pwc-archive/papers-with-abstracts huggingface dataset:
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Authors: Dawei Du, Yuankai Qi, Hongyang Yu, Yifan Yang, Kaiwen Duan, Guorong Li, Weigang Zhang, Qingming Huang, Qi Tian
Date: 2018-03-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking, Visual Tracking

With the advantage of high mobility, Unmanned Aerial Vehicles (UAVs) are used
to fuel numerous important applications in computer vision, delivering more
efficiency and convenience than surveillance cameras with fixed camera angle,
scale and view. However, very limited UAV datasets are proposed, and they focus
only on a specific task such as visual tracking or object detection in
relatively constrained scenarios. Consequently, it is of great importance to
develop an unconstrained UAV benchmark to boost related researches. In this
paper, we construct a new UAV benchmark focusing on complex scenarios with new
level challenges. Selected from 10 hours raw videos, about 80,000
representative frames are fully annotated with bounding boxes as well as up to
14 kinds of attributes (e.g., weather condition, flying altitude, camera view,
vehicle category, and occlusion) for three fundamental computer vision tasks:
object detection, single object tracking, and multiple object tracking. Then, a
detailed quantitative study is performed using most recent state-of-the-art
algorithms for each task. Experimental results show that the current
state-of-the-art methods perform relative worse on our dataset, due to the new
challenges appeared in UAV based real scenes, e.g., high density, small object,
and camera motion. To our knowledge, our work is the first time to explore such
issues in unconstrained scenes comprehensively.
Unsupervised Separation of Transliterable and Native Words for Malayalam
Authors: Deepak P
Date: 2018-03-26 00:00:00
Proceeding: WS 2017 12
Tasks: Diversity

Differentiating intrinsic language words from transliterable words is a key
step aiding text processing tasks involving different natural languages. We
consider the problem of unsupervised separation of transliterable words from
native words for text in Malayalam language. Outlining a key observation on the
diversity of characters beyond the word stem, we develop an optimization method
to score words based on their nativeness. Our method relies on the usage of
probability distributions over character n-grams that are refined in step with
the nativeness scorings in an iterative optimization formulation. Using an
empirical evaluation, we illustrate that our method, DTIM, provides significant
improvements in nativeness scoring for Malayalam, establishing DTIM as the
preferred method for the task.
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples
Authors: Pei-Hsuan Lu, Pin-Yu Chen, Chia-Mu Yu
Date: 2018-03-26 00:00:00

Understanding and characterizing the subspaces of adversarial examples aid in
studying the robustness of deep neural networks (DNNs) to adversarial
perturbations. Very recently, Ma et al. (ICLR 2018) proposed to use local
intrinsic dimensionality (LID) in layer-wise hidden representations of DNNs to
study adversarial subspaces. It was demonstrated that LID can be used to
characterize the adversarial subspaces associated with different attack
methods, e.g., the Carlini and Wagner's (C&W) attack and the fast gradient sign
attack.
  In this paper, we use MNIST and CIFAR-10 to conduct two new sets of
experiments that are absent in existing LID analysis and report the limitation
of LID in characterizing the corresponding adversarial subspaces, which are (i)
oblivious attacks and LID analysis using adversarial examples with different
confidence levels; and (ii) black-box transfer attacks. For (i), we find that
the performance of LID is very sensitive to the confidence parameter deployed
by an attack, and the LID learned from ensembles of adversarial examples with
varying confidence levels surprisingly gives poor performance. For (ii), we
find that when adversarial examples are crafted from another DNN model, LID is
ineffective in characterizing their adversarial subspaces. These two findings
together suggest the limited capability of LID in characterizing the subspaces
of adversarial examples.
Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
Authors: Ibrahim Omara, Hongzhi Zhang, Faqiang Wang, WangMeng Zuo
Date: 2018-03-26 00:00:00
Tasks: Metric Learning

Ear recognition task is known as predicting whether two ear images belong to
the same person or not. In this paper, we present a novel metric learning
method for ear recognition. This method is formulated as a pairwise constrained
optimization problem. In each training cycle, this method selects the nearest
similar and dissimilar neighbors of each sample to construct the pairwise
constraints, and then solve the optimization problem by the iterated Bregman
projections. Experiments are conducted on AMI, USTB II and WPUT databases. The
results show that the proposed approach can achieve promising recognition rates
in ear recognition, and its training process is much more efficient than the
other competing metric learning methods.
Learning the Multiple Traveling Salesmen Problem with Permutation Invariant Pooling Networks
Authors: Yoav Kaempfer, Lior Wolf
Date: 2018-03-26 00:00:00

