pwc-archive/papers-with-abstracts huggingface dataset:
POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors
Authors: Marcel Sheeny, Andrew Wallace, Mehryar Emambakhsh, Sen Wang, Barry Connor
Date: 2018-04-07 00:00:00
Tasks: vehicle detection

For vehicle autonomy, driver assistance and situational awareness, it is
necessary to operate at day and night, and in all weather conditions. In
particular, long wave infrared (LWIR) sensors that receive predominantly
emitted radiation have the capability to operate at night as well as during the
day. In this work, we employ a polarised LWIR (POL-LWIR) camera to acquire data
from a mobile vehicle, to compare and contrast four different convolutional
neural network (CNN) configurations to detect other vehicles in video
sequences. We evaluate two distinct and promising approaches, two-stage
detection (Faster-RCNN) and one-stage detection (SSD), in four different
configurations. We also employ two different image decompositions: the first
based on the polarisation ellipse and the second on the Stokes parameters
themselves. To evaluate our approach, the experimental trials were quantified
by mean average precision (mAP) and processing time, showing a clear trade-off
between the two factors. For example, the best mAP result of 80.94% was
achieved using Faster-RCNN, but at a frame rate of 6.4 fps. In contrast,
MobileNet SSD achieved only 64.51% mAP, but at 53.4 fps.
Wasserstein Introspective Neural Networks
Authors: Kwonjoon Lee, Weijian Xu, Fan Fan, Zhuowen Tu
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification

We present Wasserstein introspective neural networks (WINN) that are both a
generator and a discriminator within a single model. WINN provides a
significant improvement over the recent introspective neural networks (INN)
method by enhancing INN's generative modeling capability. WINN has three
interesting properties: (1) A mathematical connection between the formulation
of the INN algorithm and that of Wasserstein generative adversarial networks
(WGAN) is made. (2) The explicit adoption of the Wasserstein distance into INN
results in a large enhancement to INN, achieving compelling results even with a
single classifier --- e.g., providing nearly a 20 times reduction in model size
over INN for unsupervised generative modeling. (3) When applied to supervised
classification, WINN also gives rise to improved robustness against adversarial
examples in terms of the error reduction. In the experiments, we report
encouraging results on unsupervised learning problems including texture, face,
and object modeling, as well as a supervised classification task against
adversarial attacks.
Application of Superpixels to Segment Several Landmarks in Running Rodents
Authors: Omid Haji Maghsoudi, Annie Vahedipour, Benjamin Robertson, Andrew Spence
Date: 2018-04-07 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

Examining locomotion has improved our basic understanding of motor control
and aided in treating motor impairment. Mice and rats are the model system of
choice for basic neuroscience studies of human disease. High frame rates are
needed to quantify the kinematics of running rodents, due to their high stride
frequency. Manual tracking, especially for multiple body landmarks, becomes
extremely time-consuming. To overcome these limitations, we proposed the use of
superpixels based image segmentation as superpixels utilized both spatial and
color information for segmentation. We segmented some parts of body and tested
the success of segmentation as a function of color space and SLIC segment size.
We used a simple merging function to connect the segmented regions considered
as neighbor and having the same intensity value range. In addition, 28 features
were extracted, and t-SNE was used to demonstrate how much the methods are
capable to differentiate the regions. Finally, we compared the segmented
regions to a manually outlined region. The results showed for segmentation,
using the RGB image was slightly better compared to the hue channel. For merg-
ing and classification, however, the hue representation was better as it
captures the relevant color information in a single channel.
Hindsight is Only 50/50: Unsuitability of MDP based Approximate POMDP Solvers for Multi-resolution Information Gathering
Authors: Sankalp Arora, Sanjiban Choudhury, Sebastian Scherer
Date: 2018-04-07 00:00:00
Tasks: Decision Making, Imitation Learning, Sequential Decision Making

