pwc-archive/papers-with-abstracts huggingface dataset:
Vision Based Railway Track Monitoring using Deep Learning
Authors: Shruti Mittal, Dattaraj Rao
Date: 2017-11-17 00:00:00
Tasks: Deep Learning, Transfer Learning

Computer vision based methods have been explored in the past for detection of
railway track defects, but full automation has always been a challenge because
both traditional image processing methods and deep learning classifiers trained
from scratch fail to generalize that well to infinite novel scenarios seen in
the real world, given limited amount of labeled data. Advancements have been
made recently to make machine learning models utilize knowledge from a
different but related domain. In this paper, we show that even though similar
domain data is not available, transfer learning provides the model
understanding of other real world objects and enables training production scale
deep learning classifiers for uncontrolled real world data. Our models
efficiently detect both track defects like sunkinks, loose ballast and railway
assets like switches and signals. Models were validated with hours of track
videos recorded in different continents resulting in different weather
conditions, different ambience and surroundings. A track health index concept
has also been proposed to monitor complete rail network.
Adaptive Feature Abstraction for Translating Video to Text
Authors: Yunchen Pu, Martin Renqiang Min, Zhe Gan, Lawrence Carin
Date: 2016-11-23 00:00:00
Tasks: Video Captioning

Previous models for video captioning often use the output from a specific
layer of a Convolutional Neural Network (CNN) as video features. However, the
variable context-dependent semantics in the video may make it more appropriate
to adaptively select features from the multiple CNN layers. We propose a new
approach for generating adaptive spatiotemporal representations of videos for
the captioning task. A novel attention mechanism is developed, that adaptively
and sequentially focuses on different layers of CNN features (levels of feature
"abstraction"), as well as local spatiotemporal regions of the feature maps at
each layer. The proposed approach is evaluated on three benchmark datasets:
YouTube2Text, M-VAD and MSR-VTT. Along with visualizing the results and how the
model works, these experiments quantitatively demonstrate the effectiveness of
the proposed adaptive spatiotemporal feature abstraction for translating videos
to sentences with rich semantics.
Predicting Driver Attention in Critical Situations
Authors: Ye Xia, Danqing Zhang, Jinkyu Kim, Ken Nakayama, Karl Zipser, David Whitney
Date: 2017-11-17 00:00:00
Tasks: Autonomous Driving, Driver Attention Monitoring

Robust driver attention prediction for critical situations is a challenging
computer vision problem, yet essential for autonomous driving. Because critical
driving moments are so rare, collecting enough data for these situations is
difficult with the conventional in-car data collection protocol---tracking eye
movements during driving. Here, we first propose a new in-lab driver attention
collection protocol and introduce a new driver attention dataset, Berkeley
DeepDrive Attention (BDD-A) dataset, which is built upon braking event videos
selected from a large-scale, crowd-sourced driving video dataset. We further
propose Human Weighted Sampling (HWS) method, which uses human gaze behavior to
identify crucial frames of a driving dataset and weights them heavily during
model training. With our dataset and HWS, we built a driver attention
prediction model that outperforms the state-of-the-art and demonstrates
sophisticated behaviors, like attending to crossing pedestrians but not giving
false alarms to pedestrians safely walking on the sidewalk. Its prediction
results are nearly indistinguishable from ground-truth to humans. Although only
being trained with our in-lab attention data, the model also predicts in-car
driver attention data of routine driving with state-of-the-art accuracy. This
result not only demonstrates the performance of our model but also proves the
validity and usefulness of our dataset and data collection protocol.
Ubenwa: Cry-based Diagnosis of Birth Asphyxia
Authors: Charles C. Onu, Innocent Udeogu, Eyenimi Ndiomu, Urbain Kengni, Doina Precup, Guilherme M. Sant'Anna, Edward Alikor, Peace Opara
Date: 2017-11-17 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

