pwc-archive/papers-with-abstracts huggingface dataset:
Person re-identification across different datasets with multi-task learning
Authors: Matthieu Ospici, Antoine Cecchi
Date: 2018-07-25 00:00:00
Tasks: Multi-Task Learning, Person Re-Identification

This paper presents an approach to tackle the re-identification problem. This
is a challenging problem due to the large variation of pose, illumination or
camera view. More and more datasets are available to train machine learning
models for person re-identification. These datasets vary in conditions: cameras
numbers, camera positions, location, season, in size, i.e. number of images,
number of different identities. Finally in labeling: there are datasets
annotated with attributes while others are not. To deal with this variety of
datasets we present in this paper an approach to take information from
different datasets to build a system which performs well on all of them. Our
model is based on a Convolutional Neural Network (CNN) and trained using
multitask learning. Several losses are used to extract the different
information available in the different datasets. Our main task is learned with
a classification loss. To reduce the intra-class variation we experiment with
the center loss. Our paper ends with a performance evaluation in which we
discuss the influence of the different losses on the global re-identification
performance. We show that with our method, we are able to build a system that
performs well on different datasets and simultaneously extracts attributes. We
also show that our system outperforms recent re-identification works on two
datasets.
Attend Before you Act: Leveraging human visual attention for continual learning
Authors: Khimya Khetarpal, Doina Precup
Date: 2018-07-25 00:00:00
Tasks: Continual Learning, Decision Making, Transfer Learning

When humans perform a task, such as playing a game, they selectively pay
attention to certain parts of the visual input, gathering relevant information
and sequentially combining it to build a representation from the sensory data.
In this work, we explore leveraging where humans look in an image as an
implicit indication of what is salient for decision making. We build on top of
the UNREAL architecture in DeepMind Lab's 3D navigation maze environment. We
train the agent both with original images and foveated images, which were
generated by overlaying the original images with saliency maps generated using
a real-time spectral residual technique. We investigate the effectiveness of
this approach in transfer learning by measuring performance in the context of
noise in the environment.
A Surprising Linear Relationship Predicts Test Performance in Deep Networks
Authors: Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, Tomaso Poggio
Date: 2018-07-25 00:00:00
Tasks: General Classification, Generalization Bounds

Given two networks with the same training loss on a dataset, when would they
have drastically different test losses and errors? Better understanding of this
question of generalization may improve practical applications of deep networks.
In this paper we show that with cross-entropy loss it is surprisingly simple to
induce significantly different generalization performances for two networks
that have the same architecture, the same meta parameters and the same training
error: one can either pretrain the networks with different levels of
"corrupted" data or simply initialize the networks with weights of different
Gaussian standard deviations. A corollary of recent theoretical results on
overfitting shows that these effects are due to an intrinsic problem of
measuring test performance with a cross-entropy/exponential-type loss, which
can be decomposed into two components both minimized by SGD -- one of which is
not related to expected classification performance. However, if we factor out
this component of the loss, a linear relationship emerges between training and
test losses. Under this transformation, classical generalization bounds are
surprisingly tight: the empirical/training loss is very close to the
expected/test loss. Furthermore, the empirical relation between classification
error and normalized cross-entropy loss seem to be approximately monotonic
Variational Bayesian Reinforcement Learning with Regret Bounds
Authors: Brendan O'Donoghue
Date: 2018-07-25 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning the Q-values summarize the expected future rewards that the agent will attain. However, they cannot capture the epistemic uncertainty about those rewards. In this work we derive a new Bellman operator with associated fixed point we call the `knowledge values'. These K-values compress both the expected future rewards and the epistemic uncertainty into a single value, so that high uncertainty, high reward, or both, can yield high K-values. The key principle is to endow the agent with a risk-seeking utility function that is carefully tuned to balance exploration and exploitation. When the agent follows a Boltzmann policy over the K-values it yields a Bayes regret bound of $\tilde O(L \sqrt{S A T})$, where $L$ is the time horizon, $S$ is the total number of states, $A$ is the number of actions, and $T$ is the number of elapsed timesteps. We show deep connections of this approach to the soft-max and maximum-entropy strands of research in reinforcement learning.
Three hypergraph eigenvector centralities
Authors: Austin R. Benson
Date: 2018-07-25 00:00:00

