. The closest
existing work requires quasi-polynomial time. Our method is based on the latest
advances in graphon estimation techniques and analysis on the concentration of
empirical Wasserstein distances. Its core is a simple yet unconventional
sampling-and-matching scheme that reduces the problem from unseeded to seeded.
Our method allows flexible efficiencies, is convenient to analyze and
potentially can be extended to more general settings. Our work enables a rich
variety of subsequent estimations and inferences.
MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach
Authors: Kamlesh Pawar, Zhaolin Chen, N. Jon Shah, Gary F. Egan
Date: 2018-07-29 00:00:00
Tasks: Decoder

Purpose: The suppression of motion artefacts from MR images is a challenging
task. The purpose of this paper is to develop a standalone novel technique to
suppress motion artefacts from MR images using a data-driven deep learning
approach. Methods: A deep learning convolutional neural network (CNN) was
developed to remove motion artefacts in brain MR images. A CNN was trained on
simulated motion corrupted images to identify and suppress artefacts due to the
motion. The network was an encoder-decoder CNN architecture where the encoder
decomposed the motion corrupted images into a set of feature maps. The feature
maps were then combined by the decoder network to generate a motion-corrected
image. The network was tested on an unseen simulated dataset and an
experimental, motion corrupted in vivo brain dataset. Results: The trained
network was able to suppress the motion artefacts in the simulated motion
corrupted images, and the mean percentage error in the motion corrected images
was 2.69 % with a standard deviation of 0.95 %. The network was able to
effectively suppress the motion artefacts from the experimental dataset,
demonstrating the generalisation capability of the trained network. Conclusion:
A novel and generic motion correction technique has been developed that can
suppress motion artefacts from motion corrupted MR images. The proposed
technique is a standalone post-processing method that does not interfere with
data acquisition or reconstruction parameters, thus making it suitable for a
multitude of MR sequences.
Opinion Spam Recognition Method for Online Reviews using Ontological Features
Authors: L. H. Nguyen, N. T. H. Pham, V. M. Ngo
Date: 2018-07-29 00:00:00
Tasks: Spam detection

Nowadays, there are a lot of people using social media opinions to make their
decision on buying products or services. Opinion spam detection is a hard
problem because fake reviews can be made by organizations as well as
individuals for different purposes. They write fake reviews to mislead readers
or automated detection system by promoting or demoting target products to
promote them or to damage their reputations. In this paper, we pro-pose a new
approach using knowledge-based Ontology to detect opinion spam with high
accuracy (higher than 75%). Keywords: Opinion spam, Fake review, E-commercial,
Ontology.
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Authors: Mohammed Ali Al-Garadi, Amr Mohamed, Abdulla Al-Ali, Xiaojiang Du, Mohsen Guizani
Date: 2018-07-29 00:00:00

The Internet of Things (IoT) integrates billions of smart devices that can
communicate with one another with minimal human intervention. It is one of the
fastest developing fields in the history of computing, with an estimated 50
billion devices by the end of 2020. On the one hand, IoT play a crucial role in
enhancing several real-life smart applications that can improve life quality.
On the other hand, the crosscutting nature of IoT systems and the
multidisciplinary components involved in the deployment of such systems
introduced new security challenges. Implementing security measures, such as
encryption, authentication, access control, network security and application
security, for IoT devices and their inherent vulnerabilities is ineffective.
Therefore, existing security methods should be enhanced to secure the IoT
system effectively. Machine learning and deep learning (ML/DL) have advanced
considerably over the last few years, and machine intelligence has transitioned
from laboratory curiosity to practical machinery in several important
applications. Consequently, ML/DL methods are important in transforming the
security of IoT systems from merely facilitating secure communication between
devices to security-based intelligence systems. The goal of this work is to
provide a comprehensive survey of ML /DL methods that can be used to develop
enhanced security methods for IoT systems. IoT security threats that are
related to inherent or newly introduced threats are presented, and various
potential IoT system attack surfaces and the possible threats related to each
surface are discussed. We then thoroughly review ML/DL methods for IoT security
and present the opportunities, advantages and shortcomings of each method. We
discuss the opportunities and challenges involved in applying ML/DL to IoT
security. These opportunities and challenges can serve as potential future
research directions.
A Margin-based MLE for Crowdsourced Partial Ranking
Authors: Qianqian Xu, Jiechao Xiong, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang, Yuan YAO
Date: 2018-07-29 00:00:00

A preference order or ranking aggregated from pairwise comparison data is
commonly understood as a strict total order. However, in real-world scenarios,
some items are intrinsically ambiguous in comparisons, which may very well be
an inherent uncertainty of the data. In this case, the conventional total order
ranking can not capture such uncertainty with mere global ranking or utility
scores. In this paper, we are specifically interested in the recent surge in
crowdsourcing applications to predict partial but more accurate (i.e., making
less incorrect statements) orders rather than complete ones. To do so, we
propose a novel framework to learn some probabilistic models of partial orders
as a \emph{margin-based Maximum Likelihood Estimate} (MLE) method. We prove
that the induced MLE is a joint convex optimization problem with respect to all
the parameters, including the global ranking scores and margin parameter.
Moreover, three kinds of generalized linear models are studied, including the
basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model,
equipped with some theoretical analysis on FDR and Power control for the
proposed methods. The validity of these models are supported by experiments
with both simulated and real-world datasets, which shows that the proposed
models exhibit improvements compared with traditional state-of-the-art
algorithms.
Sidekick Policy Learning for Active Visual Exploration
Authors: Santhosh K. Ramakrishnan, Kristen Grauman
Date: 2018-07-29 00:00:00
Proceeding: ECCV 2018 9

We consider an active visual exploration scenario, where an agent must
intelligently select its camera motions to efficiently reconstruct the full
environment from only a limited set of narrow field-of-view glimpses. While the
agent has full observability of the environment during training, it has only
partial observability once deployed, being constrained by what portions it has
seen and what camera motions are permissible. We introduce sidekick policy
learning to capitalize on this imbalance of observability. The main idea is a
preparatory learning phase that attempts simplified versions of the eventual
exploration task, then guides the agent via reward shaping or initial policy
supervision. To support interpretation of the resulting policies, we also
develop a novel policy visualization technique. Results on active visual
exploration tasks with 360 scenes and 3D objects show that sidekicks
consistently improve performance and convergence rates over existing methods.
Code, data and demos are available.