pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Deep Features for Material Recognition
Authors: Yan Zhang, Mete Ozay, Xing Liu, Takayuki Okatani
Date: 2015-11-20 00:00:00
Tasks: feature selection, Material Recognition, Transfer Learning

We propose a method for integration of features extracted using deep
representations of Convolutional Neural Networks (CNNs) each of which is
learned using a different image dataset of objects and materials for material
recognition. Given a set of representations of multiple pre-trained CNNs, we
first compute activations of features using the representations on the images
to select a set of samples which are best represented by the features. Then, we
measure the uncertainty of the features by computing the entropy of class
distributions for each sample set. Finally, we compute the contribution of each
feature to representation of classes for feature selection and integration. We
examine the proposed method on three benchmark datasets for material
recognition. Experimental results show that the proposed method achieves
state-of-the-art performance by integrating deep features. Additionally, we
introduce a new material dataset called EFMD by extending Flickr Material
Database (FMD). By the employment of the EFMD with transfer learning for
updating the learned CNN models, we achieve 84.0%+/-1.8% accuracy on the FMD
dataset which is close to human performance that is 84.9%.
Local Binary Pattern for Word Spotting in Handwritten Historical Document
Authors: Sounak Dey, Anguelos Nicolaou, Josep Llados, Umapada Pal
Date: 2016-04-20 00:00:00
Tasks: Information Retrieval, Retrieval

Digital libraries store images which can be highly degraded and to index this
kind of images we resort to word spot- ting as our information retrieval
system. Information retrieval for handwritten document images is more
challenging due to the difficulties in complex layout analysis, large
variations of writing styles, and degradation or low quality of historical
manuscripts. This paper presents a simple innovative learning-free method for
word spotting from large scale historical documents combining Local Binary
Pattern (LBP) and spatial sampling. This method offers three advantages:
firstly, it operates in completely learning free paradigm which is very
different from unsupervised learning methods, secondly, the computational time
is significantly low because of the LBP features which are very fast to
compute, and thirdly, the method can be used in scenarios where annotations are
not available. Finally we compare the results of our proposed retrieval method
with the other methods in the literature.
Feature Learning based Deep Supervised Hashing with Pairwise Labels
Authors: Wu-Jun Li, Sheng Wang, Wang-Cheng Kang
Date: 2015-11-12 00:00:00
Tasks: Deep Hashing, Image Retrieval, Retrieval, Triplet

Recent years have witnessed wide application of hashing for large-scale image
retrieval. However, most existing hashing methods are based on hand-crafted
features which might not be optimally compatible with the hashing procedure.
Recently, deep hashing methods have been proposed to perform simultaneous
feature learning and hash-code learning with deep neural networks, which have
shown better performance than traditional hashing methods with hand-crafted
features. Most of these deep hashing methods are supervised whose supervised
information is given with triplet labels. For another common application
scenario with pairwise labels, there have not existed methods for simultaneous
feature learning and hash-code learning. In this paper, we propose a novel deep
hashing method, called deep pairwise-supervised hashing(DPSH), to perform
simultaneous feature learning and hash-code learning for applications with
pairwise labels. Experiments on real datasets show that our DPSH method can
outperform other methods to achieve the state-of-the-art performance in image
retrieval applications.
OCR Error Correction Using Character Correction and Feature-Based Word Classification
Authors: Ido Kissos, Nachum Dershowitz
Date: 2016-04-21 00:00:00
Tasks: General Classification, Language Modeling, Language Modelling, Optical Character Recognition (OCR), Segmentation

This paper explores the use of a learned classifier for post-OCR text
correction. Experiments with the Arabic language show that this approach, which
integrates a weighted confusion matrix and a shallow language model, improves
the vast majority of segmentation and recognition errors, the most frequent
types of error on our dataset.
Task scheduling system for UAV operations in indoor environment
Authors: Yohanes Khosiawan, Young Soo Park, Ilkyeong Moon, Janardhanan Mukund Nilakantan, Izabela Nielsen
Date: 2016-04-21 00:00:00
Tasks: Scheduling

Application of UAV in indoor environment is emerging nowadays due to the
advancements in technology. UAV brings more space-flexibility in an occupied or
hardly-accessible indoor environment, e.g., shop floor of manufacturing
industry, greenhouse, nuclear powerplant. UAV helps in creating an autonomous
manufacturing system by executing tasks with less human intervention in
time-efficient manner. Consequently, a scheduler is one essential component to
be focused on; yet the number of reported studies on UAV scheduling has been
minimal. This work proposes a methodology with a heuristic (based on Earliest
Available Time algorithm) which assigns tasks to UAVs with an objective of
minimizing the makespan. In addition, a quick response towards uncertain events
and a quick creation of new high-quality feasible schedule are needed. Hence,
the proposed heuristic is incorporated with Particle Swarm Optimization (PSO)
algorithm to find a quick near optimal schedule. This proposed methodology is
implemented into a scheduler and tested on a few scales of datasets generated
based on a real flight demonstration. Performance evaluation of scheduler is
discussed in detail and the best solution obtained from a selected set of
parameters is reported.
Norm Monitoring under Partial Action Observability
Authors: Natalia Criado, Jose M. Such
Date: 2015-05-15 00:00:00

In the context of using norms for controlling multi-agent systems, a vitally
important question that has not yet been addressed in the literature is the
development of mechanisms for monitoring norm compliance under partial action
observability. This paper proposes the reconstruction of unobserved actions to
tackle this problem. In particular, we formalise the problem of reconstructing
unobserved actions, and propose an information model and algorithms for
monitoring norms under partial action observability using two different
processes for reconstructing unobserved actions. Our evaluation shows that
reconstructing unobserved actions increases significantly the number of norm
violations and fulfilments detected.
Articulated Hand Pose Estimation Review
Authors: Emad Barsoum
Date: 2016-04-21 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation

With the increase number of companies focusing on commercializing Augmented
Reality (AR), Virtual Reality (VR) and wearable devices, the need for a hand
based input mechanism is becoming essential in order to make the experience
natural, seamless and immersive. Hand pose estimation has progressed
drastically in recent years due to the introduction of commodity depth cameras.
  Hand pose estimation based on vision is still a challenging problem due to
its complexity from self-occlusion (between fingers), close similarity between
fingers, dexterity of the hands, speed of the pose and the high dimension of
the hand kinematic parameters. Articulated hand pose estimation is still an
open problem and under intensive research from both academia and industry.
  The 2 approaches used for hand pose estimation are: discriminative and
generative. Generative approach is a model based that tries to fit a hand model
to the observed data. Discriminative approach is appearance based, usually
implemented with machine learning (ML) and require a large amount of training
data. Recent hand pose estimation uses hybrid approach by combining both
discriminative and generative methods into a single hand pipeline.
  In this paper, we focus on reviewing recent progress of hand pose estimation
from depth sensor. We will survey discriminative methods, generative methods
and hybrid methods. This paper is not a comprehensive review of all hand pose
estimation techniques, it is a subset of some of the recent state-of-the-art
techniques.
Dynamic matrix factorization with social influence
Authors: Aleksandr Y. Aravkin, Kush R. Varshney, Liu Yang
Date: 2016-04-21 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Matrix factorization is a key component of collaborative filtering-based
recommendation systems because it allows us to complete sparse user-by-item
ratings matrices under a low-rank assumption that encodes the belief that
similar users give similar ratings and that similar items garner similar
ratings. This paradigm has had immeasurable practical success, but it is not
the complete story for understanding and inferring the preferences of people.
First, peoples' preferences and their observable manifestations as ratings
evolve over time along general patterns of trajectories. Second, an individual
person's preferences evolve over time through influence of their social
connections. In this paper, we develop a unified process model for both types
of dynamics within a state space approach, together with an efficient
optimization scheme for estimation within that model. The model combines
elements from recent developments in dynamic matrix factorization, opinion
dynamics and social learning, and trust-based recommendation. The estimation
builds upon recent advances in numerical nonlinear optimization. Empirical
results on a large-scale data set from the Epinions website demonstrate
consistent reduction in root mean squared error by consideration of the two
types of dynamics.
What we write about when we write about causality: Features of causal statements across large-scale social discourse
Authors: Thomas C. McAndrew, Joshua C. Bongard, Christopher M. Danforth, Peter S. Dodds, Paul D. H. Hines, James P. Bagrow
Date: 2016-04-20 00:00:00
Tasks: Sentiment Analysis, Topic Models

Identifying and communicating relationships between causes and effects is
important for understanding our world, but is affected by language structure,
cognitive and emotional biases, and the properties of the communication medium.
Despite the increasing importance of social media, much remains unknown about
causal statements made online. To study real-world causal attribution, we
extract a large-scale corpus of causal statements made on the Twitter social
network platform as well as a comparable random control corpus. We compare
causal and control statements using statistical language and sentiment analysis
tools. We find that causal statements have a number of significant lexical and
grammatical differences compared with controls and tend to be more negative in
sentiment than controls. Causal statements made online tend to focus on news
and current events, medicine and health, or interpersonal relationships, as
shown by topic models. By quantifying the features and potential biases of
causality communication, this study improves our understanding of the accuracy
of information and opinions found online.
Evolutionary Image Transition Based on Theoretical Insights of Random Processes
Authors: Aneta Neumann, Bradley Alexander, Frank Neumann
Date: 2016-04-21 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary algorithms have been widely studied from a theoretical
perspective. In particular, the area of runtime analysis has contributed
significantly to a theoretical understanding and provided insights into the
working behaviour of these algorithms. We study how these insights into
evolutionary processes can be used for evolutionary art. We introduce the
notion of evolutionary image transition which transfers a given starting image
into a target image through an evolutionary process. Combining standard
mutation effects known from the optimization of the classical benchmark
function OneMax and different variants of random walks, we present ways of
performing evolutionary image transition with different artistic effects.
The THUMOS Challenge on Action Recognition for Videos "in the Wild"
Authors: Haroon Idrees, Amir R. Zamir, Yu-Gang Jiang, Alex Gorban, Ivan Laptev, Rahul Sukthankar, Mubarak Shah
Date: 2016-04-21 00:00:00
Tasks: Action Classification, Action Recognition, General Classification, Temporal Action Localization, Video Understanding

Automatically recognizing and localizing wide ranges of human actions has
crucial importance for video understanding. Towards this goal, the THUMOS
challenge was introduced in 2013 to serve as a benchmark for action
recognition. Until then, video action recognition, including THUMOS challenge,
had focused primarily on the classification of pre-segmented (i.e., trimmed)
videos, which is an artificial task. In THUMOS 2014, we elevated action
recognition to a more practical level by introducing temporally untrimmed
videos. These also include `background videos' which share similar scenes and
backgrounds as action videos, but are devoid of the specific actions. The three
editions of the challenge organized in 2013--2015 have made THUMOS a common
benchmark for action classification and detection and the annual challenge is
widely attended by teams from around the world.
  In this paper we describe the THUMOS benchmark in detail and give an overview
of data collection and annotation procedures. We present the evaluation
protocols used to quantify results in the two THUMOS tasks of action
classification and temporal detection. We also present results of submissions
to the THUMOS 2015 challenge and review the participating approaches.
Additionally, we include a comprehensive empirical study evaluating the
differences in action recognition between trimmed and untrimmed videos, and how
well methods trained on trimmed videos generalize to untrimmed videos. We
conclude by proposing several directions and improvements for future THUMOS
challenges.
Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections
Authors: Xichuan Zhou, Shengli Li, Kai Qin, Kunping Li, Fang Tang, Shengdong Hu, Shujun Liu, Zhi Lin
Date: 2016-04-21 00:00:00
Tasks: General Classification

