pwc-archive/papers-with-abstracts huggingface dataset:
The MATLAB Toolbox SciXMiner: User's Manual and Programmer's Guide
Authors: Ralf Mikut, Andreas Bartschat, Wolfgang Doneit, Jorge Ángel González Ordiano, Benjamin Schott, Johannes Stegmaier, Simon Waczowicz, Markus Reischl
Date: 2017-04-11 00:00:00
Tasks: Time Series Analysis

The Matlab toolbox SciXMiner is designed for the visualization and analysis
of time series and features with a special focus to classification problems. It
was developed at the Institute of Applied Computer Science of the Karlsruhe
Institute of Technology (KIT), a member of the Helmholtz Association of German
Research Centres in Germany. The aim was to provide an open platform for the
development and improvement of data mining methods and its applications to
various medical and technical problems. SciXMiner bases on Matlab (tested for
the version 2017a). Many functions do not require additional standard toolboxes
but some parts of Signal, Statistics and Wavelet toolboxes are used for special
cases. The decision to a Matlab-based solution was made to use the wide
mathematical functionality of this package provided by The Mathworks Inc.
SciXMiner is controlled by a graphical user interface (GUI) with menu items and
control elements like popup lists, checkboxes and edit elements. This makes it
easier to work with SciXMiner for inexperienced users. Furthermore, an
automatization and batch standardization of analyzes is possible using macros.
The standard Matlab style using the command line is also available. SciXMiner
is an open source software. The download page is
http://sourceforge.net/projects/SciXMiner. It is licensed under the conditions
of the GNU General Public License (GNU-GPL) of The Free Software Foundation.
Automatic segmentation of MR brain images with a convolutional neural network
Authors: Pim Moeskops, Max A. Viergever, Adriënne M. Mendrik, Linda S. de Vries, Manon J. N. L. Benders, Ivana Išgum
Date: 2017-04-11 00:00:00
Tasks: Segmentation

Automatic segmentation in MR brain images is important for quantitative
analysis in large-scale studies with images acquired at all ages.
  This paper presents a method for the automatic segmentation of MR brain
images into a number of tissue classes using a convolutional neural network. To
ensure that the method obtains accurate segmentation details as well as spatial
consistency, the network uses multiple patch sizes and multiple convolution
kernel sizes to acquire multi-scale information about each voxel. The method is
not dependent on explicit features, but learns to recognise the information
that is important for the classification based on training data. The method
requires a single anatomical MR image only.
  The segmentation method is applied to five different data sets: coronal
T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age
(PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired
at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an
average age of 70 years, and T1-weighted images of young adults acquired at an
average age of 23 years. The method obtained the following average Dice
coefficients over all segmented tissue classes for each data set, respectively:
0.87, 0.82, 0.84, 0.86 and 0.91.
  The results demonstrate that the method obtains accurate segmentations in all
five sets, and hence demonstrates its robustness to differences in age and
acquisition protocol.
Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors
Authors: Piotr Koniusz, Yusuf Tas, Fatih Porikli
Date: 2016-11-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Domain Adaptation

In this paper, we propose an approach to the domain adaptation, dubbed
Second- or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of
alignments of second- or higher-order scatter statistics between the source and
target domains. The human ability to learn from few labeled samples is a
recurring motivation in the literature for domain adaptation. Towards this end,
we investigate the supervised target scenario for which few labeled target
training samples per category exist. Specifically, we utilize two CNN streams:
the source and target networks fused at the classifier level. Features from the
fully connected layers fc7 of each network are used to compute second- or even
higher-order scatter tensors; one per network stream per class. As the source
and target distributions are somewhat different despite being related, we align
the scatters of the two network streams of the same class (within-class
scatters) to a desired degree with our bespoke loss while maintaining good
separation of the between-class scatters. We train the entire network in
end-to-end fashion. We provide evaluations on the standard Office benchmark
(visual domains), RGB-D combined with Caltech256 (depth-to-rgb transfer) and
Pascal VOC2007 combined with the TU Berlin dataset (image-to-sketch transfer).
We attain state-of-the-art results.
Online Video Deblurring via Dynamic Temporal Blending Network
Authors: Tae Hyun Kim, Kyoung Mu Lee, Bernhard Schölkopf, Michael Hirsch
Date: 2017-04-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deblurring, Video Deblurring

