pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Person Pose Estimation via Column Generation
Authors: Shaofei Wang, Chong Zhang, Miguel A. Gonzalez-Ballester, Alexander Ihler, Julian Yarkony
Date: 2017-09-18 00:00:00
Tasks: Multi-Person Pose Estimation, Pose Estimation

We study the problem of multi-person pose estimation in natural images. A
pose estimate describes the spatial position and identity (head, foot, knee,
etc.) of every non-occluded body part of a person. Pose estimation is difficult
due to issues such as deformation and variation in body configurations and
occlusion of parts, while multi-person settings add complications such as an
unknown number of people, with unknown appearance and possible interactions in
their poses and part locations. We give a novel integer program formulation of
the multi-person pose estimation problem, in which variables correspond to
assignments of parts in the image to poses in a two-tier, hierarchical way.
This enables us to develop an efficient custom optimization procedure based on
column generation, where columns are produced by exact optimization of very
small scale integer programs. We demonstrate improved accuracy and speed for
our method on the MPII multi-person pose estimation benchmark.
Communicating with sentences: A multi-word naming game model
Authors: Yang Lou, Guanrong Chen, Jianwei Hu
Date: 2015-12-28 00:00:00
Tasks: Sentence

Naming game simulates the process of naming an object by a single word, in
which a population of communicating agents can reach global consensus
asymptotically through iteratively pair-wise conversations. We propose an
extension of the single-word model to a multi-word naming game (MWNG),
simulating the case of describing a complex object by a sentence (multiple
words). Words are defined in categories, and then organized as sentences by
combining them from different categories. We refer to a formatted combination
of several words as a pattern. In such an MWNG, through a pair-wise
conversation, it requires the hearer to achieve consensus with the speaker with
respect to both every single word in the sentence as well as the sentence
pattern, so as to guarantee the correct meaning of the saying, otherwise, they
fail reaching consensus in the interaction. We validate the model in three
typical topologies as the underlying communication network, and employ both
conventional and man-designed patterns in performing the MWNG.
Variational Methods for Normal Integration
Authors: Yvain Quéau, Jean-Denis Durou, Jean-François Aujol
Date: 2017-09-18 00:00:00

The need for an efficient method of integration of a dense normal field is
inspired by several computer vision tasks, such as shape-from-shading,
photometric stereo, deflectometry, etc. Inspired by edge-preserving methods
from image processing, we study in this paper several variational approaches
for normal integration, with a focus on non-rectangular domains, free boundary
and depth discontinuities. We first introduce a new discretization for
quadratic integration, which is designed to ensure both fast recovery and the
ability to handle non-rectangular domains with a free boundary. Yet, with this
solver, discontinuous surfaces can be handled only if the scene is first
segmented into pieces without discontinuity. Hence, we then discuss several
discontinuity-preserving strategies. Those inspired, respectively, by the
Mumford-Shah segmentation method and by anisotropic diffusion, are shown to be
the most effective for recovering discontinuities.
Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification
Authors: Hajin Shim, Sung Ju Hwang, Eunho Yang
Date: 2017-09-18 00:00:00
Tasks: General Classification, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem of active feature acquisition, where we sequentially
select the subset of features in order to achieve the maximum prediction
performance in the most cost-effective way. In this work, we formulate this
active feature acquisition problem as a reinforcement learning problem, and
provide a novel framework for jointly learning both the RL agent and the
classifier (environment). We also introduce a more systematic way of encoding
subsets of features that can properly handle innate challenge with missing
entries in active feature acquisition problems, that uses the orderless
LSTM-based set encoding mechanism that readily fits in the joint learning
framework. We evaluate our model on a carefully designed synthetic dataset for
the active feature acquisition as well as several real datasets such as
electric health record (EHR) datasets, on which it outperforms all baselines in
terms of prediction performance as well feature acquisition cost.
Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations
Authors: Christian Beck, Weinan E, Arnulf Jentzen
Date: 2017-09-18 00:00:00
Tasks: Portfolio Optimization

