pwc-archive/papers-with-abstracts huggingface dataset:
Video Re-localization
Authors: Yang Feng, Lin Ma, Wei Liu, Tong Zhang, Jiebo Luo
Date: 2018-08-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: Copy Detection

Many methods have been developed to help people find the video contents they
want efficiently. However, there are still some unsolved problems in this area.
For example, given a query video and a reference video, how to accurately
localize a segment in the reference video such that the segment semantically
corresponds to the query video? We define a distinctively new task, namely
\textbf{video re-localization}, to address this scenario. Video re-localization
is an important emerging technology implicating many applications, such as fast
seeking in videos, video copy detection, video surveillance, etc. Meanwhile, it
is also a challenging research task because the visual appearance of a semantic
concept in videos can have large variations. The first hurdle to clear for the
video re-localization task is the lack of existing datasets. It is labor
expensive to collect pairs of videos with semantic coherence or correspondence
and label the corresponding segments. We first exploit and reorganize the
videos in ActivityNet to form a new dataset for video re-localization research,
which consists of about 10,000 videos of diverse visual appearances associated
with localized boundary information. Subsequently, we propose an innovative
cross gated bilinear matching model such that every time-step in the reference
video is matched against the attentively weighted query video. Consequently,
the prediction of the starting and ending time is formulated as a
classification problem based on the matching results. Extensive experimental
results show that the proposed method outperforms the competing methods. Our
code is available at: https://github.com/fengyang0317/video_reloc.
Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining
Authors: Guanbin Li, Xiang He, Wei zhang, Huiyou Chang, Le Dong, Liang Lin
Date: 2018-08-04 00:00:00
Tasks: Decoder

Single image rain streaks removal has recently witnessed substantial progress
due to the development of deep convolutional neural networks. However, existing
deep learning based methods either focus on the entrance and exit of the
network by decomposing the input image into high and low frequency information
and employing residual learning to reduce the mapping range, or focus on the
introduction of cascaded learning scheme to decompose the task of rain streaks
removal into multi-stages. These methods treat the convolutional neural network
as an encapsulated end-to-end mapping module without deepening into the
rationality and superiority of neural network design. In this paper, we delve
into an effective end-to-end neural network structure for stronger feature
expression and spatial correlation learning. Specifically, we propose a
non-locally enhanced encoder-decoder network framework, which consists of a
pooling indices embedded encoder-decoder network to efficiently learn
increasingly abstract feature representation for more accurate rain streaks
modeling while perfectly preserving the image detail. The proposed
encoder-decoder framework is composed of a series of non-locally enhanced dense
blocks that are designed to not only fully exploit hierarchical features from
all the convolutional layers but also well capture the long-distance
dependencies and structural information. Extensive experiments on synthetic and
real datasets demonstrate that the proposed method can effectively remove
rain-streaks on rainy image of various densities while well preserving the
image details, which achieves significant improvements over the recent
state-of-the-art methods.
Fast and accurate alignment of long bisulfite-seq reads
Authors: Brent S. Pedersen, Kenneth Eyring, Subhajyoti De, Ivana V. Yang, David A. Schwartz
Date: 2014-01-06 00:00:00
Tasks: Benchmarking

Summary: Longer sequencing reads, with at least 200 bases per template are
now common. While traditional aligners have adopted new strategies to improve
the mapping of longer reads, aligners specific to bisulfite-sequencing were
optimized when much shorter reads were the norm. We sought to perform the first
comparison using longer reads to determine which aligners were most accurate
and efficient and to evaluate a novel software tool, bwa-meth, built on a
traditional mapper that supports insertions, deletions and clipped alignments.
We gauge accuracy by comparing the number of on and off-target reads from a
targeted sequencing project and by simulations. Availability and
Implementation: The benchmarking scripts and the bwa-meth software are
available at https://github/com/brentp/bwa-meth/ under the MIT License.
pyRecLab: A Software Library for Quick Prototyping of Recommender Systems
Authors: Gabriel Sepulveda, Vicente Dominguez, Denis Parra
Date: 2017-06-20 00:00:00
Tasks: Recommendation Systems