Deep neural networks are state-of-the-art models for understanding the
content of images, video and raw input data. However, implementing a deep
neural network in embedded systems is a challenging task, because a typical
deep neural network, such as a Deep Belief Network using 128x128 images as
input, could exhaust Giga bytes of memory and result in bandwidth and computing
bottleneck. To address this challenge, this paper presents a hardware-oriented
deep learning algorithm, named as the Deep Adaptive Network, which attempts to
exploit the sparsity in the neural connections. The proposed method adaptively
reduces the weights associated with negligible features to zero, leading to
sparse feedforward network architecture. Furthermore, since the small
proportion of important weights are significantly larger than zero, they can be
robustly thresholded and represented using single-bit integers (-1 and +1),
leading to implementations of deep neural networks with sparse and binary
connections. Our experiments showed that, for the application of recognizing
MNIST handwritten digits, the features extracted by a two-layer Deep Adaptive
Network with about 25% reserved important connections achieved 97.2%
classification accuracy, which was almost the same with the standard Deep
Belief Network (97.3%). Furthermore, for efficient hardware implementations,
the sparse-and-binary-weighted deep neural network could save about 99.3%
memory and 99.9% computation units without significant loss of classification
accuracy for pattern recognition applications.
Nonextensive information theoretical machine
Authors: Chaobing Song, Shu-Tao Xia
Date: 2016-04-21 00:00:00

In this paper, we propose a new discriminative model named \emph{nonextensive
information theoretical machine (NITM)} based on nonextensive generalization of
Shannon information theory. In NITM, weight parameters are treated as random
variables. Tsallis divergence is used to regularize the distribution of weight
parameters and maximum unnormalized Tsallis entropy distribution is used to
evaluate fitting effect. On the one hand, it is showed that some well-known
margin-based loss functions such as $\ell_{0/1}$ loss, hinge loss, squared
hinge loss and exponential loss can be unified by unnormalized Tsallis entropy.
On the other hand, Gaussian prior regularization is generalized to Student-t
prior regularization with similar computational complexity. The model can be
solved efficiently by gradient-based convex optimization and its performance is
illustrated on standard datasets.
Persian Heritage Image Binarization Competition (PHIBC 2012)
Authors: Seyed Morteza Ayatollahi, Hossein Ziaei Nafchi
Date: 2013-06-26 00:00:00
Tasks: Binarization

The first competition on the binarization of historical Persian documents and
manuscripts (PHIBC 2012) has been organized in conjunction with the first
Iranian conference on pattern recognition and image analysis (PRIA 2013). The
main objective of PHIBC 2012 is to evaluate performance of the binarization
methodologies, when applied on the Persian heritage images. This paper provides
a report on the methodology and performance of the three submitted algorithms
based on evaluation measures has been used.
Embedded all relevant feature selection with Random Ferns
Authors: Miron Bartosz Kursa
Date: 2016-04-20 00:00:00
Tasks: All, feature selection

Many machine learning methods can produce variable importance scores
expressing the usability of each feature in context of the produced model;
those scores on their own are yet not sufficient to generate feature selection,
especially when an all relevant selection is required. Although there are
wrapper methods aiming to solve this problem, they introduce a substantial
increase in the required computational effort.
  In this paper I investigate an idea of incorporating all relevant selection
within the training process by producing importance for implicitly generated
shadows, attributes irrelevant by design. I propose and evaluate such a method
in context of random ferns classifier. Experiment results confirm the
effectiveness of such approach, although show that fully stochastic nature of
random ferns limits its applicability either to small dimensions or as a part
of a broader feature selection procedure.
Learning Social Affordance for Human-Robot Interaction
Authors: Tianmin Shu, M. S. Ryoo, Song-Chun Zhu
Date: 2016-04-13 00:00:00
Tasks: Weakly-supervised Learning