High-dimensional partial differential equations (PDE) appear in a number of
models from the financial industry, such as in derivative pricing models,
credit valuation adjustment (CVA) models, or portfolio optimization models. The
PDEs in such applications are high-dimensional as the dimension corresponds to
the number of financial assets in a portfolio. Moreover, such PDEs are often
fully nonlinear due to the need to incorporate certain nonlinear phenomena in
the model such as default risks, transaction costs, volatility uncertainty
(Knightian uncertainty), or trading constraints in the model. Such
high-dimensional fully nonlinear PDEs are exceedingly difficult to solve as the
computational effort for standard approximation methods grows exponentially
with the dimension. In this work we propose a new method for solving
high-dimensional fully nonlinear second-order PDEs. Our method can in
particular be used to sample from high-dimensional nonlinear expectations. The
method is based on (i) a connection between fully nonlinear second-order PDEs
and second-order backward stochastic differential equations (2BSDEs), (ii) a
merged formulation of the PDE and the 2BSDE problem, (iii) a temporal forward
discretization of the 2BSDE and a spatial approximation via deep neural nets,
and (iv) a stochastic gradient descent-type optimization procedure. Numerical
results obtained using ${\rm T{\small ENSOR}F{\small LOW}}$ in ${\rm P{\small
YTHON}}$ illustrate the efficiency and the accuracy of the method in the cases
of a $100$-dimensional Black-Scholes-Barenblatt equation, a $100$-dimensional
Hamilton-Jacobi-Bellman equation, and a nonlinear expectation of a $ 100
$-dimensional $ G $-Brownian motion.
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
Authors: Mohammad Javad Shafiee, Brendan Chywl, Francis Li, Alexander Wong
Date: 2017-09-18 00:00:00
Tasks: GPU, Object, object-detection, Object Detection, Object Localization, Real-Time Object Detection

Object detection is considered one of the most challenging problems in this
field of computer vision, as it involves the combination of object
classification and object localization within a scene. Recently, deep neural
networks (DNNs) have been demonstrated to achieve superior object detection
performance compared to other approaches, with YOLOv2 (an improved You Only
Look Once model) being one of the state-of-the-art in DNN-based object
detection methods in terms of both speed and accuracy. Although YOLOv2 can
achieve real-time performance on a powerful GPU, it still remains very
challenging for leveraging this approach for real-time object detection in
video on embedded computing devices with limited computational power and
limited memory. In this paper, we propose a new framework called Fast YOLO, a
fast You Only Look Once framework which accelerates YOLOv2 to be able to
perform object detection in video on embedded devices in a real-time manner.
First, we leverage the evolutionary deep intelligence framework to evolve the
YOLOv2 network architecture and produce an optimized architecture (referred to
as O-YOLOv2 here) that has 2.8X fewer parameters with just a ~2% IOU drop. To
further reduce power consumption on embedded devices while maintaining
performance, a motion-adaptive inference method is introduced into the proposed
Fast YOLO framework to reduce the frequency of deep inference with O-YOLOv2
based on temporal motion characteristics. Experimental results show that the
proposed Fast YOLO framework can reduce the number of deep inferences by an
average of 38.13%, and an average speedup of ~3.3X for objection detection in
video compared to the original YOLOv2, leading Fast YOLO to run an average of
~18FPS on a Nvidia Jetson TX1 embedded system.
Normal Integration: A Survey
Authors: Yvain Quéau, Jean-Denis Durou, Jean-François Aujol
Date: 2017-09-18 00:00:00
Tasks: Survey