State-of-the-art video deblurring methods are capable of removing non-uniform
blur caused by unwanted camera shake and/or object motion in dynamic scenes.
However, most existing methods are based on batch processing and thus need
access to all recorded frames, rendering them computationally demanding and
time consuming and thus limiting their practical use. In contrast, we propose
an online (sequential) video deblurring method based on a spatio-temporal
recurrent network that allows for real-time performance. In particular, we
introduce a novel architecture which extends the receptive field while keeping
the overall size of the network small to enable fast execution. In doing so,
our network is able to remove even large blur caused by strong camera shake
and/or fast moving objects. Furthermore, we propose a novel network layer that
enforces temporal consistency between consecutive frames by dynamic temporal
blending which compares and adaptively (at test time) shares features obtained
at different time steps. We show the superiority of the proposed method in an
extensive experimental evaluation.
Harmonic Networks: Deep Translation and Rotation Equivariance
Authors: Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow
Date: 2016-12-14 00:00:00
Proceeding: CVPR 2017 7
Tasks: Data Augmentation, Rotated MNIST, Translation

Translating or rotating an input image should not affect the results of many
computer vision tasks. Convolutional neural networks (CNNs) are already
translation equivariant: input image translations produce proportionate feature
map translations. This is not the case for rotations. Global rotation
equivariance is typically sought through data augmentation, but patch-wise
equivariance is more difficult. We present Harmonic Networks or H-Nets, a CNN
exhibiting equivariance to patch-wise translation and 360-rotation. We achieve
this by replacing regular CNN filters with circular harmonics, returning a
maximal response and orientation for every receptive field patch.
  H-Nets use a rich, parameter-efficient and low computational complexity
representation, and we show that deep feature maps within the network encode
complicated rotational invariants. We demonstrate that our layers are general
enough to be used in conjunction with the latest architectures and techniques,
such as deep supervision and batch normalization. We also achieve
state-of-the-art classification on rotated-MNIST, and competitive results on
other benchmark challenges.
Ensemble classifier approach in breast cancer detection and malignancy grading- A review
Authors: Deepti Ameta
Date: 2017-04-11 00:00:00
Tasks: BIG-bench Machine Learning, Breast Cancer Detection, General Classification

The diagnosed cases of Breast cancer is increasing annually and unfortunately
getting converted into a high mortality rate. Cancer, at the early stages, is
hard to detect because the malicious cells show similar properties (density) as
shown by the non-malicious cells. The mortality ratio could have been minimized
if the breast cancer could have been detected in its early stages. But the
current systems have not been able to achieve a fully automatic system which is
not just capable of detecting the breast cancer but also can detect the stage
of it. Estimation of malignancy grading is important in diagnosing the degree
of growth of malicious cells as well as in selecting a proper therapy for the
patient. Therefore, a complete and efficient clinical decision support system
is proposed which is capable of achieving breast cancer malignancy grading
scheme very efficiently. The system is based on Image processing and machine
learning domains. Classification Imbalance problem, a machine learning problem,
occurs when instances of one class is much higher than the instances of the
other class resulting in an inefficient classification of samples and hence a
bad decision support system. Therefore EUSBoost, ensemble based classifier is
proposed which is efficient and is able to outperform other classifiers as it
takes the benefits of both-boosting algorithm with Random Undersampling
techniques. Also comparison of EUSBoost with other techniques is shown in the
paper.
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
Authors: Liyuan Pan, Yuchao Dai, Miaomiao Liu, Fatih Porikli
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deblurring, Scene Flow Estimation, Video Deblurring

Videos for outdoor scene often show unpleasant blur effects due to the large
relative motion between the camera and the dynamic objects and large depth
variations. Existing works typically focus monocular video deblurring. In this
paper, we propose a novel approach to deblurring from stereo videos. In
particular, we exploit the piece-wise planar assumption about the scene and
leverage the scene flow information to deblur the image. Unlike the existing
approach [31] which used a pre-computed scene flow, we propose a single
framework to jointly estimate the scene flow and deblur the image, where the
motion cues from scene flow estimation and blur information could reinforce
each other, and produce superior results than the conventional scene flow
estimation or stereo deblurring methods. We evaluate our method extensively on
two available datasets and achieve significant improvement in flow estimation
and removing the blur effect over the state-of-the-art methods.
A Sample Complexity Measure with Applications to Learning Optimal Auctions
Authors: Vasilis Syrgkanis
Date: 2017-04-09 00:00:00
Proceeding: NeurIPS 2017 12

