pwc-archive/papers-with-abstracts huggingface dataset:
Towards Good Practices for Multi-modal Fusion in Large-scale Video Classification
Authors: Jinlai Liu, Zehuan Yuan, Changhu Wang
Date: 2018-09-16 00:00:00
Tasks: Classification, General Classification, Video Classification

Leveraging both visual frames and audio has been experimentally proven
effective to improve large-scale video classification. Previous research on
video classification mainly focuses on the analysis of visual content among
extracted video frames and their temporal feature aggregation. In contrast,
multimodal data fusion is achieved by simple operators like average and
concatenation. Inspired by the success of bilinear pooling in the visual and
language fusion, we introduce multi-modal factorized bilinear pooling (MFB) to
fuse visual and audio representations. We combine MFB with different
video-level features and explore its effectiveness in video classification.
Experimental results on the challenging Youtube-8M v2 dataset demonstrate that
MFB significantly outperforms simple fusion methods in large-scale video
classification.
A Generic Multi-modal Dynamic Gesture Recognition System using Machine Learning
Authors: Gautham Krishna G, Karthik Subramanian Nathan, Yogesh Kumar B, Ankith A Prabhu, Ajay Kannan, Vineeth Vijayaraghavan
Date: 2018-09-16 00:00:00
Tasks: BIG-bench Machine Learning, Gesture Recognition, Intelligent Communication

Human computer interaction facilitates intelligent communication between
humans and computers, in which gesture recognition plays a prominent role. This
paper proposes a machine learning system to identify dynamic gestures using
tri-axial acceleration data acquired from two public datasets. These datasets,
uWave and Sony, were acquired using accelerometers embedded in Wii remotes and
smartwatches, respectively. A dynamic gesture signed by the user is
characterized by a generic set of features extracted across time and frequency
domains. The system was analyzed from an end-user perspective and was modelled
to operate in three modes. The modes of operation determine the subsets of data
to be used for training and testing the system. From an initial set of seven
classifiers, three were chosen to evaluate each dataset across all modes
rendering the system towards mode-neutrality and dataset-independence. The
proposed system is able to classify gestures performed at varying speeds with
minimum preprocessing, making it computationally efficient. Moreover, this
system was found to run on a low-cost embedded platform - Raspberry Pi Zero
(USD 5), making it economically viable.
Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network
Authors: Mohsen Hajabdollahi, Reza Esfandiarpoor, Elyas Sabeti, Nader Karimi, Kayvan Najarian, S. M. Reza Soroushmehr, Shadrokh Samavi
Date: 2018-09-16 00:00:00
Tasks: Anomaly Detection, General Classification, Segmentation

Automating classification and segmentation process of abnormal regions in
different body organs has a crucial role in most of medical imaging
applications such as funduscopy, endoscopy, and dermoscopy. Detecting multiple
abnormalities in each type of images is necessary for better and more accurate
diagnosis procedure and medical decisions. In recent years portable medical
imaging devices such as capsule endoscopy and digital dermatoscope have been
introduced and made the diagnosis procedure easier and more efficient. However,
these portable devices have constrained power resources and limited
computational capability. To address this problem, we propose a bifurcated
structure for convolutional neural networks performing both classification and
segmentation of multiple abnormalities simultaneously. The proposed network is
first trained by each abnormality separately. Then the network is trained using
all abnormalities. In order to reduce the computational complexity, the network
is redesigned to share some features which are common among all abnormalities.
Later, these shared features are used in different settings (directions) to
segment and classify the abnormal region of the image. Finally, results of the
classification and segmentation directions are fused to obtain the classified
segmentation map. Proposed framework is simulated using four frequent
gastrointestinal abnormalities as well as three dermoscopic lesions and for
evaluation of the proposed framework the results are compared with the
corresponding ground truth map. Properties of the bifurcated network like low
complexity and resource sharing make it suitable to be implemented as a part of
portable medical imaging devices.
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images
Authors: Dongwon Park, Yonghyeok Seo, Se Young Chun
Date: 2018-09-16 00:00:00