The need for efficient normal integration methods is driven by several
computer vision tasks such as shape-from-shading, photometric stereo,
deflectometry, etc. In the first part of this survey, we select the most
important properties that one may expect from a normal integration method,
based on a thorough study of two pioneering works by Horn and Brooks [28] and
by Frankot and Chellappa [19]. Apart from accuracy, an integration method
should at least be fast and robust to a noisy normal field. In addition, it
should be able to handle several types of boundary condition, including the
case of a free boundary, and a reconstruction domain of any shape i.e., which
is not necessarily rectangular. It is also much appreciated that a minimum
number of parameters have to be tuned, or even no parameter at all. Finally, it
should preserve the depth discontinuities. In the second part of this survey,
we review most of the existing methods in view of this analysis, and conclude
that none of them satisfies all of the required properties. This work is
complemented by a companion paper entitled Variational Methods for Normal
Integration, in which we focus on the problem of normal integration in the
presence of depth discontinuities, a problem which occurs as soon as there are
occlusions.
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
Authors: Benjamin Bischke, Patrick Helber, Joachim Folz, Damian Borth, Andreas Dengel
Date: 2017-09-18 00:00:00
Tasks: Multi-Task Learning, Segmentation, Semantic Segmentation

The increased availability of high resolution satellite imagery allows to
sense very detailed structures on the surface of our planet. Access to such
information opens up new directions in the analysis of remote sensing imagery.
However, at the same time this raises a set of new challenges for existing
pixel-based prediction methods, such as semantic segmentation approaches. While
deep neural networks have achieved significant advances in the semantic
segmentation of high resolution images in the past, most of the existing
approaches tend to produce predictions with poor boundaries. In this paper, we
address the problem of preserving semantic segmentation boundaries in high
resolution satellite imagery by introducing a new cascaded multi-task loss. We
evaluate our approach on Inria Aerial Image Labeling Dataset which contains
large-scale and high resolution images. Our results show that we are able to
outperform state-of-the-art methods by 8.3\% without any additional
post-processing step.
Detecting Faces Using Region-based Fully Convolutional Networks
Authors: Yitong Wang, Xing Ji, Zheng Zhou, Hao Wang, Zhifeng Li
Date: 2017-09-14 00:00:00
Tasks: Face Detection, Position

Face detection has achieved great success using the region-based methods. In
this report, we propose a region-based face detector applying deep networks in
a fully convolutional fashion, named Face R-FCN. Based on Region-based Fully
Convolutional Networks (R-FCN), our face detector is more accurate and
computational efficient compared with the previous R-CNN based face detectors.
In our approach, we adopt the fully convolutional Residual Network (ResNet) as
the backbone network. Particularly, We exploit several new techniques including
position-sensitive average pooling, multi-scale training and testing and
on-line hard example mining strategy to improve the detection accuracy. Over
two most popular and challenging face detection benchmarks, FDDB and WIDER
FACE, Face R-FCN achieves superior performance over state-of-the-arts.
Limitations of Cross-Lingual Learning from Image Search
Authors: Mareike Hartmann, Anders Soegaard
Date: 2017-09-18 00:00:00
Proceeding: WS 2018 7
Tasks: Bilingual Lexicon Induction, Image Retrieval, Representation Learning, Translation

Cross-lingual representation learning is an important step in making NLP
scale to all the world's languages. Recent work on bilingual lexicon induction
suggests that it is possible to learn cross-lingual representations of words
based on similarities between images associated with these words. However, that
work focused on the translation of selected nouns only. In our work, we
investigate whether the meaning of other parts-of-speech, in particular
adjectives and verbs, can be learned in the same way. We also experiment with
combining the representations learned from visual data with embeddings learned
from textual data. Our experiments across five language pairs indicate that
previous work does not scale to the problem of learning cross-lingual
representations beyond simple nouns.
Variational Gaussian Approximation for Poisson Data
Authors: Simon Arridge, Kazufumi Ito, Bangti Jin, Chen Zhang
Date: 2017-09-18 00:00:00

