pwc-archive/papers-with-abstracts huggingface dataset:
The RGNLP Machine Translation Systems for WAT 2018
Authors: Atul Kr. Ojha, Koel Dutta Chowdhury, Chao-Hong Liu, Karan Saxena
Date: 2018-12-03 00:00:00
Proceeding: PACLIC 2018 12
Tasks: Machine Translation, Translation

This paper presents the system description of Machine Translation (MT)
system(s) for Indic Languages Multilingual Task for the 2018 edition of the WAT
Shared Task. In our experiments, we (the RGNLP team) explore both statistical
and neural methods across all language pairs. (We further present an extensive
comparison of language-related problems for both the approaches in the context
of low-resourced settings.) Our PBSMT models were highest score on all
automatic evaluation metrics in the English into Telugu, Hindi, Bengali, Tamil
portion of the shared task.
Disentangling Adversarial Robustness and Generalization
Authors: David Stutz, Matthias Hein, Bernt Schiele
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Adversarial Robustness

Obtaining deep networks that are robust against adversarial examples and
generalize well is an open problem. A recent hypothesis even states that both
robust and accurate models are impossible, i.e., adversarial robustness and
generalization are conflicting goals. In an effort to clarify the relationship
between robustness and generalization, we assume an underlying, low-dimensional
data manifold and show that: 1. regular adversarial examples leave the
manifold; 2. adversarial examples constrained to the manifold, i.e.,
on-manifold adversarial examples, exist; 3. on-manifold adversarial examples
are generalization errors, and on-manifold adversarial training boosts
generalization; 4. regular robustness and generalization are not necessarily
contradicting goals. These assumptions imply that both robust and accurate
models are possible. However, different models (architectures, training
strategies etc.) can exhibit different robustness and generalization
characteristics. To confirm our claims, we present extensive experiments on
synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and
CelebA.
Nose, eyes and ears: Head pose estimation by locating facial keypoints
Authors: Aryaman Gupta, Kalpit Thakkar, Vineet Gandhi, P. J. Narayanan
Date: 2018-12-03 00:00:00
Tasks: Head Pose Estimation, Pose Estimation

Monocular head pose estimation requires learning a model that computes the
intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human
face. Annotating ground truth head pose angles for images in the wild is
difficult and requires ad-hoc fitting procedures (which provides only coarse
and approximate annotations). This highlights the need for approaches which can
train on data captured in controlled environment and generalize on the images
in the wild (with varying appearance and illumination of the face). Most
present day deep learning approaches which learn a regression function directly
on the input images fail to do so. To this end, we propose to use a higher
level representation to regress the head pose while using deep learning
architectures. More specifically, we use the uncertainty maps in the form of 2D
soft localization heatmap images over five facial keypoints, namely left ear,
right ear, left eye, right eye and nose, and pass them through an convolutional
neural network to regress the head-pose. We show head pose estimation results
on two challenging benchmarks BIWI and AFLW and our approach surpasses the
state of the art on both the datasets.
Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions
Authors: Masanori Suganuma, Xing Liu, Takayuki Okatani
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Restoration

Many studies have been conducted so far on image restoration, the problem of
restoring a clean image from its distorted version. There are many different
types of distortion which affect image quality. Previous studies have focused
on single types of distortion, proposing methods for removing them. However,
image quality degrades due to multiple factors in the real world. Thus,
depending on applications, e.g., vision for autonomous cars or surveillance
cameras, we need to be able to deal with multiple combined distortions with
unknown mixture ratios. For this purpose, we propose a simple yet effective
layer architecture of neural networks. It performs multiple operations in
parallel, which are weighted by an attention mechanism to enable selection of
proper operations depending on the input. The layer can be stacked to form a
deep network, which is differentiable and thus can be trained in an end-to-end
fashion by gradient descent. The experimental results show that the proposed
method works better than previous methods by a good margin on tasks of
restoring images with multiple combined distortions.
CRAVES: Controlling Robotic Arm with a Vision-based Economic System
Authors: Yiming Zuo, Weichao Qiu, Lingxi Xie, Fangwei Zhong, Yizhou Wang, Alan L. Yuille
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Pose Estimation, Domain Adaptation, Pose Estimation, Reinforcement Learning

