pwc-archive/papers-with-abstracts huggingface dataset:
Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems
Authors: Christoph Schöller, Maximilian Schnettler, Annkathrin Krämmer, Gereon Hinz, Maida Bakovic, Müge Güzet, Alois Knoll
Date: 2019-04-18 00:00:00
Tasks: Camera Calibration

Most intelligent transportation systems use a combination of radar sensors and cameras for robust vehicle perception. The calibration of these heterogeneous sensor types in an automatic fashion during system operation is challenging due to differing physical measurement principles and the high sparsity of traffic radars. We propose - to the best of our knowledge - the first data-driven method for automatic rotational radar-camera calibration without dedicated calibration targets. Our approach is based on a coarse and a fine convolutional neural network. We employ a boosting-inspired training algorithm, where we train the fine network on the residual error of the coarse network. Due to the unavailability of public datasets combining radar and camera measurements, we recorded our own real-world data. We demonstrate that our method is able to reach precise and robust sensor registration and show its generalization capabilities to different sensor alignments and perspectives.
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
Authors: Zhe Wu, Li Su, Qingming Huang
Date: 2019-04-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: Camouflaged Object Segmentation, Decoder, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Existing state-of-the-art salient object detection networks rely on
aggregating multi-level features of pre-trained convolutional neural networks
(CNNs). Compared to high-level features, low-level features contribute less to
performance but cost more computations because of their larger spatial
resolutions. In this paper, we propose a novel Cascaded Partial Decoder (CPD)
framework for fast and accurate salient object detection. On the one hand, the
framework constructs partial decoder which discards larger resolution features
of shallower layers for acceleration. On the other hand, we observe that
integrating features of deeper layers obtain relatively precise saliency map.
Therefore we directly utilize generated saliency map to refine the features of
backbone network. This strategy efficiently suppresses distractors in the
features and significantly improves their representation ability. Experiments
conducted on five benchmark datasets exhibit that the proposed model not only
achieves state-of-the-art performance but also runs much faster than existing
models. Besides, the proposed framework is further applied to improve existing
multi-level feature aggregation models and significantly improve their
efficiency and accuracy.
Societal Controversies in Wikipedia Articles
Authors: Erik Borra, Andreas Kaltenbrunner, Michele Mauri, Esther Weltevrede, David Laniado, Richard Rogers, Paolo Ciuccarelli, Giovanni Magni, Tommaso Venturini
Date: 2019-04-18 00:00:00
Tasks: Articles

Collaborative content creation inevitably reaches situations where different
points of view lead to conflict. We focus on Wikipedia, the free encyclopedia
anyone may edit, where disputes about content in controversial articles often
reflect larger societal debates. While Wikipedia has a public edit history and
discussion section for every article, the substance of these sections is
difficult to phantom for Wikipedia users interested in the development of an
article and in locating which topics were most controversial. In this paper we
present Contropedia, a tool that augments Wikipedia articles and gives insight
into the development of controversial topics. Contropedia uses an efficient
language agnostic measure based on the edit history that focuses on wiki links
to easily identify which topics within a Wikipedia article have been most
controversial and when.
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning
Authors: Thanh-Toan Do, Toan Tran, Ian Reid, Vijay Kumar, Tuan Hoang, Gustavo Carneiro
Date: 2019-04-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: Metric Learning, Retrieval, Triplet