The Poisson model is frequently employed to describe count data, but in a
Bayesian context it leads to an analytically intractable posterior probability
distribution. In this work, we analyze a variational Gaussian approximation to
the posterior distribution arising from the Poisson model with a Gaussian
prior. This is achieved by seeking an optimal Gaussian distribution minimizing
the Kullback-Leibler divergence from the posterior distribution to the
approximation, or equivalently maximizing the lower bound for the model
evidence. We derive an explicit expression for the lower bound, and show the
existence and uniqueness of the optimal Gaussian approximation. The lower bound
functional can be viewed as a variant of classical Tikhonov regularization that
penalizes also the covariance. Then we develop an efficient alternating
direction maximization algorithm for solving the optimization problem, and
analyze its convergence. We discuss strategies for reducing the computational
complexity via low rank structure of the forward operator and the sparsity of
the covariance. Further, as an application of the lower bound, we discuss
hierarchical Bayesian modeling for selecting the hyperparameter in the prior
distribution, and propose a monotonically convergent algorithm for determining
the hyperparameter. We present extensive numerical experiments to illustrate
the Gaussian approximation and the algorithms.
Object Detection Using Deep CNNs Trained on Synthetic Images
Authors: Param S. Rajpura, Hristo Bojinov, Ravi S. Hegde
Date: 2017-06-21 00:00:00
Tasks: Object, object-detection, Object Detection, Transfer Learning

The need for large annotated image datasets for training Convolutional Neural
Networks (CNNs) has been a significant impediment for their adoption in
computer vision applications. We show that with transfer learning an effective
object detector can be trained almost entirely on synthetically rendered
datasets. We apply this strategy for detecting pack- aged food products
clustered in refrigerator scenes. Our CNN trained only with 4000 synthetic
images achieves mean average precision (mAP) of 24 on a test set with 55
distinct products as objects of interest and 17 distractor objects. A further
increase of 12% in the mAP is obtained by adding only 400 real images to these
4000 synthetic images in the training set. A high degree of photorealism in the
synthetic images was not essential in achieving this performance. We analyze
factors like training data set size and 3D model dictionary size for their
influence on detection performance. Additionally, training strategies like
fine-tuning with selected layers and early stopping which affect transfer
learning from synthetic scenes to real scenes are explored. Training CNNs with
synthetic datasets is a novel application of high-performance computing and a
promising approach for object detection applications in domains where there is
a dearth of large annotated image data.
Dataset Augmentation with Synthetic Images Improves Semantic Segmentation
Authors: Manik Goyal, Param Rajpura, Hristo Bojinov, Ravi Hegde
Date: 2017-09-04 00:00:00
Tasks: Semantic Segmentation

Although Deep Convolutional Neural Networks trained with strong pixel-level
annotations have significantly pushed the performance in semantic segmentation,
annotation efforts required for the creation of training data remains a
roadblock for further improvements. We show that augmentation of the weakly
annotated training dataset with synthetic images minimizes both the annotation
efforts and also the cost of capturing images with sufficient variety.
Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU
from 52.80% to 55.47% by adding just 100 synthetic images per object class. Our
approach is thus a promising solution to the problems of annotation and dataset
collection.
ZhuSuan: A Library for Bayesian Deep Learning
Authors: Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou
Date: 2017-09-18 00:00:00
Tasks: Bayesian Inference, Deep Learning, Probabilistic Programming, regression

In this paper we introduce ZhuSuan, a python probabilistic programming
library for Bayesian deep learning, which conjoins the complimentary advantages
of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike
existing deep learning libraries, which are mainly designed for deterministic
neural networks and supervised tasks, ZhuSuan is featured for its deep root
into Bayesian inference, thus supporting various kinds of probabilistic models,
including both the traditional hierarchical Bayesian models and recent deep
generative models. We use running examples to illustrate the probabilistic
programming on ZhuSuan, including Bayesian logistic regression, variational
auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural
networks.
A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models
Authors: Beilun Wang, Ritambhara Singh, Yanjun Qi
Date: 2016-05-11 00:00:00

