pwc-archive/papers-with-abstracts huggingface dataset:
Synchronized Detection and Recovery of Steganographic Messages with Adversarial Learning
Authors: Haichao Shi, Xiao-Yu Zhang
Date: 2018-01-31 00:00:00

In this work, we mainly study the mechanism of learning the steganographic
algorithm as well as combining the learning process with adversarial learning
to learn a good steganographic algorithm. To handle the problem of embedding
secret messages into the specific medium, we design a novel adversarial modules
to learn the steganographic algorithm, and simultaneously train three modules
called generator, discriminator and steganalyzer. Different from existing
methods, the three modules are formalized as a game to communicate with each
other. In the game, the generator and discriminator attempt to communicate with
each other using secret messages hidden in an image. While the steganalyzer
attempts to analyze whether there is a transmission of confidential
information. We show that through unsupervised adversarial training, the
adversarial model can produce robust steganographic solutions, which act like
an encryption. Furthermore, we propose to utilize supervised adversarial
training method to train a robust steganalyzer, which is utilized to
discriminate whether an image contains secret information. Numerous experiments
are conducted on publicly available dataset to demonstrate the effectiveness of
the proposed method.
Global Relation Embedding for Relation Extraction
Authors: Yu Su, Honglei Liu, Semih Yavuz, Izzeddin Gur, Huan Sun, Xifeng Yan
Date: 2017-04-19 00:00:00
Proceeding: NAACL 2018 6
Tasks: Relation, Relation Extraction

We study the problem of textual relation embedding with distant supervision.
To combat the wrong labeling problem of distant supervision, we propose to
embed textual relations with global statistics of relations, i.e., the
co-occurrence statistics of textual and knowledge base relations collected from
the entire corpus. This approach turns out to be more robust to the training
noise introduced by distant supervision. On a popular relation extraction
dataset, we show that the learned textual relation embedding can be used to
augment existing relation extraction models and significantly improve their
performance. Most remarkably, for the top 1,000 relational facts discovered by
the best existing model, the precision can be improved from 83.9% to 89.3%.
Derivate-based Component-Trees for Multi-Channel Image Segmentation
Authors: Tobias Böttger, Dominik Gutermuth
Date: 2017-05-04 00:00:00
Tasks: Image Segmentation, Optical Character Recognition (OCR), Semantic Segmentation, Translation

We introduce the concept of derivate-based component-trees for images with an
arbitrary number of channels. The approach is a natural extension of the
classical component-tree devoted to gray-scale images. The similar structure
enables the translation of many gray-level image processing techniques based on
the component-tree to hyperspectral and color images. As an example
application, we present an image segmentation approach that extracts Maximally
Stable Homogeneous Regions (MSHR). The approach very similar to MSER but can be
applied to images with an arbitrary number of channels. As opposed to MSER, our
approach implicitly segments regions with are both lighter and darker than
their background for gray-scale images and can be used in OCR applications
where MSER will fail. We introduce a local flooding-based immersion for the
derivate-based component-tree construction which is linear in the number of
pixels. In the experiments, we show that the runtime scales favorably with an
increasing number of channels and may improve algorithms which build on MSER.
To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression
Authors: Yitian Yuan, Tao Mei, Wenwu Zhu
Date: 2018-04-19 00:00:00
Tasks: regression, Sentence, Temporal Localization