This paper introduces pyRecLab, a software library written in C++ with Python
bindings which allows to quickly train, test and develop recommender systems.
Although there are several software libraries for this purpose, only a few let
developers to get quickly started with the most traditional methods, permitting
them to try different parameters and approach several tasks without a
significant loss of performance. Among the few libraries that have all these
features, they are available in languages such as Java, Scala or C#, what is a
disadvantage for less experienced programmers more used to the popular Python
programming language. In this article we introduce details of pyRecLab, showing
as well performance analysis in terms of error metrics (MAE and RMSE) and
train/test time. We benchmark it against the popular Java-based library LibRec,
showing similar results. We expect programmers with little experience and
people interested in quickly prototyping recommender systems to be benefited
from pyRecLab.
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems
Authors: Anton Puzanov, Kobi Cohen
Date: 2018-08-04 00:00:00
Tasks: Classification, General Classification, Intrusion Detection, One-Shot Learning

In recent years there has been a sharp rise in networking applications, in
which significant events need to be classified but only a few training
instances are available. These are known as cases of one-shot learning.
Examples include analyzing network traffic under zero-day attacks, and computer
vision tasks by sensor networks deployed in the field. To handle this
challenging task, organizations often use human analysts to classify events
under high uncertainty. Existing algorithms use a threshold-based mechanism to
decide whether to classify an object automatically or send it to an analyst for
deeper inspection. However, this approach leads to a significant waste of
resources since it does not take the practical temporal constraints of system
resources into account. Our contribution is threefold. First, we develop a
novel Deep Reinforcement One-shot Learning (DeROL) framework to address this
challenge. The basic idea of the DeROL algorithm is to train a deep-Q network
to obtain a policy which is oblivious to the unseen classes in the testing
data. Then, in real-time, DeROL maps the current state of the one-shot learning
process to operational actions based on the trained deep-Q network, to maximize
the objective function. Second, we develop the first open-source software for
practical artificially intelligent one-shot classification systems with limited
resources for the benefit of researchers in related fields. Third, we present
an extensive experimental study using the OMNIGLOT dataset for computer vision
tasks and the UNSW-NB15 dataset for intrusion detection tasks that demonstrates
the versatility and efficiency of the DeROL framework.
Learning Multi-scale Features for Foreground Segmentation
Authors: Long Ang Lim, Hacer Yalim Keles
Date: 2018-08-04 00:00:00
Tasks: Decoder, Foreground Segmentation, Segmentation

Foreground segmentation algorithms aim segmenting moving objects from the
background in a robust way under various challenging scenarios. Encoder-decoder
type deep neural networks that are used in this domain recently perform
impressive segmentation results. In this work, we propose a novel robust
encoder-decoder structure neural network that can be trained end-to-end using
only a few training examples. The proposed method extends the Feature Pooling
Module (FPM) of FgSegNet by introducing features fusions inside this module,
which is capable of extracting multi-scale features within images; resulting in
a robust feature pooling against camera motion, which can alleviate the need of
multi-scale inputs to the network. Our method outperforms all existing
state-of-the-art methods in CDnet2014 dataset by an average overall F-Measure
of 0.9847. We also evaluate the effectiveness of our method on SBI2015 and UCSD
Background Subtraction datasets. The source code of the proposed method is made
available at https://github.com/lim-anggun/FgSegNet_v2 .
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection
Authors: Meng Ye, Yuhong Guo
Date: 2018-08-07 00:00:00
Tasks: image-classification, Image Classification, Multi-Label Image Classification, Multi-label zero-shot learning, Transfer Learning, Zero-Shot Learning