Training a robotic arm to accomplish real-world tasks has been attracting increasing attention in both academia and industry. This work discusses the role of computer vision algorithms in this field. We focus on low-cost arms on which no sensors are equipped and thus all decisions are made upon visual recognition, e.g., real-time 3D pose estimation. This requires annotating a lot of training data, which is not only time-consuming but also laborious. In this paper, we present an alternative solution, which uses a 3D model to create a large number of synthetic data, trains a vision model in this virtual domain, and applies it to real-world images after domain adaptation. To this end, we design a semi-supervised approach, which fully leverages the geometric constraints among keypoints. We apply an iterative algorithm for optimization. Without any annotations on real images, our algorithm generalizes well and produces satisfying results on 3D pose estimation, which is evaluated on two real-world datasets. We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world. Moreover, our approach, with merely a 3D model being required, has the potential to generalize to other types of multi-rigid-body dynamic systems.
EnsNet: Ensconce Text in the Wild
Authors: Shuaitao Zhang, Yuliang Liu, Lianwen Jin, Yaoxiong Huang, Songxuan Lai
Date: 2018-12-03 00:00:00
Tasks: CPU, Generative Adversarial Network, Image Text Removal

A new method is proposed for removing text from natural images. The challenge
is to first accurately localize text on the stroke-level and then replace it
with a visually plausible background. Unlike previous methods that require
image patches to erase scene text, our method, namely ensconce network
(EnsNet), can operate end-to-end on a single image without any prior knowledge.
The overall structure is an end-to-end trainable FCN-ResNet-18 network with a
conditional generative adversarial network (cGAN). The feature of the former is
first enhanced by a novel lateral connection structure and then refined by four
carefully designed losses: multiscale regression loss and content loss, which
capture the global discrepancy of different level features; texture loss and
total variation loss, which primarily target filling the text region and
preserving the reality of the background. The latter is a novel local-sensitive
GAN, which attentively assesses the local consistency of the text erased
regions. Both qualitative and quantitative sensitivity experiments on synthetic
images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet
is essential to achieve a good performance. Moreover, our EnsNet can
significantly outperform previous state-of-the-art methods in terms of all
metrics. In addition, a qualitative experiment conducted on the SMBNet dataset
further demonstrates that the proposed method can also preform well on general
object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can
preform at 333 fps on an i5-8600 CPU device.
SUSiNet: See, Understand and Summarize it
Authors: Petros Koutras, Petros Maragos
Date: 2018-12-03 00:00:00
Tasks: Action Recognition, Saliency Prediction, Temporal Action Localization, Video Summarization

In this work we propose a multi-task spatio-temporal network, called SUSiNet,
that can jointly tackle the spatio-temporal problems of saliency estimation,
action recognition and video summarization. Our approach employs a single
network that is jointly end-to-end trained for all tasks with multiple and
diverse datasets related to the exploring tasks. The proposed network uses a
unified architecture that includes global and task specific layer and produces
multiple output types, i.e., saliency maps or classification labels, by
employing the same video input. Moreover, one additional contribution is that
the proposed network can be deeply supervised through an attention module that
is related to human attention as it is expressed by eye-tracking data. From the
extensive evaluation, on seven different datasets, we have observed that the
multi-task network performs as well as the state-of-the-art single-task methods
(or in some cases better), while it requires less computational budget than
having one independent network per each task.
Enhancing Perceptual Attributes with Bayesian Style Generation
Authors: Aliaksandr Siarohin, Gloria Zen, Nicu Sebe, Elisa Ricci
Date: 2018-12-03 00:00:00
Tasks: Attribute, Deep Learning, Style Transfer

Deep learning has brought an unprecedented progress in computer vision and
significant advances have been made in predicting subjective properties
inherent to visual data (e.g., memorability, aesthetic quality, evoked
emotions, etc.). Recently, some research works have even proposed deep learning
approaches to modify images such as to appropriately alter these properties.
Following this research line, this paper introduces a novel deep learning
framework for synthesizing images in order to enhance a predefined perceptual
attribute. Our approach takes as input a natural image and exploits recent
models for deep style transfer and generative adversarial networks to change
its style in order to modify a specific high-level attribute. Differently from
previous works focusing on enhancing a specific property of a visual content,
we propose a general framework and demonstrate its effectiveness in two use
cases, i.e. increasing image memorability and generating scary pictures. We
evaluate the proposed approach on publicly available benchmarks, demonstrating
its advantages over state of the art methods.
Care2Vec: A Deep learning approach for the classification of self-care problems in physically disabled children
Authors: Sayan Putatunda
Date: 2018-12-03 00:00:00
Tasks: Classification, General Classification

Accurate classification of self-care problems in children who suffer from physical and motor affliction is an important problem in the healthcare industry. This is a difficult and a time consumming process and it needs the expertise of occupational therapists. In recent years, healthcare professionals have opened up to the idea of using expert systems and artificial intelligence in the diagnosis and classification of self care problems. In this study, we propose a new deep learning based approach named Care2Vec for solving these kind of problems and use a real world self care activities dataset that is based on a conceptual framework designed by the World Health Organization (WHO). Care2Vec is a mix of unsupervised and supervised learning where we use Autoencoders and Deep neural networks as a two step modeling process. We found that Care2Vec has a better prediction accuracy than some of the traditional methods reported in the literature for solving the self care classification problem viz. Decision trees and Artificial neural networks.
Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks
Authors: Yizhak Ben-Shabat, Michael Lindenbaum, Anath Fischer
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Mixture-of-Experts, Surface Normals Estimation