We propose a method that substantially improves the efficiency of deep
distance metric learning based on the optimization of the triplet loss
function. One epoch of such training process based on a naive optimization of
the triplet loss function has a run-time complexity O(N^3), where N is the
number of training samples. Such optimization scales poorly, and the most
common approach proposed to address this high complexity issue is based on
sub-sampling the set of triplets needed for the training process. Another
approach explored in the field relies on an ad-hoc linearization (in terms of
N) of the triplet loss that introduces class centroids, which must be optimized
using the whole training set for each mini-batch - this means that a naive
implementation of this approach has run-time complexity O(N^2). This complexity
issue is usually mitigated with poor, but computationally cheap, approximate
centroid optimization methods. In this paper, we first propose a solid theory
on the linearization of the triplet loss with the use of class centroids, where
the main conclusion is that our new linear loss represents a tight upper-bound
to the triplet loss. Furthermore, based on the theory above, we propose a
training algorithm that no longer requires the centroid optimization step,
which means that our approach is the first in the field with a guaranteed
linear run-time complexity. We show that the training of deep distance metric
learning methods using the proposed upper-bound is substantially faster than
triplet-based methods, while producing competitive retrieval accuracy results
on benchmark datasets (CUB-200-2011 and CAR196).
Knowledge-rich Image Gist Understanding Beyond Literal Meaning
Authors: Lydia Weiland, Ioana Hulpus, Simone Paolo Ponzetto, Wolfgang Effelsberg, Laura Dietz
Date: 2019-04-18 00:00:00
Tasks: Articles

We investigate the problem of understanding the message (gist) conveyed by
images and their captions as found, for instance, on websites or news articles.
To this end, we propose a methodology to capture the meaning of image-caption
pairs on the basis of large amounts of machine-readable knowledge that has
previously been shown to be highly effective for text understanding. Our method
identifies the connotation of objects beyond their denotation: where most
approaches to image understanding focus on the denotation of objects, i.e.,
their literal meaning, our work addresses the identification of connotations,
i.e., iconic meanings of objects, to understand the message of images. We view
image understanding as the task of representing an image-caption pair on the
basis of a wide-coverage vocabulary of concepts such as the one provided by
Wikipedia, and cast gist detection as a concept-ranking problem with
image-caption pairs as queries. To enable a thorough investigation of the
problem of gist understanding, we produce a gold standard of over 300
image-caption pairs and over 8,000 gist annotations covering a wide variety of
topics at different levels of abstraction. We use this dataset to
experimentally benchmark the contribution of signals from heterogeneous
sources, namely image and text. The best result with a Mean Average Precision
(MAP) of 0.69 indicate that by combining both dimensions we are able to better
understand the meaning of our image-caption pairs than when using language or
vision information alone. We test the robustness of our gist detection approach
when receiving automatically generated input, i.e., using automatically
generated image tags or generated captions, and prove the feasibility of an
end-to-end automated process.
Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition
Authors: Devraj Mandal, Sanath Narayan, Saikumar Dwivedi, Vikram Gupta, Shuaib Ahmed, Fahad Shahbaz Khan, Ling Shao
Date: 2019-04-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Action Recognition In Videos, Out-of-Distribution Detection, Temporal Action Localization, Zero-Shot Action Recognition

Generalized zero-shot action recognition is a challenging problem, where the task is to recognize new action categories that are unavailable during the training stage, in addition to the seen action categories. Existing approaches suffer from the inherent bias of the learned classifier towards the seen action categories. As a consequence, unseen category samples are incorrectly classified as belonging to one of the seen action categories. In this paper, we set out to tackle this issue by arguing for a separate treatment of seen and unseen action categories in generalized zero-shot action recognition. We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category. To train our out-of-distribution detector, video features for unseen action categories are synthesized using generative adversarial networks trained on seen action category features. To the best of our knowledge, we are the first to propose an out-of-distribution detector based GZSL framework for action recognition in videos. Experiments are performed on three action recognition datasets: Olympic Sports, HMDB51 and UCF101. For generalized zero-shot action recognition, our proposed approach outperforms the baseline (f-CLSWGAN) with absolute gains (in classification accuracy) of 7.0%, 3.4%, and 4.9%, respectively, on these datasets.
Weighted Lifted Codes: Local Correctabilities and Application to Robust Private Information Retrieval
Authors: Julien Lavauzelle, Jade Nardi
Date: 2019-04-18 00:00:00
Tasks: Information Retrieval, Retrieval

