pwc-archive/papers-with-abstracts huggingface dataset:
Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering
Authors: Samuel Scheidegger, Joachim Benjaminsson, Emil Rosenberg, Amrit Krishnan, Karl Granstrom
Date: 2018-02-27 00:00:00
Tasks: 3D Multi-Object Tracking, Autonomous Vehicles, Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking

Monocular cameras are one of the most commonly used sensors in the automotive
industry for autonomous vehicles. One major drawback using a monocular camera
is that it only makes observations in the two dimensional image plane and can
not directly measure the distance to objects. In this paper, we aim at filling
this gap by developing a multi-object tracking algorithm that takes an image as
input and produces trajectories of detected objects in a world coordinate
system. We solve this by using a deep neural network trained to detect and
estimate the distance to objects from a single input image. The detections from
a sequence of images are fed in to a state-of-the art Poisson multi-Bernoulli
mixture tracking filter. The combination of the learned detector and the PMBM
filter results in an algorithm that achieves 3D tracking using only mono-camera
images as input. The performance of the algorithm is evaluated both in 3D world
coordinates, and 2D image coordinates, using the publicly available KITTI
object tracking dataset. The algorithm shows the ability to accurately track
objects, correctly handle data associations, even when there is a big overlap
of the objects in the image, and is one of the top performing algorithms on the
KITTI object tracking benchmark. Furthermore, the algorithm is efficient,
running on average close to 20 frames per second.
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Authors: Dayan Guan, Yanpeng Cao, Jun Liang, Yanlong Cao, Michael Ying Yang
Date: 2018-02-27 00:00:00
Tasks: Autonomous Driving, Multispectral Object Detection, Multi-Task Learning, Pedestrian Detection, Semantic Segmentation

Multispectral pedestrian detection has received extensive attention in recent
years as a promising solution to facilitate robust human target detection for
around-the-clock applications (e.g. security surveillance and autonomous
driving). In this paper, we demonstrate illumination information encoded in
multispectral images can be utilized to significantly boost performance of
pedestrian detection. A novel illumination-aware weighting mechanism is present
to accurately depict illumination condition of a scene. Such illumination
information is incorporated into two-stream deep convolutional neural networks
to learn multispectral human-related features under different illumination
conditions (daytime and nighttime). Moreover, we utilized illumination
information together with multispectral data to generate more accurate semantic
segmentation which are used to boost pedestrian detection accuracy. Putting all
of the pieces together, we present a powerful framework for multispectral
pedestrian detection based on multi-task learning of illumination-aware
pedestrian detection and semantic segmentation. Our proposed method is trained
end-to-end using a well-designed multi-task loss function and outperforms
state-of-the-art approaches on KAIST multispectral pedestrian dataset.
Real-World Repetition Estimation by Div, Grad and Curl
Authors: Tom F. H. Runia, Cees G. M. Snoek, Arnold W. M. Smeulders
Date: 2018-02-27 00:00:00
Proceeding: CVPR 2018 6

We consider the problem of estimating repetition in video, such as performing
push-ups, cutting a melon or playing violin. Existing work shows good results
under the assumption of static and stationary periodicity. As realistic video
is rarely perfectly static and stationary, the often preferred Fourier-based
measurements is inapt. Instead, we adopt the wavelet transform to better handle
non-static and non-stationary video dynamics. From the flow field and its
differentials, we derive three fundamental motion types and three motion
continuities of intrinsic periodicity in 3D. On top of this, the 2D perception
of 3D periodicity considers two extreme viewpoints. What follows are 18
fundamental cases of recurrent perception in 2D. In practice, to deal with the
variety of repetitive appearance, our theory implies measuring time-varying
flow and its differentials (gradient, divergence and curl) over segmented
foreground motion. For experiments, we introduce the new QUVA Repetition
dataset, reflecting reality by including non-static and non-stationary videos.
On the task of counting repetitions in video, we obtain favorable results
compared to a deep learning alternative.
A Hybrid Word-Character Approach to Abstractive Summarization
Authors: Chieh-Teng Chang, Chi-Chia Huang, Chih-Yuan Yang, Jane Yung-jen Hsu
Date: 2018-02-27 00:00:00
Tasks: Abstractive Text Summarization, Articles, Text Summarization