Every year, 3 million newborns die within the first month of life. Birth
asphyxia and other breathing-related conditions are a leading cause of
mortality during the neonatal phase. Current diagnostic methods are too
sophisticated in terms of equipment, required expertise, and general logistics.
Consequently, early detection of asphyxia in newborns is very difficult in many
parts of the world, especially in resource-poor settings. We are developing a
machine learning system, dubbed Ubenwa, which enables diagnosis of asphyxia
through automated analysis of the infant cry. Deployed via smartphone and
wearable technology, Ubenwa will drastically reduce the time, cost and skill
required to make accurate and potentially life-saving diagnoses.
Improving Palliative Care with Deep Learning
Authors: Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, Nigam H. Shah
Date: 2017-11-17 00:00:00
Tasks: Deep Learning

Improving the quality of end-of-life care for hospitalized patients is a
priority for healthcare organizations. Studies have shown that physicians tend
to over-estimate prognoses, which in combination with treatment inertia results
in a mismatch between patients wishes and actual care at the end of life. We
describe a method to address this problem using Deep Learning and Electronic
Health Record (EHR) data, which is currently being piloted, with Institutional
Review Board approval, at an academic medical center. The EHR data of admitted
patients are automatically evaluated by an algorithm, which brings patients who
are likely to benefit from palliative care services to the attention of the
Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR
data from previous years, to predict all-cause 3-12 month mortality of patients
as a proxy for patients that could benefit from palliative care. Our
predictions enable the Palliative Care team to take a proactive approach in
reaching out to such patients, rather than relying on referrals from treating
physicians, or conduct time consuming chart reviews of all patients. We also
present a novel interpretation technique which we use to provide explanations
of the model's predictions.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Authors: Yin Zhou, Oncel Tuzel
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Object Detection, Birds Eye View Object Detection, Descriptive, Feature Engineering, Object Detection, Object Localization, Region Proposal

Accurate detection of objects in 3D point clouds is a central problem in many
applications, such as autonomous navigation, housekeeping robots, and
augmented/virtual reality. To interface a highly sparse LiDAR point cloud with
a region proposal network (RPN), most existing efforts have focused on
hand-crafted feature representations, for example, a bird's eye view
projection. In this work, we remove the need of manual feature engineering for
3D point clouds and propose VoxelNet, a generic 3D detection network that
unifies feature extraction and bounding box prediction into a single stage,
end-to-end trainable deep network. Specifically, VoxelNet divides a point cloud
into equally spaced 3D voxels and transforms a group of points within each
voxel into a unified feature representation through the newly introduced voxel
feature encoding (VFE) layer. In this way, the point cloud is encoded as a
descriptive volumetric representation, which is then connected to a RPN to
generate detections. Experiments on the KITTI car detection benchmark show that
VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a
large margin. Furthermore, our network learns an effective discriminative
representation of objects with various geometries, leading to encouraging
results in 3D detection of pedestrians and cyclists, based on only LiDAR.
Generating Music Medleys via Playing Music Puzzle Games
Authors: Yu-Siang Huang, Szu-Yu Chou, Yi-Hsuan Yang
Date: 2017-09-13 00:00:00
Tasks: Self-Supervised Learning

Generating music medleys is about finding an optimal permutation of a given
set of music clips. Toward this goal, we propose a self-supervised learning
task, called the music puzzle game, to train neural network models to learn the
sequential patterns in music. In essence, such a game requires machines to
correctly sort a few multisecond music fragments. In the training stage, we
learn the model by sampling multiple non-overlapping fragment pairs from the
same songs and seeking to predict whether a given pair is consecutive and is in
the correct chronological order. For testing, we design a number of puzzle
games with different difficulty levels, the most difficult one being music
medley, which requiring sorting fragments from different songs. On the basis of
state-of-the-art Siamese convolutional network, we propose an improved
architecture that learns to embed frame-level similarity scores computed from
the input fragment pairs to a common space, where fragment pairs in the correct
order can be more easily identified. Our result shows that the resulting model,
dubbed as the similarity embedding network (SEN), performs better than
competing models across different games, including music jigsaw puzzle, music
sequencing, and music medley. Example results can be found at our project
website, https://remyhuang.github.io/DJnet.
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective
Authors: Tianci Liu, Zelin Shi, Yun-Peng Liu
Date: 2017-11-17 00:00:00
Tasks: Dimensionality Reduction, Riemannian optimization

