pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics
Authors: Ertugrul Bayraktar, Pinar Boyraz
Date: 2017-10-17 00:00:00

The purpose of this study is to provide a detailed performance comparison of
feature detector/descriptor methods, particularly when their various
combinations are used for image-matching. The localization experiments of a
mobile robot in an indoor environment are presented as a case study. In these
experiments, 3090 query images and 127 dataset images were used. This study
includes five methods for feature detectors (features from accelerated segment
test (FAST), oriented FAST and rotated binary robust independent elementary
features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant
feature transform (SIFT), and binary robust invariant scalable keypoints
(BRISK)) and five other methods for feature descriptors (BRIEF, BRISK, SIFT,
SURF, and ORB). These methods were used in 23 different combinations and it was
possible to obtain meaningful and consistent comparison results using the
performance criteria defined in this study. All of these methods were used
independently and separately from each other as either feature detector or
descriptor. The performance analysis shows the discriminative power of various
combinations of detector and descriptor methods. The analysis is completed
using five parameters: (i) accuracy, (ii) time, (iii) angle difference between
keypoints, (iv) number of correct matches, and (v) distance between correctly
matched keypoints. In a range of 60{\deg}, covering five rotational pose points
for our system, the FAST-SURF combination had the lowest distance and angle
difference values and the highest number of matched keypoints. SIFT-SURF was
the most accurate combination with a 98.41% correct classification rate. The
fastest algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to
match 560 images captured during motion with 127 dataset images.
3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances
Authors: Wim Abbeloos, Esra Ataer-Cansizoglu, Sergio Caccamo, Yuichi Taguchi, Yukiyasu Domae
Date: 2017-10-17 00:00:00
Tasks: Descriptive, Object, Object Discovery, Triplet

Unsupervised object modeling is important in robotics, especially for
handling a large set of objects. We present a method for unsupervised 3D object
discovery, reconstruction, and localization that exploits multiple instances of
an identical object contained in a single RGB-D image. The proposed method does
not rely on segmentation, scene knowledge, or user input, and thus is easily
scalable. Our method aims to find recurrent patterns in a single RGB-D image by
utilizing appearance and geometry of the salient regions. We extract keypoints
and match them in pairs based on their descriptors. We then generate triplets
of the keypoints matching with each other using several geometric criteria to
minimize false matches. The relative poses of the matched triplets are computed
and clustered to discover sets of triplet pairs with similar relative poses.
Triplets belonging to the same set are likely to belong to the same object and
are used to construct an initial object model. Detection of remaining instances
with the initial object model using RANSAC allows to further expand and refine
the model. The automatically generated object models are both compact and
descriptive. We show quantitative and qualitative results on RGB-D images with
various objects including some from the Amazon Picking Challenge. We also
demonstrate the use of our method in an object picking scenario with a robotic
arm.
On the (Statistical) Detection of Adversarial Examples
Authors: Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, Patrick McDaniel
Date: 2017-02-21 00:00:00
Tasks: Intrusion Detection, Malware Classification, Network Intrusion Detection

Machine Learning (ML) models are applied in a variety of tasks such as
network intrusion detection or Malware classification. Yet, these models are
vulnerable to a class of malicious inputs known as adversarial examples. These
are slightly perturbed inputs that are classified incorrectly by the ML model.
The mitigation of these adversarial inputs remains an open problem. As a step
towards understanding adversarial examples, we show that they are not drawn
from the same distribution than the original data, and can thus be detected
using statistical tests. Using thus knowledge, we introduce a complimentary
approach to identify specific inputs that are adversarial. Specifically, we
augment our ML model with an additional output, in which the model is trained
to classify all adversarial inputs. We evaluate our approach on multiple
adversarial example crafting methods (including the fast gradient sign and
saliency map methods) with several datasets. The statistical test flags sample
sets containing adversarial inputs confidently at sample sizes between 10 and
100 data points. Furthermore, our augmented model either detects adversarial
examples as outliers with high accuracy (> 80%) or increases the adversary's
cost - the perturbation added - by more than 150%. In this way, we show that
statistical properties of adversarial examples are essential to their
detection.
Projective reconstruction in algebraic vision
Authors: Atsushi Ito, Makoto Miura, Kazushi Ueda
Date: 2017-10-17 00:00:00

We discuss the geometry of rational maps from a projective space of an arbitrary dimension to the product of projective spaces of lower dimensions induced by linear projections. In particular, we give an algebro-geometric variant of the projective reconstruction theorem by Hartley and Schaffalitzky [HS09].
Continuous 3D Label Stereo Matching using Local Expansion Moves
Authors: Tatsunori Taniai, Yasuyuki Matsushita, Yoichi Sato, Takeshi Naemura
Date: 2016-03-28 00:00:00
Tasks: Patch Matching, Stereo Matching, Stereo Matching Hand

