pwc-archive/papers-with-abstracts huggingface dataset:
How Robust is 3D Human Pose Estimation to Occlusion?
Authors: István Sárándi, Timm Linder, Kai O. Arras, Bastian Leibe
Date: 2018-08-28 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Data Augmentation, Pose Estimation

Occlusion is commonplace in realistic human-robot shared environments, yet
its effects are not considered in standard 3D human pose estimation benchmarks.
This leaves the question open: how robust are state-of-the-art 3D pose
estimation methods against partial occlusions? We study several types of
synthetic occlusions over the Human3.6M dataset and find a method with
state-of-the-art benchmark performance to be sensitive even to low amounts of
occlusion. Addressing this issue is key to progress in applications such as
collaborative and service robotics. We take a first step in this direction by
improving occlusion-robustness through training data augmentation with
synthetic occlusions. This also turns out to be an effective regularizer that
is beneficial even for non-occluded test cases.
Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models
Authors: Mohammad Taher Pilehvar, Dimitri Kartsaklis, Victor Prokhorov, Nigel Collier
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Word Embeddings, Word Similarity

Rare word representation has recently enjoyed a surge of interest, owing to
the crucial role that effective handling of infrequent words can play in
accurate semantic understanding. However, there is a paucity of reliable
benchmarks for evaluation and comparison of these techniques. We show in this
paper that the only existing benchmark (the Stanford Rare Word dataset) suffers
from low-confidence annotations and limited vocabulary; hence, it does not
constitute a solid comparison framework. In order to fill this evaluation gap,
we propose CAmbridge Rare word Dataset (Card-660), an expert-annotated word
similarity dataset which provides a highly reliable, yet challenging, benchmark
for rare word representation techniques. Through a set of experiments we show
that even the best mainstream word embeddings, with millions of words in their
vocabularies, are unable to achieve performances higher than 0.43 (Pearson
correlation) on the dataset, compared to a human-level upperbound of 0.90. We
release the dataset and the annotation materials at
https://pilehvar.github.io/card-660/.
Motorcycle Classification in Urban Scenarios using Convolutional Neural Networks for Feature Extraction
Authors: Jorge E. Espinosa, Sergio A. Velastin, John W. Branch
Date: 2018-08-28 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

This paper presents a motorcycle classification system for urban scenarios
using Convolutional Neural Network (CNN). Significant results on image
classification has been achieved using CNNs at the expense of a high
computational cost for training with thousands or even millions of examples.
Nevertheless, features can be extracted from CNNs already trained. In this work
AlexNet, included in the framework CaffeNet, is used to extract features from
frames taken on a real urban scenario. The extracted features from the CNN are
used to train a support vector machine (SVM) classifier to discriminate
motorcycles from other road users. The obtained results show a mean accuracy of
99.40% and 99.29% on a classification task of three and five classes
respectively. Further experiments are performed on a validation set of images
showing a satisfactory classification.
Distance Based Source Domain Selection for Sentiment Classification
Authors: Lex Razoux Schultz, Marco Loog, Peyman Mohajerin Esfahani
Date: 2018-08-28 00:00:00
Tasks: Classification, domain classification, General Classification, Sentiment Analysis, Sentiment Classification

Automated sentiment classification (SC) on short text fragments has received
increasing attention in recent years. Performing SC on unseen domains with few
or no labeled samples can significantly affect the classification performance
due to different expression of sentiment in source and target domain. In this
study, we aim to mitigate this undesired impact by proposing a methodology
based on a predictive measure, which allows us to select an optimal source
domain from a set of candidates. The proposed measure is a linear combination
of well-known distance functions between probability distributions supported on
the source and target domains (e.g. Earth Mover's distance and Kullback-Leibler
divergence). The performance of the proposed methodology is validated through
an SC case study in which our numerical experiments suggest a significant
improvement in the cross domain classification error in comparison with a
random selected source domain for both a naive and adaptive learning setting.
In the case of more heterogeneous datasets, the predictability feature of the
proposed model can be utilized to further select a subset of candidate domains,
where the corresponding classifier outperforms the one trained on all available
source domains. This observation reinforces a hypothesis that our proposed
model may also be deployed as a means to filter out redundant information
during a training phase of SC.
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks
Authors: Martin Schmitt, Simon Steinheber, Konrad Schreiber, Benjamin Roth
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), General Classification, Sentiment Analysis

