pwc-archive/papers-with-abstracts huggingface dataset:
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
Authors: Waleed Ali, Sherif Abdelkarim, Mohamed Zahran, Mahmoud Zidan, Ahmad El Sallab
Date: 2018-08-07 00:00:00
Tasks: 3D Point Cloud Reconstruction, GPU, Object, object-detection, Object Detection, Point cloud reconstruction, regression

Object detection and classification in 3D is a key task in Automated Driving
(AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction
of the surrounding environment, while the task of 3D object bounding box
detection in real time remains a strong algorithmic challenge. In this paper,
we build on the success of the one-shot regression meta-architecture in the 2D
perspective image space and extend it to generate oriented 3D object bounding
boxes from LiDAR point cloud. Our main contribution is in extending the loss
function of YOLO v2 to include the yaw angle, the 3D box center in Cartesian
coordinates and the height of the box as a direct regression problem. This
formulation enables real-time performance, which is essential for automated
driving. Our results are showing promising figures on KITTI benchmark,
achieving real-time performance (40 fps) on Titan X GPU.
Optimal stopping via reinforced regression
Authors: Denis Belomestny, John Schoenmakers, Vladimir Spokoiny, Bakhyt Zharkynbay
Date: 2018-08-07 00:00:00
Tasks: regression

In this note we propose a new approach towards solving numerically optimal stopping problems via reinforced regression based Monte Carlo algorithms. The main idea of the method is to reinforce standard linear regression algorithms in each backward induction step by adding new basis functions based on previously estimated continuation values. The proposed methodology is illustrated by a numerical example from mathematical finance.
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
Authors: Omid Mohamad Nezami, Mark Dras, Len Hamey, Deborah Richards, Stephen Wan, Cecile Paris
Date: 2018-08-07 00:00:00
Tasks: Deep Learning, Facial Expression Recognition, Facial Expression Recognition (FER)

Engagement is a key indicator of the quality of learning experience, and one that plays a major role in developing intelligent educational interfaces. Any such interface requires the ability to recognise the level of engagement in order to respond appropriately; however, there is very little existing data to learn from, and new data is expensive and difficult to acquire. This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data. In the first of two steps, a facial expression recognition model is trained to provide a rich face representation using deep learning. In the second step, we use the model's weights to initialize our deep learning based model to recognize engagement; we term this the engagement model. We train the model on our new engagement recognition dataset with 4627 engaged and disengaged samples. We find that the engagement model outperforms effective deep learning architectures that we apply for the first time to engagement recognition, as well as approaches using histogram of oriented gradients and support vector machines.
Modelling hidden structure of signals in group data analysis with modified (Lr, 1) and block-term decompositions
Authors: Pavel Kharyuk, Ivan Oseledets
Date: 2018-08-07 00:00:00
Tasks: Clustering, General Classification, Tensor Decomposition

This work is devoted to elaboration on the idea to use block term
decomposition for group data analysis and to raise the possibility of modelling
group activity with (Lr, 1) and Tucker blocks. A new generalization of block
tensor decomposition was considered in application to group data analysis.
Suggested approach was evaluated on multilabel classification task for a set of
images. This contribution also reports results of investigation on clustering
with proposed tensor models in comparison with known matrix models, namely
common orthogonal basis extraction and group independent component analysis.
Deep Factorised Inverse-Sketching
Authors: Kaiyue Pang, Da Li, Jifei Song, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales
Date: 2018-08-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Retrieval, Retrieval, Sketch-Based Image Retrieval, Style Transfer

Modelling human free-hand sketches has become topical recently, driven by
practical applications such as fine-grained sketch based image retrieval
(FG-SBIR). Sketches are clearly related to photo edge-maps, but a human
free-hand sketch of a photo is not simply a clean rendering of that photo's
edge map. Instead there is a fundamental process of abstraction and iconic
rendering, where overall geometry is warped and salient details are selectively
included. In this paper we study this sketching process and attempt to invert
it. We model this inversion by translating iconic free-hand sketches to
contours that resemble more geometrically realistic projections of object
boundaries, and separately factorise out the salient added details. This
factorised re-representation makes it easier to match a free-hand sketch to a
photo instance of an object. Specifically, we propose a novel unsupervised
image style transfer model based on enforcing a cyclic embedding consistency
constraint. A deep FG-SBIR model is then formulated to accommodate
complementary discriminative detail from each factorised sketch for better
matching with the corresponding photo. Our method is evaluated both
qualitatively and quantitatively to demonstrate its superiority over a number
of state-of-the-art alternatives for style transfer and FG-SBIR.
Universal Perceptual Grouping
Authors: Ke Li, Kaiyue Pang, Jifei Song, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales, Honggang Zhang
Date: 2018-08-07 00:00:00
Tasks: Image Retrieval, Object, Retrieval, Sketch-Based Image Retrieval