Automatic abstractive text summarization is an important and challenging
research topic of natural language processing. Among many widely used
languages, the Chinese language has a special property that a Chinese character
contains rich information comparable to a word. Existing Chinese text
summarization methods, either adopt totally character-based or word-based
representations, fail to fully exploit the information carried by both
representations. To accurately capture the essence of articles, we propose a
hybrid word-character approach (HWC) which preserves the advantages of both
word-based and character-based representations. We evaluate the advantage of
the proposed HWC approach by applying it to two existing methods, and discover
that it generates state-of-the-art performance with a margin of 24 ROUGE points
on a widely used dataset LCSTS. In addition, we find an issue contained in the
LCSTS dataset and offer a script to remove overlapping pairs (a summary and a
short text) to create a clean dataset for the community. The proposed HWC
approach also generates the best performance on the new, clean LCSTS dataset.
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions
Authors: Jing Peng, Anna Feldman, Ekaterina Vylomova
Date: 2018-02-27 00:00:00
Proceeding: EMNLP 2014 10
Tasks: Clustering, Outlier Detection, Topic Models

We describe an algorithm for automatic classification of idiomatic and
literal expressions. Our starting point is that words in a given text segment,
such as a paragraph, that are highranking representatives of a common topic of
discussion are less likely to be a part of an idiomatic expression. Our
additional hypothesis is that contexts in which idioms occur, typically, are
more affective and therefore, we incorporate a simple analysis of the intensity
of the emotions expressed by the contexts. We investigate the bag of words
topic representation of one to three paragraphs containing an expression that
should be classified as idiomatic or literal (a target phrase). We extract
topics from paragraphs containing idioms and from paragraphs containing
literals using an unsupervised clustering method, Latent Dirichlet Allocation
(LDA) (Blei et al., 2003). Since idiomatic expressions exhibit the property of
non-compositionality, we assume that they usually present different semantics
than the words used in the local topic. We treat idioms as semantic outliers,
and the identification of a semantic shift as outlier detection. Thus, this
topic representation allows us to differentiate idioms from literals using
local semantic contexts. Our results are encouraging.
Semi-Supervised Learning with Competitive Infection Models
Authors: Nir Rosenfeld, Amir Globerson
Date: 2017-03-19 00:00:00

The goal in semi-supervised learning is to effectively combine labeled and
unlabeled data. One way to do this is by encouraging smoothness across edges in
a graph whose nodes correspond to input examples. In many graph-based methods,
labels can be thought of as propagating over the graph, where the underlying
propagation mechanism is based on random walks or on averaging dynamics. While
theoretically elegant, these dynamics suffer from several drawbacks which can
hurt predictive performance.
  Our goal in this work is to explore alternative mechanisms for propagating
labels. In particular, we propose a method based on dynamic infection
processes, where unlabeled nodes can be "infected" with the label of their
already infected neighbors. Our algorithm is efficient and scalable, and an
analysis of the underlying optimization objective reveals a surprising relation
to other Laplacian approaches. We conclude with a thorough set of experiments
across multiple benchmarks and various learning settings.
Convolutional Neural Networks for Toxic Comment Classification
Authors: Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Vassilis P. Plagianakos
Date: 2018-02-27 00:00:00
Tasks: Classification, Cloud Computing, General Classification, Management, text-classification, Text Classification, Toxic Comment Classification

