pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Source Fusion Operations in Subjective Logic
Authors: Rens Wouter van der Heijden, Henning Kopp, Frank Kargl
Date: 2018-05-03 00:00:00
Tasks: valid

The purpose of multi-source fusion is to combine information from more than
two evidence sources, or subjective opinions from multiple actors. For
subjective logic, a number of different fusion operators have been proposed,
each matching a fusion scenario with different assumptions. However, not all of
these operators are associative, and therefore multi-source fusion is not
well-defined for these settings. In this paper, we address this challenge, and
define multi-source fusion for weighted belief fusion (WBF) and consensus &
compromise fusion (CCF). For WBF, we show the definition to be equivalent to
the intuitive formulation under the bijective mapping between subjective logic
and Dirichlet evidence PDFs. For CCF, since there is no independent
generalization, we show that the resulting multi-source fusion produces valid
opinions, and explain why our generalization is sound. For completeness, we
also provide corrections to previous results for averaging and cumulative
belief fusion (ABF and CBF), as well as belief constraint fusion (BCF), which
is an extension of Dempster's rule. With our generalizations of fusion
operators, fusing information from multiple sources is now well-defined for all
different fusion types defined in subjective logic. This enables wider
applicability of subjective logic in applications where multiple actors
interact.
Boosting Domain Adaptation by Discovering Latent Domains
Authors: Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci
Date: 2018-05-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation

Current Domain Adaptation (DA) methods based on deep architectures assume
that the source samples arise from a single distribution. However, in practice,
most datasets can be regarded as mixtures of multiple domains. In these cases
exploiting single-source DA methods for learning target classifiers may lead to
sub-optimal, if not poor, results. In addition, in many applications it is
difficult to manually provide the domain labels for all source data points,
i.e. latent domains should be automatically discovered. This paper introduces a
novel Convolutional Neural Network (CNN) architecture which (i) automatically
discovers latent domains in visual datasets and (ii) exploits this information
to learn robust target classifiers. Our approach is based on the introduction
of two main components, which can be embedded into any existing CNN
architecture: (i) a side branch that automatically computes the assignment of a
source sample to a latent domain and (ii) novel layers that exploit domain
membership information to appropriately align the distribution of the CNN
internal feature representations to a reference distribution. We test our
approach on publicly-available datasets, showing that it outperforms
state-of-the-art multi-source DA methods by a large margin.
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising
Authors: Dong Xia, Fan Zhou
Date: 2017-07-05 00:00:00
Tasks: Clustering, Denoising

The higher order singular value decomposition (HOSVD) of tensors is a
generalization of matrix SVD. The perturbation analysis of HOSVD under random
noise is more delicate than its matrix counterpart. Recently, polynomial time
algorithms have been proposed where statistically optimal estimates of the
singular subspaces and the low rank tensors are attainable in the Euclidean
norm. In this article, we analyze the sup-norm perturbation bounds of HOSVD and
introduce estimators of the singular subspaces with sharp deviation bounds in
the sup-norm. We also investigate a low rank tensor denoising estimator and
demonstrate its fast convergence rate with respect to the entry-wise errors.
The sup-norm perturbation bounds reveal unconventional phase transitions for
statistical learning applications such as the exact clustering in high
dimensional Gaussian mixture model and the exact support recovery in sub-tensor
localizations. In addition, the bounds established for HOSVD also elaborate the
one-sided sup-norm perturbation bounds for the singular subspaces of unbalanced
(or fat) matrices.
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks
Authors: Konstantin Sozykin, Stanislav Protasov, Adil Khan, Rasheed Hussain, Jooyoung Lee
Date: 2017-09-05 00:00:00
Tasks: Action Recognition, Activity Recognition, Human Activity Recognition, Multi-Label Learning, Temporal Action Localization

Automatic analysis of the video is one of most complex problems in the fields
of computer vision and machine learning. A significant part of this research
deals with (human) activity recognition (HAR) since humans, and the activities
that they perform, generate most of the video semantics. Video-based HAR has
applications in various domains, but one of the most important and challenging
is HAR in sports videos. Some of the major issues include high inter- and
intra-class variations, large class imbalance, the presence of both group
actions and single player actions, and recognizing simultaneous actions, i.e.,
the multi-label learning problem. Keeping in mind these challenges and the
recent success of CNNs in solving various computer vision problems, in this
work, we implement a 3D CNN based multi-label deep HAR system for multi-label
class-imbalanced action recognition in hockey videos. We test our system for
two different scenarios: an ensemble of $k$ binary networks vs. a single
$k$-output network, on a publicly available dataset. We also compare our
results with the system that was originally designed for the chosen dataset.
Experimental results show that the proposed approach performs better than the
existing solution.
Framewise approach in multimodal emotion recognition in OMG challenge
Authors: Grigoriy Sterling, Andrey Belyaev, Maxim Ryabov
Date: 2018-05-03 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition, Speaker Recognition