Low degree Reed-Muller codes are known to satisfy local decoding properties
which find applications in private information retrieval (PIR) protocols, for
instance. However, their practical instantiation encounters a first barrier due
to their poor information rate in the low degree regime. This lead the
community to design codes with similar local properties but larger dimension,
namely the lifted Reed-Solomon codes.
  However, a second practical barrier appears when one requires that the PIR
protocol resists collusions of servers. In this paper, we propose a solution to
this problem by considering \emph{weighted} Reed-Muller codes. We prove that
such codes allow us to build PIR protocols with optimal computation complexity
and resisting to a small number of colluding servers.
  In order to improve the dimension of the codes, we then introduce an analogue
of the lifting process for weigthed degrees. With a careful analysis of their
degree sets, we notably show that the weighted lifting of Reed-Solomon codes
produces families of codes with remarkable asymptotic parameters.
Convolutional neural networks: a magic bullet for gravitational-wave detection?
Authors: Timothy D. Gebhard, Niki Kilbertus, Ian Harry, Bernhard Schölkopf
Date: 2019-04-18 00:00:00
Tasks: Astronomy, BIG-bench Machine Learning, Gravitational Wave Detection

In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes. However, to date, these methods have not yet been successfully applied to the analysis of long stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave observatories. In this work, we critically examine the use of convolutional neural networks as a tool to search for merging black holes. We identify the strengths and limitations of this approach, highlight some common pitfalls in translating between machine learning and gravitational-wave astronomy, and discuss the interdisciplinary challenges. In particular, we explain in detail why convolutional neural networks alone cannot be used to claim a statistically significant gravitational-wave detection. However, we demonstrate how they can still be used to rapidly flag the times of potential signals in the data for a more detailed follow-up. Our convolutional neural network architecture as well as the proposed performance metrics are better suited for this task than a standard binary classifications scheme. A detailed evaluation of our approach on Advanced LIGO data demonstrates the potential of such systems as trigger generators. Finally, we sound a note of caution by constructing adversarial examples, which showcase interesting "failure modes" of our model, where inputs with no visible resemblance to real gravitational-wave signals are identified as such by the network with high confidence.
Exquisitor: Interactive Learning at Large
Authors: Björn Þór Jónsson, Omar Shahbaz Khan, Hanna Ragnarsdóttir, Þórhildur Þorleiksdóttir, Jan Zahálka, Stevan Rudinac, Gylfi Þór Guðmundsson, Laurent Amsaleg, Marcel Worring
Date: 2019-04-18 00:00:00
Tasks: CPU, Retrieval

Increasing scale is a dominant trend in today's multimedia collections, which especially impacts interactive applications. To facilitate interactive exploration of large multimedia collections, new approaches are needed that are capable of learning on the fly new analytic categories based on the visual and textual content. To facilitate general use on standard desktops, laptops, and mobile devices, they must furthermore work with limited computing resources. We present Exquisitor, a highly scalable interactive learning approach, capable of intelligent exploration of the large-scale YFCC100M image collection with extremely efficient responses from the interactive classifier. Based on relevance feedback from the user on previously suggested items, Exquisitor uses semantic features, extracted from both visual and text attributes, to suggest relevant media items to the user. Exquisitor builds upon the state of the art in large-scale data representation, compression and indexing, introducing a cluster-based retrieval mechanism that facilitates the efficient suggestions. With Exquisitor, each interaction round over the full YFCC100M collection is completed in less than 0.3 seconds using a single CPU core. That is 4x less time using 16x smaller computational resources than the most efficient state-of-the-art method, with a positive impact on result quality. These results open up many interesting research avenues, both for exploration of industry-scale media collections and for media exploration on mobile devices.
Examining the Capability of GANs to Replace Real Biomedical Images in Classification Models Training
Authors: Vassili Kovalev, Siarhei Kazlouski
Date: 2019-04-18 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