Eigenvector centrality is a standard network analysis tool for determining
the importance of (or ranking of) entities in a connected system that is
represented by a graph. However, many complex systems and datasets have natural
multi-way interactions that are more faithfully modeled by a hypergraph. Here
we extend the notion of graph eigenvector centrality to uniform hypergraphs.
Traditional graph eigenvector centralities are given by a positive eigenvector
of the adjacency matrix, which is guaranteed to exist by the Perron-Frobenius
theorem under some mild conditions. The natural representation of a hypergraph
is a hypermatrix (colloquially, a tensor). Using recently established
Perron-Frobenius theory for tensors, we develop three tensor eigenvectors
centralities for hypergraphs, each with different interpretations. We show that
these centralities can reveal different information on real-world data by
analyzing hypergraphs constructed from n-gram frequencies, co-tagging on stack
exchange, and drug combinations observed in patient emergency room visits.
Finding Better Subword Segmentation for Neural Machine Translation
Authors: Yingting Wu, Hai Zhao
Date: 2018-07-25 00:00:00
Tasks: Machine Translation, NMT, Segmentation, Translation

For different language pairs, word-level neural machine translation (NMT)
models with a fixed-size vocabulary suffer from the same problem of
representing out-of-vocabulary (OOV) words. The common practice usually
replaces all these rare or unknown words with a <UNK> token, which limits the
translation performance to some extent. Most of recent work handled such a
problem by splitting words into characters or other specially extracted subword
units to enable open-vocabulary translation. Byte pair encoding (BPE) is one of
the successful attempts that has been shown extremely competitive by providing
effective subword segmentation for NMT systems. In this paper, we extend the
BPE style segmentation to a general unsupervised framework with three
statistical measures: frequency (FRQ), accessor variety (AV) and description
length gain (DLG). We test our approach on two translation tasks: German to
English and Chinese to English. The experimental results show that AV and DLG
enhanced systems outperform the FRQ baseline in the frequency weighted schemes
at different significant levels.
Video-based computer aided arthroscopy for patient specific reconstruction of the Anterior Cruciate Ligament
Authors: Carolina Raposo, Cristovao Sousa, Luis Ribeiro, Rui Melo, Joao P. Barreto, Joao Oliveira, Pedro Marques, Fernando Fonseca
Date: 2018-07-25 00:00:00
Tasks: Anatomy, Medical Procedure

The Anterior Cruciate Ligament (ACL) tear is a common medical condition that
is treated using arthroscopy by pulling a tissue graft through a tunnel opened
with a drill. The correct anatomical position and orientation of this tunnel is
crucial for knee stability, and drilling an adequate bone tunnel is the most
technically challenging part of the procedure. This paper presents, for the
first time, a guidance system based solely on intra-operative video for guiding
the drilling of the tunnel. Our solution uses small, easily recognizable visual
markers that are attached to the bone and tools for estimating their relative
pose. A recent registration algorithm is employed for aligning a pre-operative
image of the patient's anatomy with a set of contours reconstructed by touching
the bone surface with an instrumented tool. Experimental validation using
ex-vivo data shows that the method enables the accurate registration of the
pre-operative model with the bone, providing useful information for guiding the
surgeon during the medical procedure.
Repartitioning of the ComplexWebQuestions Dataset
Authors: Alon Talmor, Jonathan Berant
Date: 2018-07-25 00:00:00
Tasks: Reading Comprehension

Recently, Talmor and Berant (2018) introduced ComplexWebQuestions - a dataset
focused on answering complex questions by decomposing them into a sequence of
simpler questions and extracting the answer from retrieved web snippets. In
their work the authors used a pre-trained reading comprehension (RC) model
(Salant and Berant, 2018) to extract the answer from the web snippets. In this
short note we show that training a RC model directly on the training data of
ComplexWebQuestions reveals a leakage from the training set to the test set
that allows to obtain unreasonably high performance. As a solution, we
construct a new partitioning of ComplexWebQuestions that does not suffer from
this leakage and publicly release it. We also perform an empirical evaluation
on these two datasets and show that training a RC model on the training data
substantially improves state-of-the-art performance.
OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas
Authors: Nikolaos Zioulis, Antonis Karakottas, Dimitrios Zarpalas, Petros Daras
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Depth Estimation, Monocular Depth Estimation

Recent work on depth estimation up to now has only focused on projective
images ignoring 360 content which is now increasingly and more easily produced.
We show that monocular depth estimation models trained on traditional images
produce sub-optimal results on omnidirectional images, showcasing the need for
training directly on 360 datasets, which however, are hard to acquire. In this
work, we circumvent the challenges associated with acquiring high quality 360
datasets with ground truth depth annotations, by re-using recently released
large scale 3D datasets and re-purposing them to 360 via rendering. This
dataset, which is considerably larger than similar projective datasets, is
publicly offered to the community to enable future research in this direction.
We use this dataset to learn in an end-to-end fashion the task of depth
estimation from 360 images. We show promising results in our synthesized data
as well as in unseen realistic images.
Multiple sclerosis lesion enhancement and white matter region estimation using hyperintensities in FLAIR images
Authors: Paulo G. L. Freire, Ricardo J. Ferrari
Date: 2018-07-25 00:00:00
Tasks: Lesion Segmentation

Multiple sclerosis (MS) is a demyelinating disease that affects more than 2
million people worldwide. The most used imaging technique to help in its
diagnosis and follow-up is magnetic resonance imaging (MRI). Fluid Attenuated
Inversion Recovery (FLAIR) images are usually acquired in the context of MS
because lesions often appear hyperintense in this particular image weight,
making it easier for physicians to identify them. Though lesions have a bright
intensity profile, it may overlap with white matter (WM) and gray matter (GM)
tissues, posing difficulties to be accurately segmented. In this sense, we
propose a lesion enhancement technique to dim down WM and GM regions and
highlight hyperintensities, making them much more distinguishable than other
tissues. We applied our technique to the ISBI 2015 MS Lesion Segmentation
Challenge and took the average gray level intensity of MS lesions, WM and GM on
FLAIR and enhanced images. The lesion intensity profile in FLAIR was on average
25% and 19% brighter than white matter and gray matter, respectively;
comparatively, the same profile in our enhanced images was on average 444% and
264% brighter. Such results mean a significant improvement on the intensity
distinction among these three clusters, which may come as aid both for experts
and automated techniques. Moreover, a byproduct of our proposal is that the
enhancement can be used to automatically estimate a mask encompassing WM and MS
lesions, which may be useful for brain tissue volume assessment and improve MS
lesion segmentation accuracy in future works.
Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images
Authors: Feng Gu, Nikolay Burlutskiy, Mats Andersson, Lena Kajland Wilen
Date: 2018-07-25 00:00:00
Tasks: Segmentation, Semantic Segmentation, whole slide images

Digital pathology provides an excellent opportunity for applying fully
convolutional networks (FCNs) to tasks, such as semantic segmentation of whole
slide images (WSIs). However, standard FCNs face challenges with respect to
multi-resolution, inherited from the pyramid arrangement of WSIs. As a result,
networks specifically designed to learn and aggregate information at different
levels are desired. In this paper, we propose two novel multi-resolution
networks based on the popular `U-Net' architecture, which are evaluated on a
benchmark dataset for binary semantic segmentation in WSIs. The proposed
methods outperform the U-Net, demonstrating superior learning and
generalization capabilities.
Linear Span Network for Object Skeleton Detection
Authors: Chang Liu, Wei Ke, Fei Qin, Qixiang Ye
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, Object Skeleton Detection

Robust object skeleton detection requires to explore rich representative
visual features and effective feature fusion strategies. In this paper, we
first re-visit the implementation of HED, the essential principle of which can
be ideally described with a linear reconstruction model. Hinted by this, we
formalize a Linear Span framework, and propose Linear Span Network (LSN)
modified by Linear Span Units (LSUs), which minimize the reconstruction error
of convolutional network. LSN further utilizes subspace linear span beside the
feature linear span to increase the independence of convolutional features and
the efficiency of feature integration, which enlarges the capability of fitting
complex ground-truth. As a result, LSN can effectively suppress the cluttered
backgrounds and reconstruct object skeletons. Experimental results validate the
state-of-the-art performance of the proposed LSN.
Change Detection between Multimodal Remote Sensing Data Using Siamese CNN
Authors: Zhenchao Zhang, George Vosselman, Markus Gerke, Devis Tuia, Michael Ying Yang
Date: 2018-07-25 00:00:00
Tasks: Change Detection

Detecting topographic changes in the urban environment has always been an
important task for urban planning and monitoring. In practice, remote sensing
data are often available in different modalities and at different time epochs.
Change detection between multimodal data can be very challenging since the data
show different characteristics. Given 3D laser scanning point clouds and 2D
imagery from different epochs, this paper presents a framework to detect
building and tree changes. First, the 2D and 3D data are transformed to image
patches, respectively. A Siamese CNN is then employed to detect candidate
changes between the two epochs. Finally, the candidate patch-based changes are
grouped and verified as individual object changes. Experiments on the urban
data show that 86.4\% of patch pairs can be correctly classified by the model.
Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study
Authors: Ahmad Hany Hossny, Terry Moschou, Grant Osborne, Lewis Mitchell, Nick Lothian
Date: 2018-07-25 00:00:00
Tasks: Clustering, Event Detection, Time Series Analysis

Extracting textual features from tweets is a challenging process due to the
noisy nature of the content and the weak signal of most of the words used. In
this paper, we propose using singular value decomposition (SVD) with clustering
to enhance the signals of the textual features in the tweets to improve the
correlation with events. The proposed technique applies SVD to the time series
vector for each feature to factorize the matrix of feature/day counts, in order
to ensure the independence of the feature vectors. Afterwards, the k-means
clustering is applied to build a look-up table that maps members of each
cluster to the cluster-centroid. The lookup table is used to map each feature
in the original data to the centroid of its cluster, then we calculate the sum
of the term frequency vectors of all features in each cluster to the
term-frequency-vector of the cluster centroid. To test the technique we
calculated the correlations of the cluster centroids with the golden standard
record (GSR) vector before and after summing the vectors of the cluster members
to the centroid-vector. The proposed method is applied to multiple correlation
techniques including the Pearson, Spearman, distance correlation and Kendal
Tao. The experiments have also considered the different word forms and lengths
of the features including keywords, n-grams, skip-grams and bags-of-words. The
correlation results are enhanced significantly as the highest correlation
scores have increased from 0.3 to 0.6, and the average correlation scores have
increased from 0.3 to 0.4.
Patch-based Evaluation of Dense Image Matching Quality
Authors: Zhenchao Zhang, Markus Gerke, George Vosselman, Michael Ying Yang
Date: 2018-07-25 00:00:00

Airborne laser scanning and photogrammetry are two main techniques to obtain
3D data representing the object surface. Due to the high cost of laser
scanning, we want to explore the potential of using point clouds derived by
dense image matching (DIM), as effective alternatives to laser scanning data.
We present a framework to evaluate point clouds from dense image matching and
derived Digital Surface Models (DSM) based on automatically extracted sample
patches. Dense matching error and noise level are evaluated quantitatively at
both the local level and whole block level. Experiments show that the optimal
vertical accuracy achieved by dense matching is as follows: the mean offset to
the reference data is 0.1 Ground Sampling Distance (GSD); the maximum offset
goes up to 1.0 GSD. When additional oblique images are used in dense matching,
the mean deviation, the variation of mean deviation and the level of random
noise all get improved. We also detect a bias between the point cloud and DSM
from a single photogrammetric workflow. This framework also allows to reveal
inhomogeneity in the distribution of the dense matching errors due to
over-fitted BBA network. Meanwhile, suggestions are given on the
photogrammetric quality control.
End-to-End Incremental Learning
Authors: Francisco M. Castro, Manuel J. Marín-Jiménez, Nicolás Guil, Cordelia Schmid, Karteek Alahari
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: image-classification, Image Classification, Incremental Learning

Although deep learning approaches have stood out in recent years due to their
state-of-the-art results, they continue to suffer from catastrophic forgetting,
a dramatic decrease in overall performance when training with new classes added
incrementally. This is due to current neural network architectures requiring
the entire dataset, consisting of all the samples from the old as well as the
new classes, to update the model -a requirement that becomes easily
unsustainable as the number of classes grows. We address this issue with our
approach to learn deep neural networks incrementally, using new data and only a
small exemplar set corresponding to samples from the old classes. This is based
on a loss composed of a distillation measure to retain the knowledge acquired
from the old classes, and a cross-entropy loss to learn the new classes. Our
incremental training is achieved while keeping the entire framework end-to-end,
i.e., learning the data representation and the classifier jointly, unlike
recent methods with no such guarantees. We evaluate our method extensively on
the CIFAR-100 and ImageNet (ILSVRC 2012) image classification datasets, and
show state-of-the-art performance.
Conditional Information Gain Networks
Authors: Ufuk Can Biçici, Cem Keskin, Lale Akarun
Date: 2018-07-25 00:00:00
Tasks: General Classification

Deep neural network models owe their representational power to the high
number of learnable parameters. It is often infeasible to run these largely
parametrized deep models in limited resource environments, like mobile phones.
Network models employing conditional computing are able to reduce computational
requirements while achieving high representational power, with their ability to
model hierarchies. We propose Conditional Information Gain Networks, which
allow the feed forward deep neural networks to execute conditionally, skipping
parts of the model based on the sample and the decision mechanisms inserted in
the architecture. These decision mechanisms are trained using cost functions
based on differentiable Information Gain, inspired by the training procedures
of decision trees. These information gain based decision mechanisms are
differentiable and can be trained end-to-end using a unified framework with a
general cost function, covering both classification and decision losses. We
test the effectiveness of the proposed method on MNIST and recently introduced
Fashion MNIST datasets and show that our information gain based conditional
execution approach can achieve better or comparable classification results
using significantly fewer parameters, compared to standard convolutional neural
network baselines.
Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings
Authors: Qi Chen, Lei Wang, Yifan Wu, Guangming Wu, Zhiling Guo, Steven L. Waslander
Date: 2018-07-25 00:00:00
Tasks: Deep Learning, Segmentation, Semantic Segmentation

As an important branch of deep learning, convolutional neural network has
largely improved the performance of building detection. For further
accelerating the development of building detection toward automatic mapping, a
benchmark dataset bears significance in fair comparisons. However, several
problems still remain in the current public datasets that address this task.
First, although building detection is generally considered equivalent to
extracting roof outlines, most datasets directly provide building footprints as
ground truths for testing and evaluation; the challenges of these benchmarks
are more complicated than roof segmentation, as relief displacement leads to
varying degrees of misalignment between roof outlines and footprints. On the
other hand, an image dataset should feature a large quantity and high spatial
resolution to effectively train a high-performance deep learning model for
accurate mapping of buildings. Unfortunately, the remote sensing community
still lacks proper benchmark datasets that can simultaneously satisfy these
requirements. In this paper, we present a new large-scale benchmark dataset
termed Aerial Imagery for Roof Segmentation (AIRS). This dataset provides a
wide coverage of aerial imagery with 7.5 cm resolution and contains over
220,000 buildings. The task posed for AIRS is defined as roof segmentation. We
implement several state-of-the-art deep learning methods of semantic
segmentation for performance evaluation and analysis of the proposed dataset.
The results can serve as the baseline for future work.