In this work, we propose a new model for aspect-based sentiment analysis. In
contrast to previous approaches, we jointly model the detection of aspects and
the classification of their polarity in an end-to-end trainable neural network.
We conduct experiments with different neural architectures and word
representations on the recent GermEval 2017 dataset. We were able to show
considerable performance gains by using the joint modeling approach in all
settings compared to pipeline approaches. The combination of a convolutional
neural network and fasttext embeddings outperformed the best submission of the
shared task in 2017, establishing a new state of the art.
Linear classifier, least-squares cost function, and outliers
Authors: Babatunde M. Ayeni
Date: 2018-08-28 00:00:00

A set of introductory notes on the subject of data classification using a linear classifier and least-squares cost function, and the negative effect of the presence of outliers on the decision boundary of the linear discriminant. We also show how a simple scaling could make the outlier less significant, thereby obtaining a much better decision boundary. We present some numerical results.
DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model
Authors: Stéphane Lathuilière, Pablo Mesejo, Xavier Alameda-Pineda, Radu Horaud
Date: 2018-08-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: Head Pose Estimation, Pose Estimation, regression

In this paper, we address the problem of how to robustly train a ConvNet for
regression, or deep robust regression. Traditionally, deep regression employs
the L2 loss function, known to be sensitive to outliers, i.e. samples that
either lie at an abnormal distance away from the majority of the training
samples, or that correspond to wrongly annotated targets. This means that,
during back-propagation, outliers may bias the training process due to the high
magnitude of their gradient. In this paper, we propose DeepGUM: a deep
regression model that is robust to outliers thanks to the use of a
Gaussian-uniform mixture model. We derive an optimization algorithm that
alternates between the unsupervised detection of outliers using
expectation-maximization, and the supervised training with cleaned samples
using stochastic gradient descent. DeepGUM is able to adapt to a continuously
evolving outlier distribution, avoiding to manually impose any threshold on the
proportion of outliers in the training set. Extensive experimental evaluations
on four different tasks (facial and fashion landmark detection, age and head
pose estimation) lead us to conclude that our novel robust technique provides
reliability in the presence of various types of noise and protection against a
high percentage of outliers.
DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth
Authors: Jameel Malik, Ahmed Elhayek, Fabrizio Nunnari, Kiran varanasi, Kiarash Tamaddon, Alexis Heloir, Didier Stricker
Date: 2018-08-28 00:00:00

Articulated hand pose and shape estimation is an important problem for
vision-based applications such as augmented reality and animation. In contrast
to the existing methods which optimize only for joint positions, we propose a
fully supervised deep network which learns to jointly estimate a full 3D hand
mesh representation and pose from a single depth image. To this end, a CNN
architecture is employed to estimate parametric representations i.e. hand pose,
bone scales and complex shape parameters. Then, a novel hand pose and shape
layer, embedded inside our deep framework, produces 3D joint positions and hand
mesh. Lack of sufficient training data with varying hand shapes limits the
generalized performance of learning based methods. Also, manually annotating
real data is suboptimal. Therefore, we present SynHand5M: a million-scale
synthetic dataset with accurate joint annotations, segmentation masks and mesh
files of depth maps. Among model based learning (hybrid) methods, we show
improved results on our dataset and two of the public benchmarks i.e. NYU and
ICVL. Also, by employing a joint training strategy with real and synthetic
data, we recover 3D hand mesh and pose from real images in 3.7ms.
Why Do Neural Response Generation Models Prefer Universal Replies?
Authors: Bowen Wu, Nan Jiang, Zhifeng Gao, Mengyuan Li, Zongsheng Wang, Suke Li, Qihang Feng, Wenge Rong, Baoxun Wang
Date: 2018-08-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Response Generation

Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task. Despite its diverse applications, existing neural models are prone to producing short and generic replies, making it infeasible to tackle open-domain challenges. In this research, we analyze this critical issue in light of the model's optimization goal and the specific characteristics of the human-to-human dialog corpus. By decomposing the black box into parts, a detailed analysis of the probability limit was conducted to reveal the reason behind these universal replies. Based on these analyses, we propose a max-margin ranking regularization term to avoid the models leaning to these replies. Finally, empirical experiments on case studies and benchmarks with several metrics validate this approach.
A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units
Authors: Wassim Swaileh, Yann Soullard, Thierry Paquet
Date: 2018-08-28 00:00:00
Tasks: Handwriting Recognition, Language Modeling, Language Modelling, POS

We address the design of a unified multilingual system for handwriting
recognition. Most of multi- lingual systems rests on specialized models that
are trained on a single language and one of them is selected at test time.
While some recognition systems are based on a unified optical model, dealing
with a unified language model remains a major issue, as traditional language
models are generally trained on corpora composed of large word lexicons per
language. Here, we bring a solution by con- sidering language models based on
sub-lexical units, called multigrams. Dealing with multigrams strongly reduces
the lexicon size and thus decreases the language model complexity. This makes
pos- sible the design of an end-to-end unified multilingual recognition system
where both a single optical model and a single language model are trained on
all the languages. We discuss the impact of the language unification on each
model and show that our system reaches state-of-the-art methods perfor- mance
with a strong reduction of the complexity.
What do character-level models learn about morphology? The case of dependency parsing
Authors: Clara Vania, Andreas Grivas, Adam Lopez
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dependency Parsing, Morphological Analysis