Robotic grasp detection for novel objects is a challenging task, but for the last few years, deep learning based approaches have achieved remarkable performance improvements, up to 96.1% accuracy, with RGB-D data. In this paper, we propose fully convolutional neural network (FCNN) based methods for robotic grasp detection. Our methods also achieved state-of-the-art detection accuracy (up to 96.6%) with state-of- the-art real-time computation time for high-resolution images (6-20ms per 360x360 image) on Cornell dataset. Due to FCNN, our proposed method can be applied to images with any size for detecting multigrasps on multiobjects. Proposed methods were evaluated using 4-axis robot arm with small parallel gripper and RGB-D camera for grasping challenging small, novel objects. With accurate vision-robot coordinate calibration through our proposed learning-based, fully automatic approach, our proposed method yielded 90% success rate.
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
Authors: Michael Danielczuk, Matthew Matl, Saurabh Gupta, Andrew Li, Andrew Lee, Jeffrey Mahler, Ken Goldberg
Date: 2018-09-16 00:00:00
Tasks: Clustering, Dataset Generation, Instance Segmentation, Object Tracking, Semantic Segmentation, Unseen Object Instance Segmentation

The ability to segment unknown objects in depth images has potential to
enhance robot skills in grasping and object tracking. Recent computer vision
research has demonstrated that Mask R-CNN can be trained to segment specific
categories of objects in RGB images when massive hand-labeled datasets are
available. As generating these datasets is time consuming, we instead train
with synthetic depth images. Many robots now use depth sensors, and recent
results suggest training on synthetic depth data can transfer successfully to
the real world. We present a method for automated dataset generation and
rapidly generate a synthetic training dataset of 50,000 depth images and
320,000 object masks using simulated heaps of 3D CAD models. We train a variant
of Mask R-CNN with domain randomization on the generated dataset to perform
category-agnostic instance segmentation without any hand-labeled data and we
evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask
R-CNN, on a set of real, high-resolution depth images of challenging,
densely-cluttered bins containing objects with highly-varied geometry. SD Mask
R-CNN outperforms point cloud clustering baselines by an absolute 15% in
Average Precision and 20% in Average Recall on COCO benchmarks, and achieves
performance levels similar to a Mask R-CNN trained on a massive, hand-labeled
RGB dataset and fine-tuned on real images from the experimental setup. We
deploy the model in an instance-specific grasping pipeline to demonstrate its
usefulness in a robotics application. Code, the synthetic training dataset, and
supplementary material are available at https://bit.ly/2letCuE.
Aesthetic-based Clothing Recommendation
Authors: Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, Zheng Qin
Date: 2018-09-16 00:00:00
Tasks: Recommendation Systems

Recently, product images have gained increasing attention in clothing
recommendation since the visual appearance of clothing products has a
significant impact on consumers' decision. Most existing methods rely on
conventional features to represent an image, such as the visual features
extracted by convolutional neural networks (CNN features) and the
scale-invariant feature transform algorithm (SIFT features), color histograms,
and so on. Nevertheless, one important type of features, the \emph{aesthetic
features}, is seldom considered. It plays a vital role in clothing
recommendation since a users' decision depends largely on whether the clothing
is in line with her aesthetics, however the conventional image features cannot
portray this directly. To bridge this gap, we propose to introduce the
aesthetic information, which is highly relevant with user preference, into
clothing recommender systems. To achieve this, we first present the aesthetic
features extracted by a pre-trained neural network, which is a brain-inspired
deep structure trained for the aesthetic assessment task. Considering that the
aesthetic preference varies significantly from user to user and by time, we
then propose a new tensor factorization model to incorporate the aesthetic
features in a personalized manner. We conduct extensive experiments on
real-world datasets, which demonstrate that our approach can capture the
aesthetic preference of users and significantly outperform several
state-of-the-art recommendation methods.
Cross-Domain Labeled LDA for Cross-Domain Text Classification
Authors: Baoyu Jing, Chenwei Lu, Deqing Wang, Fuzhen Zhuang, Cheng Niu
Date: 2018-09-16 00:00:00
Tasks: Classification, Cross-Domain Text Classification, General Classification, text-classification, Text Classification