Partially Observable Markov Decision Processes (POMDPs) offer an elegant
framework to model sequential decision making in uncertain environments.
Solving POMDPs online is an active area of research and given the size of
real-world problems approximate solvers are used. Recently, a few approaches
have been suggested for solving POMDPs by using MDP solvers in conjunction with
imitation learning. MDP based POMDP solvers work well for some cases, while
catastrophically failing for others. The main failure point of such solvers is
the lack of motivation for MDP solvers to gain information, since under their
assumption the environment is either already known as much as it can be or the
uncertainty will disappear after the next step. However for solving POMDP
problems gaining information can lead to efficient solutions. In this paper we
derive a set of conditions where MDP based POMDP solvers are provably
sub-optimal. We then use the well-known tiger problem to demonstrate such
sub-optimality. We show that multi-resolution, budgeted information gathering
cannot be addressed using MDP based POMDP solvers. The contribution of the
paper helps identify the properties of a POMDP problem for which the use of MDP
based POMDP solvers is inappropriate, enabling better design choices.
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
Authors: Yuhua Chen, Wen Li, Luc van Gool
Date: 2017-11-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation, Synthetic-to-Real Translation

Exploiting synthetic data to learn deep models has attracted increasing
attention in recent years. However, the intrinsic domain difference between
synthetic and real images usually causes a significant performance drop when
applying the learned model to real world scenarios. This is mainly due to two
reasons: 1) the model overfits to synthetic images, making the convolutional
filters incompetent to extract informative representation for real images; 2)
there is a distribution difference between synthetic and real data, which is
also known as the domain adaptation problem. To this end, we propose a new
reality oriented adaptation approach for urban scene semantic segmentation by
learning from synthetic data. First, we propose a target guided distillation
approach to learn the real image style, which is achieved by training the
segmentation model to imitate a pretrained real style model using real images.
Second, we further take advantage of the intrinsic spatial structure presented
in urban scene images, and propose a spatial-aware adaptation scheme to
effectively align the distribution of two domains. These two modules can be
readily integrated with existing state-of-the-art semantic segmentation
networks to improve their generalizability when adapting from synthetic to real
urban scenes. We evaluate the proposed method on Cityscapes dataset by adapting
from GTAV and SYNTHIA datasets, where the results demonstrate the effectiveness
of our method.
A Comprehensive Study on the Applications of Machine Learning for the Medical Diagnosis and Prognosis of Asthma
Authors: Saksham Kukreja
Date: 2018-04-07 00:00:00
Tasks: Medical Diagnosis, Prognosis

An estimated 300 million people worldwide suffer from asthma, and this number
is expected to increase to 400 million by 2025. Approximately 250,000 people
die prematurely each year from asthma out of which, almost all deaths are
avoidable. Most of these deaths occur because the patients are unaware of their
asthmatic morbidity. If detected early, asthmatic mortality rate can be reduced
by 78%, provided that the patients carry appropriate medication for the same
and/or are in lose vicinity to medical equipment like nebulizers. This study
focuses on the development and valuation of algorithms to diagnose asthma
through symptom intensive questionary, clinical data and medical reports.
Machine Learning Algorithms like Back-propagation model, Context Sensitive
Auto-Associative Memory Neural Network Model, C4.5 Algorithm, Bayesian Network
and Particle Swarm Optimization have been employed for the diagnosis of asthma
and later a comparison is made between their respective prospects. All
algorithms received an accuracy of over 80%. However, the use of Auto
Associative Memory Model (on a layered Artificial Neural Network) displayed
much better results. It reached to an accuracy of over 90% and an inconclusive
diagnosis rate of less than 1% when trained with adequate data. In the end,
na\"ive mobile based applications were developed on Android and iOS that made
use of the self-training auto associative memory model to achieve an accuracy
of nearly 94.2%.
Guiding Neural Machine Translation with Retrieved Translation Pieces
Authors: Jingyi Zhang, Masao Utiyama, Eiichro Sumita, Graham Neubig, Satoshi Nakamura
Date: 2018-04-07 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, NMT, Retrieval, Sentence, Translation