This paper proposes a generalized framework with joint normalization which
learns lower-dimensional subspaces with maximum discriminative power by making
use of the Riemannian geometry. In particular, we model the
similarity/dissimilarity between subspaces using various metrics defined on
Grassmannian and formulate dimen-sionality reduction as a non-linear constraint
optimization problem considering the orthogonalization. To obtain the linear
mapping, we derive the components required to per-form Riemannian optimization
(e.g., Riemannian conju-gate gradient) from the original Grassmannian through
an orthonormal projection. We respect the Riemannian ge-ometry of the Grassmann
manifold and search for this projection directly from one Grassmann manifold to
an-other face-to-face without any additional transformations. In this natural
geometry-aware way, any metric on the Grassmann manifold can be resided in our
model theoreti-cally. We have combined five metrics with our model and the
learning process can be treated as an unconstrained optimization problem on a
Grassmann manifold. Exper-iments on several datasets demonstrate that our
approach leads to a significant accuracy gain over state-of-the-art methods.
Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks
Authors: Weiyue Wang, Qiangui Huang, Suya You, Chao Yang, Ulrich Neumann
Date: 2017-11-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Decoder, Generative Adversarial Network, GPU

Recent advances in convolutional neural networks have shown promising results
in 3D shape completion. But due to GPU memory limitations, these methods can
only produce low-resolution outputs. To inpaint 3D models with semantic
plausibility and contextual details, we introduce a hybrid framework that
combines a 3D Encoder-Decoder Generative Adversarial Network (3D-ED-GAN) and a
Long-term Recurrent Convolutional Network (LRCN). The 3D-ED-GAN is a 3D
convolutional neural network trained with a generative adversarial paradigm to
fill missing 3D data in low-resolution. LRCN adopts a recurrent neural network
architecture to minimize GPU memory usage and incorporates an Encoder-Decoder
pair into a Long Short-term Memory Network. By handling the 3D model as a
sequence of 2D slices, LRCN transforms a coarse 3D shape into a more complete
and higher resolution volume. While 3D-ED-GAN captures global contextual
structure of the 3D shape, LRCN localizes the fine-grained details.
Experimental results on both real-world and synthetic data show reconstructions
from corrupted models result in complete and high-resolution 3D objects.
Priming Neural Networks
Authors: Amir Rosenfeld, Mahdi Biparva, John K. Tsotsos
Date: 2017-11-16 00:00:00
Tasks: Object, object-detection, Object Detection, Segmentation

Visual priming is known to affect the human visual system to allow detection
of scene elements, even those that may have been near unnoticeable before, such
as the presence of camouflaged animals. This process has been shown to be an
effect of top-down signaling in the visual system triggered by the said cue. In
this paper, we propose a mechanism to mimic the process of priming in the
context of object detection and segmentation. We view priming as having a
modulatory, cue dependent effect on layers of features within a network. Our
results show how such a process can be complementary to, and at times more
effective than simple post-processing applied to the output of the network,
notably so in cases where the object is hard to detect such as in severe noise.
Moreover, we find the effects of priming are sometimes stronger when early
visual layers are affected. Overall, our experiments confirm that top-down
signals can go a long way in improving object detection and segmentation.
Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries
Authors: Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, Anton Van Den Hengel
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, Object Discovery, Referring Expression, Referring Expression Comprehension