Zero-shot learning transfers knowledge from seen classes to novel unseen
classes to reduce human labor of labelling data for building new classifiers.
Much effort on zero-shot learning however has focused on the standard
multi-class setting, the more challenging multi-label zero-shot problem has
received limited attention. In this paper we propose a transfer-aware embedding
projection approach to tackle multi-label zero-shot learning. The approach
projects the label embedding vectors into a low-dimensional space to induce
better inter-label relationships and explicitly facilitate information transfer
from seen labels to unseen labels, while simultaneously learning a max-margin
multi-label classifier with the projected label embeddings. Auxiliary
information can be conveniently incorporated to guide the label embedding
projection to further improve label relation structures for zero-shot knowledge
transfer. We conduct experiments for zero-shot multi-label image
classification. The results demonstrate the efficacy of the proposed approach.
SketchyScene: Richly-Annotated Scene Sketches
Authors: Changqing Zou, Qian Yu, Ruofei Du, Haoran Mo, Yi-Zhe Song, Tao Xiang, Chengying Gao, Baoquan Chen, Hao Zhang
Date: 2018-08-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: Colorization, Image Retrieval, Retrieval, Semantic Segmentation, Sketch Colorization

We contribute the first large-scale dataset of scene sketches, SketchyScene,
with the goal of advancing research on sketch understanding at both the object
and scene level. The dataset is created through a novel and carefully designed
crowdsourcing pipeline, enabling users to efficiently generate large quantities
of realistic and diverse scene sketches. SketchyScene contains more than 29,000
scene-level sketches, 7,000+ pairs of scene templates and photos, and 11,000+
object sketches. All objects in the scene sketches have ground-truth semantic
and instance masks. The dataset is also highly scalable and extensible, easily
allowing augmenting and/or changing scene composition. We demonstrate the
potential impact of SketchyScene by training new computational models for
semantic segmentation of scene sketches and showing how the new dataset enables
several applications including image retrieval, sketch colorization, editing,
and captioning, etc. The dataset and code can be found at
https://github.com/SketchyScene/SketchyScene.
The Sample Complexity of Up-to-$\varepsilon$ Multi-Dimensional Revenue Maximization
Authors: Yannai A. Gonczarowski, S. Matthew Weinberg
Date: 2018-08-07 00:00:00
Tasks: Computational Efficiency

We consider the sample complexity of revenue maximization for multiple bidders in unrestricted multi-dimensional settings. Specifically, we study the standard model of $n$ additive bidders whose values for $m$ heterogeneous items are drawn independently. For any such instance and any $\varepsilon>0$, we show that it is possible to learn an $\varepsilon$-Bayesian Incentive Compatible auction whose expected revenue is within $\varepsilon$ of the optimal $\varepsilon$-BIC auction from only polynomially many samples. Our fully nonparametric approach is based on ideas that hold quite generally, and completely sidestep the difficulty of characterizing optimal (or near-optimal) auctions for these settings. Therefore, our results easily extend to general multi-dimensional settings, including valuations that are not necessarily even subadditive, and arbitrary allocation constraints. For the cases of a single bidder and many goods, or a single parameter (good) and many bidders, our analysis yields exact incentive compatibility (and for the latter also computational efficiency). Although the single-parameter case is already well-understood, our corollary for this case extends slightly the state-of-the-art.
Data augmentation using synthetic data for time series classification with deep residual networks
Authors: Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller
Date: 2018-08-07 00:00:00
Tasks: Data Augmentation, Dynamic Time Warping, General Classification, Time Series, Time Series Analysis, Time Series Classification

Data augmentation in deep neural networks is the process of generating
artificial data in order to reduce the variance of the classifier with the goal
to reduce the number of errors. This idea has been shown to improve deep neural
network's generalization capabilities in many computer vision tasks such as
image recognition and object localization. Apart from these applications, deep
Convolutional Neural Networks (CNNs) have also recently gained popularity in
the Time Series Classification (TSC) community. However, unlike in image
recognition problems, data augmentation techniques have not yet been
investigated thoroughly for the TSC task. This is surprising as the accuracy of
deep learning models for TSC could potentially be improved, especially for
small datasets that exhibit overfitting, when a data augmentation method is
adopted. In this paper, we fill this gap by investigating the application of a
recently proposed data augmentation technique based on the Dynamic Time Warping
distance, for a deep learning model for TSC. To evaluate the potential of
augmenting the training set, we performed extensive experiments using the UCR
TSC benchmark. Our preliminary experiments reveal that data augmentation can
drastically increase deep CNN's accuracy on some datasets and significantly
improve the deep model's accuracy when the method is used in an ensemble
approach.
Overhead Detection: Beyond 8-bits and RGB
Authors: Eliza Mace, Keith Manville, Monica Barbu-McInnis, Michael Laielli, Matthew Klaric, Samuel Dooley
Date: 2018-08-07 00:00:00

This study uses the challenging and publicly available SpaceNet dataset to
establish a performance baseline for a state-of-the-art object detector in
satellite imagery. Specifically, we examine how various features of the data
affect building detection accuracy with respect to the Intersection over Union
metric. We demonstrate that the performance of the R-FCN detection algorithm on
imagery with a 1.5 meter ground sample distance and three spectral bands
increases by over 32% by using 13-bit data, as opposed to 8-bit data at the
same spatial and spectral resolution. We also establish accuracy trends with
respect to building size and scene density. Finally, we propose and evaluate
multiple methods for integrating additional spectral information into
off-the-shelf deep learning architectures. Interestingly, our methods are
robust to the choice of spectral bands and we note no significant performance
improvement when adding additional bands.
Mixed Integer Linear Programming for Feature Selection in Support Vector Machine
Authors: Martine Labbé, Luisa I. Martínez-Merino, Antonio M. Rodríguez-Chía
Date: 2018-08-07 00:00:00
Tasks: Classification, feature selection, General Classification

This work focuses on support vector machine (SVM) with feature selection. A
MILP formulation is proposed for the problem. The choice of suitable features
to construct the separating hyperplanes has been modelled in this formulation
by including a budget constraint that sets in advance a limit on the number of
features to be used in the classification process. We propose both an exact and
a heuristic procedure to solve this formulation in an efficient way. Finally,
the validation of the model is done by checking it with some well-known data
sets and comparing it with classical classification methods.
Robust Implicit Backpropagation
Authors: Francois Fagan, Garud Iyengar
Date: 2018-08-07 00:00:00
Tasks: Sensitivity

Arguably the biggest challenge in applying neural networks is tuning the
hyperparameters, in particular the learning rate. The sensitivity to the
learning rate is due to the reliance on backpropagation to train the network.
In this paper we present the first application of Implicit Stochastic Gradient
Descent (ISGD) to train neural networks, a method known in convex optimization
to be unconditionally stable and robust to the learning rate. Our key
contribution is a novel layer-wise approximation of ISGD which makes its
updates tractable for neural networks. Experiments show that our method is more
robust to high learning rates and generally outperforms standard
backpropagation on a variety of tasks.
Fast and Accurate Camera Covariance Computation for Large 3D Reconstruction
Authors: Michal Polic, Wolfgang Förstner, Tomas Pajdla
Date: 2018-08-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Reconstruction

Estimating uncertainty of camera parameters computed in Structure from Motion
(SfM) is an important tool for evaluating the quality of the reconstruction and
guiding the reconstruction process. Yet, the quality of the estimated
parameters of large reconstructions has been rarely evaluated due to the
computational challenges. We present a new algorithm which employs the sparsity
of the uncertainty propagation and speeds the computation up about ten times
\wrt previous approaches. Our computation is accurate and does not use any
approximations. We can compute uncertainties of thousands of cameras in tens of
seconds on a standard PC. We also demonstrate that our approach can be
effectively used for reconstructions of any size by applying it to smaller
sub-reconstructions.
Spinal Cord Gray Matter-White Matter Segmentation on Magnetic Resonance AMIRA Images with MD-GRU
Authors: Antal Horvath, Charidimos Tsagkas, Simon Andermatt, Simon Pezold, Katrin Parmar, Philippe Cattin
Date: 2018-08-07 00:00:00
Tasks: Segmentation