We introduce a new sample complexity measure, which we refer to as
split-sample growth rate. For any hypothesis $H$ and for any sample $S$ of size
$m$, the split-sample growth rate $\hat{\tau}_H(m)$ counts how many different
hypotheses can empirical risk minimization output on any sub-sample of $S$ of
size $m/2$. We show that the expected generalization error is upper bounded by
$O\left(\sqrt{\frac{\log(\hat{\tau}_H(2m))}{m}}\right)$. Our result is enabled
by a strengthening of the Rademacher complexity analysis of the expected
generalization error. We show that this sample complexity measure, greatly
simplifies the analysis of the sample complexity of optimal auction design, for
many auction classes studied in the literature. Their sample complexity can be
derived solely by noticing that in these auction classes, ERM on any sample or
sub-sample will pick parameters that are equal to one of the points in the
sample.
Deep Unsupervised Similarity Learning using Partially Ordered Sets
Authors: Miguel A. Bautista, Artsiom Sanakoyeu, Björn Ommer
Date: 2017-04-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Pose Estimation

Unsupervised learning of visual similarities is of paramount importance to
computer vision, particularly due to lacking training data for fine-grained
similarities. Deep learning of similarities is often based on relationships
between pairs or triplets of samples. Many of these relations are unreliable
and mutually contradicting, implying inconsistencies when trained without
supervision information that relates different tuples or triplets to each
other. To overcome this problem, we use local estimates of reliable
(dis-)similarities to initially group samples into compact surrogate classes
and use local partial orders of samples to classes to link classes to each
other. Similarity learning is then formulated as a partial ordering task with
soft correspondences of all samples to classes. Adopting a strategy of
self-supervision, a CNN is trained to optimally represent samples in a mutually
consistent manner while updating the classes. The similarity learning and
grouping procedure are integrated in a single model and optimized jointly. The
proposed unsupervised approach shows competitive performance on detailed pose
estimation and object classification.
Learning Deep CNN Denoiser Prior for Image Restoration
Authors: Kai Zhang, WangMeng Zuo, Shuhang Gu, Lei Zhang
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Color Image Denoising, Deblurring, Denoising, Image Denoising, Image Restoration

Model-based optimization methods and discriminative learning methods have
been the two dominant strategies for solving various inverse problems in
low-level vision. Typically, those two kinds of methods have their respective
merits and drawbacks, e.g., model-based optimization methods are flexible for
handling different inverse problems but are usually time-consuming with
sophisticated priors for the purpose of good performance; in the meanwhile,
discriminative learning methods have fast testing speed but their application
range is greatly restricted by the specialized task. Recent works have revealed
that, with the aid of variable splitting techniques, denoiser prior can be
plugged in as a modular part of model-based optimization methods to solve other
inverse problems (e.g., deblurring). Such an integration induces considerable
advantage when the denoiser is obtained via discriminative learning. However,
the study of integration with fast discriminative denoiser prior is still
lacking. To this end, this paper aims to train a set of fast and effective CNN
(convolutional neural network) denoisers and integrate them into model-based
optimization method to solve other inverse problems. Experimental results
demonstrate that the learned set of denoisers not only achieve promising
Gaussian denoising results but also can be used as prior to deliver good
performance for various low-level vision applications.
General models for rational cameras and the case of two-slit projections
Authors: Matthew Trager, Bernd Sturmfels, John Canny, Martial Hebert, Jean Ponce
Date: 2016-12-04 00:00:00
Proceeding: CVPR 2017 7

The rational camera model recently introduced in [19] provides a general
methodology for studying abstract nonlinear imaging systems and their
multi-view geometry. This paper builds on this framework to study "physical
realizations" of rational cameras. More precisely, we give an explicit account
of the mapping between between physical visual rays and image points (missing
in the original description), which allows us to give simple analytical
expressions for direct and inverse projections. We also consider "primitive"
camera models, that are orbits under the action of various projective
transformations, and lead to a general notion of intrinsic parameters. The
methodology is general, but it is illustrated concretely by an in-depth study
of two-slit cameras, that we model using pairs of linear projections. This
simple analytical form allows us to describe models for the corresponding
primitive cameras, to introduce intrinsic parameters with a clear geometric
meaning, and to define an epipolar tensor characterizing two-view
correspondences. In turn, this leads to new algorithms for structure from
motion and self-calibration.
Automatic Keyword Extraction for Text Summarization: A Survey
Authors: Santosh Kumar Bharti, Korra Sathya Babu
Date: 2017-04-11 00:00:00
Tasks: Keyword Extraction, Survey, Text Summarization

In recent times, data is growing rapidly in every domain such as news, social
media, banking, education, etc. Due to the excessiveness of data, there is a
need of automatic summarizer which will be capable to summarize the data
especially textual data in original document without losing any critical
purposes. Text summarization is emerged as an important research area in recent
past. In this regard, review of existing work on text summarization process is
useful for carrying out further research. In this paper, recent literature on
automatic keyword extraction and text summarization are presented since text
summarization process is highly depend on keyword extraction. This literature
includes the discussion about different methodology used for keyword extraction
and text summarization. It also discusses about different databases used for
text summarization in several domains along with evaluation matrices. Finally,
it discusses briefly about issues and research challenges faced by researchers
along with future direction.
Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations
Authors: Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Teddy Furon, Ondrej Chum
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Retrieval, Retrieval