When parsing morphologically-rich languages with neural models, it is
beneficial to model input at the character level, and it has been claimed that
this is because character-level models learn morphology. We test these claims
by comparing character-level models to an oracle with access to explicit
morphological analysis on twelve languages with varying morphological
typologies. Our results highlight many strengths of character-level models, but
also show that they are poor at disambiguating some words, particularly in the
face of case syncretism. We then demonstrate that explicitly modeling
morphological case improves our best model, showing that character-level models
can benefit from targeted forms of explicit morphological modeling.
Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue
Authors: Dieuwke Hupkes, Sanne Bouwmeester, Raquel Fernández
Date: 2018-08-28 00:00:00
Proceeding: WS 2018 11
Tasks: Decoder, Diagnostic

We investigate how encoder-decoder models trained on a synthetic dataset of
task-oriented dialogues process disfluencies, such as hesitations and
self-corrections. We find that, contrary to earlier results, disfluencies have
very little impact on the task success of seq-to-seq models with attention.
Using visualisation and diagnostic classifiers, we analyse the representations
that are incrementally built by the model, and discover that models develop
little to no awareness of the structure of disfluencies. However, adding
disfluencies to the data appears to help the model create clearer
representations overall, as evidenced by the attention patterns the different
models exhibit.
A Multi-channel DART Algorithm
Authors: Mathé Zeegers, Felix Lucka, Kees Joost Batenburg
Date: 2018-08-28 00:00:00

Tomography deals with the reconstruction of objects from their projections,
acquired along a range of angles. Discrete tomography is concerned with objects
that consist of a small number of materials, which makes it possible to compute
accurate reconstructions from highly limited projection data. For cases where
the allowed intensity values in the reconstruction are known a priori, the
discrete algebraic reconstruction technique (DART) has shown to yield accurate
reconstructions from few projections. However, a key limitation is that the
benefit of DART diminishes as the number of different materials increases. Many
tomographic imaging techniques can simultaneously record tomographic data at
multiple channels, each corresponding to a different weighting of the materials
in the object. Whenever projection data from more than one channel is
available, this additional information can potentially be exploited by the
reconstruction algorithm. In this paper we present Multi-Channel DART
(MC-DART), which deals effectively with multi-channel data. This class of
algorithms is a generalization of DART to multiple channels and combines the
information for each separate channel-reconstruction in a multi-channel
segmentation step. We demonstrate that in a range of simulation experiments,
MC-DART is capable of producing more accurate reconstructions compared to
single-channel DART.
Removing out-of-focus blur from a single image
Authors: Guodong Xu, Chaoqiang Liu, Hui Ji
Date: 2018-08-28 00:00:00

Reproducing an all-in-focus image from an image with defocus regions is of
practical value in many applications, eg, digital photography, and robotics.
Using the output of some existing defocus map estimator, existing approaches
first segment a de-focused image into multiple regions blurred by Gaussian
kernels with different variance each, and then de-blur each region using the
corresponding Gaussian kernel. In this paper, we proposed a blind deconvolution
method specifically designed for removing defocus blurring from an image, by
providing effective solutions to two critical problems: 1) suppressing the
artifacts caused by segmentation error by introducing an additional variable
regularized by weighted $\ell_0$-norm; and 2) more accurate defocus kernel
estimation using non-parametric symmetry and low-rank based constraints on the
kernel. The experiments on real datasets showed the advantages of the proposed
method over existing ones, thanks to the effective treatments of the two
important issues mentioned above during deconvolution.
Cognitive Action Laws: The Case of Visual Features
Authors: Alessandro Betti, Marco Gori, Stefano Melacci
Date: 2018-08-28 00:00:00
Tasks: BIG-bench Machine Learning

This paper proposes a theory for understanding perceptual learning processes
within the general framework of laws of nature. Neural networks are regarded as
systems whose connections are Lagrangian variables, namely functions depending
on time. They are used to minimize the cognitive action, an appropriate
functional index that measures the agent interactions with the environment. The
cognitive action contains a potential and a kinetic term that nicely resemble
the classic formulation of regularization in machine learning. A special choice
of the functional index, which leads to forth-order differential
equations---Cognitive Action Laws (CAL)---exhibits a structure that mirrors
classic formulation of machine learning. In particular, unlike the action of
mechanics, the stationarity condition corresponds with the global minimum.
Moreover, it is proven that typical asymptotic learning conditions on the
weights can coexist with the initialization provided that the system dynamics
is driven under a policy referred to as information overloading control.
Finally, the theory is experimented for the problem of feature extraction in
computer vision.
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation
Authors: Hardy, Andreas Vlachos
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Abstractive Text Summarization, Abstract Meaning Representation, Decoder, Text Generation