We present an accurate stereo matching method using local expansion moves
based on graph cuts. This new move-making scheme is used to efficiently infer
per-pixel 3D plane labels on a pairwise Markov random field (MRF) that
effectively combines recently proposed slanted patch matching and curvature
regularization terms. The local expansion moves are presented as many
alpha-expansions defined for small grid regions. The local expansion moves
extend traditional expansion moves by two ways: localization and spatial
propagation. By localization, we use different candidate alpha-labels according
to the locations of local alpha-expansions. By spatial propagation, we design
our local alpha-expansions to propagate currently assigned labels for nearby
regions. With this localization and spatial propagation, our method can
efficiently infer MRF models with a continuous label space using randomized
search. Our method has several advantages over previous approaches that are
based on fusion moves or belief propagation; it produces submodular moves
deriving a subproblem optimality; it helps find good, smooth, piecewise linear
disparity maps; it is suitable for parallelization; it can use cost-volume
filtering techniques for accelerating the matching cost computations. Even
using a simple pairwise MRF, our method is shown to have best performance in
the Middlebury stereo benchmark V2 and V3.
A New Coherence-Penalized Minimal Path Model with Application to Retinal Vessel Centerline Delineation
Authors: Da Chen, Laurent D. Cohen
Date: 2017-10-17 00:00:00

In this paper, we propose a new minimal path model for minimally interactive
retinal vessel centerline extraction. The main contribution lies at the
construction of a novel coherence-penalized Riemannian metric in a lifted
space, dependently of the local geometry of tubularity and an external
scalar-valued reference feature map. The globally minimizing curves associated
to the proposed metric favour to pass through a set of retinal vessel segments
with low variations of the feature map, thus can avoid the short branches
combination problem and shortcut problem, commonly suffered by the existing
minimal path models in the application of retinal imaging. We validate our
model on a series of retinal vessel patches obtained from the DRIVE and IOSTAR
datasets, showing that our model indeed get promising results.
Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task
Authors: Stephen James, Andrew J. Davison, Edward Johns
Date: 2017-07-07 00:00:00
Tasks: Robotic Grasping, Robot Manipulation

End-to-end control for robot manipulation and grasping is emerging as an
attractive alternative to traditional pipelined approaches. However, end-to-end
methods tend to either be slow to train, exhibit little or no generalisability,
or lack the ability to accomplish long-horizon or multi-stage tasks. In this
paper, we show how two simple techniques can lead to end-to-end (image to
velocity) execution of a multi-stage task, which is analogous to a simple
tidying routine, without having seen a single real image. This involves
locating, reaching for, and grasping a cube, then locating a basket and
dropping the cube inside. To achieve this, robot trajectories are computed in a
simulator, to collect a series of control velocities which accomplish the task.
Then, a CNN is trained to map observed images to velocities, using domain
randomisation to enable generalisation to real world images. Results show that
we are able to successfully accomplish the task in the real world with the
ability to generalise to novel environments, including those with dynamic
lighting conditions, distractor objects, and moving objects, including the
basket itself. We believe our approach to be simple, highly scalable, and
capable of learning long-horizon tasks that have until now not been shown with
the state-of-the-art in end-to-end robot control.
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Authors: Diego Moussallem, Ricardo Usbeck, Michael Röder, Axel-Cyrille Ngonga Ngomo
Date: 2017-07-17 00:00:00
Tasks: Entity Linking, Retrieval

Entity linking has recently been the subject of a significant body of
research. Currently, the best performing approaches rely on trained
mono-lingual models. Porting these approaches to other languages is
consequently a difficult endeavor as it requires corresponding training data
and retraining of the models. We address this drawback by presenting a novel
multilingual, knowledge-based agnostic and deterministic approach to entity
linking, dubbed MAG. MAG is based on a combination of context-based retrieval
on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data
sets and in 7 languages. Our results show that the best approach trained on
English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse
on datasets in other languages. MAG, on the other hand, achieves
state-of-the-art performance on English datasets and reaches a micro F-measure
that is up to 0.6 higher than that of PBOH on non-English languages.
Learning to Learn Image Classifiers with Visual Analogy
Authors: Linjun Zhou, Peng Cui, Shiqiang Yang, Wenwu Zhu, Qi Tian
Date: 2017-10-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, General Classification, Image Classification