While there are optimal TSP solvers, as well as recent learning-based
approaches, the generalization of the TSP to the Multiple Traveling Salesmen
Problem is much less studied. Here, we design a neural network solution that
treats the salesmen, cities and depot as three different sets of varying
cardinalities. We apply a novel technique that combines elements from recent
architectures that were developed for sets, as well as elements from graph
networks. Coupled with new constraint enforcing output layers, a dedicated
loss, and a search method, our solution is shown to outperform all the
meta-heuristics of the leading solver in the field.
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Authors: Andreas Hauptmann, Felix Lucka, Marta Betcke, Nam Huynh, Jonas Adler, Ben Cox, Paul Beard, Sebastien Ourselin, Simon Arridge
Date: 2017-08-31 00:00:00
Tasks: Tomographic Reconstructions

Recent advances in deep learning for tomographic reconstructions have shown
great potential to create accurate and high quality images with a considerable
speed-up. In this work we present a deep neural network that is specifically
designed to provide high resolution 3D images from restricted photoacoustic
measurements. The network is designed to represent an iterative scheme and
incorporates gradient information of the data fit to compensate for limited
view artefacts. Due to the high complexity of the photoacoustic forward
operator, we separate training and computation of the gradient information. A
suitable prior for the desired image structures is learned as part of the
training. The resulting network is trained and tested on a set of segmented
vessels from lung CT scans and then applied to in-vivo photoacoustic
measurement data.
Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study
Authors: Fabian Schrumpf, Gerold Bausch, Matthias Sturm, Mirco Fuchs
Date: 2018-03-26 00:00:00
Tasks: Clustering

This paper introduces a new unsupervised method for the clustering of
physiological data into health states based on their similarity. We propose an
iterative hierarchical clustering approach that combines health states
according to a similarity constraint to new arbitrary health states. We applied
method to experimental data in which the physical strain of subjects was
systematically varied. We derived health states based on parameters extracted
from ECG data. The occurrence of health states shows a high temporal
correlation to the experimental phases of the physical exercise. We compared
our method to other clustering algorithms and found a significantly higher
accuracy with respect to the identification of health states.
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy
Authors: Florian Scheidegger, Roxana Istrate, Giovanni Mariani, Luca Benini, Costas Bekas, Cristiano Malossi
Date: 2018-03-26 00:00:00
Tasks: General Classification, image-classification, Image Classification

In the deep-learning community new algorithms are published at an incredible
pace. Therefore, solving an image classification problem for new datasets
becomes a challenging task, as it requires to re-evaluate published algorithms
and their different configurations in order to find a close to optimal
classifier. To facilitate this process, before biasing our decision towards a
class of neural networks or running an expensive search over the network space,
we propose to estimate the classification difficulty of the dataset. Our method
computes a single number that characterizes the dataset difficulty 27x faster
than training state-of-the-art networks. The proposed method can be used in
combination with network topology and hyper-parameter search optimizers to
efficiently drive the search towards promising neural-network configurations.
Evaluation of Session-based Recommendation Algorithms
Authors: Malte Ludewig, Dietmar Jannach
Date: 2018-03-26 00:00:00
Tasks: Recommendation Systems, Session-Based Recommendations

Recommender systems help users find relevant items of interest, for example
on e-commerce or media streaming sites. Most academic research is concerned
with approaches that personalize the recommendations according to long-term
user profiles. In many real-world applications, however, such long-term
profiles often do not exist and recommendations therefore have to be made
solely based on the observed behavior of a user during an ongoing session.
Given the high practical relevance of the problem, an increased interest in
this problem can be observed in recent years, leading to a number of proposals
for session-based recommendation algorithms that typically aim to predict the
user's immediate next actions. In this work, we present the results of an
in-depth performance comparison of a number of such algorithms, using a variety
of datasets and evaluation measures. Our comparison includes the most recent
approaches based on recurrent neural networks like GRU4REC, factorized Markov
model approaches such as FISM or FOSSIL, as well as simpler methods based,
e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of
this latter class, despite their sometimes almost trivial nature, often perform
equally well or significantly better than today's more complex approaches based
on deep neural networks. Our results therefore suggest that there is
substantial room for improvement regarding the development of more
sophisticated session-based recommendation algorithms.
Transfer Learning for Improving Speech Emotion Classification Accuracy
Authors: Siddique Latif, Rajib Rana, Shahzad Younis, Junaid Qadir, Julien Epps
Date: 2018-01-19 00:00:00
Tasks: Classification, Cross-corpus, Emotion Classification, Emotion Recognition, General Classification, Speech Emotion Recognition, Transfer Learning

