pwc-archive/papers-with-abstracts huggingface dataset:
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
Authors: Samuel Dodge, Lina Karam
Date: 2017-02-01 00:00:00
Tasks: Mixture-of-Experts, Saliency Prediction

Visual saliency models have recently begun to incorporate deep learning to
achieve predictive capacity much greater than previous unsupervised methods.
However, most existing models predict saliency using local mechanisms limited
to the receptive field of the network. We propose a model that incorporates
global scene semantic information in addition to local information gathered by
a convolutional neural network. Our model is formulated as a mixture of
experts. Each expert network is trained to predict saliency for a set of
closely related images. The final saliency map is computed as a weighted
mixture of the expert networks' output, with weights determined by a separate
gating network. This gating network is guided by global scene information to
predict weights. The expert networks and the gating network are trained
simultaneously in an end-to-end manner. We show that our mixture formulation
leads to improvement in performance over an otherwise identical non-mixture
model that does not incorporate global scene information.
Optimal Experimental Design of Field Trials using Differential Evolution
Authors: Vitaliy Feoktistov, Stephane Pietravalle, Nicolas Heslot
Date: 2017-02-01 00:00:00
Tasks: Experimental Design

When setting up field experiments, to test and compare a range of genotypes (e.g. maize hybrids), it is important to account for any possible field effect that may otherwise bias performance estimates of genotypes. To do so, we propose a model-based method aimed at optimizing the allocation of the tested genotypes and checks between fields and placement within field, according to their kinship. This task can be formulated as a combinatorial permutation-based problem. We used Differential Evolution concept to solve this problem. We then present results of optimal strategies for between-field and within-field placements of genotypes and compare them to existing optimization strategies, both in terms of convergence time and result quality. The new algorithm gives promising results in terms of convergence and search space exploration.
Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval
Authors: Eng-Jon Ong, Sameed Husain, Miroslaw Bober
Date: 2017-02-01 00:00:00
Tasks: Image Retrieval, Retrieval

This paper addresses the problem of large scale image retrieval, with the aim
of accurately ranking the similarity of a large number of images to a given
query image. To achieve this, we propose a novel Siamese network. This network
consists of two computational strands, each comprising of a CNN component
followed by a Fisher vector component. The CNN component produces dense, deep
convolutional descriptors that are then aggregated by the Fisher Vector method.
Crucially, we propose to simultaneously learn both the CNN filter weights and
Fisher Vector model parameters. This allows us to account for the evolving
distribution of deep descriptors over the course of the learning process. We
show that the proposed approach gives significant improvements over the
state-of-the-art methods on the Oxford and Paris image retrieval datasets.
Additionally, we provide a baseline performance measure for both these datasets
with the inclusion of 1 million distractors.
TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild
Authors: Lluis Gomez-Bigorda, Dimosthenis Karatzas
Date: 2016-04-10 00:00:00
Tasks: Object

Motivated by the success of powerful while expensive techniques to recognize
words in a holistic way, object proposals techniques emerge as an alternative
to the traditional text detectors. In this paper we introduce a novel object
proposals method that is specifically designed for text. We rely on a
similarity based region grouping algorithm that generates a hierarchy of word
hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic
word recognition method in an efficient way.
  Our experiments demonstrate that the presented method is superior in its
ability of producing good quality word proposals when compared with
class-independent algorithms. We show impressive recall rates with a few
thousand proposals in different standard benchmarks, including focused or
incidental text datasets, and multi-language scenarios. Moreover, the
combination of our object proposals with existing whole-word recognizers shows
competitive performance in end-to-end word spotting, and, in some benchmarks,
outperforms previously published results. Concretely, in the challenging
ICDAR2015 Incidental Text dataset, we overcome in more than 10 percent f-score
the best-performing method in the last ICDAR Robust Reading Competition. Source
code of the complete end-to-end system is available at
https://github.com/lluisgomez/TextProposals
A Hybrid Evolutionary Algorithm Based on Solution Merging for the Longest Arc-Preserving Common Subsequence Problem
Authors: Christian Blum, Maria J. Blesa
Date: 2017-02-01 00:00:00
Tasks: ARC, Combinatorial Optimization

The longest arc-preserving common subsequence problem is an NP-hard
combinatorial optimization problem from the field of computational biology.
This problem finds applications, in particular, in the comparison of
arc-annotated Ribonucleic acid (RNA) sequences. In this work we propose a
simple, hybrid evolutionary algorithm to tackle this problem. The most
important feature of this algorithm concerns a crossover operator based on
solution merging. In solution merging, two or more solutions to the problem are
merged, and an exact technique is used to find the best solution within this
union. It is experimentally shown that the proposed algorithm outperforms a
heuristic from the literature.
Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks
Authors: Žiga Emeršič, Luka Lan Gabriel, Vitomir Štruc, Peter Peer
Date: 2017-02-01 00:00:00
Tasks: Decoder, object-detection, Object Detection, Segmentation