Recent work on abstractive summarization has made progress with neural
encoder-decoder architectures. However, such models are often challenged due to
their lack of explicit semantic modeling of the source document and its
summary. In this paper, we extend previous work on abstractive summarization
using Abstract Meaning Representation (AMR) with a neural language generation
stage which we guide using the source document. We demonstrate that this
guidance improves summarization results by 7.4 and 10.5 points in ROUGE-2 using
gold standard AMR parses and parses obtained from an off-the-shelf parser
respectively. We also find that the summarization performance using the latter
is 2 ROUGE-2 points higher than that of a well-established neural
encoder-decoder approach trained on a larger dataset. Code is available at
\url{https://github.com/sheffieldnlp/AMR2Text-summ}
Toward Fast and Accurate Neural Discourse Segmentation
Authors: Yizhong Wang, Sujian Li, Jingfeng Yang
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Discourse Segmentation

Discourse segmentation, which segments texts into Elementary Discourse Units,
is a fundamental step in discourse analysis. Previous discourse segmenters rely
on complicated hand-crafted features and are not practical in actual use. In
this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF
framework. To improve its accuracy, we address the problem of data
insufficiency by transferring a word representation model that is trained on a
large corpus. We also propose a restricted self-attention mechanism in order to
capture useful information within a neighborhood. Experiments on the RST-DT
corpus show that our model is significantly faster than previous methods, while
achieving new state-of-the-art performance.
Weighted total variation based convex clustering
Authors: Guodong Xu, Yu Xia, Hui Ji
Date: 2018-08-28 00:00:00
Tasks: Clustering

Data clustering is a fundamental problem with a wide range of applications.
Standard methods, eg the $k$-means method, usually require solving a non-convex
optimization problem. Recently, total variation based convex relaxation to the
$k$-means model has emerged as an attractive alternative for data clustering.
However, the existing results on its exact clustering property, ie, the
condition imposed on data so that the method can provably give correct
identification of all cluster memberships, is only applicable to very specific
data and is also much more restrictive than that of some other methods. This
paper aims at the revisit of total variation based convex clustering, by
proposing a weighted sum-of-$\ell_1$-norm relating convex model. Its exact
clustering property established in this paper, in both deterministic and
probabilistic context, is applicable to general data and is much sharper than
the existing results. These results provided good insights to advance the
research on convex clustering. Moreover, the experiments also demonstrated that
the proposed convex model has better empirical performance when be compared to
standard clustering methods, and thus it can see its potential in practice.
Mapping Natural Language Commands to Web Elements
Authors: Panupong Pasupat, Tian-Shun Jiang, Evan Zheran Liu, Kelvin Guu, Percy Liang
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Relational Reasoning, Visual Reasoning

The web provides a rich, open-domain environment with textual, structural,
and spatial properties. We propose a new task for grounding language in this
environment: given a natural language command (e.g., "click on the second
article"), choose the correct element on the web page (e.g., a hyperlink or
text box). We collected a dataset of over 50,000 commands that capture various
phenomena such as functional references (e.g. "find who made this site"),
relational reasoning (e.g. "article by john"), and visual reasoning (e.g.
"top-most article"). We also implemented and analyzed three baseline models
that capture different phenomena present in the dataset.
Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation
Authors: Han Ma, Yixin Ma, Jianhao Jiao, M Usman Maqbool Bhutta, Mohammud Junaid Bocus, Lujia Wang, Ming Liu, Rui Fan
Date: 2018-08-28 00:00:00
Tasks: Disparity Estimation, Lane Detection

Lane detection is very important for self-driving vehicles. In recent years,
computer stereo vision has been prevalently used to enhance the accuracy of the
lane detection systems. This paper mainly presents a multiple lane detection
algorithm developed based on optimised dense disparity map estimation, where
the disparity information obtained at time t_{n} is utilised to optimise the
process of disparity estimation at time t_{n+1}. This is achieved by estimating
the road model at time t_{n} and then controlling the search range for the
disparity estimation at time t_{n+1}. The lanes are then detected using our
previously published algorithm, where the vanishing point information is used
to model the lanes. The experimental results illustrate that the runtime of the
disparity estimation is reduced by around 37% and the accuracy of the lane
detection is about 99%.