Given an untrimmed video and a sentence description, temporal sentence
localization aims to automatically determine the start and end points of the
described sentence within the video. The problem is challenging as it needs the
understanding of both video and sentence. Existing research predominantly
employs a costly "scan and localize" framework, neglecting the global video
context and the specific details within sentences which play as critical issues
for this problem. In this paper, we propose a novel Attention Based Location
Regression (ABLR) approach to solve the temporal sentence localization from a
global perspective. Specifically, to preserve the context information, ABLR
first encodes both video and sentence via Bidirectional LSTM networks. Then, a
multi-modal co-attention mechanism is introduced to generate not only video
attention which reflects the global video structure, but also sentence
attention which highlights the crucial details for temporal localization.
Finally, a novel attention based location regression network is designed to
predict the temporal coordinates of sentence query from the previous attention.
ABLR is jointly trained in an end-to-end manner. Comprehensive experiments on
ActivityNet Captions and TACoS datasets demonstrate both the effectiveness and
the efficiency of the proposed ABLR approach.
High Dimensional Time Series Generators
Authors: Jörg P. Bachmann, Johann-Christoph Freytag
Date: 2018-04-17 00:00:00
Tasks: Dynamic Time Warping, ERP, Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

Multidimensional time series are sequences of real valued vectors. They occur
in different areas, for example handwritten characters, GPS tracking, and
gestures of modern virtual reality motion controllers. Within these areas, a
common task is to search for similar time series. Dynamic Time Warping (DTW) is
a common distance function to compare two time series. The Edit Distance with
Real Penalty (ERP) and the Dog Keeper Distance (DK) are two more distance
functions on time series. Their behaviour has been analyzed on 1-dimensional
time series. However, it is not easy to evaluate their behaviour in relation to
growing dimensionality. For this reason we propose two new data synthesizers
generating multidimensional time series. The first synthesizer extends the well
known cylinder-bell-funnel (CBF) dataset to multidimensional time series. Here,
each time series has an arbitrary type (cylinder, bell, or funnel) in each
dimension, thus for $d$-dimensional time series there are $3^{d}$ different
classes. The second synthesizer (RAM) creates time series with ideas adapted
from Brownian motions which is a common model of movement in physics. Finally,
we evaluate the applicability of a 1-nearest neighbor classifier using DTW on
datasets generated by our synthesizers.
An Integrated Development Environment for Planning Domain Modeling
Authors: Yuncong Li, Hankz Hankui Zhuo
Date: 2018-04-19 00:00:00

In order to make the task, description of planning domains and problems, more
comprehensive for non-experts in planning, the visual representation has been
used in planning domain modeling in recent years. However, current knowledge
engineering tools with visual modeling, like itSIMPLE (Vaquero et al. 2012) and
VIZ (Vodr\'a\v{z}ka and Chrpa 2010), are less efficient than the traditional
method of hand-coding by a PDDL expert using a text editor, and rarely involved
in finetuning planning domains depending on the plan validation. Aim at this,
we present an integrated development environment KAVI for planning domain
modeling inspired by itSIMPLE and VIZ. KAVI using an abstract domain knowledge
base to improve the efficiency of planning domain visual modeling. By
integrating planners and a plan validator, KAVI proposes a method to fine-tune
planning domains based on the plan validation.
DetNet: A Backbone network for Object Detection
Authors: Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun
Date: 2018-04-17 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Instance Segmentation, Object, object-detection, Object Detection, Semantic Segmentation, valid

Recent CNN based object detectors, no matter one-stage methods like YOLO,
SSD, and RetinaNe or two-stage detectors like Faster R-CNN, R-FCN and FPN are
usually trying to directly finetune from ImageNet pre-trained models designed
for image classification. There has been little work discussing on the backbone
feature extractor specifically designed for the object detection. More
importantly, there are several differences between the tasks of image
classification and object detection. 1. Recent object detectors like FPN and
RetinaNet usually involve extra stages against the task of image classification
to handle the objects with various scales. 2. Object detection not only needs
to recognize the category of the object instances but also spatially locate the
position. Large downsampling factor brings large valid receptive field, which
is good for image classification but compromises the object location ability.
Due to the gap between the image classification and object detection, we
propose DetNet in this paper, which is a novel backbone network specifically
designed for object detection. Moreover, DetNet includes the extra stages
against traditional backbone network for image classification, while maintains
high spatial resolution in deeper layers. Without any bells and whistles,
state-of-the-art results have been obtained for both object detection and
instance segmentation on the MSCOCO benchmark based on our DetNet~(4.8G FLOPs)
backbone. The code will be released for the reproduction.
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations
Authors: Maziar Raissi
Date: 2018-04-19 00:00:00

