pwc-archive/papers-with-abstracts huggingface dataset:
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds
Authors: Hongyi Zhang, Sashank J. Reddi, Suvrit Sra
Date: 2016-05-23 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Riemannian optimization, Stochastic Optimization

We study optimization of finite sums of geodesically smooth functions on
Riemannian manifolds. Although variance reduction techniques for optimizing
finite-sums have witnessed tremendous attention in the recent years, existing
work is limited to vector space problems. We introduce Riemannian SVRG (RSVRG),
a new variance reduced Riemannian optimization method. We analyze RSVRG for
both geodesically convex and nonconvex (smooth) functions. Our analysis reveals
that RSVRG inherits advantages of the usual SVRG method, but with factors
depending on curvature of the manifold that influence its convergence. To our
knowledge, RSVRG is the first provably fast stochastic Riemannian method.
Moreover, our paper presents the first non-asymptotic complexity analysis
(novel even for the batch setting) for nonconvex Riemannian optimization. Our
results have several implications; for instance, they offer a Riemannian
perspective on variance reduced PCA, which promises a short, transparent
convergence analysis.
A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification
Authors: Yaoyuan Zhang, Zhenxu Ye, Yansong Feng, Dongyan Zhao, Rui Yan
Date: 2017-04-07 00:00:00
Tasks: Sentence

Sentence simplification reduces semantic complexity to benefit people with
language impairments. Previous simplification studies on the sentence level and
word level have achieved promising results but also meet great challenges. For
sentence-level studies, sentences after simplification are fluent but sometimes
are not really simplified. For word-level studies, words are simplified but
also have potential grammar errors due to different usages of words before and
after simplification. In this paper, we propose a two-step simplification
framework by combining both the word-level and the sentence-level
simplifications, making use of their corresponding advantages. Based on the
two-step framework, we implement a novel constrained neural generation model to
simplify sentences given simplified words. The final results on Wikipedia and
Simple Wikipedia aligned datasets indicate that our method yields better
performance than various baselines.
Comparison of Global Algorithms in Word Sense Disambiguation
Authors: Loïc Vial, Andon Tchechmedjiev, Didier Schwab
Date: 2017-04-07 00:00:00
Tasks: Word Sense Disambiguation

This article compares four probabilistic algorithms (global algorithms) for
Word Sense Disambiguation (WSD) in terms of the number of scorer calls (local
algo- rithm) and the F1 score as determined by a gold-standard scorer. Two
algorithms come from the state of the art, a Simulated Annealing Algorithm
(SAA) and a Genetic Algorithm (GA) as well as two algorithms that we first
adapt from WSD that are state of the art probabilistic search algorithms,
namely a Cuckoo search algorithm (CSA) and a Bat Search algorithm (BS). As WSD
requires to evaluate exponentially many word sense combinations (with branching
factors of up to 6 or more), probabilistic algorithms allow to find approximate
solution in a tractable time by sampling the search space. We find that CSA, GA
and SA all eventually converge to similar results (0.98 F1 score), but CSA gets
there faster (in fewer scorer calls) and reaches up to 0.95 F1 before SA in
fewer scorer calls. In BA a strict convergence criterion prevents it from
reaching above 0.89 F1.
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System
Authors: Elike Hodo, Xavier Bellekens, Andrew Hamilton, Pierre-louis Dubouilh, Ephraim Iorkyase, Christos Tachtatzis, Robert Atkinson
Date: 2017-04-07 00:00:00
Tasks: General Classification, Intrusion Detection, Network Intrusion Detection

The Internet of things (IoT) is still in its infancy and has attracted much
interest in many industrial sectors including medical fields, logistics
tracking, smart cities and automobiles. However as a paradigm, it is
susceptible to a range of significant intrusion threats. This paper presents a
threat analysis of the IoT and uses an Artificial Neural Network (ANN) to
combat these threats. A multi-level perceptron, a type of supervised ANN, is
trained using internet packet traces, then is assessed on its ability to thwart
Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the
classification of normal and threat patterns on an IoT Network. The ANN
procedure is validated against a simulated IoT network. The experimental
results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS
attacks.
NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis
Authors: Edilson A. Corrêa Jr., Vanessa Queiroz Marinho, Leandro Borges dos Santos
Date: 2017-04-07 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, Twitter Sentiment Analysis, Word Embeddings

