pwc-archive/papers-with-abstracts huggingface dataset:
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign
Authors: Pham Quang Nhat Minh
Date: 2018-03-22 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition, TAG

In this report, we describe our participant named-entity recognition system
at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling
problem using BIO encoding scheme. We applied a feature-based model which
combines word, word-shape features, Brown-cluster-based features, and
word-embedding-based features. We compare several methods to deal with nested
entities in the dataset. We showed that combining tags of entities at all
levels for training a sequence labeling model (joint-tag model) improved the
accuracy of nested named-entity recognition.
Towards Universal Representation for Unseen Action Recognition
Authors: Yi Zhu, Yang Long, Yu Guan, Shawn Newsam, Ling Shao
Date: 2018-03-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Multiple Instance Learning, Temporal Action Localization, Zero-Shot Action Recognition

Unseen Action Recognition (UAR) aims to recognise novel action categories
without training examples. While previous methods focus on inner-dataset
seen/unseen splits, this paper proposes a pipeline using a large-scale training
source to achieve a Universal Representation (UR) that can generalise to a more
realistic Cross-Dataset UAR (CD-UAR) scenario. We first address UAR as a
Generalised Multiple-Instance Learning (GMIL) problem and discover
'building-blocks' from the large-scale ActivityNet dataset using distribution
kernels. Essential visual and semantic components are preserved in a shared
space to achieve the UR that can efficiently generalise to new datasets.
Predicted UR exemplars can be improved by a simple semantic adaptation, and
then an unseen action can be directly recognised using UR during the test.
Without further training, extensive experiments manifest significant
improvements over the UCF101 and HMDB51 benchmarks.
Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft
Authors: Stephan Alaniz
Date: 2018-03-22 00:00:00
Tasks: Deep Reinforcement Learning, Minecraft, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has been successfully applied to several
visual-input tasks using model-free methods. In this paper, we propose a
model-based approach that combines learning a DNN-based transition model with
Monte Carlo tree search to solve a block-placing task in Minecraft. Our learned
transition model predicts the next frame and the rewards one step ahead given
the last four frames of the agent's first-person-view image and the current
action. Then a Monte Carlo tree search algorithm uses this model to plan the
best sequence of actions for the agent to perform. On the proposed task in
Minecraft, our model-based approach reaches the performance comparable to the
Deep Q-Network's, but learns faster and, thus, is more training sample
efficient.
Deep saliency: What is learnt by a deep network about saliency?
Authors: Sen He, Nicolas Pugeault
Date: 2018-01-12 00:00:00
Tasks: Saliency Detection

Deep convolutional neural networks have achieved impressive performance on a
broad range of problems, beating prior art on established benchmarks, but it
often remains unclear what are the representations learnt by those systems and
how they achieve such performance. This article examines the specific problem
of saliency detection, where benchmarks are currently dominated by CNN-based
approaches, and investigates the properties of the learnt representation by
visualizing the artificial neurons' receptive fields.
  We demonstrate that fine tuning a pre-trained network on the saliency
detection task lead to a profound transformation of the network's deeper
layers. Moreover we argue that this transformation leads to the emergence of
receptive fields conceptually similar to the centre-surround filters
hypothesized by early research on visual saliency.
A Comprehensive Analysis of Deep Regression
Authors: Stéphane Lathuilière, Pablo Mesejo, Xavier Alameda-Pineda, Radu Horaud
Date: 2018-03-22 00:00:00
Tasks: Pose Estimation, regression

Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, together with the stochastic nature of the optimization procedures, produce notably different results, making extremely difficult to sift methods that significantly outperform others. This situation motivates the current study, in which we perform a systematic evaluation and statistical analysis of vanilla deep regression, i.e. convolutional neural networks with a linear regression top layer. This is the first comprehensive analysis of deep regression techniques. We perform experiments on four vision problems, and report confidence intervals for the median performance as well as the statistical significance of the results, if any. Surprisingly, the variability due to different data pre-processing procedures generally eclipses the variability due to modifications in the network architecture. Our results reinforce the hypothesis according to which, in general, a general-purpose network (e.g. VGG-16 or ResNet-50) adequately tuned can yield results close to the state-of-the-art without having to resort to more complex and ad-hoc regression models.
Self-Attentive Residual Decoder for Neural Machine Translation
Authors: Lesly Miculicich Werlen, Nikolaos Pappas, Dhananjay Ram, Andrei Popescu-Belis
Date: 2017-09-14 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Machine Translation, Translation