In this work we aim to develop a universal sketch grouper. That is, a grouper
that can be applied to sketches of any category in any domain to group
constituent strokes/segments into semantically meaningful object parts. The
first obstacle to this goal is the lack of large-scale datasets with grouping
annotation. To overcome this, we contribute the largest sketch perceptual
grouping (SPG) dataset to date, consisting of 20,000 unique sketches evenly
distributed over 25 object categories. Furthermore, we propose a novel deep
universal perceptual grouping model. The model is learned with both generative
and discriminative losses. The generative losses improve the generalisation
ability of the model to unseen object categories and datasets. The
discriminative losses include a local grouping loss and a novel global grouping
loss to enforce global grouping consistency. We show that the proposed model
significantly outperforms the state-of-the-art groupers. Further, we show that
our grouper is useful for a number of sketch analysis tasks including sketch
synthesis and fine-grained sketch-based image retrieval (FG-SBIR).
Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN
Authors: Jorge E. Espinosa, Sergio A. Velastin, John W. Branch
Date: 2018-08-07 00:00:00
Tasks: General Classification

This paper introduces a Deep Learning Convolutional Neural Network model
based on Faster-RCNN for motorcycle detection and classification on urban
environments. The model is evaluated in occluded scenarios where more than 60%
of the vehicles present a degree of occlusion. For training and evaluation, we
introduce a new dataset of 7500 annotated images, captured under real traffic
scenes, using a drone mounted camera. Several tests were carried out to design
the network, achieving promising results of 75% in average precision (AP), even
with the high number of occluded motorbikes, the low angle of capture and the
moving camera. The model is also evaluated on low occlusions datasets, reaching
results of up to 92% in AP.
The Window Validity Problem in Rule-Based Stream Reasoning
Authors: Alessandro Ronca, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks
Date: 2018-08-07 00:00:00

Rule-based temporal query languages provide the expressive power and
flexibility required to capture in a natural way complex analysis tasks over
streaming data. Stream processing applications, however, typically require near
real-time response using limited resources. In particular, it becomes essential
that the underpinning query language has favourable computational properties
and that stream processing algorithms are able to keep only a small number of
previously received facts in memory at any point in time without sacrificing
correctness. In this paper, we propose a recursive fragment of temporal Datalog
with tractable data complexity and study the properties of a generic stream
reasoning algorithm for this fragment. We focus on the window validity problem
as a way to minimise the number of time points for which the stream reasoning
algorithm needs to keep data in memory at any point in time.
How did the discussion go: Discourse act classification in social media conversations
Authors: Subhabrata Dutta, Tanmoy Chakraborty, Dipankar Das
Date: 2018-08-07 00:00:00
Tasks: General Classification, Sentence, Stance Detection

We propose a novel attention based hierarchical LSTM model to classify
discourse act sequences in social media conversations, aimed at mining data
from online discussion using textual meanings beyond sentence level. The very
uniqueness of the task is the complete categorization of possible pragmatic
roles in informal textual discussions, contrary to extraction of
question-answers, stance detection or sarcasm identification which are very
much role specific tasks. Early attempt was made on a Reddit discussion
dataset. We train our model on the same data, and present test results on two
different datasets, one from Reddit and one from Facebook. Our proposed model
outperformed the previous one in terms of domain independence; without using
platform-dependent structural features, our hierarchical LSTM with word
relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively
to predict discourse roles of comments in Reddit and Facebook discussions.
Efficiency of recurrent and convolutional architectures in order to learn
discursive representation on the same task has been presented and analyzed,
with different word and comment embedding schemes. Our attention mechanism
enables us to inquire into relevance ordering of text segments according to
their roles in discourse. We present a human annotator experiment to unveil
important observations about modeling and data annotation. Equipped with our
text-based discourse identification model, we inquire into how heterogeneous
non-textual features like location, time, leaning of information etc. play
their roles in charaterizing online discussions on Facebook.
Predicting Visual Context for Unsupervised Event Segmentation in Continuous Photo-streams
Authors: Ana Garcia del Molino, Joo-Hwee Lim, Ah-Hwee Tan
Date: 2018-08-07 00:00:00
Tasks: Event Segmentation, Retrieval

