pwc-archive/papers-with-abstracts huggingface dataset:
Influence of Image Classification Accuracy on Saliency Map Estimation
Authors: Taiki Oyama, Takao Yamanaka
Date: 2018-07-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Saliency map estimation in computer vision aims to estimate the locations
where people gaze in images. Since people tend to look at objects in images,
the parameters of the model pretrained on ImageNet for image classification are
useful for the saliency map estimation. However, there is no research on the
relationship between the image classification accuracy and the performance of
the saliency map estimation. In this paper, it is shown that there is a strong
correlation between image classification accuracy and saliency map estimation
accuracy. We also investigated the effective architecture based on multi scale
images and the upsampling layers to refine the saliency-map resolution. Our
model achieved the state-of-the-art accuracy on the PASCAL-S, OSIE, and MIT1003
datasets. In the MIT Saliency Benchmark, our model achieved the best
performance in some metrics and competitive results in the other metrics.
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas
Authors: Esther Puyol-Anton, Bram Ruijsink, Helene Langet, Mathieu De Craene, Paolo Piro, Julia A. Schnabel, Andrew P. King
Date: 2018-07-27 00:00:00
Tasks: Dimensionality Reduction

The availability of large scale databases containing imaging and non-imaging
data, such as the UK Biobank, represents an opportunity to improve our
understanding of healthy and diseased bodily function. Cardiac motion atlases
provide a space of reference in which the motion fields of a cohort of subjects
can be directly compared. In this work, a cardiac motion atlas is built from
cine MR data from the UK Biobank (~ 6000 subjects). Two automated quality
control strategies are proposed to reject subjects with insufficient image
quality. Based on the atlas, three dimensionality reduction algorithms are
evaluated to learn data-driven cardiac motion descriptors, and statistical
methods used to study the association between these descriptors and non-imaging
data. Results show a positive correlation between the atlas motion descriptors
and body fat percentage, basal metabolic rate, hypertension, smoking status and
alcohol intake frequency. The proposed method outperforms the ability to
identify changes in cardiac function due to these known cardiovascular risk
factors compared to ejection fraction, the most commonly used descriptor of
cardiac function. In conclusion, this work represents a framework for further
investigation of the factors influencing cardiac health.
General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
Authors: Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Favory, Jordi Pons, Xavier Serra
Date: 2018-07-26 00:00:00
Tasks: Audio Tagging, Task 2

This paper describes Task 2 of the DCASE 2018 Challenge, titled
"General-purpose audio tagging of Freesound content with AudioSet labels". This
task was hosted on the Kaggle platform as "Freesound General-Purpose Audio
Tagging Challenge". The goal of the task is to build an audio tagging system
that can recognize the category of an audio clip from a subset of 41 diverse
categories drawn from the AudioSet Ontology. We present the task, the dataset
prepared for the competition, and a baseline system.
Experimental Implementation of a Quantum Autoencoder via Quantum Adders
Authors: Yongcheng Ding, Lucas Lamata, Mikel Sanz, Xi Chen, Enrique Solano
Date: 2018-07-27 00:00:00

Quantum autoencoders allow for reducing the amount of resources in a quantum
computation by mapping the original Hilbert space onto a reduced space with the
relevant information. Recently, it was proposed to employ approximate quantum
adders to implement quantum autoencoders in quantum technologies. Here, we
carry out the experimental implementation of this proposal in the Rigetti cloud
quantum computer employing up to three qubits. The experimental fidelities are
in good agreement with the theoretical prediction, thus proving the feasibility
to realize quantum autoencoders via quantum adders in state-of-the-art
superconducting quantum technologies.
Revealing the Unobserved by Linking Collaborative Behavior and Side Knowledge
Authors: Evgeny Frolov, Ivan Oseledets
Date: 2018-07-27 00:00:00

We propose a tensor-based model that fuses a more granular representation of
user preferences with the ability to take additional side information into
account. The model relies on the concept of ordinal nature of utility, which
better corresponds to actual user perception. In addition to that, unlike the
majority of hybrid recommenders, the model ties side information directly to
collaborative data, which not only addresses the problem of extreme data
sparsity, but also allows to naturally exploit patterns in the observed
behavior for a more meaningful representation of user intents. We demonstrate
the effectiveness of the proposed model on several standard benchmark datasets.
The general formulation of the approach imposes no restrictions on the type of
observed interactions and makes it potentially applicable for joint modelling
of context information along with side data.
Diverse feature visualizations reveal invariances in early layers of deep neural networks
Authors: Santiago A. Cadena, Marissa A. Weis, Leon A. Gatys, Matthias Bethge, Alexander S. Ecker
Date: 2018-07-27 00:00:00
Proceeding: ECCV 2018 9