In this report we described our approach achieves $53\%$ of unweighted
accuracy over $7$ emotions and $0.05$ and $0.09$ mean squared errors for
arousal and valence in OMG emotion recognition challenge. Our results were
obtained with ensemble of single modality models trained on voice and face data
from video separately. We consider each stream as a sequence of frames. Next we
estimated features from frames and handle it with recurrent neural network. As
audio frame we mean short $0.4$ second spectrogram interval. For features
estimation for face pictures we used own ResNet neural network pretrained on
AffectNet database. Each short spectrogram was considered as a picture and
processed by convolutional network too. As a base audio model we used ResNet
pretrained in speaker recognition task. Predictions from both modalities were
fused on decision level and improve single-channel approaches by a few percent
Open Loop Execution of Tree-Search Algorithms, extended version
Authors: Erwan Lecarpentier, Guillaume Infantes, Charles Lesire, Emmanuel Rachelson
Date: 2018-05-03 00:00:00

In the context of tree-search stochastic planning algorithms where a
generative model is available, we consider on-line planning algorithms building
trees in order to recommend an action. We investigate the question of avoiding
re-planning in subsequent decision steps by directly using sub-trees as action
recommender. Firstly, we propose a method for open loop control via a new
algorithm taking the decision of re-planning or not at each time step based on
an analysis of the statistics of the sub-tree. Secondly, we show that the
probability of selecting a suboptimal action at any depth of the tree can be
upper bounded and converges towards zero. Moreover, this upper bound decays in
a logarithmic way between subsequent depths. This leads to a distinction
between node-wise optimality and state-wise optimality. Finally, we empirically
demonstrate that our method achieves a compromise between loss of performance
and computational gain.
Machine learning regression on hyperspectral data to estimate multiple water parameters
Authors: Philipp M. Maier, Sina Keller
Date: 2018-05-03 00:00:00
Tasks: BIG-bench Machine Learning, regression

In this paper, we present a regression framework involving several machine
learning models to estimate water parameters based on hyperspectral data.
Measurements from a multi-sensor field campaign, conducted on the River Elbe,
Germany, represent the benchmark dataset. It contains hyperspectral data and
the five water parameters chlorophyll a, green algae, diatoms, CDOM and
turbidity. We apply a PCA for the high-dimensional data as a possible
preprocessing step. Then, we evaluate the performance of the regression
framework with and without this preprocessing step. The regression results of
the framework clearly reveal the potential of estimating water parameters based
on hyperspectral data with machine learning. The proposed framework provides
the basis for further investigations, such as adapting the framework to
estimate water parameters of different inland waters.
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings
Authors: Daniele Zambon, Lorenzo Livi, Cesare Alippi
Date: 2018-05-03 00:00:00
Tasks: Anomaly Detection, Change Detection

Mapping complex input data into suitable lower dimensional manifolds is a
common procedure in machine learning. This step is beneficial mainly for two
reasons: (1) it reduces the data dimensionality and (2) it provides a new data
representation possibly characterised by convenient geometric properties.
Euclidean spaces are by far the most widely used embedding spaces, thanks to
their well-understood structure and large availability of consolidated
inference methods. However, recent research demonstrated that many types of
complex data (e.g., those represented as graphs) are actually better described
by non-Euclidean geometries. Here, we investigate how embedding graphs on
constant-curvature manifolds (hyper-spherical and hyperbolic manifolds) impacts
on the ability to detect changes in sequences of attributed graphs. The
proposed methodology consists in embedding graphs into a geometric space and
perform change detection there by means of conventional methods for numerical
streams. The curvature of the space is a parameter that we learn to reproduce
the geometry of the original application-dependent graph space. Preliminary
experimental results show the potential capability of representing graphs by
means of curved manifold, in particular for change and anomaly detection
problems.
SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning
Authors: Titus Cieslewski, Konstantinos G. Derpanis, Davide Scaramuzza
Date: 2018-05-03 00:00:00
Tasks: Interest Point Detection, Pose Estimation, Visual Odometry

A wide range of computer vision algorithms rely on identifying sparse interest points in images and establishing correspondences between them. However, only a subset of the initially identified interest points results in true correspondences (inliers). In this paper, we seek a detector that finds the minimum number of points that are likely to result in an application-dependent "sufficient" number of inliers k. To quantify this goal, we introduce the "k-succinctness" metric. Extracting a minimum number of interest points is attractive for many applications, because it can reduce computational load, memory, and data transmission. Alongside succinctness, we introduce an unsupervised training methodology for interest point detectors that is based on predicting the probability of a given pixel being an inlier. In comparison to previous learned detectors, our method requires the least amount of data pre-processing. Our detector and other state-of-the-art detectors are extensively evaluated with respect to succinctness on popular public datasets covering both indoor and outdoor scenes, and both wide and narrow baselines. In certain cases, our detector is able to obtain an equivalent amount of inliers with as little as 60% of the amount of points of other detectors. The code and trained networks are provided at https://github.com/uzh-rpg/sips2_open .
Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling
Authors: Chenyan Xiong, Zhengzhong Liu, Jamie Callan, Tie-Yan Liu
Date: 2018-05-03 00:00:00
Tasks: Retrieval

This paper presents a Kernel Entity Salience Model (KESM) that improves text
understanding and retrieval by better estimating entity salience (importance)
in documents. KESM represents entities by knowledge enriched distributed
representations, models the interactions between entities and words by kernels,
and combines the kernel scores to estimate entity salience. The whole model is
learned end-to-end using entity salience labels. The salience model also
improves ad hoc search accuracy, providing effective ranking features by
modeling the salience of query entities in candidate documents. Our experiments
on two entity salience corpora and two TREC ad hoc search datasets demonstrate
the effectiveness of KESM over frequency-based and feature-based methods. We
also provide examples showing how KESM conveys its text understanding ability
learned from entity salience to search.
Evaluation of CNN-based Single-Image Depth Estimation Methods
Authors: Tobias Koch, Lukas Liebel, Friedrich Fraundorfer, Marco Körner
Date: 2018-05-03 00:00:00
Tasks: Depth Estimation

While an increasing interest in deep models for single-image depth estimation
methods can be observed, established schemes for their evaluation are still
limited. We propose a set of novel quality criteria, allowing for a more
detailed analysis by focusing on specific characteristics of depth maps. In
particular, we address the preservation of edges and planar regions, depth
consistency, and absolute distance accuracy. In order to employ these metrics
to evaluate and compare state-of-the-art single-image depth estimation
approaches, we provide a new high-quality RGB-D dataset. We used a DSLR camera
together with a laser scanner to acquire high-resolution images and highly
accurate depth maps. Experimental results show the validity of our proposed
evaluation protocol.
Topology Control of wireless sensor network using Quantum Inspired Genetic algorithm
Authors: Sajid Ullah, Mussarat Wahid
Date: 2015-08-11 00:00:00

In this work, an evolving Linked Quantum register has been introduced, which
are group vector of binary pair of genes, which in its local proximity
represent those nodes that will have high connectivity and keep the energy
consumption at low, and which are taken into account for topology control. The
register works in higher dimension. Here order-2 Quantum inspired genetic
algorithm has been used and also higher order can be used to achieve greater
versatility in topology control of nodes. Numerical result has been obtained,
analysis is done as how the result has previously been obtained with Quantum
genetic algorithm and results are compared too. For future work, factor is
hinted which would exploit the algorithm to work in more computational
intensive problem.
Facial Landmarks Localization using Cascaded Neural Networks
Authors: Shahar Mahpod, Rig Das, Emanuele Maiorana, Yosi Keller, Patrizio Campisi
Date: 2018-05-03 00:00:00
Tasks: Face Recognition, Facial Landmark Detection, regression

The accurate localization of facial landmarks is at the core of face analysis tasks, such as face recognition and facial expression analysis, to name a few. In this work, we propose a novel localization approach based on a deep learning architecture that utilizes cascaded subnetworks with convolutional neural network units. The cascaded units of the first subnetwork estimate heatmap-based encodings of the landmarks locations, while the cascaded units of the second subnetwork receive as input the output of the corresponding heatmap estimation units, and refine them through regression. The proposed scheme is experimentally shown to compare favorably with contemporary state-of-the-art schemes, especially when applied to images depicting challenging localization conditions.
Multi-task Learning of Cascaded CNN for Facial Attribute Classification
Authors: Ni Zhuang, Yan Yan, Si Chen, Hanzi Wang
Date: 2018-05-03 00:00:00
Tasks: Attribute, Classification, Face Alignment, Face Detection, Facial Attribute Classification, General Classification, Multi-Task Learning

Recently, facial attribute classification (FAC) has attracted significant
attention in the computer vision community. Great progress has been made along
with the availability of challenging FAC datasets. However, conventional FAC
methods usually firstly pre-process the input images (i.e., perform face
detection and alignment) and then predict facial attributes. These methods
ignore the inherent dependencies among these tasks (i.e., face detection,
facial landmark localization and FAC). Moreover, some methods using
convolutional neural network are trained based on the fixed loss weights
without considering the differences between facial attributes. In order to
address the above problems, we propose a novel multi-task learning of cas-
caded convolutional neural network method, termed MCFA, for predicting multiple
facial attributes simultaneously. Specifically, the proposed method takes
advantage of three cascaded sub-networks (i.e., S_Net, M_Net and L_Net
corresponding to the neural networks under different scales) to jointly train
multiple tasks in a coarse-to-fine manner, which can achieve end-to-end
optimization. Furthermore, the proposed method automatically assigns the loss
weight to each facial attribute based on a novel dynamic weighting scheme, thus
making the proposed method concentrate on predicting the more difficult facial
attributes. Experimental results show that the proposed method outperforms
several state-of-the-art FAC methods on the challenging CelebA and LFWA
datasets.
Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification
Authors: Ni Zhuang, Yan Yan, Si Chen, Hanzi Wang, Chunhua Shen
Date: 2018-05-03 00:00:00
Tasks: Attribute, Classification, Domain Adaptation, Face Detection, Facial Attribute Classification, General Classification, Multi-Label Learning, Transfer Learning, Unsupervised Domain Adaptation