Classical numerical methods for solving partial differential equations suffer
from the curse dimensionality mainly due to their reliance on meticulously
generated spatio-temporal grids. Inspired by modern deep learning based
techniques for solving forward and inverse problems associated with partial
differential equations, we circumvent the tyranny of numerical discretization
by devising an algorithm that is scalable to high-dimensions. In particular, we
approximate the unknown solution by a deep neural network which essentially
enables us to benefit from the merits of automatic differentiation. To train
the aforementioned neural network we leverage the well-known connection between
high-dimensional partial differential equations and forward-backward stochastic
differential equations. In fact, independent realizations of a standard
Brownian motion will act as training data. We test the effectiveness of our
approach for a couple of benchmark problems spanning a number of scientific
domains including Black-Scholes-Barenblatt and Hamilton-Jacobi-Bellman
equations, both in 100-dimensions.
CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction
Authors: Murari Mandal, Prafulla Saxena, Santosh Kumar Vipparthi, Subrahmanyam Murala
Date: 2018-04-19 00:00:00
Tasks: Change Detection

Background subtraction in video provides the preliminary information which is
essential for many computer vision applications. In this paper, we propose a
sequence of approaches named CANDID to handle the change detection problem in
challenging video scenarios. The CANDID adaptively initializes the pixel-level
distance threshold and update rate. These parameters are updated by computing
the change dynamics at a location. Further, the background model is maintained
by formulating a deterministic update policy. The performance of the proposed
method is evaluated over various challenging scenarios such as dynamic
background and extreme weather conditions. The qualitative and quantitative
measures of the proposed method outperform the existing state-of-the-art
approaches.
QuaSE: Accurate Text Style Transfer under Quantifiable Guidance
Authors: Yi Liao, Lidong Bing, Piji Li, Shuming Shi, Wai Lam, Tong Zhang
Date: 2018-04-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Disentanglement, Sentence, Style Transfer, Text Style Transfer

We propose the task of Quantifiable Sequence Editing (QuaSE): editing an
input sequence to generate an output sequence that satisfies a given numerical
outcome value measuring a certain property of the sequence, with the
requirement of keeping the main content of the input sequence. For example, an
input sequence could be a word sequence, such as review sentence and
advertisement text. For a review sentence, the outcome could be the review
rating; for an advertisement, the outcome could be the click-through rate. The
major challenge in performing QuaSE is how to perceive the outcome-related
wordings, and only edit them to change the outcome. In this paper, the proposed
framework contains two latent factors, namely, outcome factor and content
factor, disentangled from the input sentence to allow convenient editing to
change the outcome and keep the content. Our framework explores the
pseudo-parallel sentences by modeling their content similarity and outcome
differences to enable a better disentanglement of the latent factors, which
allows generating an output to better satisfy the desired outcome and keep the
content. The dual reconstruction structure further enhances the capability of
generating expected output by exploiting the couplings of latent factors of
pseudo-parallel sentences. For evaluation, we prepared a dataset of Yelp review
sentences with the ratings as outcome. Extensive experimental results are
reported and discussed to elaborate the peculiarities of our framework.
Large Margin Structured Convolution Operator for Thermal Infrared Object Tracking
Authors: Peng Gao, Yipeng Ma, Ke Song, Chao Li, Fei Wang, Liyi Xiao
Date: 2018-04-19 00:00:00
Tasks: Object, Object Tracking, Thermal Infrared Object Tracking