Visualizing features in deep neural networks (DNNs) can help understanding
their computations. Many previous studies aimed to visualize the selectivity of
individual units by finding meaningful images that maximize their activation.
However, comparably little attention has been paid to visualizing to what image
transformations units in DNNs are invariant. Here we propose a method to
discover invariances in the responses of hidden layer units of deep neural
networks. Our approach is based on simultaneously searching for a batch of
images that strongly activate a unit while at the same time being as distinct
from each other as possible. We find that even early convolutional layers in
VGG-19 exhibit various forms of response invariance: near-perfect phase
invariance in some units and invariance to local diffeomorphic transformations
in others. At the same time, we uncover representational differences with
ResNet-50 in its corresponding layers. We conclude that invariance
transformations are a major computational component learned by DNNs and we
provide a systematic method to study them.
Auto-Encoding Variational Neural Machine Translation
Authors: Bryan Eikema, Wilker Aziz
Date: 2018-07-27 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Sentence, Translation, Variational Inference

We present a deep generative model of bilingual sentence pairs for machine translation. The model generates source and target sentences jointly from a shared latent representation and is parameterised by neural networks. We perform efficient training using amortised variational inference and reparameterised gradients. Additionally, we discuss the statistical implications of joint modelling and propose an efficient approximation to maximum a posteriori decoding for fast test-time predictions. We demonstrate the effectiveness of our model in three machine translation scenarios: in-domain training, mixed-domain training, and learning from a mix of gold-standard and synthetic data. Our experiments show consistently that our joint formulation outperforms conditional modelling (i.e. standard neural machine translation) in all such scenarios.
Towards an Embodied Semantic Fovea: Semantic 3D scene reconstruction from ego-centric eye-tracker videos
Authors: Mickey Li, Noyan Songur, Pavel Orlov, Stefan Leutenegger, A. Aldo Faisal
Date: 2018-07-27 00:00:00
Tasks: 3D Scene Reconstruction, Semantic SLAM, Simultaneous Localization and Mapping

Incorporating the physical environment is essential for a complete
understanding of human behavior in unconstrained every-day tasks. This is
especially important in ego-centric tasks where obtaining 3 dimensional
information is both limiting and challenging with the current 2D video analysis
methods proving insufficient. Here we demonstrate a proof-of-concept system
which provides real-time 3D mapping and semantic labeling of the local
environment from an ego-centric RGB-D video-stream with 3D gaze point
estimation from head mounted eye tracking glasses. We augment existing work in
Semantic Simultaneous Localization And Mapping (Semantic SLAM) with collected
gaze vectors. Our system can then find and track objects both inside and
outside the user field-of-view in 3D from multiple perspectives with reasonable
accuracy. We validate our concept by producing a semantic map from images of
the NYUv2 dataset while simultaneously estimating gaze position and gaze
classes from recorded gaze data of the dataset images.
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network
Authors: Yongxiang Huang, Albert Chi-shing Chung
Date: 2018-07-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Vocal Bursts Intensity Prediction

Histology imaging is an essential diagnosis method to finalize the grade and
stage of cancer of different tissues, especially for breast cancer diagnosis.
Specialists often disagree on the final diagnosis on biopsy tissue due to the
complex morphological variety. Although convolutional neural networks (CNN)
have advantages in extracting discriminative features in image classification,
directly training a CNN on high resolution histology images is computationally
infeasible currently. Besides, inconsistent discriminative features often
distribute over the whole histology image, which incurs challenges in
patch-based CNN classification method. In this paper, we propose a novel
architecture for automatic classification of high resolution histology images.
First, an adapted residual network is employed to explore hierarchical features
without attenuation. Second, we develop a robust deep fusion network to utilize
the spatial relationship between patches and learn to correct the prediction
bias generated from inconsistent discriminative feature distribution. The
proposed method is evaluated using 10-fold cross-validation on 400 high
resolution breast histology images with balanced labels and reports 95%
accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class
classification (carcinoma and non-carcinoma), which substantially outperforms
previous methods and close to pathologist performance.
X2Face: A network for controlling face generation by using images, audio, and pose codes
Authors: Olivia Wiles, A. Sophia Koepke, Andrew Zisserman
Date: 2018-07-27 00:00:00
Tasks: Face Generation