Segmenting video content into events provides semantic structures for
indexing, retrieval, and summarization. Since motion cues are not available in
continuous photo-streams, and annotations in lifelogging are scarce and costly,
the frames are usually clustered into events by comparing the visual features
between them in an unsupervised way. However, such methodologies are
ineffective to deal with heterogeneous events, e.g. taking a walk, and
temporary changes in the sight direction, e.g. at a meeting. To address these
limitations, we propose Contextual Event Segmentation (CES), a novel
segmentation paradigm that uses an LSTM-based generative network to model the
photo-stream sequences, predict their visual context, and track their
evolution. CES decides whether a frame is an event boundary by comparing the
visual context generated from the frames in the past, to the visual context
predicted from the future. We implemented CES on a new and massive lifelogging
dataset consisting of more than 1.5 million images spanning over 1,723 days.
Experiments on the popular EDUB-Seg dataset show that our model outperforms the
state-of-the-art by over 16% in f-measure. Furthermore, CES' performance is
only 3 points below that of human annotators.
ODSQA: Open-domain Spoken Question Answering Dataset
Authors: Chia-Hsuan Lee, Shang-Ming Wang, Huan-Cheng Chang, Hung-Yi Lee
Date: 2018-08-07 00:00:00
Tasks: Data Augmentation, Question Answering, Reading Comprehension

Reading comprehension by machine has been widely studied, but machine
comprehension of spoken content is still a less investigated problem. In this
paper, we release Open-Domain Spoken Question Answering Dataset (ODSQA) with
more than three thousand questions. To the best of our knowledge, this is the
largest real SQA dataset. On this dataset, we found that ASR errors have
catastrophic impact on SQA. To mitigate the effect of ASR errors, subword units
are involved, which brings consistent improvements over all the models. We
further found that data augmentation on text-based QA training examples can
improve SQA.
MatchBench: An Evaluation of Feature Matchers
Authors: Jia-Wang Bian, Ruihan Yang, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid, WenHai Wu
Date: 2018-08-07 00:00:00

Feature matching is one of the most fundamental and active research areas in
computer vision. A comprehensive evaluation of feature matchers is necessary,
since it would advance both the development of this field and also high-level
applications such as Structure-from-Motion or Visual SLAM. However, to the best
of our knowledge, no previous work targets the evaluation of feature matchers
while they only focus on evaluating feature detectors and descriptors. This
leads to a critical absence in this field that there is no standard datasets
and evaluation metrics to evaluate different feature matchers fairly. To this
end, we present the first uniform feature matching benchmark to facilitate the
evaluation of feature matchers. In the proposed benchmark, matchers are
evaluated in different aspects, involving matching ability, correspondence
sufficiency, and efficiency. Also, their performances are investigated in
different scenes and in different matching types. Subsequently, we carry out an
extensive evaluation of different state-of-the-art matchers on the benchmark
and make in-depth analyses based on the reported results. This can be used to
design practical matching systems in real applications and also advocates the
potential future research directions in the field of feature matching.
Multi-Output Convolution Spectral Mixture for Gaussian Processes
Authors: Kai Chen, Twan van Laarhoven, Perry Groot, Jinsong Chen, Elena Marchiori
Date: 2018-08-07 00:00:00
Tasks: Gaussian Processes

Multi-output Gaussian processes (MOGPs) are an extension of Gaussian Processes (GPs) for predicting multiple output variables (also called channels, tasks) simultaneously. In this paper we use the convolution theorem to design a new kernel for MOGPs, by modeling cross channel dependencies through cross convolution of time and phase delayed components in the spectral domain. The resulting kernel is called Multi-Output Convolution Spectral Mixture (MOCSM) kernel. Results of extensive experiments on synthetic and real-life datasets demonstrate the advantages of the proposed kernel and its state of the art performance. MOCSM enjoys the desirable property to reduce to the well known Spectral Mixture (SM) kernel when a single-channel is considered. A comparison with the recently introduced Multi-Output Spectral Mixture kernel reveals that this is not the case for the latter kernel, which contains quadratic terms that generate undesirable scale effects when the spectral densities of different channels are either very close or very far from each other in the frequency domain.
SAM-RCNN: Scale-Aware Multi-Resolution Multi-Channel Pedestrian Detection
Authors: Tianrui Liu, Mohamed Elmikaty, Tania Stathaki
Date: 2018-08-07 00:00:00
Tasks: Pedestrian Detection, Region Proposal