Object detection and segmentation represents the basis for many tasks in
computer and machine vision. In biometric recognition systems the detection of
the region-of-interest (ROI) is one of the most crucial steps in the overall
processing pipeline, significantly impacting the performance of the entire
recognition system. Existing approaches to ear detection, for example, are
commonly susceptible to the presence of severe occlusions, ear accessories or
variable illumination conditions and often deteriorate in their performance if
applied on ear images captured in unconstrained settings. To address these
shortcomings, we present in this paper a novel ear detection technique based on
convolutional encoder-decoder networks (CEDs). For our technique, we formulate
the problem of ear detection as a two-class segmentation problem and train a
convolutional encoder-decoder network based on the SegNet architecture to
distinguish between image-pixels belonging to either the ear or the non-ear
class. The output of the network is then post-processed to further refine the
segmentation result and return the final locations of the ears in the input
image. Different from competing techniques from the literature, our approach
does not simply return a bounding box around the detected ear, but provides
detailed, pixel-wise information about the location of the ears in the image.
Our experiments on a dataset gathered from the web (a.k.a. in the wild) show
that the proposed technique ensures good detection results in the presence of
various covariate factors and significantly outperforms the existing
state-of-the-art.
OCR of historical printings with an application to building diachronic corpora: A case study using the RIDGES herbal corpus
Authors: U. Springmann, A. Lüdeling
Date: 2016-08-06 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

This article describes the results of a case study that applies Neural
Network-based Optical Character Recognition (OCR) to scanned images of books
printed between 1487 and 1870 by training the OCR engine OCRopus
[@breuel2013high] on the RIDGES herbal text corpus [@OdebrechtEtAlSubmitted].
Training specific OCR models was possible because the necessary *ground truth*
is available as error-corrected diplomatic transcriptions. The OCR results have
been evaluated for accuracy against the ground truth of unseen test sets.
Character and word accuracies (percentage of correctly recognized items) for
the resulting machine-readable texts of individual documents range from 94% to
more than 99% (character level) and from 76% to 97% (word level). This includes
the earliest printed books, which were thought to be inaccessible by OCR
methods until recently. Furthermore, OCR models trained on one part of the
corpus consisting of books with different printing dates and different typesets
*(mixed models)* have been tested for their predictive power on the books from
the other part containing yet other fonts, mostly yielding character accuracies
well above 90%. It therefore seems possible to construct generalized models
trained on a range of fonts that can be applied to a wide variety of historical
printings still giving good results. A moderate postcorrection effort of some
pages will then enable the training of individual models with even better
accuracies. Using this method, diachronic corpora including early printings can
be constructed much faster and cheaper than by manual transcription. The OCR
methods reported here open up the possibility of transforming our printed
textual cultural heritage into electronic text by largely automatic means,
which is a prerequisite for the mass conversion of scanned books.
Improving patch-based scene text script identification with ensembles of conjoined networks
Authors: Lluis Gomez, Anguelos Nicolaou, Dimosthenis Karatzas
Date: 2016-02-24 00:00:00
Tasks: General Classification, Optical Character Recognition (OCR)

This paper focuses on the problem of script identification in scene text
images. Facing this problem with state of the art CNN classifiers is not
straightforward, as they fail to address a key characteristic of scene text
instances: their extremely variable aspect ratio. Instead of resizing input
images to a fixed aspect ratio as in the typical use of holistic CNN
classifiers, we propose here a patch-based classification framework in order to
preserve discriminative parts of the image that are characteristic of its
class. We describe a novel method based on the use of ensembles of conjoined
networks to jointly learn discriminative stroke-parts representations and their
relative importance in a patch-based classification scheme. Our experiments
with this learning procedure demonstrate state-of-the-art results in two public
script identification datasets. In addition, we propose a new public benchmark
dataset for the evaluation of multi-lingual scene text end-to-end reading
systems. Experiments done in this dataset demonstrate the key role of script
identification in a complete end-to-end system that combines our script
identification method with a previously published text detector and an
off-the-shelf OCR engine.
Foreign-language Reviews: Help or Hindrance?
Authors: Scott A. Hale, Irene Eleta
Date: 2017-02-01 00:00:00