Flood of information is produced in a daily basis through the global Internet
usage arising from the on-line interactive communications among users. While
this situation contributes significantly to the quality of human life,
unfortunately it involves enormous dangers, since on-line texts with high
toxicity can cause personal attacks, on-line harassment and bullying behaviors.
This has triggered both industrial and research community in the last few years
while there are several tries to identify an efficient model for on-line toxic
comment prediction. However, these steps are still in their infancy and new
approaches and frameworks are required. On parallel, the data explosion that
appears constantly, makes the construction of new machine learning
computational tools for managing this information, an imperative need.
Thankfully advances in hardware, cloud computing and big data management allow
the development of Deep Learning approaches appearing very promising
performance so far. For text classification in particular the use of
Convolutional Neural Networks (CNN) have recently been proposed approaching
text analytics in a modern manner emphasizing in the structure of words in a
document. In this work, we employ this approach to discover toxic comments in a
large pool of documents provided by a current Kaggle's competition regarding
Wikipedia's talk page edits. To justify this decision we choose to compare CNNs
against the traditional bag-of-words approach for text analysis combined with a
selection of algorithms proven to be very effective in text classification. The
reported results provide enough evidence that CNN enhance toxic comment
classification reinforcing research interest towards this direction.
Discriminative Learning of Prediction Intervals
Authors: Nir Rosenfeld, Yishay Mansour, Elad Yom-Tov
Date: 2017-10-16 00:00:00
Tasks: Prediction, Prediction Intervals

In this work we consider the task of constructing prediction intervals in an
inductive batch setting. We present a discriminative learning framework which
optimizes the expected error rate under a budget constraint on the interval
sizes. Most current methods for constructing prediction intervals offer
guarantees for a single new test point. Applying these methods to multiple test
points can result in a high computational overhead and degraded statistical
guarantees. By focusing on expected errors, our method allows for variability
in the per-example conditional error rates. As we demonstrate both analytically
and empirically, this flexibility can increase the overall accuracy, or
alternatively, reduce the average interval size.
  While the problem we consider is of a regressive flavor, the loss we use is
combinatorial. This allows us to provide PAC-style, finite-sample guarantees.
Computationally, we show that our original objective is NP-hard, and suggest a
tractable convex surrogate. We conclude with a series of experimental
evaluations.
Discovering Bayesian Market Views for Intelligent Asset Allocation
Authors: Frank Z. Xing, Erik Cambria, Lorenzo Malandri, Carlo Vercellis
Date: 2018-02-27 00:00:00
Tasks: Opinion Mining, Stock Market Prediction

Along with the advance of opinion mining techniques, public mood has been
found to be a key element for stock market prediction. However, how market
participants' behavior is affected by public mood has been rarely discussed.
Consequently, there has been little progress in leveraging public mood for the
asset allocation problem, which is preferred in a trusted and interpretable
way. In order to address the issue of incorporating public mood analyzed from
social media, we propose to formalize public mood into market views, because
market views can be integrated into the modern portfolio theory. In our
framework, the optimal market views will maximize returns in each period with a
Bayesian asset allocation model. We train two neural models to generate the
market views, and benchmark the model performance on other popular asset
allocation strategies. Our experimental results suggest that the formalization
of market views significantly increases the profitability (5% to 10% annually)
of the simulated portfolio at a given risk level.
A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit
Authors: Seong Jin Cho, Sunghun Kang, Chang D. Yoo
Date: 2017-11-17 00:00:00
Proceeding: ICLR 2019 5

Determining the appropriate batch size for mini-batch gradient descent is
always time consuming as it often relies on grid search. This paper considers a
resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed
bandit for achieving best performance in grid search by selecting an
appropriate batch size at each epoch with a probability defined as a function
of its previous success/failure. This probability encourages exploration of
different batch size and then later exploitation of batch size with history of
success. At each epoch, the RMGD samples a batch size from its probability
distribution, then uses the selected batch size for mini-batch gradient
descent. After obtaining the validation loss at each epoch, the probability
distribution is updated to incorporate the effectiveness of the sampled batch
size. The RMGD essentially assists the learning process to explore the possible
domain of the batch size and exploit successful batch size. Experimental
results show that the RMGD achieves performance better than the best performing
single batch size. Furthermore, it, obviously, attains this performance in a
shorter amount of time than grid search. It is surprising that the RMGD
achieves better performance than grid search.
Live Blog Corpus for Summarization
Authors: Avinesh P. V. S., Maxime Peyrard, Christian M. Meyer
Date: 2018-02-27 00:00:00
Proceeding: LREC 2018 5