Convolutional neural networks (CNN) have enabled significant improvements in
pedestrian detection owing to the strong representation ability of the CNN
features. Recently, aggregating features from multiple layers of a CNN has been
considered as an effective approach, however, the same approach regarding
feature representation is used for detecting pedestrians of varying scales.
Consequently, it is not guaranteed that the feature representation for
pedestrians of a particular scale is optimised. In this paper, we propose a
Scale-Aware Multi-resolution (SAM) method for pedestrian detection which can
adaptively select multi-resolution convolutional features according to
pedestrian sizes. The proposed SAM method extracts the appropriate CNN features
that have strong representation ability as well as sufficient feature
resolution, given the size of the pedestrian candidate output from a region
proposal network. Moreover, we propose an enhanced SAM method, termed as SAM+,
which incorporates complementary features channels and achieves further
performance improvement. Evaluations on the challenging Caltech and KITTI
pedestrian benchmarks demonstrate the superiority of our proposed method.
A Generic Multi-Projection-Center Model and Calibration Method for Light Field Cameras
Authors: Qi Zhang, Chunping Zhang, Jinbo Ling, Qing Wang, Jingyi Yu
Date: 2018-08-07 00:00:00
Tasks: 3D geometry, 3D Reconstruction

Light field cameras can capture both spatial and angular information of light
rays, enabling 3D reconstruction by a single exposure. The geometry of 3D
reconstruction is affected by intrinsic parameters of a light field camera
significantly. In the paper, we propose a multi-projection-center (MPC) model
with 6 intrinsic parameters to characterize light field cameras based on
traditional two-parallel-plane (TPP) representation. The MPC model can
generally parameterize light field in different imaging formations, including
conventional and focused light field cameras. By the constraints of 4D ray and
3D geometry, a 3D projective transformation is deduced to describe the
relationship between geometric structure and the MPC coordinates. Based on the
MPC model and projective transformation, we propose a calibration algorithm to
verify our light field camera model. Our calibration method includes a
close-form solution and a non-linear optimization by minimizing re-projection
errors. Experimental results on both simulated and real scene data have
verified the performance of our algorithm.
Speeding Up Distributed Gradient Descent by Utilizing Non-persistent Stragglers
Authors: Emre Ozfatura, Deniz Gunduz, Sennur Ulukus
Date: 2018-08-07 00:00:00

Distributed gradient descent (DGD) is an efficient way of implementing
gradient descent (GD), especially for large data sets, by dividing the
computation tasks into smaller subtasks and assigning to different computing
servers (CSs) to be executed in parallel. In standard parallel execution,
per-iteration waiting time is limited by the execution time of the straggling
servers. Coded DGD techniques have been introduced recently, which can tolerate
straggling servers via assigning redundant computation tasks to the CSs. In
most of the existing DGD schemes, either with coded computation or coded
communication, the non-straggling CSs transmit one message per iteration once
they complete all their assigned computation tasks. However, although the
straggling servers cannot complete all their assigned tasks, they are often
able to complete a certain portion of them. In this paper, we allow multiple
transmissions from each CS at each iteration in order to make sure a maximum
number of completed computations can be reported to the aggregating server
(AS), including the straggling servers. We numerically show that the average
completion time per iteration can be reduced significantly by slightly
increasing the communication load per server.
DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome
Authors: Behrooz Azarkhalili, Ali Saberi, Hamidreza Chitsaz, Ali Sharifi-Zarchi
Date: 2018-08-07 00:00:00
Tasks: Missing Values, Multi-Task Learning, Transfer Learning

Despite great advances, molecular cancer pathology is often limited to the use of a small number of biomarkers rather than the whole transcriptome, partly due to computational challenges. Here, we introduce a novel architecture of Deep Neural Networks (DNNs) that is capable of simultaneous inference of various properties of biological samples, through multi-task and transfer learning. It encodes the whole transcription profile into a strikingly low-dimensional latent vector of size 8, and then recovers mRNA and miRNA expression profiles, tissue and disease type from this vector. This latent space is significantly better than the original gene expression profiles for discriminating samples based on their tissue and disease. We employed this architecture on mRNA transcription profiles of 10787 clinical samples from 34 classes (one healthy and 33 different types of cancer) from 27 tissues. Our method significantly outperforms prior works and classical machine learning approaches in predicting tissue-of-origin, normal or disease state and cancer type of each sample. For tissues with more than one type of cancer, it reaches 99.4\% accuracy in identifying the correct cancer subtype. We also show this system is very robust against noise and missing values. Collectively, our results highlight applications of artificial intelligence in molecular cancer pathology and oncological research. DeePathology is freely available at \url{https://github.com/SharifBioinf/DeePathology}.
Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams
Authors: Mahardhika Pratama, Dianhui Wang
Date: 2018-08-07 00:00:00
Tasks: Continual Learning, Lifelong learning

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology.