pwc-archive/papers-with-abstracts huggingface dataset:
Scene Text Eraser
Authors: Toshiki Nakamura, Anna Zhu, Keiji Yanai, Seiichi Uchida
Date: 2017-05-08 00:00:00
Tasks: Scene Text Detection, Text Detection

The character information in natural scene images contains various personal
information, such as telephone numbers, home addresses, etc. It is a high risk
of leakage the information if they are published. In this paper, we proposed a
scene text erasing method to properly hide the information via an inpainting
convolutional neural network (CNN) model. The input is a scene text image, and
the output is expected to be text erased image with all the character regions
filled up the colors of the surrounding background pixels. This work is
accomplished by a CNN model through convolution to deconvolution with
interconnection process. The training samples and the corresponding inpainting
images are considered as teaching signals for training. To evaluate the text
erasing performance, the output images are detected by a novel scene text
detection method. Subsequently, the same measurement on text detection is
utilized for testing the images in benchmark dataset ICDAR2013. Compared with
direct text detection way, the scene text erasing process demonstrates a
drastically decrease on the precision, recall and f-score. That proves the
effectiveness of proposed method for erasing the text in natural scene images.
Deep Descriptor Transforming for Image Co-Localization
Authors: Xiu-Shen Wei, Chen-Lin Zhang, Yao Li, Chen-Wei Xie, Jianxin Wu, Chunhua Shen, Zhi-Hua Zhou
Date: 2017-05-08 00:00:00

Reusable model design becomes desirable with the rapid expansion of machine
learning applications. In this paper, we focus on the reusability of
pre-trained deep convolutional models. Specifically, different from treating
pre-trained models as feature extractors, we reveal more treasures beneath
convolutional layers, i.e., the convolutional activations could act as a
detector for the common object in the image co-localization problem. We propose
a simple but effective method, named Deep Descriptor Transforming (DDT), for
evaluating the correlations of descriptors and then obtaining the
category-consistent regions, which can accurately locate the common object in a
set of images. Empirical studies validate the effectiveness of the proposed DDT
method. On benchmark image co-localization datasets, DDT consistently
outperforms existing state-of-the-art methods by a large margin. Moreover, DDT
also demonstrates good generalization ability for unseen categories and
robustness for dealing with noisy data.
What Can Help Pedestrian Detection?
Authors: Jiayuan Mao, Tete Xiao, Yuning Jiang, Zhimin Cao
Date: 2017-05-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Pedestrian Detection

Aggregating extra features has been considered as an effective approach to
boost traditional pedestrian detection methods. However, there is still a lack
of studies on whether and how CNN-based pedestrian detectors can benefit from
these extra features. The first contribution of this paper is exploring this
issue by aggregating extra features into CNN-based pedestrian detection
framework. Through extensive experiments, we evaluate the effects of different
kinds of extra features quantitatively. Moreover, we propose a novel network
architecture, namely HyperLearner, to jointly learn pedestrian detection as
well as the given extra feature. By multi-task training, HyperLearner is able
to utilize the information of given features and improve detection performance
without extra inputs in inference. The experimental results on multiple
pedestrian benchmarks validate the effectiveness of the proposed HyperLearner.
High-Level Concepts for Affective Understanding of Images
Authors: Afsheen Rafaqat Ali, Usman Shahid, Mohsen Ali, Jeffrey Ho
Date: 2017-05-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper aims to bridge the affective gap between image content and the
emotional response of the viewer it elicits by using High-Level Concepts
(HLCs). In contrast to previous work that relied solely on low-level features
or used convolutional neural network (CNN) as a black-box, we use HLCs
generated by pretrained CNNs in an explicit way to investigate the
relations/associations between these HLCs and a (small) set of Ekman's
emotional classes. As a proof-of-concept, we first propose a linear admixture
model for modeling these relations, and the resulting computational framework
allows us to determine the associations between each emotion class and certain
HLCs (objects and places). This linear model is further extended to a nonlinear
model using support vector regression (SVR) that aims to predict the viewer's
emotional response using both low-level image features and HLCs extracted from
images. These class-specific regressors are then assembled into a regressor
ensemble that provide a flexible and effective predictor for predicting
viewer's emotional responses from images. Experimental results have
demonstrated that our results are comparable to existing methods, with a clear
view of the association between HLCs and emotional classes that is ostensibly
missing in most existing work.
Density Estimation for Geolocation via Convolutional Mixture Density Network
Authors: Hayate Iso, Shoko Wakamiya, Eiji Aramaki
Date: 2017-05-08 00:00:00
Tasks: Density Estimation

Nowadays, geographic information related to Twitter is crucially important
for fine-grained applications. However, the amount of geographic information
avail- able on Twitter is low, which makes the pursuit of many applications
challenging. Under such circumstances, estimating the location of a tweet is an
important goal of the study. Unlike most previous studies that estimate the
pre-defined district as the classification task, this study employs a
probability distribution to represent richer information of the tweet, not only
the location but also its ambiguity. To realize this modeling, we propose the
convolutional mixture density network (CMDN), which uses text data to estimate
the mixture model parameters. Experimentally obtained results reveal that CMDN
achieved the highest prediction performance among the method for predicting the
exact coordinates. It also provides a quantitative representation of the
location ambiguity for each tweet that properly works for extracting the
reliable location estimations.
Combating Human Trafficking with Deep Multimodal Models
Authors: Edmund Tong, Amir Zadeh, Cara Jones, Louis-Philippe Morency
Date: 2017-05-08 00:00:00

Human trafficking is a global epidemic affecting millions of people across
the planet. Sex trafficking, the dominant form of human trafficking, has seen a
significant rise mostly due to the abundance of escort websites, where human
traffickers can openly advertise among at-will escort advertisements. In this
paper, we take a major step in the automatic detection of advertisements
suspected to pertain to human trafficking. We present a novel dataset called
Trafficking-10k, with more than 10,000 advertisements annotated for this task.
The dataset contains two sources of information per advertisement: text and
images. For the accurate detection of trafficking advertisements, we designed
and trained a deep multimodal model called the Human Trafficking Deep Network
(HTDN).
Implicitly Incorporating Morphological Information into Word Embedding
Authors: Yang Xu, Jiawei Liu
Date: 2017-01-10 00:00:00
Tasks: Word Similarity

In this paper, we propose three novel models to enhance word embedding by
implicitly using morphological information. Experiments on word similarity and
syntactic analogy show that the implicit models are superior to traditional
explicit ones. Our models outperform all state-of-the-art baselines and
significantly improve the performance on both tasks. Moreover, our performance
on the smallest corpus is similar to the performance of CBOW on the corpus
which is five times the size of ours. Parameter analysis indicates that the
implicit models can supplement semantic information during the word embedding
training process.
Learning Local Dependence In Ordered Data
Authors: Guo Yu, Jacob Bien
Date: 2016-04-25 00:00:00
Tasks: regression

In many applications, data come with a natural ordering. This ordering can
often induce local dependence among nearby variables. However, in complex data,
the width of this dependence may vary, making simple assumptions such as a
constant neighborhood size unrealistic. We propose a framework for learning
this local dependence based on estimating the inverse of the Cholesky factor of
the covariance matrix. Penalized maximum likelihood estimation of this matrix
yields a simple regression interpretation for local dependence in which
variables are predicted by their neighbors. Our proposed method involves
solving a convex, penalized Gaussian likelihood problem with a hierarchical
group lasso penalty. The problem decomposes into independent subproblems which
can be solved efficiently in parallel using first-order methods. Our method
yields a sparse, symmetric, positive definite estimator of the precision
matrix, encoding a Gaussian graphical model. We derive theoretical results not
found in existing methods attaining this structure. In particular, our
conditions for signed support recovery and estimation consistency rates in
multiple norms are as mild as those in a regression problem. Empirical results
show our method performing favorably compared to existing methods. We apply our
method to genomic data to flexibly model linkage disequilibrium. Our method is
also applied to improve the performance of discriminant analysis in sound
recording classification.
Automatic Recognition of Mammal Genera on Camera-Trap Images using Multi-Layer Robust Principal Component Analysis and Mixture Neural Networks
Authors: Jhony-Heriberto Giraldo-Zuluaga, Augusto Salazar, Alexander Gomez, Angélica Diaz-Pulido
Date: 2017-05-08 00:00:00
Tasks: General Classification

The segmentation and classification of animals from camera-trap images is due
to the conditions under which the images are taken, a difficult task. This work
presents a method for classifying and segmenting mammal genera from camera-trap
images. Our method uses Multi-Layer Robust Principal Component Analysis (RPCA)
for segmenting, Convolutional Neural Networks (CNNs) for extracting features,
Least Absolute Shrinkage and Selection Operator (LASSO) for selecting features,
and Artificial Neural Networks (ANNs) or Support Vector Machines (SVM) for
classifying mammal genera present in the Colombian forest. We evaluated our
method with the camera-trap images from the Alexander von Humboldt Biological
Resources Research Institute. We obtained an accuracy of 92.65% classifying 8
mammal genera and a False Positive (FP) class, using automatic-segmented
images. On the other hand, we reached 90.32% of accuracy classifying 10 mammal
genera, using ground-truth images only. Unlike almost all previous works, we
confront the animal segmentation and genera classification in the camera-trap
recognition. This method shows a new approach toward a fully-automatic
detection of animals from camera-trap images.
A Correlative Denoising Autoencoder to Model Social Influence for Top-N Recommender System
Authors: Yiteng Pan, Fazhi He, Haiping Yu
Date: 2017-03-06 00:00:00
Tasks: Denoising, Recommendation Systems

In recent years, there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation performance. In most cases, it requires a larger number of data to train a robust deep learning model, which contains a lot of parameters to fit training data. However, both data of user ratings and social networks are facing critical sparse problem, which makes it not easy to train a robust deep neural network model. Towards this problem, we propose a novel Correlative Denoising Autoencoder (CoDAE) method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for recommendation. We develop the CoDAE model by utilizing three separated autoencoders to learn user features with roles of rater, truster and trustee, respectively. Especially, on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user, we propose to utilize shared parameters to learn common information of the units that corresponding to same users. Moreover, we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE model. We further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation task. The experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.
Generating Memorable Mnemonic Encodings of Numbers
Authors: Vincent Fiorentini, Megan Shao, Julie Medero
Date: 2017-05-07 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence

The major system is a mnemonic system that can be used to memorize sequences
of numbers. In this work, we present a method to automatically generate
sentences that encode a given number. We propose several encoding models and
compare the most promising ones in a password memorability study. The results
of the study show that a model combining part-of-speech sentence templates with
an $n$-gram language model produces the most memorable password
representations.
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Authors: Haiyang Yu, Zhihai Wu, Shuqin Wang, Yunpeng Wang, Xiaolei Ma
Date: 2017-05-07 00:00:00
Tasks: motion prediction, Traffic Prediction

Predicting large-scale transportation network traffic has become an important
and challenging topic in recent decades. Inspired by the domain knowledge of
motion prediction, in which the future motion of an object can be predicted
based on previous scenes, we propose a network grid representation method that
can retain the fine-scale structure of a transportation network. Network-wide
traffic speeds are converted into a series of static images and input into a
novel deep architecture, namely, spatiotemporal recurrent convolutional
networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the
advantages of deep convolutional neural networks (DCNNs) and long short-term
memory (LSTM) neural networks. The spatial dependencies of network-wide traffic
can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An
experiment on a Beijing transportation network with 278 links demonstrates that
SRCNs outperform other deep learning-based algorithms in both short-term and
long-term traffic prediction.
Multimodal Affect Analysis for Product Feedback Assessment
Authors: Amol S Patwardhan, Gerald M Knapp
Date: 2017-05-07 00:00:00

Consumers often react expressively to products such as food samples, perfume,
jewelry, sunglasses, and clothing accessories. This research discusses a
multimodal affect recognition system developed to classify whether a consumer
likes or dislikes a product tested at a counter or kiosk, by analyzing the
consumer's facial expression, body posture, hand gestures, and voice after
testing the product. A depth-capable camera and microphone system - Kinect for
Windows - is utilized. An emotion identification engine has been developed to
analyze the images and voice to determine affective state of the customer. The
image is segmented using skin color and adaptive threshold. Face, body and
hands are detected using the Haar cascade classifier. Canny edges are
identified and the lip, body and hand contours are extracted using spatial
filtering. Edge count and orientation around the mouth, cheeks, eyes,
shoulders, fingers and the location of the edges are used as features.
Classification is done by an emotion template mapping algorithm and training a
classifier using support vector machines. The real-time performance, accuracy
and feasibility for multimodal affect recognition in feedback assessment are
evaluated.
The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives
Authors: Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daumé III, Larry Davis
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7

Visual narrative is often a combination of explicit information and judicious
omissions, relying on the viewer to supply missing details. In comics, most
movements in time and space are hidden in the "gutters" between panels. To
follow the story, readers logically connect panels together by inferring unseen
actions through a process called "closure". While computers can now describe
what is explicitly depicted in natural images, in this paper we examine whether
they can understand the closure-driven narratives conveyed by stylized artwork
and dialogue in comic book panels. We construct a dataset, COMICS, that
consists of over 1.2 million panels (120 GB) paired with automatic textbox
transcriptions. An in-depth analysis of COMICS demonstrates that neither text
nor image alone can tell a comic book story, so a computer must understand both
modalities to keep up with the plot. We introduce three cloze-style tasks that
ask models to predict narrative and character-centric aspects of a panel given
n preceding panels as context. Various deep neural architectures underperform
human baselines on these tasks, suggesting that COMICS contains fundamental
challenges for both vision and language.
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
Authors: Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker Jr., Alberto Rodriguez, Jianxiong Xiao
Date: 2016-09-29 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGBD, Pose Estimation, Segmentation