Live blogs are an increasingly popular news format to cover breaking news and
live events in online journalism. Online news websites around the world are
using this medium to give their readers a minute by minute update on an event.
Good summaries enhance the value of the live blogs for a reader but are often
not available. In this paper, we study a way of collecting corpora for
automatic live blog summarization. In an empirical evaluation using well-known
state-of-the-art summarization systems, we show that live blogs corpus poses
new challenges in the field of summarization. We make our tools publicly
available to reconstruct the corpus to encourage the research community and
replicate our results.
Multilingual Adaptation of RNN Based ASR Systems
Authors: Markus Müller, Sebastian Stüker, Alex Waibel
Date: 2017-11-13 00:00:00

In this work, we focus on multilingual systems based on recurrent neural
networks (RNNs), trained using the Connectionist Temporal Classification (CTC)
loss function. Using a multilingual set of acoustic units poses difficulties.
To address this issue, we proposed Language Feature Vectors (LFVs) to train
language adaptive multilingual systems. Language adaptation, in contrast to
speaker adaptation, needs to be applied not only on the feature level, but also
to deeper layers of the network. In this work, we therefore extended our
previous approach by introducing a novel technique which we call "modulation".
Based on this method, we modulated the hidden layers of RNNs using LFVs. We
evaluated this approach in both full and low resource conditions, as well as
for grapheme and phone based systems. Lower error rates throughout the
different conditions could be achieved by the use of the modulation.
Efficient kernel-based variable selection with sparsistency
Authors: Xin He, Junhui Wang, Shaogao Lv
Date: 2018-02-26 00:00:00
Tasks: Variable Selection

Variable selection is central to high-dimensional data analysis, and various algorithms have been developed. Ideally, a variable selection algorithm shall be flexible, scalable, and with theoretical guarantee, yet most existing algorithms cannot attain these properties at the same time. In this article, a three-step variable selection algorithm is developed, involving kernel-based estimation of the regression function and its gradient functions as well as a hard thresholding. Its key advantage is that it assumes no explicit model assumption, admits general predictor effects, allows for scalable computation, and attains desirable asymptotic sparsistency. The proposed algorithm can be adapted to any reproducing kernel Hilbert space (RKHS) with different kernel functions, and can be extended to interaction selection with slight modification. Its computational cost is only linear in the data dimension, and can be further improved through parallel computing. The sparsistency of the proposed algorithm is established for general RKHS under mild conditions, including linear and Gaussian kernels as special cases. Its effectiveness is also supported by a variety of simulated and real examples.
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
Authors: Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, Benno Stein
Date: 2017-08-04 00:00:00
Proceeding: NAACL 2018 6
Tasks: 2k, Common Sense Reasoning

Reasoning is a crucial part of natural language argumentation. To comprehend
an argument, one must analyze its warrant, which explains why its claim follows
from its premises. As arguments are highly contextualized, warrants are usually
presupposed and left implicit. Thus, the comprehension does not only require
language understanding and logic skills, but also depends on common sense. In
this paper we develop a methodology for reconstructing warrants systematically.
We operationalize it in a scalable crowdsourcing process, resulting in a freely
licensed dataset with warrants for 2k authentic arguments from news comments.
On this basis, we present a new challenging task, the argument reasoning
comprehension task. Given an argument with a claim and a premise, the goal is
to choose the correct implicit warrant from two options. Both warrants are
plausible and lexically close, but lead to contradicting claims. A solution to
this task will define a substantial step towards automatic warrant
reconstruction. However, experiments with several neural attention and language
models reveal that current approaches do not suffice.
Deep factorization for speech signal
Authors: Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, Thomas Fang Zheng
Date: 2018-02-27 00:00:00
Tasks: Emotion Recognition, Speaker Recognition