Cross-domain text classification aims at building a classifier for a target
domain which leverages data from both source and target domain. One promising
idea is to minimize the feature distribution differences of the two domains.
Most existing studies explicitly minimize such differences by an exact
alignment mechanism (aligning features by one-to-one feature alignment,
projection matrix etc.). Such exact alignment, however, will restrict models'
learning ability and will further impair models' performance on classification
tasks when the semantic distributions of different domains are very different.
To address this problem, we propose a novel group alignment which aligns the
semantics at group level. In addition, to help the model learn better semantic
groups and semantics within these groups, we also propose a partial supervision
for model's learning in source domain. To this end, we embed the group
alignment and a partial supervision into a cross-domain topic model, and
propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and
Reuters dataset, extensive quantitative (classification, perplexity etc.) and
qualitative (topic detection) experiments are conducted to show the
effectiveness of the proposed group alignment and partial supervision.
Linear Independent Component Analysis over Finite Fields: Algorithms and Bounds
Authors: Amichai Painsky, Saharon Rosset, Meir Feder
Date: 2018-09-16 00:00:00

Independent Component Analysis (ICA) is a statistical tool that decomposes an
observed random vector into components that are as statistically independent as
possible. ICA over finite fields is a special case of ICA, in which both the
observations and the decomposed components take values over a finite alphabet.
This problem is also known as minimal redundancy representation or factorial
coding. In this work we focus on linear methods for ICA over finite fields. We
introduce a basic lower bound which provides a fundamental limit to the ability
of any linear solution to solve this problem. Based on this bound, we present a
greedy algorithm that outperforms all currently known methods. Importantly, we
show that the overhead of our suggested algorithm (compared with the lower
bound) typically decreases, as the scale of the problem grows. In addition, we
provide a sub-optimal variant of our suggested method that significantly
reduces the computational complexity at a relatively small cost in performance.
Finally, we discuss the universal abilities of linear transformations in
decomposing random vectors, compared with existing non-linear solutions.
Development of deep learning algorithms to categorize free-text notes pertaining to diabetes: convolution neural networks achieve higher accuracy than support vector machines
Authors: Boyi Yang, Adam Wright
Date: 2018-09-16 00:00:00
Tasks: Deep Learning

Health professionals can use natural language processing (NLP) technologies
when reviewing electronic health records (EHR). Machine learning free-text
classifiers can help them identify problems and make critical decisions. We aim
to develop deep learning neural network algorithms that identify EHR progress
notes pertaining to diabetes and validate the algorithms at two institutions.
The data used are 2,000 EHR progress notes retrieved from patients with
diabetes and all notes were annotated manually as diabetic or non-diabetic.
Several deep learning classifiers were developed, and their performances were
evaluated with the area under the ROC curve (AUC). The convolutional neural
network (CNN) model with a separable convolution layer accurately identified
diabetes-related notes in the Brigham and Womens Hospital testing set with the
highest AUC of 0.975. Deep learning classifiers can be used to identify EHR
progress notes pertaining to diabetes. In particular, the CNN-based classifier
can achieve a higher AUC than an SVM-based classifier.
Dual Memory Network Model for Biased Product Review Classification
Authors: Yunfei Long, Mingyu Ma, Qin Lu, Rong Xiang, Chu-Ren Huang
Date: 2018-09-16 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, General Classification, Sentiment Analysis

In sentiment analysis (SA) of product reviews, both user and product
information are proven to be useful. Current tasks handle user profile and
product information in a unified model which may not be able to learn salient
features of users and products effectively. In this work, we propose a dual
user and product memory network (DUPMN) model to learn user profiles and
product reviews using separate memory networks. Then, the two representations
are used jointly for sentiment prediction. The use of separate models aims to
capture user profiles and product information more effectively. Compared to
state-of-the-art unified prediction models, the evaluations on three benchmark
datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives
performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are
also deemed very significant measured by p-values.
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Authors: Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi
Date: 2018-09-16 00:00:00

Spiking neural networks (SNNs) that enables energy efficient implementation
on emerging neuromorphic hardware are gaining more attention. Yet now, SNNs
have not shown competitive performance compared with artificial neural networks
(ANNs), due to the lack of effective learning algorithms and efficient
programming frameworks. We address this issue from two aspects: (1) We propose
a neuron normalization technique to adjust the neural selectivity and develop a
direct learning algorithm for deep SNNs. (2) Via narrowing the rate coding
window and converting the leaky integrate-and-fire (LIF) model into an
explicitly iterative version, we present a Pytorch-based implementation method
towards the training of large-scale SNNs. In this way, we are able to train
deep SNNs with tens of times speedup. As a result, we achieve significantly
better accuracy than the reported works on neuromorphic datasets (N-MNIST and
DVS-CIFAR10), and comparable accuracy as existing ANNs and pre-trained SNNs on
non-spiking datasets (CIFAR10). {To our best knowledge, this is the first work
that demonstrates direct training of deep SNNs with high performance on
CIFAR10, and the efficient implementation provides a new way to explore the
potential of SNNs.
Mobility Mode Detection Using WiFi Signals
Authors: Arash Kalatian, Bilal Farooq
Date: 2018-09-16 00:00:00

We utilize Wi-Fi communications from smartphones to predict their mobility
mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four
strategic locations in a closed loop on streets in downtown Toronto. Deep
neural network (Multilayer Perceptron) along with three decision tree based
classifiers (Decision Tree, Bagged Decision Tree and Random Forest) are
developed. Results show that the best prediction accuracy is achieved by
Multilayer Perceptron, with 86.52% correct predictions of mobility modes.
GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks
Authors: Yasin Almalioglu, Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Andrew Markham, Niki Trigoni
Date: 2018-09-16 00:00:00
Tasks: Depth Estimation, Monocular Visual Odometry, Pose Estimation, Visual Odometry

In the last decade, supervised deep learning approaches have been extensively
employed in visual odometry (VO) applications, which is not feasible in
environments where labelled data is not abundant. On the other hand,
unsupervised deep learning approaches for localization and mapping in unknown
environments from unlabelled data have received comparatively less attention in
VO research. In this study, we propose a generative unsupervised learning
framework that predicts 6-DoF pose camera motion and monocular depth map of the
scene from unlabelled RGB image sequences, using deep convolutional Generative
Adversarial Networks (GANs). We create a supervisory signal by warping view
sequences and assigning the re-projection minimization to the objective loss
function that is adopted in multi-view pose estimation and single-view depth
generation network. Detailed quantitative and qualitative evaluations of the
proposed framework on the KITTI and Cityscapes datasets show that the proposed
method outperforms both existing traditional and unsupervised deep VO methods
providing better results for both pose estimation and depth recovery.
CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis
Authors: Ankit Shah, Jean Baptiste Lamare, Tuan Nguyen Anh, Alexander Hauptmann
Date: 2018-09-16 00:00:00
Tasks: Object, object-detection, Object Detection, Pedestrian Detection

This paper presents a novel dataset for traffic accidents analysis. Our goal
is to resolve the lack of public data for research about automatic
spatio-temporal annotations for traffic safety in the roads. Through the
analysis of the proposed dataset, we observed a significant degradation of
object detection in pedestrian category in our dataset, due to the object sizes
and complexity of the scenes. To this end, we propose to integrate contextual
information into conventional Faster R-CNN using Context Mining (CM) and
Augmented Context Mining (ACM) to complement the accuracy for small pedestrian
detection. Our experiments indicate a considerable improvement in object
detection accuracy: +8.51% for CM and +6.20% for ACM. Finally, we demonstrate
the performance of accident forecasting in our dataset using Faster R-CNN and
an Accident LSTM architecture. We achieved an average of 1.684 seconds in terms
of Time-To-Accident measure with an Average Precision of 47.25%. Our Webpage
for the paper is https://goo.gl/cqK2wE
Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning
Authors: Melvin Wong, Bilal Farooq
Date: 2018-09-15 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Survey

In this paper, we implement an information-theoretic approach to travel
behaviour analysis by introducing a generative modelling framework to identify
informative latent characteristics in travel decision making. It involves
developing a joint tri-partite Bayesian graphical network model using a
Restricted Boltzmann Machine (RBM) generative modelling framework. We apply
this framework on a mode choice survey data to identify abstract latent
variables and compare the performance with a traditional latent variable model
with specific latent preferences -- safety, comfort, and environmental. Data
collected from a joint stated and revealed preference mode choice survey in
Quebec, Canada were used to calibrate the RBM model. Results show that a
signficant impact on model likelihood statistics and suggests that machine
learning tools are highly suitable for modelling complex networks of
conditional independent behaviour interactions.
Sampled Policy Gradient for Learning to Play the Game Agar.io
Authors: Anton Orell Wiehe, Nil Stolt Ansó, Madalina M. Drugan, Marco A. Wiering
Date: 2018-09-15 00:00:00
Tasks: Game Design, Q-Learning

In this paper, a new offline actor-critic learning algorithm is introduced:
Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an
approximated policy gradient by using the critic to evaluate the samples. This
sampling allows SPG to search the action-Q-value space more globally than
deterministic policy gradient (DPG), enabling it to theoretically avoid more
local optima. SPG is compared to Q-learning and the actor-critic algorithms
CACLA and DPG in a pellet collection task and a self play environment in the
game Agar.io. The online game Agar.io has become massively popular on the
internet due to intuitive game design and the ability to instantly compete
against players around the world. From the point of view of artificial
intelligence this game is also very intriguing: The game has a continuous input
and action space and allows to have diverse agents with complex strategies
compete against each other. The experimental results show that Q-Learning and
CACLA outperform a pre-programmed greedy bot in the pellet collection task, but
all algorithms fail to outperform this bot in a fighting scenario. The SPG
algorithm is analyzed to have great extendability through offline exploration
and it matches DPG in performance even in its basic form without extensive
sampling.
Using Artificial Intelligence to Support Compliance with the General Data Protection Regulation
Authors: John KC Kingston
Date: 2018-09-15 00:00:00
Tasks: BIG-bench Machine Learning

The General Data Protection Regulation (GDPR) is a European Union regulation
that will replace the existing Data Protection Directive on 25 May 2018. The
most significant change is a huge increase in the maximum fine that can be
levied for breaches of the regulation. Yet fewer than half of UK companies are
fully aware of GDPR - and a number of those who were preparing for it stopped
doing so when the Brexit vote was announced. A last-minute rush to become
compliant is therefore expected, and numerous companies are starting to offer
advice, checklists and consultancy on how to comply with GDPR. In such an
environment, artificial intelligence technologies ought to be able to assist by
providing best advice; asking all and only the relevant questions; monitoring
activities; and carrying out assessments. The paper considers four areas of
GDPR compliance where rule based technologies and/or machine learning
techniques may be relevant: * Following compliance checklists and codes of
conduct; * Supporting risk assessments; * Complying with the new regulations
regarding technologies that perform automatic profiling; * Complying with the
new regulations concerning recognising and reporting breaches of security. It
concludes that AI technology can support each of these four areas. The
requirements that GDPR (or organisations that need to comply with GDPR) state
for explanation and justification of reasoning imply that rule-based approaches
are likely to be more helpful than machine learning approaches. However, there
may be good business reasons to take a different approach in some
circumstances.