One of the difficulties of neural machine translation (NMT) is the recall and
appropriate translation of low-frequency words or phrases. In this paper, we
propose a simple, fast, and effective method for recalling previously seen
translation examples and incorporating them into the NMT decoding process.
Specifically, for an input sentence, we use a search engine to retrieve
sentence pairs whose source sides are similar with the input sentence, and then
collect $n$-grams that are both in the retrieved target sentences and aligned
with words that match in the source sentences, which we call "translation
pieces". We compute pseudo-probabilities for each retrieved sentence based on
similarities between the input sentence and the retrieved source sentences, and
use these to weight the retrieved translation pieces. Finally, an existing NMT
model is used to translate the input sentence, with an additional bonus given
to outputs that contain the collected translation pieces. We show our method
improves NMT translation results up to 6 BLEU points on three narrow domain
translation tasks where repetitiveness of the target sentences is particularly
salient. It also causes little increase in the translation time, and compares
favorably to another alternative retrieval-based method with respect to
accuracy, speed, and simplicity of implementation.
What are the Receptive, Effective Receptive, and Projective Fields of Neurons in Convolutional Neural Networks?
Authors: Hung Le, Ali Borji
Date: 2017-05-19 00:00:00

In this work, we explain in detail how receptive fields, effective receptive
fields, and projective fields of neurons in different layers, convolution or
pooling, of a Convolutional Neural Network (CNN) are calculated. While our
focus here is on CNNs, the same operations, but in the reverse order, can be
used to calculate these quantities for deconvolutional neural networks. These
are important concepts, not only for better understanding and analyzing
convolutional and deconvolutional networks, but also for optimizing their
performance in real-world applications.
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Authors: Hirokatsu Kataoka, Teppei Suzuki, Shoko Oikawa, Yasuhiro Matsui, Yutaka Satoh
Date: 2018-04-07 00:00:00
Tasks: Self-Driving Cars

Because of their recent introduction, self-driving cars and advanced driver
assistance system (ADAS) equipped vehicles have had little opportunity to
learn, the dangerous traffic (including near-miss incident) scenarios that
provide normal drivers with strong motivation to drive safely. Accordingly, as
a means of providing learning depth, this paper presents a novel traffic
database that contains information on a large number of traffic near-miss
incidents that were obtained by mounting driving recorders in more than 100
taxis over the course of a decade. The study makes the following two main
contributions: (i) In order to assist automated systems in detecting near-miss
incidents based on database instances, we created a large-scale traffic
near-miss incident database (NIDB) that consists of video clip of dangerous
events captured by monocular driving recorders. (ii) To illustrate the
applicability of NIDB traffic near-miss incidents, we provide two primary
database-related improvements: parameter fine-tuning using various near-miss
scenes from NIDB, and foreground/background separation into motion
representation. Then, using our new database in conjunction with a monocular
driving recorder, we developed a near-miss recognition method that provides
automated systems with a performance level that is comparable to a human-level
understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition,
61.3% vs. 78.7% at near-miss detection).
Efficient No-Reference Quality Assessment and Classification Model for Contrast Distorted Images
Authors: Hossein Ziaei Nafchi, Mohamed Cheriet
Date: 2018-04-07 00:00:00
Tasks: General Classification

In this paper, an efficient Minkowski Distance based Metric (MDM) for
no-reference (NR) quality assessment of contrast distorted images is proposed.
It is shown that higher orders of Minkowski distance and entropy provide
accurate quality prediction for the contrast distorted images. The proposed
metric performs predictions by extracting only three features from the
distorted images followed by a regression analysis. Furthermore, the proposed
features are able to classify type of the contrast distorted images with a high
accuracy. Experimental results on four datasets CSIQ, TID2013, CCID2014, and
SIQAD show that the proposed metric with a very low complexity provides better
quality predictions than the state-of-the-art NR metrics. The MATLAB source
code of the proposed metric is available to public at
http://www.synchromedia.ca/system/files/MDM.zip.
A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis
Authors: Xin Wang, Jaime Lorenzo-Trueba, Shinji Takaki, Lauri Juvela, Junichi Yamagishi
Date: 2018-04-07 00:00:00
Tasks: Speech Synthesis