The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions. The performance of such systems has been shown to drop significantly in cross-corpus and cross-language scenarios. To address the problem, this paper exploits a transfer learning technique to improve the performance of speech emotion recognition systems that is novel in cross-language and cross-corpus scenarios. Evaluations on five different corpora in three different languages show that Deep Belief Networks (DBNs) offer better accuracy than previous approaches on cross-corpus emotion recognition, relative to a Sparse Autoencoder and SVM baseline system. Results also suggest that using a large number of languages for training and using a small fraction of the target data in training can significantly boost accuracy compared with baseline also for the corpus with limited training examples.
Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches
Authors: Nils Reimers, Iryna Gurevych
Date: 2018-03-26 00:00:00
Tasks: BIG-bench Machine Learning, NER

Developing state-of-the-art approaches for specific tasks is a major driving
force in our research community. Depending on the prestige of the task,
publishing it can come along with a lot of visibility. The question arises how
reliable are our evaluation methodologies to compare approaches?
  One common methodology to identify the state-of-the-art is to partition data
into a train, a development and a test set. Researchers can train and tune
their approach on some part of the dataset and then select the model that
worked best on the development set for a final evaluation on unseen test data.
Test scores from different approaches are compared, and performance differences
are tested for statistical significance.
  In this publication, we show that there is a high risk that a statistical
significance in this type of evaluation is not due to a superior learning
approach. Instead, there is a high risk that the difference is due to chance.
For example for the CoNLL 2003 NER dataset we observed in up to 26% of the
cases type I errors (false positives) with a threshold of p < 0.05, i.e.,
falsely concluding a statistically significant difference between two identical
approaches.
  We prove that this evaluation setup is unsuitable to compare learning
approaches. We formalize alternative evaluation setups based on score
distributions.
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses
Authors: Zheng Dang, Kwang Moo Yi, Yinlin Hu, Fei Wang, Pascal Fua, Mathieu Salzmann
Date: 2018-03-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Pose Estimation, Pose Estimation

Many classical Computer Vision problems, such as essential matrix computation
and pose estimation from 3D to 2D correspondences, can be solved by finding the
eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix
representing a linear system. Incorporating this in deep learning frameworks
would allow us to explicitly encode known notions of geometry, instead of
having the network implicitly learn them from data. However, performing
eigendecomposition within a network requires the ability to differentiate this
operation. Unfortunately, while theoretically doable, this introduces numerical
instability in the optimization process in practice.
  In this paper, we introduce an eigendecomposition-free approach to training a
deep network whose loss depends on the eigenvector corresponding to a zero
eigenvalue of a matrix predicted by the network. We demonstrate on several
tasks, including keypoint matching and 3D pose estimation, that our approach is
much more robust than explicit differentiation of the eigendecomposition, It
has better convergence properties and yields state-of-the-art results on both
tasks.
Calibrated Prediction Intervals for Neural Network Regressors
Authors: Gil Keren, NIcholas Cummins, Björn Schuller
Date: 2018-03-26 00:00:00
Tasks: Prediction, Prediction Intervals

Ongoing developments in neural network models are continually advancing the
state of the art in terms of system accuracy. However, the predicted labels
should not be regarded as the only core output; also important is a
well-calibrated estimate of the prediction uncertainty. Such estimates and
their calibration are critical in many practical applications. Despite their
obvious aforementioned advantage in relation to accuracy, contemporary neural
networks can, generally, be regarded as poorly calibrated and as such do not
produce reliable output probability estimates. Further, while post-processing
calibration solutions can be found in the relevant literature, these tend to be
for systems performing classification. In this regard, we herein present two
novel methods for acquiring calibrated predictions intervals for neural network
regressors: empirical calibration and temperature scaling. In experiments using
different regression tasks from the audio and computer vision domains, we find
that both our proposed methods are indeed capable of producing calibrated
prediction intervals for neural network regressors with any desired confidence
level, a finding that is consistent across all datasets and neural network
architectures we experimented with. In addition, we derive an additional
practical recommendation for producing more accurate calibrated prediction
intervals. We release the source code implementing our proposed methods for
computing calibrated predicted intervals. The code for computing calibrated
predicted intervals is publicly available.
Scale Coding Bag of Deep Features for Human Attribute and Action Recognition
Authors: Fahad Shahbaz Khan, Joost Van de Weijer, Rao Muhammad Anwer, Andrew D. Bagdanov, Michael Felsberg, Jorma Laaksonen
Date: 2016-12-14 00:00:00
Tasks: Action Recognition, Action Recognition In Still Images, Attribute, Temporal Action Localization