Identifying context-specific entity networks from aggregated data is an
important task, arising often in bioinformatics and neuroimaging.
Computationally, this task can be formulated as jointly estimating multiple
different, but related, sparse Undirected Graphical Models (UGM) from
aggregated samples across several contexts. Previous joint-UGM studies have
mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify
context-specific edge patterns directly. We, therefore, propose a novel
approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs
Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE
automatically infers both specific edge patterns that are unique to each
context and shared interactions preserved among all the contexts. Through the
L1 constrained formulation, this problem is cast as multiple independent
subtasks of linear programming that can be solved efficiently in parallel. In
addition to Gaussian data, SIMULE can also handle multivariate Nonparanormal
data that greatly relaxes the normality assumption that many real-world
applications do not follow. We provide a novel theoretical proof showing that
SIMULE achieves a consistent result at the rate O(log(Kp)/n_{tot}). On multiple
synthetic datasets and two biomedical datasets, SIMULE shows significant
improvement over state-of-the-art multi-sGGM and single-UGM baselines.
Combinational neural network using Gabor filters for the classification of handwritten digits
Authors: N. Joshi
Date: 2017-09-18 00:00:00
Tasks: General Classification, Object Recognition

A classification algorithm that combines the components of k-nearest
neighbours and multilayer neural networks has been designed and tested. With
this method the computational time required for training the dataset has been
reduced substancially. Gabor filters were used for the feature extraction to
ensure a better performance. This algorithm is tested with MNIST dataset and it
will be integrated as a module in the object recognition software which is
currently under development.
Depression Scale Recognition from Audio, Visual and Text Analysis
Authors: Shubham Dham, Anirudh Sharma, Abhinav Dhall
Date: 2017-09-18 00:00:00
Tasks: Clustering

Depression is a major mental health disorder that is rapidly affecting lives
worldwide. Depression not only impacts emotional but also physical and
psychological state of the person. Its symptoms include lack of interest in
daily activities, feeling low, anxiety, frustration, loss of weight and even
feeling of self-hatred. This report describes work done by us for Audio Visual
Emotion Challenge (AVEC) 2017 during our second year BTech summer internship.
With the increase in demand to detect depression automatically with the help of
machine learning algorithms, we present our multimodal feature extraction and
decision level fusion approach for the same. Features are extracted by
processing on the provided Distress Analysis Interview Corpus-Wizard of Oz
(DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector
approach were applied on the visual data; statistical descriptors on gaze,
pose; low level audio features and head pose and text features were also
extracted. Classification is done on fused as well as independent features
using Support Vector Machine (SVM) and neural networks. The results obtained
were able to cross the provided baseline on validation data set by 17% on audio
features and 24.5% on video features.
The Shape of a Benedictine Monastery: The SaintGall Ontology (Extended Version)
Authors: Claudia Cantale, Domenico Cantone, Manuela Lupica Rinato, Marianna Nicolosi-Asmundo, Daniele Francesco Santamaria
Date: 2017-09-08 00:00:00

We present an OWL 2 ontology representing the Saint Gall plan, one of the most ancient documents arrived intact to us, which describes the ideal model of a Benedictine monastic complex that inspired the design of many European monasteries.
A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+
Authors: Peng-Bo Zhang, Zhi-Xin Yang
Date: 2017-08-28 00:00:00

In school, a teacher plays an important role in various classroom teaching
patterns. Likewise to this human learning activity, the learning using
privileged information (LUPI) paradigm provides additional information
generated by the teacher to 'teach' learning models during the training stage.
Therefore, this novel learning paradigm is a typical Teacher-Student
Interaction mechanism. This paper is the first to present a random vector
functional link network based on the LUPI paradigm, called RVFL+. Rather than
simply combining two existing approaches, the newly-derived RVFL+ fills the gap
between classical randomized neural networks and the newfashioned LUPI
paradigm, which offers an alternative way to train RVFL networks. Moreover, the
proposed RVFL+ can perform in conjunction with the kernel trick for highly
complicated nonlinear feature learning, which is termed KRVFL+. Furthermore,
the statistical property of the proposed RVFL+ is investigated, and we present
a sharp and high-quality generalization error bound based on the Rademacher
complexity. Competitive experimental results on 14 real-world datasets
illustrate the great effectiveness and efficiency of the novel RVFL+ and
KRVFL+, which can achieve better generalization performance than
state-of-the-art methods.