Recent advances in speech synthesis suggest that limitations such as the
lossy nature of the amplitude spectrum with minimum phase approximation and the
over-smoothing effect in acoustic modeling can be overcome by using advanced
machine learning approaches. In this paper, we build a framework in which we
can fairly compare new vocoding and acoustic modeling techniques with
conventional approaches by means of a large scale crowdsourced evaluation.
Results on acoustic models showed that generative adversarial networks and an
autoregressive (AR) model performed better than a normal recurrent network and
the AR model performed best. Evaluation on vocoders by using the same AR
acoustic model demonstrated that a Wavenet vocoder outperformed classical
source-filter-based vocoders. Particularly, generated speech waveforms from the
combination of AR acoustic model and Wavenet vocoder achieved a similar score
of speech quality to vocoded speech.
Not quite unreasonable effectiveness of machine learning algorithms
Authors: Egor Illarionov, Roman Khudorozhkov
Date: 2018-04-07 00:00:00
Tasks: BIG-bench Machine Learning

State-of-the-art machine learning algorithms demonstrate close to absolute
performance in selected challenges. We provide arguments that the reason can be
in low variability of the samples and high effectiveness in learning typical
patterns. Due to this fact, standard performance metrics do not reveal model
capacity and new metrics are required for the better understanding of
state-of-the-art.
Statistical transformer networks: learning shape and appearance models via self supervision
Authors: Anil Bas, William A. P. Smith
Date: 2018-04-07 00:00:00

We generalise Spatial Transformer Networks (STN) by replacing the parametric
transformation of a fixed, regular sampling grid with a deformable, statistical
shape model which is itself learnt. We call this a Statistical Transformer
Network (StaTN). By training a network containing a StaTN end-to-end for a
particular task, the network learns the optimal nonrigid alignment of the input
data for the task. Moreover, the statistical shape model is learnt with no
direct supervision (such as landmarks) and can be reused for other tasks.
Besides training for a specific task, we also show that a StaTN can learn a
shape model using generic loss functions. This includes a loss inspired by the
minimum description length principle in which an appearance model is also
learnt from scratch. In this configuration, our model learns an active
appearance model and a means to fit the model from scratch with no supervision
at all, even identity labels.
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Authors: Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood
Date: 2018-04-07 00:00:00
Tasks: Probabilistic Programming

Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference
algorithm used in most popular "first-order differentiable" Probabilistic
Programming Languages (PPLs). However, the fact that HMC uses derivative
information causes complications when the target distribution is
non-differentiable with respect to one or more of the latent variables. In this
paper, we show how to use extensions to HMC to perform inference in
probabilistic programs that contain discontinuities. To do this, we design a
Simple first-order Probabilistic Programming Language (SPPL) that contains a
sufficient set of language restrictions together with a compilation scheme.
This enables us to preserve both the statistical and syntactic interpretation
of if-else statements in the probabilistic program, within the scope of
first-order PPLs. We also provide a corresponding mathematical formalism that
ensures any joint density denoted in such a language has a suitably low measure
of discontinuities.
Visual Analytics for Explainable Deep Learning
Authors: Jaegul Choo, Shixia Liu
Date: 2018-04-07 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Deep Learning

Recently, deep learning has been advancing the state of the art in artificial
intelligence to a new level, and humans rely on artificial intelligence
techniques more than ever. However, even with such unprecedented advancements,
the lack of explanation regarding the decisions made by deep learning models
and absence of control over their internal processes act as major drawbacks in
critical decision-making processes, such as precision medicine and law
enforcement. In response, efforts are being made to make deep learning
interpretable and controllable by humans. In this paper, we review visual
analytics, information visualization, and machine learning perspectives
relevant to this aim, and discuss potential challenges and future research
directions.
Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping
Authors: Dario Pavllo, Tiziano Piccardi, Robert West
Date: 2018-04-07 00:00:00
Tasks: Articles

