pwc-archive/papers-with-abstracts huggingface dataset:
Faceness-Net: Face Detection through Deep Facial Part Responses
Authors: Shuo Yang, Ping Luo, Chen Change Loy, Xiaoou Tang
Date: 2017-01-29 00:00:00
Tasks: Face Detection

We propose a deep convolutional neural network (CNN) for face detection
leveraging on facial attributes based supervision. We observe a phenomenon that
part detectors emerge within CNN trained to classify attributes from uncropped
face images, without any explicit part supervision. The observation motivates a
new method for finding faces through scoring facial parts responses by their
spatial structure and arrangement. The scoring mechanism is data-driven, and
carefully formulated considering challenging cases where faces are only
partially visible. This consideration allows our network to detect faces under
severe occlusion and unconstrained pose variations. Our method achieves
promising performance on popular benchmarks including FDDB, PASCAL Faces, AFW,
and WIDER FACE.
Rotation equivariant vector field networks
Authors: Diego Marcos, Michele Volpi, Nikos Komodakis, Devis Tuia
Date: 2016-12-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: Breast Tumour Classification, Colorectal Gland Segmentation:, image-classification, Image Classification, Image Segmentation, Multi-tissue Nucleus Segmentation, Patch Matching, Semantic Segmentation

In many computer vision tasks, we expect a particular behavior of the output
with respect to rotations of the input image. If this relationship is
explicitly encoded, instead of treated as any other variation, the complexity
of the problem is decreased, leading to a reduction in the size of the required
model. In this paper, we propose the Rotation Equivariant Vector Field Networks
(RotEqNet), a Convolutional Neural Network (CNN) architecture encoding rotation
equivariance, invariance and covariance. Each convolutional filter is applied
at multiple orientations and returns a vector field representing magnitude and
angle of the highest scoring orientation at every spatial location. We develop
a modified convolution operator relying on this representation to obtain deep
architectures. We test RotEqNet on several problems requiring different
responses with respect to the inputs' rotation: image classification,
biomedical image segmentation, orientation estimation and patch matching. In
all cases, we show that RotEqNet offers extremely compact models in terms of
number of parameters and provides results in line to those of networks orders
of magnitude larger.
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization
Authors: Demian Gholipour Ghalandari
Date: 2017-08-25 00:00:00
Proceeding: WS 2017 9
Tasks: Document Summarization, Extractive Document Summarization, Extractive Text Summarization, Multi-Document Summarization

The centroid-based model for extractive document summarization is a simple
and fast baseline that ranks sentences based on their similarity to a centroid
vector. In this paper, we apply this ranking to possible summaries instead of
sentences and use a simple greedy algorithm to find the best summary.
Furthermore, we show possi- bilities to scale up to larger input docu- ment
collections by selecting a small num- ber of sentences from each document prior
to constructing the summary. Experiments were done on the DUC2004 dataset for
multi-document summarization. We ob- serve a higher performance over the orig-
inal model, on par with more complex state-of-the-art methods.
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Authors: Sebastian Lapuschkin, Alexander Binder, Klaus-Robert Müller, Wojciech Samek
Date: 2017-08-25 00:00:00
Tasks: Age And Gender Classification, Gender Classification, General Classification

Recently, deep neural networks have demonstrated excellent performances in
recognizing the age and gender on human face images. However, these models were
applied in a black-box manner with no information provided about which facial
features are actually used for prediction and how these features depend on
image preprocessing, model initialization and architecture choice. We present a
study investigating these different effects.
  In detail, our work compares four popular neural network architectures,
studies the effect of pretraining, evaluates the robustness of the considered
alignment preprocessings via cross-method test set swapping and intuitively
visualizes the model's prediction strategies in given preprocessing conditions
using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our
evaluations on the challenging Adience benchmark show that suitable parameter
initialization leads to a holistic perception of the input, compensating
artefactual data representations. With a combination of simple preprocessing
steps, we reach state of the art performance in gender recognition.
Standard Steady State Genetic Algorithms Can Hillclimb Faster than Mutation-only Evolutionary Algorithms
Authors: Dogan Corus, Pietro S. Oliveto
Date: 2017-08-04 00:00:00
Tasks: Evolutionary Algorithms