The number and quality of user reviews greatly affects consumer purchasing
decisions. While reviews in all languages are increasing, it is still often the
case (especially for non-English speakers) that there are only a few reviews in
a person's first language. Using an online experiment, we examine the value
that potential purchasers receive from interfaces showing additional reviews in
a second language. The results paint a complicated picture with both positive
and negative reactions to the inclusion of foreign-language reviews. Roughly
26-28% of subjects clicked to see translations of the foreign-language content
when given the opportunity, and those who did so were more likely to select the
product with foreign-language reviews than those who did not.
Communication-Optimal Distributed Clustering
Authors: Jiecao Chen, He Sun, David P. Woodruff, Qin Zhang
Date: 2017-02-01 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Clustering

Clustering large datasets is a fundamental problem with a number of
applications in machine learning. Data is often collected on different sites
and clustering needs to be performed in a distributed manner with low
communication. We would like the quality of the clustering in the distributed
setting to match that in the centralized setting for which all the data resides
on a single site. In this work, we study both graph and geometric clustering
problems in two distributed models: (1) a point-to-point model, and (2) a model
with a broadcast channel. We give protocols in both models which we show are
nearly optimal by proving almost matching communication lower bounds. Our work
highlights the surprising power of a broadcast channel for clustering problems;
roughly speaking, to spectrally cluster $n$ points or $n$ vertices in a graph
distributed across $s$ servers, for a worst-case partitioning the communication
complexity in a point-to-point model is $n \cdot s$, while in the broadcast
model it is $n + s$. A similar phenomenon holds for the geometric setting as
well. We implement our algorithms and demonstrate this phenomenon on real life
datasets, showing that our algorithms are also very efficient in practice.
ImageNet MPEG-7 Visual Descriptors - Technical Report
Authors: Frédéric Rayar
Date: 2017-02-01 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object, Object Recognition

ImageNet is a large scale and publicly available image database. It currently
offers more than 14 millions of images, organised according to the WordNet
hierarchy. One of the main objective of the creators is to provide to the
research community a relevant database for visual recognition applications such
as object recognition, image classification or object localisation. However,
only a few visual descriptors of the images are available to be used by the
researchers. Only SIFT-based features have been extracted from a subset of the
collection. This technical report presents the extraction of some MPEG-7 visual
descriptors from the ImageNet database. These descriptors are made publicly
available in an effort towards open research.
A Kinematic Chain Space for Monocular Motion Capture
Authors: Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
Date: 2017-02-01 00:00:00
Tasks: Industrial Robots

This paper deals with motion capture of kinematic chains (e.g. human
skeletons) from monocular image sequences taken by uncalibrated cameras. We
present a method based on projecting an observation into a kinematic chain
space (KCS). An optimization of the nuclear norm is proposed that implicitly
enforces structural properties of the kinematic chain. Unlike other approaches
our method does not require specific camera or object motion and is not relying
on training data or previously determined constraints such as particular body
lengths. The proposed algorithm is able to reconstruct scenes with limited
camera motion and previously unseen motions. It is not only applicable to human
skeletons but also to other kinematic chains for instance animals or industrial
robots. We achieve state-of-the-art results on different benchmark data bases
and real world scenes.
PCA-Initialized Deep Neural Networks Applied To Document Image Analysis
Authors: Mathias Seuret, Michele Alberti, Rolf Ingold, Marcus Liwicki
Date: 2017-02-01 00:00:00
Tasks: Transfer Learning

In this paper, we present a novel approach for initializing deep neural
networks, i.e., by turning PCA into neural layers. Usually, the initialization
of the weights of a deep neural network is done in one of the three following
ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or
as auto-encoder, and 3) re-use of layers from another network (transfer
learning). Therefore, typically, many training epochs are needed before
meaningful weights are learned, or a rather similar dataset is required for
seeding a fine-tuning of transfer learning. In this paper, we describe how to
turn a PCA into an auto-encoder, by generating an encoder layer of the PCA
parameters and furthermore adding a decoding layer. We analyze the
initialization technique on real documents. First, we show that a PCA-based
initialization is quick and leads to a very stable initialization. Furthermore,
for the task of layout analysis we investigate the effectiveness of PCA-based
initialization and show that it outperforms state-of-the-art random weight
initialization methods.
Reading Comprehension using Entity-based Memory Network
Authors: Xun Wang, Katsuhito Sudoh, Masaaki Nagata, Tomohide Shibata, Daisuke Kawahara, Sadao Kurohashi
Date: 2016-12-12 00:00:00
Tasks: Question Answering, Reading Comprehension