This paper describes our multi-view ensemble approach to SemEval-2017 Task 4
on Sentiment Analysis in Twitter, specifically, the Message Polarity
Classification subtask for English (subtask A). Our system is a voting
ensemble, where each base classifier is trained in a different feature space.
The first space is a bag-of-words model and has a Linear SVM as base
classifier. The second and third spaces are two different strategies of
combining word embeddings to represent sentences and use a Linear SVM and a
Logistic Regressor as base classifiers. The proposed system was ranked 18th out
of 38 systems considering F1 score and 20th considering recall.
Combining policy gradient and Q-learning
Authors: Brendan O'Donoghue, Remi Munos, Koray Kavukcuoglu, Volodymyr Mnih
Date: 2016-11-05 00:00:00
Tasks: Atari Games, Q-Learning, Reinforcement Learning

Policy gradient is an efficient technique for improving a policy in a
reinforcement learning setting. However, vanilla online variants are on-policy
only and not able to take advantage of off-policy data. In this paper we
describe a new technique that combines policy gradient with off-policy
Q-learning, drawing experience from a replay buffer. This is motivated by
making a connection between the fixed points of the regularized policy gradient
algorithm and the Q-values. This connection allows us to estimate the Q-values
from the action preferences of the policy, to which we apply Q-learning
updates. We refer to the new technique as 'PGQL', for policy gradient and
Q-learning. We also establish an equivalency between action-value fitting
techniques and actor-critic algorithms, showing that regularized policy
gradient techniques can be interpreted as advantage function learning
algorithms. We conclude with some numerical examples that demonstrate improved
data efficiency and stability of PGQL. In particular, we tested PGQL on the
full suite of Atari games and achieved performance exceeding that of both
asynchronous advantage actor-critic (A3C) and Q-learning.
PoseTrack: Joint Multi-Person Pose Estimation and Tracking
Authors: Umar Iqbal, Anton Milan, Juergen Gall
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Multi-Person Pose Estimation, Multi-Person Pose Estimation and Tracking, Pose Estimation, Pose Tracking

In this work, we introduce the challenging problem of joint multi-person pose
estimation and tracking of an unknown number of persons in unconstrained
videos. Existing methods for multi-person pose estimation in images cannot be
applied directly to this problem, since it also requires to solve the problem
of person association over time in addition to the pose estimation for each
person. We therefore propose a novel method that jointly models multi-person
pose estimation and tracking in a single formulation. To this end, we represent
body joint detections in a video by a spatio-temporal graph and solve an
integer linear program to partition the graph into sub-graphs that correspond
to plausible body pose trajectories for each person. The proposed approach
implicitly handles occlusion and truncation of persons. Since the problem has
not been addressed quantitatively in the literature, we introduce a challenging
"Multi-Person PoseTrack" dataset, and also propose a completely unconstrained
evaluation protocol that does not make any assumptions about the scale, size,
location or the number of persons. Finally, we evaluate the proposed approach
and several baseline methods on our new dataset.
Clothing and People - A Social Signal Processing Perspective
Authors: Maedeh Aghaei, Federico Parezzan, Mariella Dimiccoli, Petia Radeva, Marco Cristani
Date: 2017-04-07 00:00:00
Tasks: Decision Making, Human Parsing