Neural sequence-to-sequence networks with attention have achieved remarkable
performance for machine translation. One of the reasons for their effectiveness
is their ability to capture relevant source-side contextual information at each
time-step prediction through an attention mechanism. However, the target-side
context is solely based on the sequence model which, in practice, is prone to a
recency bias and lacks the ability to capture effectively non-sequential
dependencies among words. To address this limitation, we propose a
target-side-attentive residual recurrent network for decoding, where attention
over previous words contributes directly to the prediction of the next word.
The residual learning facilitates the flow of information from the distant past
and is able to emphasize any of the previously translated words, hence it gains
access to a wider context. The proposed model outperforms a neural MT baseline
as well as a memory and self-attention network on three language pairs. The
analysis of the attention learned by the decoder confirms that it emphasizes a
wider context, and that it captures syntactic-like structures.
Image Dataset for Visual Objects Classification in 3D Printing
Authors: Hongjia Li, Xiaolong Ma, Aditya Singh Rathore, Zhe Li, Qiyuan An, Chen Song, Wenyao Xu, Yanzhi Wang
Date: 2018-02-15 00:00:00
Tasks: Classification, General Classification

The rapid development in additive manufacturing (AM), also known as 3D
printing, has brought about potential risk and security issues along with
significant benefits. In order to enhance the security level of the 3D printing
process, the present research aims to detect and recognize illegal components
using deep learning. In this work, we collected a dataset of 61,340 2D images
(28x28 for each image) of 10 classes including guns and other non-gun objects,
corresponding to the projection results of the original 3D models. To validate
the dataset, we train a convolutional neural network (CNN) model for gun
classification which can achieve 98.16% classification accuracy.
Guided Image Inpainting: Replacing an Image Region by Pulling Content from Another Image
Authors: Yinan Zhao, Brian Price, Scott Cohen, Danna Gurari
Date: 2018-03-22 00:00:00
Tasks: Image Inpainting

Deep generative models have shown success in automatically synthesizing
missing image regions using surrounding context. However, users cannot directly
decide what content to synthesize with such approaches. We propose an
end-to-end network for image inpainting that uses a different image to guide
the synthesis of new content to fill the hole. A key challenge addressed by our
approach is synthesizing new content in regions where the guidance image and
the context of the original image are inconsistent. We conduct four studies
that demonstrate our results yield more realistic image inpainting results over
seven baselines.
Incremental Color Quantization for Color-Vision-Deficient Observers Using Mobile Gaming Data
Authors: Jose Cambronero, Phillip Stanley-Marbell, Martin Rinard
Date: 2018-03-22 00:00:00
Tasks: Image Compression, Quantization

The sizes of compressed images depend on their spatial resolution (number of
pixels) and on their color resolution (number of color quantization levels). We
introduce DaltonQuant, a new color quantization technique for image compression
that cloud services can apply to images destined for a specific user with known
color vision deficiencies. DaltonQuant improves compression in a user-specific
but reversible manner thereby improving a user's network bandwidth and data
storage efficiency. DaltonQuant quantizes image data to account for
user-specific color perception anomalies, using a new method for incremental
color quantization based on a large corpus of color vision acuity data obtained
from a popular mobile game. Servers that host images can revert DaltonQuant's
image requantization and compression when those images must be transmitted to a
different user, making the technique practical to deploy on a large scale. We
evaluate DaltonQuant's compression performance on the Kodak PC reference image
set and show that it improves compression by an additional 22%-29% over the
state-of-the-art compressors TinyPNG and pngquant.
Demystifying Deep Learning: A Geometric Approach to Iterative Projections
Authors: Ashkan Panahi, Hamid Krim, Liyi Dai
Date: 2018-03-22 00:00:00
Tasks: Deep Learning, regression