This paper introduces a novel neural network model for question answering,
the \emph{entity-based memory network}. It enhances neural networks' ability of
representing and calculating information over a long period by keeping records
of entities contained in text. The core component is a memory pool which
comprises entities' states. These entities' states are continuously updated
according to the input text. Questions with regard to the input text are used
to search the memory pool for related entities and answers are further
predicted based on the states of retrieved entities. Compared with previous
memory network models, the proposed model is capable of handling fine-grained
information and more sophisticated relations based on entities. We formulated
several different tasks as question answering problems and tested the proposed
model. Experiments reported satisfying results.
Robust Order Scheduling in the Fashion Industry: A Multi-Objective Optimization Approach
Authors: Wei Du, Yang Tang, Sunney Yung Sun Leung, Le Tong, Athanasios V. Vasilakos, Feng Qian
Date: 2017-02-01 00:00:00
Tasks: Scheduling

In the fashion industry, order scheduling focuses on the assignment of
production orders to appropriate production lines. In reality, before a new
order can be put into production, a series of activities known as
pre-production events need to be completed. In addition, in real production
process, owing to various uncertainties, the daily production quantity of each
order is not always as expected. In this research, by considering the
pre-production events and the uncertainties in the daily production quantity,
robust order scheduling problems in the fashion industry are investigated with
the aid of a multi-objective evolutionary algorithm (MOEA) called nondominated
sorting adaptive differential evolution (NSJADE). The experimental results
illustrate that it is of paramount importance to consider pre-production events
in order scheduling problems in the fashion industry. We also unveil that the
existence of the uncertainties in the daily production quantity heavily affects
the order scheduling.
Design, Analysis and Application of A Volumetric Convolutional Neural Network
Authors: Xiaqing Pan, Yueru Chen, C. -C. Jay Kuo
Date: 2017-02-01 00:00:00
Tasks: 3D Shape Classification, Classification, Clustering, General Classification

The design, analysis and application of a volumetric convolutional neural
network (VCNN) are studied in this work. Although many CNNs have been proposed
in the literature, their design is empirical. In the design of the VCNN, we
propose a feed-forward K-means clustering algorithm to determine the filter
number and size at each convolutional layer systematically. For the analysis of
the VCNN, the cause of confusing classes in the output of the VCNN is explained
by analyzing the relationship between the filter weights (also known as anchor
vectors) from the last fully-connected layer to the output. Furthermore, a
hierarchical clustering method followed by a random forest classification
method is proposed to boost the classification performance among confusing
classes. For the application of the VCNN, we examine the 3D shape
classification problem and conduct experiments on a popular ModelNet40 dataset.
The proposed VCNN offers the state-of-the-art performance among all
volume-based CNN methods.
Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program
Authors: Eric Eaton, Sven Koenig, Claudia Schulz, Francesco Maurelli, John Lee, Joshua Eckroth, Mark Crowley, Richard G. Freedman, Rogelio E. Cardona-Rivera, Tiago Machado, Tom Williams
Date: 2017-02-01 00:00:00
Tasks: Ethics

The 7th Symposium on Educational Advances in Artificial Intelligence
(EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and
Future AI Educator Program to support the training of early-career university
faculty, secondary school faculty, and future educators (PhD candidates or
postdocs who intend a career in academia). As part of the program, awardees
were asked to address one of the following "blue sky" questions:
  * How could/should Artificial Intelligence (AI) courses incorporate ethics
into the curriculum?
  * How could we teach AI topics at an early undergraduate or a secondary
school level?
  * AI has the potential for broad impact to numerous disciplines. How could we
make AI education more interdisciplinary, specifically to benefit
non-engineering fields?
  This paper is a collection of their responses, intended to help motivate
discussion around these issues in AI education.
Combinatorial Multi-Armed Bandit with General Reward Functions
Authors: Wei Chen, Wei Hu, Fu Li, Jian Li, Yu Liu, Pinyan Lu
Date: 2016-10-20 00:00:00
Proceeding: NeurIPS 2016 12

In this paper, we study the stochastic combinatorial multi-armed bandit
(CMAB) framework that allows a general nonlinear reward function, whose
expected value may not depend only on the means of the input random variables
but possibly on the entire distributions of these variables. Our framework
enables a much larger class of reward functions such as the $\max()$ function
and nonlinear utility functions. Existing techniques relying on accurate
estimations of the means of random variables, such as the upper confidence
bound (UCB) technique, do not work directly on these functions. We propose a
new algorithm called stochastically dominant confidence bound (SDCB), which
estimates the distributions of underlying random variables and their
stochastically dominant confidence bounds. We prove that SDCB can achieve
$O(\log{T})$ distribution-dependent regret and $\tilde{O}(\sqrt{T})$
distribution-independent regret, where $T$ is the time horizon. We apply our
results to the $K$-MAX problem and expected utility maximization problems. In
particular, for $K$-MAX, we provide the first polynomial-time approximation
scheme (PTAS) for its offline problem, and give the first $\tilde{O}(\sqrt T)$
bound on the $(1-\epsilon)$-approximation regret of its online problem, for any
$\epsilon>0$.