Query expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. This work focuses on diffusion, a mechanism that captures the image manifold in the feature space. The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches. An efficient off-line stage allows optional reduction in the number of stored regions. In the on-line stage, the proposed handling of unseen queries in the indexing stage removes additional computation to adjust the precomputed data. We perform diffusion through a sparse linear system solver, yielding practical query times well below one second. Experimentally, we observe a significant boost in performance of image retrieval with compact CNN descriptors on standard benchmarks, especially when the query object covers only a small part of the image. Small objects have been a common failure case of CNN-based retrieval.
F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media
Authors: Hangfeng He, Xu sun
Date: 2016-11-14 00:00:00
Proceeding: EACL 2017 4
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We focus on named entity recognition (NER) for Chinese social media. With
massive unlabeled text and quite limited labelled corpus, we propose a
semi-supervised learning model based on B-LSTM neural network. To take
advantage of traditional methods in NER such as CRF, we combine transition
probability with deep learning in our model. To bridge the gap between label
accuracy and F-score of NER, we construct a model which can be directly trained
on F-score. When considering the instability of F-score driven method and
meaningful information provided by label accuracy, we propose an integrated
method to train on both F-score and label accuracy. Our integrated model yields
7.44\% improvement over previous state-of-the-art result.
Video Propagation Networks
Authors: Varun Jampani, Raghudeep Gadde, Peter V. Gehler
Date: 2016-12-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Propagation, Video Segmentation, Video Semantic Segmentation, Visual Object Tracking

We propose a technique that propagates information forward through video
data. The method is conceptually simple and can be applied to tasks that
require the propagation of structured information, such as semantic labels,
based on video content. We propose a 'Video Propagation Network' that processes
video frames in an adaptive manner. The model is applied online: it propagates
information forward without the need to access future frames. In particular we
combine two components, a temporal bilateral network for dense and video
adaptive filtering, followed by a spatial network to refine features and
increased flexibility. We present experiments on video object segmentation and
semantic video segmentation and show increased performance comparing to the
best previous task-specific methods, while having favorable runtime.
Additionally we demonstrate our approach on an example regression task of color
propagation in a grayscale video.
Reconstruction of three-dimensional porous media using generative adversarial neural networks
Authors: Lukas Mosser, Olivier Dubrule, Martin J. Blunt
Date: 2017-04-11 00:00:00
Tasks: Computational Efficiency, Image Reconstruction

To evaluate the variability of multi-phase flow properties of porous media at
the pore scale, it is necessary to acquire a number of representative samples
of the void-solid structure. While modern x-ray computer tomography has made it
possible to extract three-dimensional images of the pore space, assessment of
the variability in the inherent material properties is often experimentally not
feasible. We present a novel method to reconstruct the solid-void structure of
porous media by applying a generative neural network that allows an implicit
description of the probability distribution represented by three-dimensional
image datasets. We show, by using an adversarial learning approach for neural
networks, that this method of unsupervised learning is able to generate
representative samples of porous media that honor their statistics. We
successfully compare measures of pore morphology, such as the Euler
characteristic, two-point statistics and directional single-phase permeability
of synthetic realizations with the calculated properties of a bead pack, Berea
sandstone, and Ketton limestone. Results show that GANs can be used to
reconstruct high-resolution three-dimensional images of porous media at
different scales that are representative of the morphology of the images used
to train the neural network. The fully convolutional nature of the trained
neural network allows the generation of large samples while maintaining
computational efficiency. Compared to classical stochastic methods of image
reconstruction, the implicit representation of the learned data distribution
can be stored and reused to generate multiple realizations of the pore
structure very rapidly.
Persian Wordnet Construction using Supervised Learning
Authors: Zahra Mousavi, Heshaam Faili
Date: 2017-04-11 00:00:00
Tasks: Classification, General Classification

This paper presents an automated supervised method for Persian wordnet
construction. Using a Persian corpus and a bi-lingual dictionary, the initial
links between Persian words and Princeton WordNet synsets have been generated.
These links will be discriminated later as correct or incorrect by employing
seven features in a trained classification system. The whole method is just a
classification system, which has been trained on a train set containing FarsNet
as a set of correct instances. State of the art results on the automatically
derived Persian wordnet is achieved. The resulted wordnet with a precision of
91.18% includes more than 16,000 words and 22,000 synsets.