Explaining to what extent the real power of genetic algorithms lies in the
ability of crossover to recombine individuals into higher quality solutions is
an important problem in evolutionary computation. In this paper we show how the
interplay between mutation and crossover can make genetic algorithms hillclimb
faster than their mutation-only counterparts. We devise a Markov Chain
framework that allows to rigorously prove an upper bound on the runtime of
standard steady state genetic algorithms to hillclimb the OneMax function. The
bound establishes that the steady-state genetic algorithms are 25% faster than
all standard bit mutation-only evolutionary algorithms with static mutation
rate up to lower order terms for moderate population sizes. The analysis also
suggests that larger populations may be faster than populations of size 2. We
present a lower bound for a greedy (2+1) GA that matches the upper bound for
populations larger than 2, rigorously proving that 2 individuals cannot
outperform larger population sizes under greedy selection and greedy crossover
up to lower order terms. In complementary experiments the best population size
is greater than 2 and the greedy genetic algorithms are faster than standard
ones, further suggesting that the derived lower bound also holds for the
standard steady state (2+1) GA.
Efficient Algorithms for Moral Lineage Tracing
Authors: Markus Rempfler, Jan-Hendrik Lange, Florian Jug, Corinna Blasse, Eugene W. Myers, Bjoern H. Menze, Bjoern Andres
Date: 2017-02-14 00:00:00
Proceeding: ICCV 2017 10
Tasks: Segmentation

Lineage tracing, the joint segmentation and tracking of living cells as they
move and divide in a sequence of light microscopy images, is a challenging
task. Jug et al. have proposed a mathematical abstraction of this task, the
moral lineage tracing problem (MLTP), whose feasible solutions define both a
segmentation of every image and a lineage forest of cells. Their branch-and-cut
algorithm, however, is prone to many cuts and slow convergence for large
instances. To address this problem, we make three contributions: (i) we devise
the first efficient primal feasible local search algorithms for the MLTP, (ii)
we improve the branch-and-cut algorithm by separating tighter cutting planes
and by incorporating our primal algorithms, (iii) we show in experiments that
our algorithms find accurate solutions on the problem instances of Jug et al.
and scale to larger instances, leveraging moral lineage tracing to practical
significance.
Compressed sensing for longitudinal MRI: An adaptive-weighted approach
Authors: Lior Weizman, Yonina C. Eldar, Dafna Ben Bashat
Date: 2014-07-10 00:00:00
Tasks: compressed sensing, Image Reconstruction

Purpose: Repeated brain MRI scans are performed in many clinical scenarios,
such as follow up of patients with tumors and therapy response assessment. In
this paper, the authors show an approach to utilize former scans of the patient
for the acceleration of repeated MRI scans.
  Methods: The proposed approach utilizes the possible similarity of the
repeated scans in longitudinal MRI studies. Since similarity is not guaranteed,
sampling and reconstruction are adjusted during acquisition to match the actual
similarity between the scans. The baseline MR scan is utilized both in the
sampling stage, via adaptive sampling, and in the reconstruction stage, with
weighted reconstruction. In adaptive sampling, k-space sampling locations are
optimized during acquisition. Weighted reconstruction uses the locations of the
nonzero coefficients in the sparse domains as a prior in the recovery process.
The approach was tested on 2D and 3D MRI scans of patients with brain tumors.
  Results: The longitudinal adaptive CS MRI (LACS-MRI) scheme provides
reconstruction quality which outperforms other CS-based approaches for rapid
MRI. Examples are shown on patients with brain tumors and demonstrate improved
spatial resolution. Compared with data sampled at Nyquist rate, LACS-MRI
exhibits Signal-to-Error Ratio (SER) of 24.8dB with undersampling factor of
16.6 in 3D MRI.
  Conclusions: The authors have presented a novel method for image
reconstruction utilizing similarity of scans in longitudinal MRI studies, where
possible. The proposed approach can play a major part and significantly reduce
scanning time in many applications that consist of disease follow-up and
monitoring of longitudinal changes in brain MRI.
Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fields
Authors: Jean-Luc Meunier
Date: 2017-08-25 00:00:00
Tasks: BIG-bench Machine Learning, document understanding