In this paper, we explore the possibility of generating artificial biomedical
images that can be used as a substitute for real image datasets in applied
machine learning tasks. We are focusing on generation of realistic chest X-ray
images as well as on the lymph node histology images using the two recent GAN
architectures including DCGAN and PGGAN. The possibility of the use of
artificial images instead of real ones for training machine learning models was
examined by benchmark classification tasks being solved using conventional and
deep learning methods. In particular, a comparison was made by replacing real
images with synthetic ones at the model training stage and comparing the
prediction results with the ones obtained while training on the real image
data. It was found that the drop of classification accuracy caused by such
training data substitution ranged between 2.2% and 3.5% for deep learning
models and between 5.5% and 13.25% for conventional methods such as LBP +
Random Forests.
Global Hashing System for Fast Image Search
Authors: Dayong Tian, DaCheng Tao
Date: 2019-04-18 00:00:00
Tasks: Image Retrieval

Hashing methods have been widely investigated for fast approximate nearest
neighbor searching in large data sets. Most existing methods use binary vectors
in lower dimensional spaces to represent data points that are usually real
vectors of higher dimensionality. We divide the hashing process into two steps.
Data points are first embedded in a low-dimensional space, and the global
positioning system method is subsequently introduced but modified for binary
embedding. We devise dataindependent and data-dependent methods to distribute
the satellites at appropriate locations. Our methods are based on finding the
tradeoff between the information losses in these two steps. Experiments show
that our data-dependent method outperforms other methods in different-sized
data sets from 100k to 10M. By incorporating the orthogonality of the code
matrix, both our data-independent and data-dependent methods are particularly
impressive in experiments on longer bits.
Coupled Learning for Facial Deblur
Authors: Dayong Tian, DaCheng Tao
Date: 2019-04-18 00:00:00
Tasks: Blind Image Quality Assessment, Face Recognition, Image Quality Assessment

Blur in facial images significantly impedes the efficiency of recognition
approaches. However, most existing blind deconvolution methods cannot generate
satisfactory results due to their dependence on strong edges, which are
sufficient in natural images but not in facial images. In this paper, we
represent point spread functions (PSFs) by the linear combination of a set of
pre-defined orthogonal PSFs, and similarly, an estimated intrinsic (EI) sharp
face image is represented by the linear combination of a set of pre-defined
orthogonal face images. In doing so, PSF and EI estimation is simplified to
discovering two sets of linear combination coefficients, which are
simultaneously found by our proposed coupled learning algorithm. To make our
method robust to different types of blurry face images, we generate several
candidate PSFs and EIs for a test image, and then, a non-blind deconvolution
method is adopted to generate more EIs by those candidate PSFs. Finally, we
deploy a blind image quality assessment metric to automatically select the
optimal EI. Thorough experiments on the facial recognition technology database,
extended Yale face database B, CMU pose, illumination, and expression (PIE)
database, and face recognition grand challenge database version 2.0 demonstrate
that the proposed approach effectively restores intrinsic sharp face images
and, consequently, improves the performance of face recognition.
Batch Tournament Selection for Genetic Programming
Authors: Vinicius V. Melo, Danilo Vasconcellos Vargas, Wolfgang Banzhaf
Date: 2019-04-18 00:00:00
Tasks: Diversity

Lexicase selection achieves very good solution quality by introducing ordered
test cases. However, the computational complexity of lexicase selection can
prohibit its use in many applications. In this paper, we introduce Batch
Tournament Selection (BTS), a hybrid of tournament and lexicase selection which
is approximately one order of magnitude faster than lexicase selection while
achieving a competitive quality of solutions. Tests on a number of regression
datasets show that BTS compares well with lexicase selection in terms of mean
absolute error while having a speed-up of up to 25 times. Surprisingly, BTS and
lexicase selection have almost no difference in both diversity and performance.
This reveals that batches and ordered test cases are completely different
mechanisms which share the same general principle fostering the specialization
of individuals. This work introduces an efficient algorithm that sheds light
onto the main principles behind the success of lexicase, potentially opening up
a new range of possibilities for algorithms to come.
Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations
Authors: Julia Rackerseder, Rüdiger Göbl, Nassir Navab, Christoph Hennersperger
Date: 2019-04-18 00:00:00
Tasks: Segmentation