Deep Neural Network (DNN) has recently achieved outstanding performance in a
variety of computer vision tasks, including facial attribute classification.
The great success of classifying facial attributes with DNN often relies on a
massive amount of labelled data. However, in real-world applications, labelled
data are only provided for some commonly used attributes (such as age, gender);
whereas, unlabelled data are available for other attributes (such as
attraction, hairline). To address the above problem, we propose a novel deep
transfer neural network method based on multi-label learning for facial
attribute classification, termed FMTNet, which consists of three sub-networks:
the Face detection Network (FNet), the Multi-label learning Network (MNet) and
the Transfer learning Network (TNet). Firstly, based on the Faster Region-based
Convolutional Neural Network (Faster R-CNN), FNet is fine-tuned for face
detection. Then, MNet is fine-tuned by FNet to predict multiple attributes with
labelled data, where an effective loss weight scheme is developed to explicitly
exploit the correlation between facial attributes based on attribute grouping.
Finally, based on MNet, TNet is trained by taking advantage of unsupervised
domain adaptation for unlabelled facial attribute classification. The three
sub-networks are tightly coupled to perform effective facial attribute
classification. A distinguishing characteristic of the proposed FMTNet method
is that the three sub-networks (FNet, MNet and TNet) are constructed in a
similar network structure. Extensive experimental results on challenging face
datasets demonstrate the effectiveness of our proposed method compared with
several state-of-the-art methods.
Learning Pretopological Spaces to Model Complex Propagation Phenomena: A Multiple Instance Learning Approach Based on a Logical Modeling
Authors: Gaëtan Caillaut, Guillaume Cleuziou
Date: 2018-05-03 00:00:00
Tasks: Multiple Instance Learning

This paper addresses the problem of learning the concept of "propagation" in
the pretopology theoretical formalism. Our proposal is first to define the
pseudo-closure operator (modeling the propagation concept) as a logical
combination of neighborhoods. We show that learning such an operator lapses
into the Multiple Instance (MI) framework, where the learning process is
performed on bags of instances instead of individual instances. Though this
framework is well suited for this task, its use for learning a pretopological
space leads to a set of bags exponential in size. To overcome this issue we
thus propose a learning method based on a low estimation of the bags covered by
a concept under construction. As an experiment, percolation processes (forest
fires typically) are simulated and the corresponding propagation models are
learned based on a subset of observations. It reveals that the proposed MI
approach is significantly more efficient on the task of propagation model
recognition than existing methods.
Two Techniques That Enhance the Performance of Multi-robot Prioritized Path Planning
Authors: Anton Andreychuk, Konstantin Yakovlev
Date: 2018-05-03 00:00:00
Tasks: Scheduling

We introduce and empirically evaluate two techniques aimed at enhancing the
performance of multi-robot prioritized path planning. The first technique is
the deterministic procedure for re-scheduling (as opposed to well-known
approach based on random restarts), the second one is the heuristic procedure
that modifies the search-space of the individual planner involved in the
prioritized path finding.
Learning-Based Compressive MRI
Authors: Baran Gözcü, Rabeeh Karimi Mahabadi, Yen-Huan Li, Efe Ilıcak, Tolga Çukur, Jonathan Scarlett, Volkan Cevher
Date: 2018-05-03 00:00:00
Tasks: Anatomy, Learning Theory

In the area of magnetic resonance imaging (MRI), an extensive range of
non-linear reconstruction algorithms have been proposed that can be used with
general Fourier subsampling patterns. However, the design of these subsampling
patterns has typically been considered in isolation from the reconstruction
rule and the anatomy under consideration. In this paper, we propose a
learning-based framework for optimizing MRI subsampling patterns for a specific
reconstruction rule and anatomy, considering both the noiseless and noisy
settings. Our learning algorithm has access to a representative set of training
signals, and searches for a sampling pattern that performs well on average for
the signals in this set. We present a novel parameter-free greedy mask
selection method, and show it to be effective for a variety of reconstruction
rules and performance metrics. Moreover we also support our numerical findings
by providing a rigorous justification of our framework via statistical learning
theory.