Parametric approaches to Learning, such as deep learning (DL), are highly
popular in nonlinear regression, in spite of their extremely difficult training
with their increasing complexity (e.g. number of layers in DL). In this paper,
we present an alternative semi-parametric framework which foregoes the
ordinarily required feedback, by introducing the novel idea of geometric
regularization. We show that certain deep learning techniques such as residual
network (ResNet) architecture are closely related to our approach. Hence, our
technique can be used to analyze these types of deep learning. Moreover, we
present preliminary results which confirm that our approach can be easily
trained to obtain complex structures.
Causal Inference on Discrete Data via Estimating Distance Correlations
Authors: Furui Liu, Laiwan Chan
Date: 2018-03-21 00:00:00
Tasks: Causal Inference

In this paper, we deal with the problem of inferring causal directions when
the data is on discrete domain. By considering the distribution of the cause
$P(X)$ and the conditional distribution mapping cause to effect $P(Y|X)$ as
independent random variables, we propose to infer the causal direction via
comparing the distance correlation between $P(X)$ and $P(Y|X)$ with the
distance correlation between $P(Y)$ and $P(X|Y)$. We infer "$X$ causes $Y$" if
the dependence coefficient between $P(X)$ and $P(Y|X)$ is smaller. Experiments
are performed to show the performance of the proposed method.
Buried object detection from B-scan ground penetrating radar data using Faster-RCNN
Authors: Minh-Tan Pham, Sébastien Lefèvre
Date: 2018-03-22 00:00:00
Tasks: GPR, object-detection, Object Detection

In this paper, we adapt the Faster-RCNN framework for the detection of
underground buried objects (i.e. hyperbola reflections) in B-scan ground
penetrating radar (GPR) images. Due to the lack of real data for training, we
propose to incorporate more simulated radargrams generated from different
configurations using the gprMax toolbox. Our designed CNN is first pre-trained
on the grayscale Cifar-10 database. Then, the Faster-RCNN framework based on
the pre-trained CNN is trained and fine-tuned on both real and simulated GPR
data. Preliminary detection results show that the proposed technique can
provide significant improvements compared to classical computer vision methods
and hence becomes quite promising to deal with this kind of specific GPR data
even with few training samples.
Group Sparsity Residual with Non-Local Samples for Image Denoising
Authors: Zhiyuan Zha, Xinggan Zhang, Qiong Wang, Yechao Bai, Lan Tang, Xin Yuan
Date: 2018-03-22 00:00:00
Tasks: Denoising, Image Denoising

Inspired by group-based sparse coding, recently proposed group sparsity
residual (GSR) scheme has demonstrated superior performance in image
processing. However, one challenge in GSR is to estimate the residual by using
a proper reference of the group-based sparse coding (GSC), which is desired to
be as close to the truth as possible. Previous researches utilized the
estimations from other algorithms (i.e., GMM or BM3D), which are either not
accurate or too slow. In this paper, we propose to use the Non-Local Samples
(NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS.
More specifically, we first obtain a good estimation of the group sparse
coefficients by the image nonlocal self-similarity, and then solve the GSR
model by an effective iterative shrinkage algorithm. Experimental results
demonstrate that the proposed GSR-NLS not only outperforms many
state-of-the-art methods, but also delivers the competitive advantage of speed.
A Smoke Removal Method for Laparoscopic Images
Authors: Congcong Wang, Faouzi Alaya Cheikh, Mounir Kaaniche, Ole Jacob Elle
Date: 2018-03-22 00:00:00