Humans are far better learners who can learn a new concept very fast with
only a few samples compared with machines. The plausible mystery making the
difference is two fundamental learning mechanisms: learning to learn and
learning by analogy. In this paper, we attempt to investigate a new human-like
learning method by organically combining these two mechanisms. In particular,
we study how to generalize the classification parameters from previously
learned concepts to a new concept. we first propose a novel Visual Analogy
Graph Embedded Regression (VAGER) model to jointly learn a low-dimensional
embedding space and a linear mapping function from the embedding space to
classification parameters for base classes. We then propose an out-of-sample
embedding method to learn the embedding of a new class represented by a few
samples through its visual analogy with base classes and derive the
classification parameters for the new class. We conduct extensive experiments
on ImageNet dataset and the results show that our method could consistently and
significantly outperform state-of-the-art baselines.
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
Authors: Maikel Leemans, Wil M. P. van der Aalst, Mark G. J. van den Brand
Date: 2017-10-17 00:00:00
Tasks: Model Discovery

This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational processes of software systems under real-life conditions at
multiple levels of granularity. The work can be positioned in-between reverse
engineering and process mining. An implementation of the proposed approach is
available as a ProM plugin. Experimental results based on real-life (software)
event logs demonstrate the feasibility and usefulness of the approach and show
the huge potential to speed up discovery by exploiting the available hierarchy.
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection
Authors: Damien Matti, Hazim Kemal Ekenel, Jean-Philippe Thiran
Date: 2017-10-17 00:00:00
Tasks: Autonomous Vehicles, Clustering, Pedestrian Detection

Pedestrian detection is an important component for safety of autonomous
vehicles, as well as for traffic and street surveillance. There are extensive
benchmarks on this topic and it has been shown to be a challenging problem when
applied on real use-case scenarios. In purely image-based pedestrian detection
approaches, the state-of-the-art results have been achieved with convolutional
neural networks (CNN) and surprisingly few detection frameworks have been built
upon multi-cue approaches. In this work, we develop a new pedestrian detector
for autonomous vehicles that exploits LiDAR data, in addition to visual
information. In the proposed approach, LiDAR data is utilized to generate
region proposals by processing the three dimensional point cloud that it
provides. These candidate regions are then further processed by a
state-of-the-art CNN classifier that we have fine-tuned for pedestrian
detection. We have extensively evaluated the proposed detection process on the
KITTI dataset. The experimental results show that the proposed LiDAR space
clustering approach provides a very efficient way of generating region
proposals leading to higher recall rates and fewer misses for pedestrian
detection. This indicates that LiDAR data can provide auxiliary information for
CNN-based approaches.
A New Family of Near-metrics for Universal Similarity
Authors: Chu Wang, Iraj Saniee, William S. Kennedy, Chris A. White
Date: 2017-07-21 00:00:00

We propose a family of near-metrics based on local graph diffusion to capture
similarity for a wide class of data sets. These quasi-metametrics, as their
names suggest, dispense with one or two standard axioms of metric spaces,
specifically distinguishability and symmetry, so that similarity between data
points of arbitrary type and form could be measured broadly and effectively.
The proposed near-metric family includes the forward k-step diffusion and its
reverse, typically on the graph consisting of data objects and their features.
By construction, this family of near-metrics is particularly appropriate for
categorical data, continuous data, and vector representations of images and
text extracted via deep learning approaches. We conduct extensive experiments
to evaluate the performance of this family of similarity measures and compare
and contrast with traditional measures of similarity used for each specific
application and with the ground truth when available. We show that for
structured data including categorical and continuous data, the near-metrics
corresponding to normalized forward k-step diffusion (k small) work as one of
the best performing similarity measures; for vector representations of text and
images including those extracted from deep learning, the near-metrics derived
from normalized and reverse k-step graph diffusion (k very small) exhibit
outstanding ability to distinguish data points from different classes.
Operational thermal load forecasting in district heating networks using machine learning and expert advice
Authors: Davy Geysen, Oscar De Somer, Christian Johansson, Jens Brage, Dirk Vanhoudt
Date: 2017-10-17 00:00:00
Tasks: BIG-bench Machine Learning, Load Forecasting, regression

Forecasting thermal load is a key component for the majority of optimization
solutions for controlling district heating and cooling systems. Recent studies
have analysed the results of a number of data-driven methods applied to thermal
load forecasting, this paper presents the results of combining a collection of
these individual methods in an expert system. The expert system will combine
multiple thermal load forecasts in a way that it always tracks the best expert
in the system. This solution is tested and validated using a thermal load
dataset of 27 months obtained from 10 residential buildings located in Rottne,
Sweden together with outdoor temperature information received from a weather
forecast service. The expert system is composed of the following data-driven
methods: linear regression, extremely randomized trees regression, feed-forward
neural network and support vector machine. The results of the proposed solution
are compared with the results of the individual methods.
Scalable Dense Monocular Surface Reconstruction
Authors: Mohammad Dawud Ansari, Vladislav Golyanik, Didier Stricker
Date: 2017-10-17 00:00:00
Tasks: Surface Reconstruction