The small butterfly shaped structure of spinal cord (SC) gray matter (GM) is
challenging to image and to delinate from its surrounding white matter (WM).
Segmenting GM is up to a point a trade-off between accuracy and precision. We
propose a new pipeline for GM-WM magnetic resonance (MR) image acquisition and
segmentation. We report superior results as compared to the ones recently
reported in the SC GM segmentation challenge and show even better results using
the averaged magnetization inversion recovery acquisitions (AMIRA) sequence.
Scan-rescan experiments with the AMIRA sequence show high reproducibility in
terms of Dice coefficient, Hausdorff distance and relative standard deviation.
We use a recurrent neural network (RNN) with multi-dimensional gated recurrent
units (MD-GRU) to train segmentation models on the AMIRA dataset of 855 slices.
We added a generalized dice loss to the cross entropy loss that MD-GRU uses and
were able to improve the results.
Application of End-to-End Deep Learning in Wireless Communications Systems
Authors: Woongsup Lee, Ohyun Jo, Minhoe Kim
Date: 2018-08-07 00:00:00
Tasks: Deep Learning

Deep learning is a potential paradigm changer for the design of wireless
communications systems (WCS), from conventional handcrafted schemes based on
sophisticated mathematical models with assumptions to autonomous schemes based
on the end-to-end deep learning using a large number of data. In this article,
we present a basic concept of the deep learning and its application to WCS by
investigating the resource allocation (RA) scheme based on a deep neural
network (DNN) where multiple goals with various constraints can be satisfied
through the end-to-end deep learning. Especially, the optimality and
feasibility of the DNN based RA are verified through simulation. Then, we
discuss the technical challenges regarding the application of deep learning in
WCS.
Word-Level Loss Extensions for Neural Temporal Relation Classification
Authors: Artuur Leeuwenberg, Marie-Francine Moens
Date: 2018-08-07 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, General Classification, Relation, Relation Classification, Relation Extraction, Temporal Relation Classification, Temporal Relation Extraction, Word Embeddings

Unsupervised pre-trained word embeddings are used effectively for many tasks
in natural language processing to leverage unlabeled textual data. Often these
embeddings are either used as initializations or as fixed word representations
for task-specific classification models. In this work, we extend our
classification model's task loss with an unsupervised auxiliary loss on the
word-embedding level of the model. This is to ensure that the learned word
representations contain both task-specific features, learned from the
supervised loss component, and more general features learned from the
unsupervised loss component. We evaluate our approach on the task of temporal
relation extraction, in particular, narrative containment relation extraction
from clinical records, and show that continued training of the embeddings on
the unsupervised objective together with the task objective gives better
task-specific embeddings, and results in an improvement over the state of the
art on the THYME dataset, using only a general-domain part-of-speech tagger as
linguistic resource.
Capturing global spatial context for accurate cell classification in skin cancer histology
Authors: Konstantinos Zormpas-Petridis, Henrik Failmezger, Ioannis Roxanis, Matthew Blackledge, Yann Jamin, Yinyin Yuan
Date: 2018-08-07 00:00:00
Tasks: Classification, General Classification, Superpixels

The spectacular response observed in clinical trials of immunotherapy in
patients with previously uncurable Melanoma, a highly aggressive form of skin
cancer, calls for a better understanding of the cancer-immune interface.
Computational pathology provides a unique opportunity to spatially dissect such
interface on digitised pathological slides. Accurate cellular classification is
a key to ensure meaningful results, but is often challenging even with
state-of-art machine learning and deep learning methods.
  We propose a hierarchical framework, which mirrors the way pathologists
perceive tumour architecture and define tumour heterogeneity to improve cell
classification methods that rely solely on cell nuclei morphology. The SLIC
superpixel algorithm was used to segment and classify tumour regions in low
resolution H&E-stained histological images of melanoma skin cancer to provide a
global context. Classification of superpixels into tumour, stroma, epidermis
and lumen/white space, yielded a 97.7% training set accuracy and 95.7% testing
set accuracy in 58 whole-tumour images of the TCGA melanoma dataset. The
superpixel classification was projected down to high resolution images to
enhance the performance of a single cell classifier, based on cell nuclear
morphological features, and resulted in increasing its accuracy from 86.4% to
91.6%. Furthermore, a voting scheme was proposed to use global context as
biological a priori knowledge, pushing the accuracy further to 92.8%.
  This study demonstrates how using the global spatial context can accurately
characterise the tumour microenvironment and allow us to extend significantly
beyond single-cell morphological classification.