In this paper, we propose a normal estimation method for unstructured 3D
point clouds. This method, called Nesti-Net, builds on a new local point cloud
representation which consists of multi-scale point statistics (MuPS), estimated
on a local coarse Gaussian grid. This representation is a suitable input to a
CNN architecture. The normals are estimated using a mixture-of-experts (MoE)
architecture, which relies on a data-driven approach for selecting the optimal
scale around each point and encourages sub-network specialization. Interesting
insights into the network's resource distribution are provided. The scale
prediction significantly improves robustness to different noise levels, point
density variations and different levels of detail. We achieve state-of-the-art
results on a benchmark synthetic dataset and present qualitative results on
real scanned scenes.
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability
Authors: Uma M. Girkar, Ryo Uchimido, Li-wei H. Lehman, Peter Szolovits, Leo Celi, Wei-Hung Weng
Date: 2018-12-03 00:00:00
Tasks: regression, Time Series, Time Series Analysis

Determining whether hypotensive patients in intensive care units (ICUs)
should receive fluid bolus therapy (FBT) has been an extremely challenging task
for intensive care physicians as the corresponding increase in blood pressure
has been hard to predict. Our study utilized regression models and
attention-based recurrent neural network (RNN) algorithms and a multi-clinical
information system large-scale database to build models that can predict the
successful response to FBT among hypotensive patients in ICUs. We investigated
both time-aggregated modeling using logistic regression algorithms with
regularization and time-series modeling using the long short term memory
network (LSTM) and the gated recurrent units network (GRU) with the attention
mechanism for clinical interpretability. Among all modeling strategies, the
stacked LSTM with the attention mechanism yielded the most predictable model
with the highest accuracy of 0.852 and area under the curve (AUC) value of
0.925. The study results may help identify hypotensive patients in ICUs who
will have sufficient blood pressure recovery after FBT.
An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT
Authors: Stefan Reinhold. Timo Damm, Lukas Huber, Reimer Andresen, Reinhard Barkmann, Claus-C. Glüer, Reinhard Koch
Date: 2018-12-03 00:00:00

Quantitative computed tomography (QCT) is a widely used tool for osteoporosis
diagnosis and monitoring. The assessment of cortical markers like cortical bone
mineral density (BMD) and thickness is a demanding task, mainly because of the
limited spatial resolution of QCT. We propose a direct model based method to
automatically identify the surface through the center of the cortex of human
vertebra. We develop a statistical bone model and analyze its probability
distribution after the imaging process. Using an as-rigid-as-possible
deformation we find the cortical surface that maximizes the likelihood of our
model given the input volume. Using the European Spine Phantom (ESP) and a high
resolution \mu CT scan of a cadaveric vertebra, we show that the proposed
method is able to accurately identify the real center of cortex ex-vivo. To
demonstrate the in-vivo applicability of our method we use manually obtained
surfaces for comparison.
Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery
Authors: Yu-Bang Zheng, Ting-Zhu Huang, Xi-Le Zhao, Tai-Xiang Jiang, Teng-Yu Ji, Tian-Hui Ma
Date: 2018-12-03 00:00:00

As low-rank modeling has achieved great success in tensor recovery, many
research efforts devote to defining the tensor rank. Among them, the recent
popular tensor tubal rank, defined based on the tensor singular value
decomposition (t-SVD), obtains promising results. However, the framework of the
t-SVD and the tensor tubal rank are applicable only to three-way tensors and
lack of flexibility to handle different correlations along different modes. To
tackle these two issues, we define a new tensor unfolding operator, named
mode-$k_1k_2$ tensor unfolding, as the process of lexicographically stacking
the mode-$k_1k_2$ slices of an $N$-way tensor into a three-way tensor, which is
a three-way extension of the well-known mode-$k$ tensor matricization. Based on
it, we define a novel tensor rank, the tensor $N$-tubal rank, as a vector whose
elements contain the tubal rank of all mode-$k_1k_2$ unfolding tensors, to
depict the correlations along different modes. To efficiently minimize the
proposed $N$-tubal rank, we establish its convex relaxation: the weighted sum
of tensor nuclear norm (WSTNN). Then, we apply WSTNN to low-rank tensor
completion (LRTC) and tensor robust principal component analysis (TRPCA). The
corresponding WSTNN-based LRTC and TRPCA models are proposed, and two efficient
alternating direction method of multipliers (ADMM)-based algorithms are
developed to solve the proposed models. Numerical experiments demonstrate that
the proposed models significantly outperform the compared ones.
Building Sequential Inference Models for End-to-End Response Selection
Authors: Jia-Chen Gu, Zhen-Hua Ling, Yu-Ping Ruan, Quan Liu
Date: 2018-12-03 00:00:00
Tasks: Conversational Response Selection, Descriptive, Word Embeddings