In laparoscopic surgery, image quality can be severely degraded by surgical
smoke, which not only introduces error for the image processing (used in image
guided surgery), but also reduces the visibility of the surgeons. In this
paper, we propose to enhance the laparoscopic images by decomposing them into
unwanted smoke part and enhanced part using a variational approach. The
proposed method relies on the observation that smoke has low contrast and low
inter-channel differences. A cost function is defined based on this prior
knowledge and is solved using an augmented Lagrangian method. The obtained
unwanted smoke component is then subtracted from the original degraded image,
resulting in the enhanced image. The obtained quantitative scores in terms of
FADE, JNBM and RE metrics show that our proposed method performs rather well.
Furthermore, the qualitative visual inspection of the results show that it
removes smoke effectively from the laparoscopic images.
Quality expectations of machine translation
Authors: Andy Way
Date: 2018-03-22 00:00:00
Tasks: Machine Translation, Translation

Machine Translation (MT) is being deployed for a range of use-cases by
millions of people on a daily basis. There should, therefore, be no doubt as to
the utility of MT. However, not everyone is convinced that MT can be useful,
especially as a productivity enhancer for human translators. In this chapter, I
address this issue, describing how MT is currently deployed, how its output is
evaluated and how this could be enhanced, especially as MT quality itself
improves. Central to these issues is the acceptance that there is no longer a
single 'gold standard' measure of quality, such that the situation in which MT
is deployed needs to be borne in mind, especially with respect to the expected
'shelf-life' of the translation itself.
Sentiment Analysis of Comments on Rohingya Movement with Support Vector Machine
Authors: Hemayet Ahmed Chowdhury, Tanvir Alam Nibir, Md. Saiful Islam
Date: 2018-03-22 00:00:00
Tasks: Sentiment Analysis

The Rohingya Movement and Crisis caused a huge uproar in the political and
economic state of Bangladesh. Refugee movement is a recurring event and a large
amount of data in the form of opinions remains on social media such as
Facebook, with very little analysis done on them.To analyse the comments based
on all Rohingya related posts, we had to create and modify a classifier based
on the Support Vector Machine algorithm. The code is implemented in python and
uses scikit-learn library. A dataset on Rohingya analysis is not currently
available so we had to use our own data set of 2500 positive and 2500 negative
comments. We specifically used a support vector machine with linear kernel. A
previous experiment was performed by us on the same dataset using the naive
bayes algorithm, but that did not yield impressive results.
Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables
Authors: Till Haug, Octavian-Eugen Ganea, Paulina Grnarova
Date: 2017-02-21 00:00:00
Tasks: Question Answering

Advances in natural language processing tasks have gained momentum in recent
years due to the increasingly popular neural network methods. In this paper, we
explore deep learning techniques for answering multi-step reasoning questions
that operate on semi-structured tables. Challenges here arise from the level of
logical compositionality expressed by questions, as well as the domain
openness. Our approach is weakly supervised, trained on question-answer-table
triples without requiring intermediate strong supervision. It performs two
phases: first, machine understandable logical forms (programs) are generated
from natural language questions following the work of [Pasupat and Liang,
2015]. Second, paraphrases of logical forms and questions are embedded in a
jointly learned vector space using word and character convolutional neural
networks. A neural scoring function is further used to rank and retrieve the
most probable logical form (interpretation) of a question. Our best single
model achieves 34.8% accuracy on the WikiTableQuestions dataset, while the best
ensemble of our models pushes the state-of-the-art score on this task to 38.7%,
thus slightly surpassing both the engineered feature scoring baseline, as well
as the Neural Programmer model of [Neelakantan et al., 2016].
Densely Connected Pyramid Dehazing Network
Authors: He Zhang, Vishal M. Patel
Date: 2018-03-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Generative Adversarial Network, Image Dehazing, Single Image Dehazing