We propose Quootstrap, a method for extracting quotations, as well as the
names of the speakers who uttered them, from large news corpora. Whereas prior
work has addressed this problem primarily with supervised machine learning, our
approach follows a fully unsupervised bootstrapping paradigm. It leverages the
redundancy present in large news corpora, more precisely, the fact that the
same quotation often appears across multiple news articles in slightly
different contexts. Starting from a few seed patterns, such as ["Q", said S.],
our method extracts a set of quotation-speaker pairs (Q, S), which are in turn
used for discovering new patterns expressing the same quotations; the process
is then repeated with the larger pattern set. Our algorithm is highly scalable,
which we demonstrate by running it on the large ICWSM 2011 Spinn3r corpus.
Validating our results against a crowdsourced ground truth, we obtain 90%
precision at 40% recall using a single seed pattern, with significantly higher
recall values for more frequently reported (and thus likely more interesting)
quotations. Finally, we showcase the usefulness of our algorithm's output for
computational social science by analyzing the sentiment expressed in our
extracted quotations.
Learning a Text-Video Embedding from Incomplete and Heterogeneous Data
Authors: Antoine Miech, Ivan Laptev, Josef Sivic
Date: 2018-04-07 00:00:00
Tasks: Retrieval, Text Retrieval, Video Retrieval, Video to Text Retrieval

Joint understanding of video and language is an active research area with many applications. Prior work in this domain typically relies on learning text-video embeddings. One difficulty with this approach, however, is the lack of large-scale annotated video-caption datasets for training. To address this issue, we aim at learning text-video embeddings from heterogeneous data sources. To this end, we propose a Mixture-of-Embedding-Experts (MEE) model with ability to handle missing input modalities during training. As a result, our framework can learn improved text-video embeddings simultaneously from image and video datasets. We also show the generalization of MEE to other input modalities such as face descriptors. We evaluate our method on the task of video retrieval and report results for the MPII Movie Description and MSR-VTT datasets. The proposed MEE model demonstrates significant improvements and outperforms previously reported methods on both text-to-video and video-to-text retrieval tasks. Code is available at: https://github.com/antoine77340/Mixture-of-Embedding-Experts
Efficient Reciprocal Collision Avoidance between Heterogeneous Agents Using CTMAT
Authors: Yuexin Ma, Dinesh Manocha, Wenping Wang
Date: 2018-04-07 00:00:00
Tasks: Collision Avoidance

We present a novel algorithm for reciprocal collision avoidance between
heterogeneous agents of different shapes and sizes. We present a novel CTMAT
representation based on medial axis transform to compute a tight fitting
bounding shape for each agent. Each CTMAT is represented using tuples, which
are composed of circular arcs and line segments. Based on the reciprocal
velocity obstacle formulation, we reduce the problem to solving a
low-dimensional linear programming between each pair of tuples belonging to
adjacent agents. We precompute the Minkowski Sums of tuples to accelerate the
runtime performance. Finally, we provide an efficient method to update the
orientation of each agent in a local manner. We have implemented the algorithm
and highlight its performance on benchmarks corresponding to road traffic
scenarios and different vehicles. The overall runtime performance is comparable
to prior multi-agent collision avoidance algorithms that use circular or
elliptical agents. Our approach is less conservative and results in fewer false
collisions.
Enabling Reasoning with LegalRuleML
Authors: Ho-Pun Lam, Mustafa Hashmi
Date: 2017-11-11 00:00:00

In order to automate verification process, regulatory rules written in
natural language need to be translated into a format that machines can
understand. However, none of the existing formalisms can fully represent the
elements that appear in legal norms. For instance, most of these formalisms do
not provide features to capture the behavior of deontic effects, which is an
important aspect in automated compliance checking. This paper presents an
approach for transforming legal norms represented using LegalRuleML to a
variant of Modal Defeasible Logic (and vice versa) such that a legal statement
represented using LegalRuleML can be transformed into a machine-readable format
that can be understood and reasoned about depending upon the client's
preferences.