This paper is concerned with structured machine learning, in a supervised
machine learning context. It discusses how to make joint structured learning on
interdependent objects of different nature, as well as how to enforce logical
con-straints when predicting labels. We explain how this need arose in a
Document Understanding task. We then discuss a general extension to Conditional
Random Field (CRF) for this purpose and present the contributed open source
implementation on top of the open source PyStruct library. We evaluate its
performance on a publicly available dataset.
Convolutional Dictionary Learning: Acceleration and Convergence
Authors: Il Yong Chun, Jeffrey A. Fessler
Date: 2017-07-03 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

Convolutional dictionary learning (CDL or sparsifying CDL) has many
applications in image processing and computer vision. There has been growing
interest in developing efficient algorithms for CDL, mostly relying on the
augmented Lagrangian (AL) method or the variant alternating direction method of
multipliers (ADMM). When their parameters are properly tuned, AL methods have
shown fast convergence in CDL. However, the parameter tuning process is not
trivial due to its data dependence and, in practice, the convergence of AL
methods depends on the AL parameters for nonconvex CDL problems. To moderate
these problems, this paper proposes a new practically feasible and convergent
Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The
BPG-M-based CDL is investigated with different block updating schemes and
majorization matrix designs, and further accelerated by incorporating some
momentum coefficient formulas and restarting techniques. All of the methods
investigated incorporate a boundary artifacts removal (or, more generally,
sampling) operator in the learning model. Numerical experiments show that,
without needing any parameter tuning process, the proposed BPG-M approach
converges more stably to desirable solutions of lower objective values than the
existing state-of-the-art ADMM algorithm and its memory-efficient variant do.
Compared to the ADMM approaches, the BPG-M method using a multi-block updating
scheme is particularly useful in single-threaded CDL algorithm handling large
datasets, due to its lower memory requirement and no polynomial computational
complexity. Image denoising experiments show that, for relatively strong
additive white Gaussian noise, the filters learned by BPG-M-based CDL
outperform those trained by the ADMM approach.
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
Authors: Kensho Hara, Hirokatsu Kataoka, Yutaka Satoh
Date: 2017-08-25 00:00:00
Tasks: Action Recognition, Hand-Gesture Recognition, Temporal Action Localization

Convolutional neural networks with spatio-temporal 3D kernels (3D CNNs) have
an ability to directly extract spatio-temporal features from videos for action
recognition. Although the 3D kernels tend to overfit because of a large number
of their parameters, the 3D CNNs are greatly improved by using recent huge
video databases. However, the architecture of 3D CNNs is relatively shallow
against to the success of very deep neural networks in 2D-based CNNs, such as
residual networks (ResNets). In this paper, we propose a 3D CNNs based on
ResNets toward a better action representation. We describe the training
procedure of our 3D ResNets in details. We experimentally evaluate the 3D
ResNets on the ActivityNet and Kinetics datasets. The 3D ResNets trained on the
Kinetics did not suffer from overfitting despite the large number of parameters
of the model, and achieved better performance than relatively shallow networks,
such as C3D. Our code and pretrained models (e.g. Kinetics and ActivityNet) are
publicly available at https://github.com/kenshohara/3D-ResNets.
SPARQL as a Foreign Language
Authors: Tommaso Soru, Edgard Marx, Diego Moussallem, Gustavo Publio, André Valdestilhas, Diego Esteves, Ciro Baron Neto
Date: 2017-08-25 00:00:00
Tasks: Graph Question Answering, Knowledge Base Question Answering, Semantic Parsing