This paper reports on a novel template-free monocular non-rigid surface
reconstruction approach. Existing techniques using motion and deformation cues
rely on multiple prior assumptions, are often computationally expensive and do
not perform equally well across the variety of data sets. In contrast, the
proposed Scalable Monocular Surface Reconstruction (SMSR) combines strengths of
several algorithms, i.e., it is scalable with the number of points, can handle
sparse and dense settings as well as different types of motions and
deformations. We estimate camera pose by singular value thresholding and
proximal gradient. Our formulation adopts alternating direction method of
multipliers which converges in linear time for large point track matrices. In
the proposed SMSR, trajectory space constraints are integrated by smoothing of
the measurement matrix. In the extensive experiments, SMSR is demonstrated to
consistently achieve state-of-the-art accuracy on a wide variety of data sets.
Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems
Authors: Damek Davis, Benjamin Grimmer
Date: 2017-07-12 00:00:00

In this paper, we introduce a stochastic projected subgradient method for
weakly convex (i.e., uniformly prox-regular) nonsmooth, nonconvex functions---a
wide class of functions which includes the additive and convex composite
classes. At a high-level, the method is an inexact proximal point iteration in
which the strongly convex proximal subproblems are quickly solved with a
specialized stochastic projected subgradient method. The primary contribution
of this paper is a simple proof that the proposed algorithm converges at the
same rate as the stochastic gradient method for smooth nonconvex problems. This
result appears to be the first convergence rate analysis of a stochastic (or
even deterministic) subgradient method for the class of weakly convex
functions.
Nesterov's Acceleration For Approximate Newton
Authors: Haishan Ye, Zhihua Zhang
Date: 2017-10-17 00:00:00
Tasks: Second-order methods

Optimization plays a key role in machine learning. Recently, stochastic
second-order methods have attracted much attention due to their low
computational cost in each iteration. However, these algorithms might perform
poorly especially if it is hard to approximate the Hessian well and
efficiently. As far as we know, there is no effective way to handle this
problem. In this paper, we resort to Nesterov's acceleration technique to
improve the convergence performance of a class of second-order methods called
approximate Newton. We give a theoretical analysis that Nesterov's acceleration
technique can improve the convergence performance for approximate Newton just
like for first-order methods. We accordingly propose an accelerated regularized
sub-sampled Newton. Our accelerated algorithm performs much better than the
original regularized sub-sampled Newton in experiments, which validates our
theory empirically. Besides, the accelerated regularized sub-sampled Newton has
good performance comparable to or even better than classical algorithms.
Nestrov's Acceleration For Second Order Method
Authors: Haishan Ye, Zhihua Zhang
Date: 2017-05-19 00:00:00
Tasks: Second-order methods

Optimization plays a key role in machine learning. Recently, stochastic
second-order methods have attracted much attention due to their low
computational cost in each iteration. However, these algorithms might perform
poorly especially if it is hard to approximate the Hessian well and
efficiently. As far as we know, there is no effective way to handle this
problem. In this paper, we resort to Nestrov's acceleration technique to
improve the convergence performance of a class of second-order methods called
approximate Newton. We give a theoretical analysis that Nestrov's acceleration
technique can improve the convergence performance for approximate Newton just
like for first-order methods. We accordingly propose an accelerated regularized
sub-sampled Newton. Our accelerated algorithm performs much better than the
original regularized sub-sampled Newton in experiments, which validates our
theory empirically. Besides, the accelerated regularized sub-sampled Newton has
good performance comparable to or even better than state-of-art algorithms.
CASICT Tibetan Word Segmentation System for MLWS2017
Authors: Jiawei Hu, Qun Liu
Date: 2017-10-17 00:00:00
Tasks: Segmentation

We participated in the MLWS 2017 on Tibetan word segmentation task, our
system is trained in a unrestricted way, by introducing a baseline system and
76w tibetan segmented sentences of ours. In the system character sequence is
processed by the baseline system into word sequence, then a subword unit (BPE
algorithm) split rare words into subwords with its corresponding features,
after that a neural network classifier is adopted to token each subword into
"B,M,E,S" label, in decoding step a simple rule is used to recover a final word
sequence. The candidate system for submition is selected by evaluating the
F-score in dev set pre-extracted from the 76w sentences. Experiment shows that
this method can fix segmentation errors of baseline system and result in a
significant performance gain.