Compared with visible object tracking, thermal infrared (TIR) object tracking
can track an arbitrary target in total darkness since it cannot be influenced
by illumination variations. However, there are many unwanted attributes that
constrain the potentials of TIR tracking, such as the absence of visual color
patterns and low resolutions. Recently, structured output support vector
machine (SOSVM) and discriminative correlation filter (DCF) have been
successfully applied to visible object tracking, respectively. Motivated by
these, in this paper, we propose a large margin structured convolution operator
(LMSCO) to achieve efficient TIR object tracking. To improve the tracking
performance, we employ the spatial regularization and implicit interpolation to
obtain continuous deep feature maps, including deep appearance features and
deep motion features, of the TIR targets. Finally, a collaborative optimization
strategy is exploited to significantly update the operators. Our approach not
only inherits the advantage of the strong discriminative capability of SOSVM
but also achieves accurate and robust tracking with higher-dimensional features
and more dense samples. To the best of our knowledge, we are the first to
incorporate the advantages of DCF and SOSVM for TIR object tracking.
Comprehensive evaluations on two thermal infrared tracking benchmarks, i.e.
VOT-TIR2015 and VOT-TIR2016, clearly demonstrate that our LMSCO tracker
achieves impressive results and outperforms most state-of-the-art trackers in
terms of accuracy and robustness with sufficient frame rate.
Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences
Authors: Marcel Trotzek, Sven Koitka, Christoph M. Friedrich
Date: 2018-04-19 00:00:00
Tasks: Word Embeddings

Depression is ranked as the largest contributor to global disability and is
also a major reason for suicide. Still, many individuals suffering from forms
of depression are not treated for various reasons. Previous studies have shown
that depression also has an effect on language usage and that many depressed
individuals use social media platforms or the internet in general to get
information or discuss their problems. This paper addresses the early detection
of depression using machine learning models based on messages on a social
platform. In particular, a convolutional neural network based on different word
embeddings is evaluated and compared to a classification based on user-level
linguistic metadata. An ensemble of both approaches is shown to achieve
state-of-the-art results in a current early detection task. Furthermore, the
currently popular ERDE score as metric for early detection systems is examined
in detail and its drawbacks in the context of shared tasks are illustrated. A
slightly modified metric is proposed and compared to the original score.
Finally, a new word embedding was trained on a large corpus of the same domain
as the described task and is evaluated as well.
Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks
Authors: Victor Dibia, Çağatay Demiralp
Date: 2018-04-09 00:00:00
Tasks: Data Visualization, Translation, valid

Rapidly creating effective visualizations using expressive grammars is
challenging for users who have limited time and limited skills in statistics
and data visualization. Even high-level, dedicated visualization tools often
require users to manually select among data attributes, decide which
transformations to apply, and specify mappings between visual encoding
variables and raw or transformed attributes.
  In this paper we introduce Data2Vis, a neural translation model for
automatically generating visualizations from given datasets. We formulate
visualization generation as a sequence to sequence translation problem where
data specifications are mapped to visualization specifications in a declarative
language (Vega-Lite). To this end, we train a multilayered attention-based
recurrent neural network (RNN) with long short-term memory (LSTM) units on a
corpus of visualization specifications.
  Qualitative results show that our model learns the vocabulary and syntax for
a valid visualization specification, appropriate transformations (count, bins,
mean) and how to use common data selection patterns that occur within data
visualizations. Data2Vis generates visualizations that are comparable to
manually-created visualizations in a fraction of the time, with potential to
learn more complex visualization strategies at scale.
SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wild
Authors: Soumyadip Sengupta, Angjoo Kanazawa, Carlos D. Castillo, David Jacobs
Date: 2017-12-02 00:00:00
Proceeding: CVPR 2018
Tasks: Inverse Rendering