The objective of this paper is a neural network model that controls the pose
and expression of a given face, using another face or modality (e.g. audio).
This model can then be used for lightweight, sophisticated video and image
editing.
  We make the following three contributions. First, we introduce a network,
X2Face, that can control a source face (specified by one or more frames) using
another face in a driving frame to produce a generated frame with the identity
of the source frame but the pose and expression of the face in the driving
frame. Second, we propose a method for training the network fully
self-supervised using a large collection of video data. Third, we show that the
generation process can be driven by other modalities, such as audio or pose
codes, without any further training of the network.
  The generation results for driving a face with another face are compared to
state-of-the-art self-supervised/supervised methods. We show that our approach
is more robust than other methods, as it makes fewer assumptions about the
input data. We also show examples of using our framework for video face
editing.
CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping
Authors: Haitian Zheng, Mengqi Ji, Haoqian Wang, Yebin Liu, Lu Fang
Date: 2018-07-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Patch Matching, Reference-based Super-Resolution, Super-Resolution

The Reference-based Super-resolution (RefSR) super-resolves a low-resolution
(LR) image given an external high-resolution (HR) reference image, where the
reference image and LR image share similar viewpoint but with significant
resolution gap x8. Existing RefSR methods work in a cascaded way such as patch
matching followed by synthesis pipeline with two independently defined
objective functions, leading to the inter-patch misalignment, grid effect and
inefficient optimization. To resolve these issues, we present CrossNet, an
end-to-end and fully-convolutional deep neural network using cross-scale
warping. Our network contains image encoders, cross-scale warping layers, and
fusion decoder: the encoder serves to extract multi-scale features from both
the LR and the reference images; the cross-scale warping layers spatially
aligns the reference feature map with the LR feature map; the decoder finally
aggregates feature maps from both domains to synthesize the HR output. Using
cross-scale warping, our network is able to perform spatial alignment at
pixel-level in an end-to-end fashion, which improves the existing schemes both
in precision (around 2dB-4dB) and efficiency (more than 100 times faster).
Automatic Short Answer Grading and Feedback Using Text Mining Methods
Authors: Neslihan Suzen, Alexander Gorban, Jeremy Levesley, Evgeny Mirkes
Date: 2018-07-27 00:00:00
Tasks: automatic short answer grading

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We then consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer.
ESCaF: Pupil Centre Localization Algorithm with Candidate Filtering
Authors: Anjith George, Aurobinda Routray
Date: 2018-07-27 00:00:00

Algorithms for accurate localization of pupil centre is essential for gaze
tracking in real world conditions. Most of the algorithms fail in real world
conditions like illumination variations, contact lenses, glasses, eye makeup,
motion blur, noise, etc. We propose a new algorithm which improves the
detection rate in real world conditions. The proposed algorithm uses both edges
as well as intensity information along with a candidate filtering approach to
identify the best pupil candidate. A simple tracking scheme has also been added
which improves the processing speed. The algorithm has been evaluated in
Labelled Pupil in the Wild (LPW) dataset, largest in its class which contains
real world conditions. The proposed algorithm outperformed the state of the art
algorithms while achieving real-time performance.
FARM: Functional Automatic Registration Method for 3D Human Bodies
Authors: Riccardo Marin, Simone Melzi, Emanuele Rodolà, Umberto Castellani
Date: 2018-07-27 00:00:00

We introduce a new method for non-rigid registration of 3D human shapes. Our
proposed pipeline builds upon a given parametric model of the human, and makes
use of the functional map representation for encoding and inferring shape maps
throughout the registration process. This combination endows our method with
robustness to a large variety of nuisances observed in practical settings,
including non-isometric transformations, downsampling, topological noise, and
occlusions; further, the pipeline can be applied invariably across different
shape representations (e.g. meshes and point clouds), and in the presence of
(even dramatic) missing parts such as those arising in real-world depth sensing
applications. We showcase our method on a selection of challenging tasks,
demonstrating results in line with, or even surpassing, state-of-the-art
methods in the respective areas.
Global and local evaluation of link prediction tasks with neural embeddings
Authors: Asan Agibetov, Matthias Samwald
Date: 2018-07-27 00:00:00
Tasks: Binary Classification, Knowledge Graphs, Link Prediction, Prediction