In our society and century, clothing is not anymore used only as a means for
body protection. Our paper builds upon the evidence, studied within the social
sciences, that clothing brings a clear communicative message in terms of social
signals, influencing the impression and behaviour of others towards a person.
In fact, clothing correlates with personality traits, both in terms of
self-assessment and assessments that unacquainted people give to an individual.
The consequences of these facts are important: the influence of clothing on the
decision making of individuals has been investigated in the literature, showing
that it represents a discriminative factor to differentiate among diverse
groups of people. Unfortunately, this has been observed after cumbersome and
expensive manual annotations, on very restricted populations, limiting the
scope of the resulting claims. With this position paper, we want to sketch the
main steps of the very first systematic analysis, driven by social signal
processing techniques, of the relationship between clothing and social signals,
both sent and perceived. Thanks to human parsing technologies, which exhibit
high robustness owing to deep learning architectures, we are now capable to
isolate visual patterns characterising a large types of garments. These
algorithms will be used to capture statistical relations on a large corpus of
evidence to confirm the sociological findings and to go beyond the state of the
art.
Hand3D: Hand Pose Estimation using 3D Neural Network
Authors: Xiaoming Deng, Shuo Yang, yinda zhang, Ping Tan, Liang Chang, Hongan Wang
Date: 2017-04-07 00:00:00
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation

We propose a novel 3D neural network architecture for 3D hand pose estimation
from a single depth image. Different from previous works that mostly run on 2D
depth image domain and require intermediate or post process to bring in the
supervision from 3D space, we convert the depth map to a 3D volumetric
representation, and feed it into a 3D convolutional neural network(CNN) to
directly produce the pose in 3D requiring no further process. Our system does
not require the ground truth reference point for initialization, and our
network architecture naturally integrates both local feature and global context
in 3D space. To increase the coverage of the hand pose space of the training
data, we render synthetic depth image by transferring hand pose from existing
real image datasets. We evaluation our algorithm on two public benchmarks and
achieve the state-of-the-art performance. The synthetic hand pose dataset will
be available.
Investigating Natural Image Pleasantness Recognition using Deep Features and Eye Tracking for Loosely Controlled Human-computer Interaction
Authors: Hamed R. -Tavakoli, Jorma Laaksonen, Esa Rahtu
Date: 2017-04-07 00:00:00

This paper revisits recognition of natural image pleasantness by employing
deep convolutional neural networks and affordable eye trackers. There exist
several approaches to recognize image pleasantness: (1) computer vision, and
(2) psychophysical signals. For natural images, computer vision approaches have
not been as successful as for abstract paintings and is lagging behind the
psychophysical signals like eye movements. Despite better results, the
scalability of eye movements is adversely affected by the sensor cost. While
the introduction of affordable sensors have helped the scalability issue by
making the sensors more accessible, the application of such sensors in a
loosely controlled human-computer interaction setup is not yet studied for
affective image tagging. On the other hand, deep convolutional neural networks
have boosted the performance of vision-based techniques significantly in recent
years. To investigate the current status in regard to affective image tagging,
we (1) introduce a new eye movement dataset using an affordable eye tracker,
(2) study the use of deep neural networks for pleasantness recognition, (3)
investigate the gap between deep features and eye movements. To meet these
ends, we record eye movements in a less controlled setup, akin to daily
human-computer interaction. We assess features from eye movements, visual
features, and their combination. Our results show that (1) recognizing natural
image pleasantness from eye movement under less restricted setup is difficult
and previously used techniques are prone to fail, and (2) visual class
categories are strong cues for predicting pleasantness, due to their
correlation with emotions, necessitating careful study of this phenomenon. This
latter finding is alerting as some deep learning approaches may fit to the
class category bias.
Hybrid Learning of Optical Flow and Next Frame Prediction to Boost Optical Flow in the Wild
Authors: Nima Sedaghat, Mohammadreza Zolfaghari, Thomas Brox
Date: 2016-12-12 00:00:00
Tasks: Action Classification, Optical Flow Estimation, Prediction