In this paper, we present an approach for robot learning of social affordance
from human activity videos. We consider the problem in the context of
human-robot interaction: Our approach learns structural representations of
human-human (and human-object-human) interactions, describing how body-parts of
each agent move with respect to each other and what spatial relations they
should maintain to complete each sub-event (i.e., sub-goal). This enables the
robot to infer its own movement in reaction to the human body motion, allowing
it to naturally replicate such interactions.
  We introduce the representation of social affordance and propose a generative
model for its weakly supervised learning from human demonstration videos. Our
approach discovers critical steps (i.e., latent sub-events) in an interaction
and the typical motion associated with them, learning what body-parts should be
involved and how. The experimental results demonstrate that our Markov Chain
Monte Carlo (MCMC) based learning algorithm automatically discovers
semantically meaningful interactive affordance from RGB-D videos, which allows
us to generate appropriate full body motion for an agent.
Speaker Cluster-Based Speaker Adaptive Training for Deep Neural Network Acoustic Modeling
Authors: Wei Chu, Ruxin Chen
Date: 2016-04-20 00:00:00
Tasks: speech-recognition, Speech Recognition

A speaker cluster-based speaker adaptive training (SAT) method under deep
neural network-hidden Markov model (DNN-HMM) framework is presented in this
paper. During training, speakers that are acoustically adjacent to each other
are hierarchically clustered using an i-vector based distance metric. DNNs with
speaker dependent layers are then adaptively trained for each cluster of
speakers. Before decoding starts, an unseen speaker in test set is matched to
the closest speaker cluster through comparing i-vector based distances. The
previously trained DNN of the matched speaker cluster is used for decoding
utterances of the test speaker. The performance of the proposed method on a
large vocabulary spontaneous speech recognition task is evaluated on a training
set of with 1500 hours of speech, and a test set of 24 speakers with 1774
utterances. Comparing to a speaker independent DNN with a baseline word error
rate of 11.6%, a relative 6.8% reduction in word error rate is observed from
the proposed method.
Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs
Authors: Zhilin Yang, Jie Tang, William Cohen
Date: 2015-08-04 00:00:00
Tasks: Knowledge Graphs

We study the extent to which online social networks can be connected to open
knowledge bases. The problem is referred to as learning social knowledge
graphs. We propose a multi-modal Bayesian embedding model, GenVector, to learn
latent topics that generate word and network embeddings. GenVector leverages
large-scale unlabeled data with embeddings and represents data of two
modalities---i.e., social network users and knowledge concepts---in a shared
latent topic space. Experiments on three datasets show that the proposed method
clearly outperforms state-of-the-art methods. We then deploy the method on
AMiner, a large-scale online academic search system with a network of
38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our
method significantly decreases the error rate in an online A/B test with live
users.
Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks
Authors: Gonçalo Oliveira, Xavier Frazão, André Pimentel, Bernardete Ribeiro
Date: 2016-04-20 00:00:00
Tasks: Data Augmentation, Logo Recognition, Marketing, Object, Object Recognition, Transfer Learning

Brand recognition is a very challenging topic with many useful applications
in localization recognition, advertisement and marketing. In this paper we
present an automatic graphic logo detection system that robustly handles
unconstrained imaging conditions. Our approach is based on Fast Region-based
Convolutional Networks (FRCN) proposed by Ross Girshick, which have shown
state-of-the-art performance in several generic object recognition tasks
(PASCAL Visual Object Classes challenges). In particular, we use two CNN models
pre-trained with the ILSVRC ImageNet dataset and we look at the selective
search of windows `proposals' in the pre-processing stage and data augmentation
to enhance the logo recognition rate. The novelty lies in the use of transfer
learning to leverage powerful Convolutional Neural Network models trained with
large-scale datasets and repurpose them in the context of graphic logo
detection. Another benefit of this framework is that it allows for multiple
detections of graphic logos using regions that are likely to have an object.
Experimental results with the FlickrLogos-32 dataset show not only the
promising performance of our developed models with respect to noise and other
transformations a graphic logo can be subject to, but also its superiority over
state-of-the-art systems with hand-crafted models and features.