Most approaches to human attribute and action recognition in still images are
based on image representation in which multi-scale local features are pooled
across scale into a single, scale-invariant encoding. Both in bag-of-words and
the recently popular representations based on convolutional neural networks,
local features are computed at multiple scales. However, these multi-scale
convolutional features are pooled into a single scale-invariant representation.
We argue that entirely scale-invariant image representations are sub-optimal
and investigate approaches to scale coding within a Bag of Deep Features
framework.
  Our approach encodes multi-scale information explicitly during the image
encoding stage. We propose two strategies to encode multi-scale information
explicitly in the final image representation. We validate our two scale coding
techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012,
Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale
coding approaches outperform both the scale-invariant method and the standard
deep features of the same network. Further, combining our scale coding
approaches with standard deep features leads to consistent improvement over the
state-of-the-art.
Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance
Authors: Zachary A. Pardos, Zihao Fan, Weijie Jiang
Date: 2018-03-26 00:00:00
Tasks: Decision Making

The aggregate behaviors of users can collectively encode deep semantic
information about the objects with which they interact. In this paper, we
demonstrate novel ways in which the synthesis of these data can illuminate the
terrain of users' environment and support them in their decision making and
wayfinding. A novel application of Recurrent Neural Networks and skip-gram
models, approaches popularized by their application to modeling language, are
brought to bear on student university enrollment sequences to create vector
representations of courses and map out traversals across them. We present
demonstrations of how scrutability from these neural networks can be gained and
how the combination of these techniques can be seen as an evolution of content
tagging and a means for a recommender to balance user preferences inferred from
data with those explicitly specified. From validation of the models to the
development of a UI, we discuss additional requisite functionality informed by
the results of a usability study leading to the ultimate deployment of the
system at a university.
Deep Representation for Patient Visits from Electronic Health Records
Authors: Jean-Baptiste Escudié, Alaa Saade, Alice Coucke, Marc Lelarge
Date: 2018-03-26 00:00:00
Tasks: General Classification

We show how to learn low-dimensional representations (embeddings) of patient
visits from the corresponding electronic health record (EHR) where
International Classification of Diseases (ICD) diagnosis codes are removed. We
expect that these embeddings will be useful for the construction of predictive
statistical models anticipated to drive personalized medicine and improve
healthcare quality. These embeddings are learned using a deep neural network
trained to predict ICD diagnosis categories. We show that our embeddings
capture relevant clinical informations and can be used directly as input to
standard machine learning algorithms like multi-output classifiers for ICD code
prediction. We also show that important medical informations correspond to
particular directions in our embedding space.
A Provably Correct Algorithm for Deep Learning that Actually Works
Authors: Eran Malach, Shai Shalev-Shwartz
Date: 2018-03-26 00:00:00
Tasks: Clustering, Deep Learning

We describe a layer-by-layer algorithm for training deep convolutional
networks, where each step involves gradient updates for a two layer network
followed by a simple clustering algorithm. Our algorithm stems from a deep
generative model that generates mages level by level, where lower resolution
images correspond to latent semantic classes. We analyze the convergence rate
of our algorithm assuming that the data is indeed generated according to this
model (as well as additional assumptions). While we do not pretend to claim
that the assumptions are realistic for natural images, we do believe that they
capture some true properties of real data. Furthermore, we show that our
algorithm actually works in practice (on the CIFAR dataset), achieving results
in the same ballpark as that of vanilla convolutional neural networks that are
being trained by stochastic gradient descent. Finally, our proof techniques may
be of independent interest.
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Authors: Rao Muhammad Anwer, Fahad Shahbaz Khan, Joost Van de Weijer, Matthieu Molinier, Jorma Laaksonen
Date: 2017-06-05 00:00:00
Tasks: Aerial Scene Classification, General Classification, Material Recognition, Scene Classification

Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.