We focus our attention on the link prediction problem for knowledge graphs,
which is treated herein as a binary classification task on neural embeddings of
the entities. By comparing, combining and extending different methodologies for
link prediction on graph-based data coming from different domains, we formalize
a unified methodology for the quality evaluation benchmark of neural embeddings
for knowledge graphs. This benchmark is then used to empirically investigate
the potential of training neural embeddings globally for the entire graph, as
opposed to the usual way of training embeddings locally for a specific
relation. This new way of testing the quality of the embeddings evaluates the
performance of binary classifiers for scalable link prediction with limited
data. Our evaluation pipeline is made open source, and with this we aim to draw
more attention of the community towards an important issue of transparency and
reproducibility of the neural embeddings evaluations.
Person Search in Videos with One Portrait Through Visual and Temporal Links
Authors: Qingqiu Huang, Wentao Liu, Dahua Lin
Date: 2018-07-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Person Re-Identification, Person Search, Retrieval, Video Retrieval

In real-world applications, e.g. law enforcement and video retrieval, one
often needs to search a certain person in long videos with just one portrait.
This is much more challenging than the conventional settings for person
re-identification, as the search may need to be carried out in the environments
different from where the portrait was taken. In this paper, we aim to tackle
this challenge and propose a novel framework, which takes into account the
identity invariance along a tracklet, thus allowing person identities to be
propagated via both the visual and the temporal links. We also develop a novel
scheme called Progressive Propagation via Competitive Consensus, which
significantly improves the reliability of the propagation process. To promote
the study of person search, we construct a large-scale benchmark, which
contains 127K manually annotated tracklets from 192 movies. Experiments show
that our approach remarkably outperforms mainstream person re-id methods,
raising the mAP from 42.16% to 62.27%.
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G
Authors: Nils Strodthoff, Barış Göktepe, Thomas Schierl, Cornelius Hellge, Wojciech Samek
Date: 2018-07-27 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Scheduling

We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency communication (URLLC). To this end, we propose machine learning methods to predict the outcome of the decoding process ahead of the end of the transmission. We discuss different input features and classification algorithms ranging from traditional methods to newly developed supervised autoencoders. These methods are evaluated based on their prospects of complying with the URLLC requirements of effective block error rates below $10^{-5}$ at small latency overheads. We provide realistic performance estimates in a system model incorporating scheduling effects to demonstrate the feasibility of E-HARQ across different signal-to-noise ratios, subcode lengths, channel conditions and system loads, and show the benefit over regular HARQ and existing E-HARQ schemes without machine learning.
DeepLink: A Novel Link Prediction Framework based on Deep Learning
Authors: Mohammad Mehdi Keikha, Maseud Rahgozar, Masoud Asadpour
Date: 2018-07-27 00:00:00
Tasks: Deep Learning, Feature Engineering, Link Prediction, Prediction

Recently, link prediction has attracted more attentions from various
disciplines such as computer science, bioinformatics and economics. In this
problem, unknown links between nodes are discovered based on numerous
information such as network topology, profile information and user generated
contents. Most of the previous researchers have focused on the structural
features of the networks. While the recent researches indicate that contextual
information can change the network topology. Although, there are number of
valuable researches which combine structural and content information, but they
face with the scalability issue due to feature engineering. Because, majority
of the extracted features are obtained by a supervised or semi supervised
algorithm. Moreover, the existing features are not general enough to indicate
good performance on different networks with heterogeneous structures. Besides,
most of the previous researches are presented for undirected and unweighted
networks. In this paper, a novel link prediction framework called "DeepLink" is
presented based on deep learning techniques. In contrast to the previous
researches which fail to automatically extract best features for the link
prediction, deep learning reduces the manual feature engineering. In this
framework, both the structural and content information of the nodes are
employed. The framework can use different structural feature vectors, which are
prepared by various link prediction methods. It considers all proximity orders
that are presented in a network during the structural feature learning. We have
evaluated the performance of DeepLink on two real social network datasets
including Telegram and irBlogs. On both datasets, the proposed framework
outperforms several structural and hybrid approaches for link prediction
problem.