CNN-based optical flow estimation has attracted attention recently, mainly
due to its impressively high frame rates. These networks perform well on
synthetic datasets, but they are still far behind the classical methods in
real-world videos. This is because there is no ground truth optical flow for
training these networks on real data. In this paper, we boost CNN-based optical
flow estimation in real scenes with the help of the freely available
self-supervised task of next-frame prediction. To this end, we train the
network in a hybrid way, providing it with a mixture of synthetic and real
videos. With the help of a sample-variant multi-tasking architecture, the
network is trained on different tasks depending on the availability of
ground-truth. We also experiment with the prediction of "next-flow" instead of
estimation of the current flow, which is intuitively closer to the task of
next-frame prediction and yields favorable results. We demonstrate the
improvement in optical flow estimation on the real-world KITTI benchmark.
Additionally, we test the optical flow indirectly in an action classification
scenario. As a side product of this work, we report significant improvements
over state-of-the-art in the task of next-frame prediction.
High-Quality Correspondence and Segmentation Estimation for Dual-Lens Smart-Phone Portraits
Authors: Xiaoyong Shen, Hongyun Gao, Xin Tao, Chao Zhou, Jiaya Jia
Date: 2017-04-07 00:00:00
Proceeding: ICCV 2017 10

Estimating correspondence between two images and extracting the foreground
object are two challenges in computer vision. With dual-lens smart phones, such
as iPhone 7Plus and Huawei P9, coming into the market, two images of slightly
different views provide us new information to unify the two topics. We propose
a joint method to tackle them simultaneously via a joint fully connected
conditional random field (CRF) framework. The regional correspondence is used
to handle textureless regions in matching and make our CRF system
computationally efficient. Our method is evaluated over 2,000 new image pairs,
and produces promising results on challenging portrait images.
Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database
Authors: Yuta Matsuzaki, Kazushige Okayasu, Takaaki Imanari, Naomichi Kobayashi, Yoshihiro Kanehara, Ryousuke Takasawa, Akio Nakamura, Hirokatsu Kataoka
Date: 2017-04-07 00:00:00
Tasks: Movie Recommendation

In this paper, we aim to estimate the Winner of world-wide film festival from
the exhibited movie poster. The task is an extremely challenging because the
estimation must be done with only an exhibited movie poster, without any film
ratings and box-office takings. In order to tackle this problem, we have
created a new database which is consist of all movie posters included in the
four biggest film festivals. The movie poster database (MPDB) contains historic
movies over 80 years which are nominated a movie award at each year. We apply a
couple of feature types, namely hand-craft, mid-level and deep feature to
extract various information from a movie poster. Our experiments showed
suggestive knowledge, for example, the Academy award estimation can be better
rate with a color feature and a facial emotion feature generally performs good
rate on the MPDB. The paper may suggest a possibility of modeling human taste
for a movie recommendation.
Distributed methods for synchronization of orthogonal matrices over graphs
Authors: Johan Thunberg, Florian Bernard, Jorge Goncalves
Date: 2017-01-25 00:00:00
Tasks: Image Registration

This paper addresses the problem of synchronizing orthogonal matrices over
directed graphs. For synchronized transformations (or matrices), composite
transformations over loops equal the identity. We formulate the synchronization
problem as a least-squares optimization problem with nonlinear constraints. The
synchronization problem appears as one of the key components in applications
ranging from 3D-localization to image registration. The main contributions of
this work can be summarized as the introduction of two novel algorithms; one
for symmetric graphs and one for graphs that are possibly asymmetric. Under
general conditions, the former has guaranteed convergence to the solution of a
spectral relaxation to the synchronization problem. The latter is stable for
small step sizes when the graph is quasi-strongly connected. The proposed
methods are verified in numerical simulations.
Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space
Authors: Matthias Vestner, Roee Litman, Emanuele Rodolà, Alex Bronstein, Daniel Cremers
Date: 2017-01-03 00:00:00
Proceeding: CVPR 2017 7
Tasks: Density Estimation

Many algorithms for the computation of correspondences between deformable
shapes rely on some variant of nearest neighbor matching in a descriptor space.
Such are, for example, various point-wise correspondence recovery algorithms
used as a post-processing stage in the functional correspondence framework.
Such frequently used techniques implicitly make restrictive assumptions (e.g.,
near-isometry) on the considered shapes and in practice suffer from lack of
accuracy and result in poor surjectivity. We propose an alternative recovery
technique capable of guaranteeing a bijective correspondence and producing
significantly higher accuracy and smoothness. Unlike other methods our approach
does not depend on the assumption that the analyzed shapes are isometric. We
derive the proposed method from the statistical framework of kernel density
estimation and demonstrate its performance on several challenging deformable 3D
shape matching datasets.
DeepVisage: Making face recognition simple yet with powerful generalization skills
Authors: Abul Hasnat, Julien Bohné, Jonathan Milgram, Stéphane Gentric, Liming Chen
Date: 2017-03-24 00:00:00
Tasks: Face Recognition, Metric Learning