We present SfSNet, an end-to-end learning framework for producing an accurate
decomposition of an unconstrained human face image into shape, reflectance and
illuminance. SfSNet is designed to reflect a physical lambertian rendering
model. SfSNet learns from a mixture of labeled synthetic and unlabeled real
world images. This allows the network to capture low frequency variations from
synthetic and high frequency details from real images through the photometric
reconstruction loss. SfSNet consists of a new decomposition architecture with
residual blocks that learns a complete separation of albedo and normal. This is
used along with the original image to predict lighting. SfSNet produces
significantly better quantitative and qualitative results than state-of-the-art
methods for inverse rendering and independent normal and illumination
estimation.
Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
Authors: Sharmistha Jat, Siddhesh Khandelwal, Partha Talukdar
Date: 2018-04-19 00:00:00
Tasks: Relation, Relation Extraction, Relationship Extraction (Distant Supervised), Sentence

Relation extraction is the problem of classifying the relationship between
two entities in a given sentence. Distant Supervision (DS) is a popular
technique for developing relation extractors starting with limited supervision.
We note that most of the sentences in the distant supervision relation
extraction setting are very long and may benefit from word attention for better
sentence representation. Our contributions in this paper are threefold.
Firstly, we propose two novel word attention models for distantly- supervised
relation extraction: (1) a Bi-directional Gated Recurrent Unit (Bi-GRU) based
word attention model (BGWA), (2) an entity-centric attention model (EA), and
(3) a combination model which combines multiple complementary models using
weighted voting method for improved relation extraction. Secondly, we introduce
GDS, a new distant supervision dataset for relation extraction. GDS removes
test data noise present in all previous distant- supervision benchmark
datasets, making credible automatic evaluation possible. Thirdly, through
extensive experiments on multiple real-world datasets, we demonstrate the
effectiveness of the proposed methods.
Replica Symmetry Breaking in Bipartite Spin Glasses and Neural Networks
Authors: Gavin Hartnett, Edward Parker, Edward Geist
Date: 2018-03-17 00:00:00
Tasks: graph partitioning

Some interesting recent advances in the theoretical understanding of neural
networks have been informed by results from the physics of disordered many-body
systems. Motivated by these findings, this work uses the replica technique to
study the mathematically tractable bipartite Sherrington-Kirkpatrick (SK) spin
glass model, which is formally similar to a Restricted Boltzmann Machine (RBM)
neural network. The bipartite SK model has been previously studied assuming
replica symmetry; here this assumption is relaxed and a replica symmetry
breaking analysis is performed. The bipartite SK model is found to have many
features in common with Parisi's solution of the original, unipartite SK model,
including the existence of a multitude of pure states which are related in a
hierarchical, ultrametric fashion. As an application of this analysis, the
optimal cost for a graph partitioning problem is shown to be simply related to
the ground state energy of the bipartite SK model. As a second application,
empirical investigations reveal that the Gibbs sampled outputs of an RBM
trained on the MNIST data set are more ultrametrically distributed than the
input data itself.
GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning
Authors: Siyu Huang, Xi Li, Zhi-Qi Cheng, Zhongfei Zhang, Alexander Hauptmann
Date: 2018-04-19 00:00:00
Tasks: Attribute, Neural Architecture Search

A key problem in deep multi-attribute learning is to effectively discover the
inter-attribute correlation structures. Typically, the conventional deep
multi-attribute learning approaches follow the pipeline of manually designing
the network architectures based on task-specific expertise prior knowledge and
careful network tunings, leading to the inflexibility for various complicated
scenarios in practice. Motivated by addressing this problem, we propose an
efficient greedy neural architecture search approach (GNAS) to automatically
discover the optimal tree-like deep architecture for multi-attribute learning.
In a greedy manner, GNAS divides the optimization of global architecture into
the optimizations of individual connections step by step. By iteratively
updating the local architectures, the global tree-like architecture gets
converged where the bottom layers are shared across relevant attributes and the
branches in top layers more encode attribute-specific features. Experiments on
three benchmark multi-attribute datasets show the effectiveness and compactness
of neural architectures derived by GNAS, and also demonstrate the efficiency of
GNAS in searching neural architectures.