In the last years, the Linked Data Cloud has achieved a size of more than 100 billion facts pertaining to a multitude of domains. However, accessing this information has been significantly challenging for lay users. Approaches to problems such as Question Answering on Linked Data and Link Discovery have notably played a role in increasing information access. These approaches are often based on handcrafted and/or statistical models derived from data observation. Recently, Deep Learning architectures based on Neural Networks called seq2seq have shown to achieve state-of-the-art results at translating sequences into sequences. In this direction, we propose Neural SPARQL Machines, end-to-end deep architectures to translate any natural language expression into sentences encoding SPARQL queries. Our preliminary results, restricted on selected DBpedia classes, show that Neural SPARQL Machines are a promising approach for Question Answering on Linked Data, as they can deal with known problems such as vocabulary mismatch and perform graph pattern composition.
Phonetic Temporal Neural Model for Language Identification
Authors: Zhiyuan Tang, Dong Wang, Yixiang Chen, Lantian Li, Andrew Abel
Date: 2017-05-09 00:00:00
Tasks: Language Identification, model

Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We present a phonetic temporal neural model for LID,
which is an LSTM-RNN LID system that accepts phonetic features produced by a
phone-discriminative DNN as the input, rather than raw acoustic features. This
new model is similar to traditional phonetic LID methods, but the phonetic
knowledge here is much richer: it is at the frame level and involves compacted
information of all phones. Our experiments conducted on the Babel database and
the AP16-OLR database demonstrate that the temporal phonetic neural approach is
very effective, and significantly outperforms existing acoustic neural models.
It also outperforms the conventional i-vector approach on short utterances and
in noisy conditions.
Automatic Identification of Sarcasm Target: An Introductory Approach
Authors: Aditya Joshi, Pranav Goel, Pushpak Bhattacharyya, Mark Carman
Date: 2016-10-22 00:00:00
Tasks: Sarcasm Detection, Sentence

Past work in computational sarcasm deals primarily with sarcasm detection. In
this paper, we introduce a novel, related problem: sarcasm target
identification i.e., extracting the target of ridicule in a sarcastic
sentence). We present an introductory approach for sarcasm target
identification. Our approach employs two types of extractors: one based on
rules, and another consisting of a statistical classifier. To compare our
approach, we use two baselines: a na\"ive baseline and another baseline based
on work in sentiment target identification. We perform our experiments on book
snippets and tweets, and show that our hybrid approach performs better than the
two baselines and also, in comparison with using the two extractors
individually. Our introductory approach establishes the viability of sarcasm
target identification, and will serve as a baseline for future work.
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
Authors: Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang
Date: 2016-04-13 00:00:00

In modern large-scale machine learning applications, the training data are
often partitioned and stored on multiple machines. It is customary to employ
the "data parallelism" approach, where the aggregated training loss is
minimized without moving data across machines. In this paper, we introduce a
novel distributed dual formulation for regularized loss minimization problems
that can directly handle data parallelism in the distributed setting. This
formulation allows us to systematically derive dual coordinate optimization
procedures, which we refer to as Distributed Alternating Dual Maximization
(DADM). The framework extends earlier studies described in (Boyd et al., 2011;
Ma et al., 2015a; Jaggi et al., 2014; Yang, 2013) and has rigorous theoretical
analyses. Moreover with the help of the new formulation, we develop the
accelerated version of DADM (Acc-DADM) by generalizing the acceleration
technique from (Shalev-Shwartz and Zhang, 2014) to the distributed setting. We
also provide theoretical results for the proposed accelerated version and the
new result improves previous ones (Yang, 2013; Ma et al., 2015a) whose runtimes
grow linearly on the condition number. Our empirical studies validate our
theory and show that our accelerated approach significantly improves the
previous state-of-the-art distributed dual coordinate optimization algorithms.
Differentially Private Regression for Discrete-Time Survival Analysis
Authors: Thông T. Nguyên, Siu Cheung Hui
Date: 2017-08-24 00:00:00
Tasks: regression, Survival Analysis