Recognising objects according to a pre-defined fixed set of class labels has
been well studied in the Computer Vision. There are a great many practical
applications where the subjects that may be of interest are not known
beforehand, or so easily delineated, however. In many of these cases natural
language dialog is a natural way to specify the subject of interest, and the
task achieving this capability (a.k.a, Referring Expression Comprehension) has
recently attracted attention. To this end we propose a unified framework, the
ParalleL AttentioN (PLAN) network, to discover the object in an image that is
being referred to in variable length natural expression descriptions, from
short phrases query to long multi-round dialogs. The PLAN network has two
attention mechanisms that relate parts of the expressions to both the global
visual content and also directly to object candidates. Furthermore, the
attention mechanisms are recurrent, making the referring process visualizable
and explainable. The attended information from these dual sources are combined
to reason about the referred object. These two attention mechanisms can be
trained in parallel and we find the combined system outperforms the
state-of-art on several benchmarked datasets with different length language
input, such as RefCOCO, RefCOCO+ and GuessWhat?!.
HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images
Authors: Abhishake Kumar Bojja, Franziska Mueller, Sri Raghu Malireddi, Markus Oberweger, Vincent Lepetit, Christian Theobalt, Kwang Moo Yi, Andrea Tagliasacchi
Date: 2017-11-16 00:00:00
Tasks: Data Augmentation, Hand Segmentation, Segmentation

We propose an automatic method for generating high-quality annotations for
depth-based hand segmentation, and introduce a large-scale hand segmentation
dataset. Existing datasets are typically limited to a single hand. By
exploiting the visual cues given by an RGBD sensor and a pair of colored
gloves, we automatically generate dense annotations for two hand segmentation.
This lowers the cost/complexity of creating high quality datasets, and makes it
easy to expand the dataset in the future. We further show that existing
datasets, even with data augmentation, are not sufficient to train a hand
segmentation algorithm that can distinguish two hands. Source and datasets will
be made publicly available.
Towards Effective Low-bitwidth Convolutional Neural Networks
Authors: Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, Ian Reid
Date: 2017-11-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Quantization

This paper tackles the problem of training a deep convolutional neural
network with both low-precision weights and low-bitwidth activations.
Optimizing a low-precision network is very challenging since the training
process can easily get trapped in a poor local minima, which results in
substantial accuracy loss. To mitigate this problem, we propose three
simple-yet-effective approaches to improve the network training. First, we
propose to use a two-stage optimization strategy to progressively find good
local minima. Specifically, we propose to first optimize a net with quantized
weights and then quantized activations. This is in contrast to the traditional
methods which optimize them simultaneously. Second, following a similar spirit
of the first method, we propose another progressive optimization approach which
progressively decreases the bit-width from high-precision to low-precision
during the course of training. Third, we adopt a novel learning scheme to
jointly train a full-precision model alongside the low-precision one. By doing
so, the full-precision model provides hints to guide the low-precision model
training. Extensive experiments on various datasets ( i.e., CIFAR-100 and
ImageNet) show the effectiveness of the proposed methods. To highlight, using
our methods to train a 4-bit precision network leads to no performance decrease
in comparison with its full-precision counterpart with standard network
architectures ( i.e., AlexNet and ResNet-50).
Exploring the Use of Shatter for AllSAT Through Ramsey-Type Problems
Authors: David E. Narváez
Date: 2017-11-17 00:00:00
Tasks: Vocal Bursts Type Prediction

In the context of SAT solvers, Shatter is a popular tool for symmetry
breaking on CNF formulas. Nevertheless, little has been said about its use in
the context of AllSAT problems: problems where we are interested in listing all
the models of a Boolean formula. AllSAT has gained much popularity in recent
years due to its many applications in domains like model checking, data mining,
etc. One example of a particularly transparent application of AllSAT to other
fields of computer science is computational Ramsey theory. In this paper we
study the effect of incorporating Shatter to the workflow of using Boolean
formulas to generate all possible edge colorings of a graph avoiding prescribed
monochromatic subgraphs. Generating complete sets of colorings is an important
building block in computational Ramsey theory. We identify two drawbacks in the
na\"ive use of Shatter to break the symmetries of Boolean formulas encoding
Ramsey-type problems for graphs: a "blow-up" in the number of models and the
generation of incomplete sets of colorings. The issues presented in this work
are not intended to discourage the use of Shatter as a preprocessing tool for
AllSAT problems in combinatorial computing but to help researchers properly use
this tool by avoiding these potential pitfalls. To this end, we provide
strategies and additional tools to cope with the negative effects of using
Shatter for AllSAT. While the specific application addressed in this paper is
that of Ramsey-type problems, the analysis we carry out applies to many other
areas in which highly-symmetrical Boolean formulas arise and we wish to find
all of their models.
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
Authors: Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor
Date: 2015-05-17 00:00:00
Tasks: Knowledge Graphs, Probabilistic Programming