Various informative factors mixed in speech signals, leading to great
difficulty when decoding any of the factors. An intuitive idea is to factorize
each speech frame into individual informative factors, though it turns out to
be highly difficult. Recently, we found that speaker traits, which were assumed
to be long-term distributional properties, are actually short-time patterns,
and can be learned by a carefully designed deep neural network (DNN). This
discovery motivated a cascade deep factorization (CDF) framework that will be
presented in this paper. The proposed framework infers speech factors in a
sequential way, where factors previously inferred are used as conditional
variables when inferring other factors. We will show that this approach can
effectively factorize speech signals, and using these factors, the original
speech spectrum can be recovered with a high accuracy. This factorization and
reconstruction approach provides potential values for many speech processing
tasks, e.g., speaker recognition and emotion recognition, as will be
demonstrated in the paper.
Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis
Authors: Mohammad Malekzadeh, Richard G. Clegg, Hamed Haddadi
Date: 2017-10-18 00:00:00
Tasks: Activity Recognition, Privacy Preserving, Time Series, Time Series Analysis

An increasing number of sensors on mobile, Internet of things (IoT), and
wearable devices generate time-series measurements of physical activities.
Though access to the sensory data is critical to the success of many beneficial
applications such as health monitoring or activity recognition, a wide range of
potentially sensitive information about the individuals can also be discovered
through access to sensory data and this cannot easily be protected using
traditional privacy approaches.
  In this paper, we propose a privacy-preserving sensing framework for managing
access to time-series data in order to provide utility while protecting
individuals' privacy. We introduce Replacement AutoEncoder, a novel algorithm
which learns how to transform discriminative features of data that correspond
to sensitive inferences, into some features that have been more observed in
non-sensitive inferences, to protect users' privacy. This efficiency is
achieved by defining a user-customized objective function for deep
autoencoders. Our replacement method will not only eliminate the possibility of
recognizing sensitive inferences, it also eliminates the possibility of
detecting the occurrence of them. That is the main weakness of other approaches
such as filtering or randomization. We evaluate the efficacy of the algorithm
with an activity recognition task in a multi-sensing environment using
extensive experiments on three benchmark datasets. We show that it can retain
the recognition accuracy of state-of-the-art techniques while simultaneously
preserving the privacy of sensitive information. Finally, we utilize the GANs
for detecting the occurrence of replacement, after releasing data, and show
that this can be done only if the adversarial network is trained on the users'
original data.
End-to-end Flow Correlation Tracking with Spatial-temporal Attention
Authors: Zheng Zhu, Wei Wu, Wei Zou, Junjie Yan
Date: 2017-11-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Optical Flow Estimation

Discriminative correlation filters (DCF) with deep convolutional features
have achieved favorable performance in recent tracking benchmarks. However,
most of existing DCF trackers only consider appearance features of current
frame, and hardly benefit from motion and inter-frame information. The lack of
temporal information degrades the tracking performance during challenges such
as partial occlusion and deformation. In this work, we focus on making use of
the rich flow information in consecutive frames to improve the feature
representation and the tracking accuracy. Firstly, individual components,
including optical flow estimation, feature extraction, aggregation and
correlation filter tracking are formulated as special layers in network. To the
best of our knowledge, this is the first work to jointly train flow and
tracking task in a deep learning framework. Then the historical feature maps at
predefined intervals are warped and aggregated with current ones by the guiding
of flow. For adaptive aggregation, we propose a novel spatial-temporal
attention mechanism. Extensive experiments are performed on four challenging
tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016, and the proposed
method achieves superior results on these benchmarks.