Intra-operative ultrasound is an increasingly important imaging modality in
neurosurgery. However, manual interaction with imaging data during the
procedures, for example to select landmarks or perform segmentation, is
difficult and can be time consuming. Yet, as registration to other imaging
modalities is required in most cases, some annotation is necessary. We propose
a segmentation method based on DeepVNet and specifically evaluate the
integration of pre-training with simulated ultrasound sweeps to improve
automatic segmentation and enable a fully automatic initialization of
registration. In this view, we show that despite training on coarse and
incomplete semi-automatic annotations, our approach is able to capture the
desired superficial structures such as \textit{sulci}, the \textit{cerebellar
tentorium}, and the \textit{falx cerebri}. We perform a five-fold
cross-validation on the publicly available RESECT dataset. Trained on the
dataset alone, we report a Dice and Jaccard coefficient of $0.45 \pm 0.09$ and
$0.30 \pm 0.07$ respectively, as well as an average distance of $0.78 \pm
0.36~mm$. With the suggested pre-training, we computed a Dice and Jaccard
coefficient of $0.47 \pm 0.10$ and $0.31 \pm 0.08$, and an average distance of
$0.71 \pm 0.38~mm$. The qualitative evaluation suggest that with pre-training
the network can learn to generalize better and provide refined and more
complete segmentations in comparison to incomplete annotations provided as
input.
Analytical Methods for Interpretable Ultradense Word Embeddings
Authors: Philipp Dufter, Hinrich Schütze
Date: 2019-04-18 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Word Embeddings

Word embeddings are useful for a wide variety of tasks, but they lack interpretability. By rotating word spaces, interpretable dimensions can be identified while preserving the information contained in the embeddings without any loss. In this work, we investigate three methods for making word spaces interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and DensRay, a new method we propose. In contrast to Densifier, DensRay can be computed in closed form, is hyperparameter-free and thus more robust than Densifier. We evaluate the three methods on lexicon induction and set-based word analogy. In addition we provide qualitative insights as to how interpretable word spaces can be used for removing gender bias from embeddings.
Fooling automated surveillance cameras: adversarial patches to attack person detection
Authors: Simen Thys, Wiebe Van Ranst, Toon Goedemé
Date: 2019-04-18 00:00:00
Tasks: Human Detection

Adversarial attacks on machine learning models have seen increasing interest
in the past years. By making only subtle changes to the input of a
convolutional neural network, the output of the network can be swayed to output
a completely different result. The first attacks did this by changing pixel
values of an input image slightly to fool a classifier to output the wrong
class. Other approaches have tried to learn "patches" that can be applied to an
object to fool detectors and classifiers. Some of these approaches have also
shown that these attacks are feasible in the real-world, i.e. by modifying an
object and filming it with a video camera. However, all of these approaches
target classes that contain almost no intra-class variety (e.g. stop signs).
The known structure of the object is then used to generate an adversarial patch
on top of it.
  In this paper, we present an approach to generate adversarial patches to
targets with lots of intra-class variety, namely persons. The goal is to
generate a patch that is able successfully hide a person from a person
detector. An attack that could for instance be used maliciously to circumvent
surveillance systems, intruders can sneak around undetected by holding a small
cardboard plate in front of their body aimed towards the surveillance camera.
  From our results we can see that our system is able significantly lower the
accuracy of a person detector. Our approach also functions well in real-life
scenarios where the patch is filmed by a camera. To the best of our knowledge
we are the first to attempt this kind of attack on targets with a high level of
intra-class variety like persons.