This paper presents an end-to-end response selection model for Track 1 of the
7th Dialogue System Technology Challenges (DSTC7). This task focuses on
selecting the correct next utterance from a set of candidates given a partial
conversation. We propose an end-to-end neural network based on enhanced
sequential inference model (ESIM) for this task. Our proposed model differs
from the original ESIM model in the following four aspects. First, a new word
representation method which combines the general pre-trained word embeddings
with those estimated on the task-specific training set is adopted in order to
address the challenge of out-of-vocabulary (OOV) words. Second, an attentive
hierarchical recurrent encoder (AHRE) is designed which is capable to encode
sentences hierarchically and generate more descriptive representations by
aggregation. Third, a new pooling method which combines multi-dimensional
pooling and last-state pooling is used instead of the simple combination of max
pooling and average pooling in the original ESIM. Last, a modification layer is
added before the softmax layer to emphasize the importance of the last
utterance in the context for response selection. In the released evaluation
results of DSTC7, our proposed method ranked second on the Ubuntu dataset and
third on the Advising dataset in subtask 1 of Track 1.
Data Driven Chiller Plant Energy Optimization with Domain Knowledge
Authors: Hoang Dung Vu, Kok Soon Chai, Bryan Keating, Nurislam Tursynbek, Boyan Xu, Kaige Yang, Xiaoyan Yang, Zhenjie Zhang
Date: 2018-12-03 00:00:00
Tasks: BIG-bench Machine Learning

Refrigeration and chiller optimization is an important and well studied topic
in mechanical engineering, mostly taking advantage of physical models, designed
on top of over-simplified assumptions, over the equipments. Conventional
optimization techniques using physical models make decisions of online
parameter tuning, based on very limited information of hardware specifications
and external conditions, e.g., outdoor weather. In recent years, new generation
of sensors is becoming essential part of new chiller plants, for the first time
allowing the system administrators to continuously monitor the running status
of all equipments in a timely and accurate way. The explosive growth of data
flowing to databases, driven by the increasing analytical power by machine
learning and data mining, unveils new possibilities of data-driven approaches
for real-time chiller plant optimization. This paper presents our research and
industrial experience on the adoption of data models and optimizations on
chiller plant and discusses the lessons learnt from our practice on real world
plants. Instead of employing complex machine learning models, we emphasize the
incorporation of appropriate domain knowledge into data analysis tools, which
turns out to be the key performance improver over state-of-the-art deep
learning techniques by a significant margin. Our empirical evaluation on a real
world chiller plant achieves savings by more than 7% on daily power
consumption.
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals
Authors: Hamed Hassanzadeh, Mahnoosh Kholghi, Anthony Nguyen, Kevin Chu
Date: 2018-12-03 00:00:00
Tasks: Classification, Document Classification, Feature Engineering, General Classification, Transfer Learning

Reviewing radiology reports in emergency departments is an essential but
laborious task. Timely follow-up of patients with abnormal cases in their
radiology reports may dramatically affect the patient's outcome, especially if
they have been discharged with a different initial diagnosis. Machine learning
approaches have been devised to expedite the process and detect the cases that
demand instant follow up. However, these approaches require a large amount of
labeled data to train reliable predictive models. Preparing such a large
dataset, which needs to be manually annotated by health professionals, is
costly and time-consuming. This paper investigates a semi-supervised learning
framework for radiology report classification across three hospitals. The main
goal is to leverage clinical unlabeled data in order to augment the learning
process where limited labeled data is available. To further improve the
classification performance, we also integrate a transfer learning technique
into the semi-supervised learning pipeline . Our experimental findings show
that (1) convolutional neural networks (CNNs), while being independent of any
problem-specific feature engineering, achieve significantly higher
effectiveness compared to conventional supervised learning approaches, (2)
leveraging unlabeled data in training a CNN-based classifier reduces the
dependency on labeled data by more than 50% to reach the same performance of a
fully supervised CNN, and (3) transferring the knowledge gained from available
labeled data in an external source hospital significantly improves the
performance of a semi-supervised CNN model over their fully supervised
counterparts in a target hospital.