In survival analysis, regression models are used to understand the effects of
explanatory variables (e.g., age, sex, weight, etc.) to the survival
probability. However, for sensitive survival data such as medical data, there
are serious concerns about the privacy of individuals in the data set when
medical data is used to fit the regression models. The closest work addressing
such privacy concerns is the work on Cox regression which linearly projects the
original data to a lower dimensional space. However, the weakness of this
approach is that there is no formal privacy guarantee for such projection. In
this work, we aim to propose solutions for the regression problem in survival
analysis with the protection of differential privacy which is a golden standard
of privacy protection in data privacy research. To this end, we extend the
Output Perturbation and Objective Perturbation approaches which are originally
proposed to protect differential privacy for the Empirical Risk Minimization
(ERM) problems. In addition, we also propose a novel sampling approach based on
the Markov Chain Monte Carlo (MCMC) method to practically guarantee
differential privacy with better accuracy. We show that our proposed approaches
achieve good accuracy as compared to the non-private results while guaranteeing
differential privacy for individuals in the private data set.
The Microsoft 2017 Conversational Speech Recognition System
Authors: W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, A. Stolcke
Date: 2017-08-21 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We describe the 2017 version of Microsoft's conversational speech recognition
system, in which we update our 2016 system with recent developments in
neural-network-based acoustic and language modeling to further advance the
state of the art on the Switchboard speech recognition task. The system adds a
CNN-BLSTM acoustic model to the set of model architectures we combined
previously, and includes character-based and dialog session aware LSTM language
models in rescoring. For system combination we adopt a two-stage approach,
whereby subsets of acoustic models are first combined at the senone/frame
level, followed by a word-level voting via confusion networks. We also added a
confusion network rescoring step after system combination. The resulting system
yields a 5.1\% word error rate on the 2000 Switchboard evaluation set.
Learning a 3D descriptor for cross-source point cloud registration from synthetic data
Authors: Xiaoshui Huang
Date: 2017-08-24 00:00:00
Tasks: Data Augmentation, Point Cloud Registration

As the development of 3D sensors, registration of 3D data (e.g. point cloud)
coming from different kind of sensor is dispensable and shows great demanding.
However, point cloud registration between different sensors is challenging
because of the variant of density, missing data, different viewpoint, noise and
outliers, and geometric transformation. In this paper, we propose a method to
learn a 3D descriptor for finding the correspondent relations between these
challenging point clouds. To train the deep learning framework, we use
synthetic 3D point cloud as input. Starting from synthetic dataset, we use
region-based sampling method to select reasonable, large and diverse training
samples from synthetic samples. Then, we use data augmentation to extend our
network be robust to rotation transformation. We focus our work on more general
cases that point clouds coming from different sensors, named cross-source point
cloud. The experiments show that our descriptor is not only able to generalize
to new scenes, but also generalize to different sensors. The results
demonstrate that the proposed method successfully aligns two 3D cross-source
point clouds which outperforms state-of-the-art method.
A Robust Indoor Scene Recognition Method based on Sparse Representation
Authors: Guilherme Nascimento, Camila Laranjeira, Vinicius Braz, Anisio Lacerda, Erickson R. Nascimento
Date: 2017-08-24 00:00:00
Tasks: Scene Recognition

In this paper, we present a robust method for scene recognition, which
leverages Convolutional Neural Networks (CNNs) features and Sparse Coding
setting by creating a new representation of indoor scenes. Although CNNs highly
benefited the fields of computer vision and pattern recognition, convolutional
layers adjust weights on a global-approach, which might lead to losing
important local details such as objects and small structures. Our proposed
scene representation relies on both: global features that mostly refers to
environment's structure, and local features that are sparsely combined to
capture characteristics of common objects of a given scene. This new
representation is based on fragments of the scene and leverages features
extracted by CNNs. The experimental evaluation shows that the resulting
representation outperforms previous scene recognition methods on Scene15 and
MIT67 datasets, and performs competitively on SUN397, while being highly robust
to perturbations in the input image such as noise and occlusion.