Face recognition (FR) methods report significant performance by adopting the
convolutional neural network (CNN) based learning methods. Although CNNs are
mostly trained by optimizing the softmax loss, the recent trend shows an
improvement of accuracy with different strategies, such as task-specific CNN
learning with different loss functions, fine-tuning on target dataset, metric
learning and concatenating features from multiple CNNs. Incorporating these
tasks obviously requires additional efforts. Moreover, it demotivates the
discovery of efficient CNN models for FR which are trained only with identity
labels. We focus on this fact and propose an easily trainable and single CNN
based FR method. Our CNN model exploits the residual learning framework.
Additionally, it uses normalized features to compute the loss. Our extensive
experiments show excellent generalization on different datasets. We obtain very
competitive and state-of-the-art results on the LFW, IJB-A, YouTube faces and
CACD datasets.
Convolutional Neural Networks for Page Segmentation of Historical Document Images
Authors: Kai Chen, Mathias Seuret
Date: 2017-04-05 00:00:00
Tasks: Segmentation

This paper presents a Convolutional Neural Network (CNN) based page
segmentation method for handwritten historical document images. We consider
page segmentation as a pixel labeling problem, i.e., each pixel is classified
as one of the predefined classes. Traditional methods in this area rely on
carefully hand-crafted features or large amounts of prior knowledge. In
contrast, we propose to learn features from raw image pixels using a CNN. While
many researchers focus on developing deep CNN architectures to solve different
problems, we train a simple CNN with only one convolution layer. We show that
the simple architecture achieves competitive results against other deep
architectures on different public datasets. Experiments also demonstrate the
effectiveness and superiority of the proposed method compared to previous
methods.
Semi-Latent GAN: Learning to generate and modify facial images from attributes
Authors: Weidong Yin, Yanwei Fu, Leonid Sigal, xiangyang xue
Date: 2017-04-07 00:00:00
Tasks: Attribute, Generative Adversarial Network

Generating and manipulating human facial images using high-level attributal
controls are important and interesting problems. The models proposed in
previous work can solve one of these two problems (generation or manipulation),
but not both coherently. This paper proposes a novel model that learns how to
both generate and modify the facial image from high-level semantic attributes.
Our key idea is to formulate a Semi-Latent Facial Attribute Space (SL-FAS) to
systematically learn relationship between user-defined and latent attributes,
as well as between those attributes and RGB imagery. As part of this newly
formulated space, we propose a new model --- SL-GAN which is a specific form of
Generative Adversarial Network. Finally, we present an iterative training
algorithm for SL-GAN. The experiments on recent CelebA and CASIA-WebFace
datasets validate the effectiveness of our proposed framework. We will also
make data, pre-trained models and code available.
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
Authors: Dan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, Nicu Sebe
Date: 2017-04-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Depth Estimation, Monocular Depth Estimation

This paper addresses the problem of depth estimation from a single still
image. Inspired by recent works on multi- scale convolutional neural networks
(CNN), we propose a deep model which fuses complementary information derived
from multiple CNN side outputs. Different from previous methods, the
integration is obtained by means of continuous Conditional Random Fields
(CRFs). In particular, we propose two different variations, one based on a
cascade of multiple CRFs, the other on a unified graphical model. By designing
a novel CNN implementation of mean-field updates for continuous CRFs, we show
that both proposed models can be regarded as sequential deep networks and that
training can be performed end-to-end. Through extensive experimental evaluation
we demonstrate the effective- ness of the proposed approach and establish new
state of the art results on publicly available datasets.