A fundamental challenge in developing high-impact machine learning
technologies is balancing the need to model rich, structured domains with the
ability to scale to big data. Many important problem areas are both richly
structured and large scale, from social and biological networks, to knowledge
graphs and the Web, to images, video, and natural language. In this paper, we
introduce two new formalisms for modeling structured data, and show that they
can both capture rich structure and scale to big data. The first, hinge-loss
Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model
that generalizes different approaches to convex inference. We unite three
approaches from the randomized algorithms, probabilistic graphical models, and
fuzzy logic communities, showing that all three lead to the same inference
objective. We then define HL-MRFs by generalizing this unified objective. The
second new formalism, probabilistic soft logic (PSL), is a probabilistic
programming language that makes HL-MRFs easy to define using a syntax based on
first-order logic. We introduce an algorithm for inferring most-probable
variable assignments (MAP inference) that is much more scalable than
general-purpose convex optimization methods, because it uses message passing to
take advantage of sparse dependency structures. We then show how to learn the
parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous
discrete models, but much more scalable. Together, these algorithms enable
HL-MRFs and PSL to model rich, structured data at scales not previously
possible.
Adaptive active queue management controller for TCP communication networks using PSO-RBF models
Authors: Mansour Sheikhan, Reza Shahnazi, Ehasn Hemmati
Date: 2017-11-16 00:00:00
Tasks: Management

Addressing performance degradations in end-to-end congestion control has been
one of the most active research areas in the last decade. Active queue
management (AQM) aims to improve the overall network throughput, while
providing lower delay and reduce packet loss and improving network. The basic
idea is to actively trigger packet dropping (or marking provided by explicit
congestion notification (ECN)) before buffer overflow. Radial bias function
(RBF)-based AQM controller is proposed in this paper. RBF controller is
suitable as an AQM scheme to control congestion in TCP communication networks
since it is nonlinear. Particle swarm optimization (PSO) algorithm is also
employed to derive RBF parameters such that the integrated-absolute error (IAE)
is minimized. Furthermore, in order to improve the robustness of RBF
controller, an error-integral term is added to RBF equation. The results of the
comparison with Drop Tail, adaptive random early detection (ARED), random
exponential marking (REM), and proportional-integral (PI) controllers are
presented. Integral-RBF has better performance not only in comparison with RBF
but also with ARED, REM and PI controllers in the case of link utilization
while packet loss rate is small.
Grounded Objects and Interactions for Video Captioning
Authors: Chih-Yao Ma, Asim Kadav, Iain Melvin, Zsolt Kira, Ghassan AlRegib, Hans Peter Graf
Date: 2017-11-16 00:00:00
Tasks: Object, Scene Understanding, Text Generation, Video Captioning, Video Understanding

We address the problem of video captioning by grounding language generation
on object interactions in the video. Existing work mostly focuses on overall
scene understanding with often limited or no emphasis on object interactions to
address the problem of video understanding. In this paper, we propose
SINet-Caption that learns to generate captions grounded over higher-order
interactions between arbitrary groups of objects for fine-grained video
understanding. We discuss the challenges and benefits of such an approach. We
further demonstrate state-of-the-art results on the ActivityNet Captions
dataset using our model, SINet-Caption based on this approach.