Robot warehouse automation has attracted significant interest in recent
years, perhaps most visibly in the Amazon Picking Challenge (APC). A fully
autonomous warehouse pick-and-place system requires robust vision that reliably
recognizes and locates objects amid cluttered environments, self-occlusions,
sensor noise, and a large variety of objects. In this paper we present an
approach that leverages multi-view RGB-D data and self-supervised, data-driven
learning to overcome those difficulties. The approach was part of the
MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and
picking tasks, respectively at APC 2016. In the proposed approach, we segment
and label multiple views of a scene with a fully convolutional neural network,
and then fit pre-scanned 3D object models to the resulting segmentation to get
the 6D object pose. Training a deep neural network for segmentation typically
requires a large amount of training data. We propose a self-supervised method
to generate a large labeled dataset without tedious manual segmentation. We
demonstrate that our system can reliably estimate the 6D pose of objects under
a variety of scenarios. All code, data, and benchmarks are available at
http://apc.cs.princeton.edu/
AirDraw: Leveraging Smart Watch Motion Sensors for Mobile Human Computer Interactions
Authors: Seyed A Sajjadi, Danial Moazen, Ani Nahapetian
Date: 2017-05-07 00:00:00
Tasks: Gesture Recognition

Wearable computing is one of the fastest growing technologies today. Smart
watches are poised to take over at least of half the wearable devices market in
the near future. Smart watch screen size, however, is a limiting factor for
growth, as it restricts practical text input. On the other hand, wearable
devices have some features, such as consistent user interaction and hands-free,
heads-up operations, which pave the way for gesture recognition methods of text
entry. This paper proposes a new text input method for smart watches, which
utilizes motion sensor data and machine learning approaches to detect letters
written in the air by a user. This method is less computationally intensive and
less expensive when compared to computer vision approaches. It is also not
affected by lighting factors, which limit computer vision solutions. The
AirDraw system prototype developed to test this approach is presented.
Additionally, experimental results close to 71% accuracy are presented.
Finding Bottlenecks: Predicting Student Attrition with Unsupervised Classifier
Authors: Seyed Sajjadi, Bruce Shapiro, Christopher McKinlay, Allen Sarkisyan, Carol Shubin, Efunwande Osoba
Date: 2017-05-07 00:00:00
Tasks: Clustering

With pressure to increase graduation rates and reduce time to degree in
higher education, it is important to identify at-risk students early. Automated
early warning systems are therefore highly desirable. In this paper, we use
unsupervised clustering techniques to predict the graduation status of declared
majors in five departments at California State University Northridge (CSUN),
based on a minimal number of lower division courses in each major. In addition,
we use the detected clusters to identify hidden bottleneck courses.
Handwritten Bangla Digit Recognition Using Deep Learning
Authors: Md Zahangir Alom, Paheding Sidike, Tarek M. Taha, Vijayan K. Asari
Date: 2017-05-07 00:00:00
Tasks: Deep Learning

In spite of the advances in pattern recognition technology, Handwritten
Bangla Character Recognition (HBCR) (such as alpha-numeric and special
characters) remains largely unsolved due to the presence of many perplexing
characters and excessive cursive in Bangla handwriting. Even the best existing
recognizers do not lead to satisfactory performance for practical applications.
To improve the performance of Handwritten Bangla Digit Recognition (HBDR), we
herein present a new approach based on deep neural networks which have recently
shown excellent performance in many pattern recognition and machine learning
applications, but has not been throughly attempted for HBDR. We introduce
Bangla digit recognition techniques based on Deep Belief Network (DBN),
Convolutional Neural Networks (CNN), CNN with dropout, CNN with dropout and
Gaussian filters, and CNN with dropout and Gabor filters. These networks have
the advantage of extracting and using feature information, improving the
recognition of two dimensional shapes with a high degree of invariance to
translation, scaling and other pattern distortions. We systematically evaluated
the performance of our method on publicly available Bangla numeral image
database named CMATERdb 3.1.1. From experiments, we achieved 98.78% recognition
rate using the proposed method: CNN with Gabor features and dropout, which
outperforms the state-of-the-art algorithms for HDBR.