We propose a new end-to-end single image dehazing method, called Densely
Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the
transmission map, atmospheric light and dehazing all together. The end-to-end
learning is achieved by directly embedding the atmospheric scattering model
into the network, thereby ensuring that the proposed method strictly follows
the physics-driven scattering model for dehazing. Inspired by the dense network
that can maximize the information flow along features from different levels, we
propose a new edge-preserving densely connected encoder-decoder structure with
multi-level pyramid pooling module for estimating the transmission map. This
network is optimized using a newly introduced edge-preserving loss function. To
further incorporate the mutual structural information between the estimated
transmission map and the dehazed result, we propose a joint-discriminator based
on generative adversarial network framework to decide whether the corresponding
dehazed image and the estimated transmission map are real or fake. An ablation
study is conducted to demonstrate the effectiveness of each module evaluated at
both estimated transmission map and dehazed result. Extensive experiments
demonstrate that the proposed method achieves significant improvements over the
state-of-the-art methods. Code will be made available at:
https://github.com/hezhangsprinter
Found a good match: should I keep searching? - Accuracy and Performance in Iris Matching Using 1-to-First Search
Authors: Andrey Kuehlkamp, Kevin Bowyer
Date: 2018-03-22 00:00:00
Tasks: Iris Recognition

Iris recognition is used in many applications around the world, with
enrollment sizes as large as over one billion persons in India's Aadhaar
program. Large enrollment sizes can require special optimizations in order to
achieve fast database searches. One such optimization that has been used in
some operational scenarios is 1:First search. In this approach, instead of
scanning the entire database, the search is terminated when the first
sufficiently good match is found. This saves time, but ignores potentially
better matches that may exist in the unexamined portion of the enrollments. At
least one prominent and successful border-crossing program used this approach
for nearly a decade, in order to allow users a fast "token-free" search. Our
work investigates the search accuracy of 1:First and compares it to the
traditional 1:N search. Several different scenarios are considered trying to
emulate real environments as best as possible: a range of enrollment sizes,
closed- and open-set configurations, two iris matchers, and different
permutations of the galleries. Results confirm the expected accuracy
degradation using 1:First search, and also allow us to identify acceptable
working parameters where significant search time reduction is achieved, while
maintaining accuracy similar to 1:N search.
DOP: Deep Optimistic Planning with Approximate Value Function Evaluation
Authors: Francesco Riccio, Roberto Capobianco, Daniele Nardi
Date: 2018-03-22 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Research on reinforcement learning has demonstrated promising results in
manifold applications and domains. Still, efficiently learning effective robot
behaviors is very difficult, due to unstructured scenarios, high uncertainties,
and large state dimensionality (e.g. multi-agent systems or hyper-redundant
robots). To alleviate this problem, we present DOP, a deep model-based
reinforcement learning algorithm, which exploits action values to both (1)
guide the exploration of the state space and (2) plan effective policies.
Specifically, we exploit deep neural networks to learn Q-functions that are
used to attack the curse of dimensionality during a Monte-Carlo tree search.
Our algorithm, in fact, constructs upper confidence bounds on the learned value
function to select actions optimistically. We implement and evaluate DOP on
different scenarios: (1) a cooperative navigation problem, (2) a fetching task
for a 7-DOF KUKA robot, and (3) a human-robot handover with a humanoid robot
(both in simulation and real). The obtained results show the effectiveness of
DOP in the chosen applications, where action values drive the exploration and
reduce the computational demand of the planning process while achieving good
performance.
Deep Learning using Rectified Linear Units (ReLU)
Authors: Abien Fred Agarap
Date: 2018-03-22 00:00:00
Tasks: Classification, Deep Learning, General Classification

We introduce the use of rectified linear units (ReLU) as the classification
function in a deep neural network (DNN). Conventionally, ReLU is used as an
activation function in DNNs, with Softmax function as their classification
function. However, there have been several studies on using a classification
function other than Softmax, and this study is an addition to those. We
accomplish this by taking the activation of the penultimate layer $h_{n - 1}$
in a neural network, then multiply it by weight parameters $\theta$ to get the
raw scores $o_{i}$. Afterwards, we threshold the raw scores $o_{i}$ by $0$,
i.e. $f(o) = \max(0, o_{i})$, where $f(o)$ is the ReLU function. We provide
class predictions $\hat{y}$ through argmax function, i.e. argmax $f(x)$.