pwc-archive/papers-with-abstracts huggingface dataset:
MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge
Authors: Suwon Shon, Ahmed Ali, James Glass
Date: 2017-08-28 00:00:00
Tasks: Arabic Speech Recognition, Dialect Identification, speech-recognition, Speech Recognition

In order to successfully annotate the Arabic speech con- tent found in
open-domain media broadcasts, it is essential to be able to process a diverse
set of Arabic dialects. For the 2017 Multi-Genre Broadcast challenge (MGB-3)
there were two possible tasks: Arabic speech recognition, and Arabic Dialect
Identification (ADI). In this paper, we describe our efforts to create an ADI
system for the MGB-3 challenge, with the goal of distinguishing amongst four
major Arabic dialects, as well as Modern Standard Arabic. Our research fo-
cused on dialect variability and domain mismatches between the training and
test domain. In order to achieve a robust ADI system, we explored both Siamese
neural network models to learn similarity and dissimilarities among Arabic
dialects, as well as i-vector post-processing to adapt domain mismatches. Both
Acoustic and linguistic features were used for the final MGB-3 submissions,
with the best primary system achieving 75% accuracy on the official 10hr test
set.
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
Authors: Markus Oberweger, Vincent Lepetit
Date: 2017-08-28 00:00:00
Tasks: 3D Hand Pose Estimation, Data Augmentation, Hand Pose Estimation, Pose Estimation

DeepPrior is a simple approach based on Deep Learning that predicts the joint
3D locations of a hand given a depth map. Since its publication early 2015, it
has been outperformed by several impressive works. Here we show that with
simple improvements: adding ResNet layers, data augmentation, and better
initial hand localization, we achieve better or similar performance than more
sophisticated recent methods on the three main benchmarks (NYU, ICVL, MSRA)
while keeping the simplicity of the original method. Our new implementation is
available at https://github.com/moberweger/deep-prior-pp .
Convergence Analysis of MAP based Blur Kernel Estimation
Authors: Sunghyun Cho, Seungyong Lee
Date: 2016-11-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Defocus Estimation

One popular approach for blind deconvolution is to formulate a maximum a
posteriori (MAP) problem with sparsity priors on the gradients of the latent
image, and then alternatingly estimate the blur kernel and the latent image.
While several successful MAP based methods have been proposed, there has been
much controversy and confusion about their convergence, because sparsity priors
have been shown to prefer blurry images to sharp natural images. In this paper,
we revisit this problem and provide an analysis on the convergence of MAP based
approaches. We first introduce a slight modification to a conventional joint
energy function for blind deconvolution. The reformulated energy function
yields the same alternating estimation process, but more clearly reveals how
blind deconvolution works. We then show the energy function can actually favor
the right solution instead of the no-blur solution under certain conditions,
which explains the success of previous MAP based approaches. The reformulated
energy function and our conditions for the convergence also provide a way to
compare the qualities of different blur kernels, and we demonstrate its
applicability to automatic blur kernel size selection, blur kernel estimation
using light streaks, and defocus estimation.
Deep Learning Sparse Ternary Projections for Compressed Sensing of Images
Authors: Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis
Date: 2017-08-28 00:00:00
Tasks: compressed sensing, Deep Learning

Compressed sensing (CS) is a sampling theory that allows reconstruction of
sparse (or compressible) signals from an incomplete number of measurements,
using of a sensing mechanism implemented by an appropriate projection matrix.
The CS theory is based on random Gaussian projection matrices, which satisfy
recovery guarantees with high probability; however, sparse ternary {0, -1, +1}
projections are more suitable for hardware implementation. In this paper, we
present a deep learning approach to obtain very sparse ternary projections for
compressed sensing. Our deep learning architecture jointly learns a pair of a
projection matrix and a reconstruction operator in an end-to-end fashion. The
experimental results on real images demonstrate the effectiveness of the
proposed approach compared to state-of-the-art methods, with significant
advantage in terms of complexity.
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising
Authors: Dufan Wu, Kyungsang Kim, Georges El Fakhri, Quanzheng Li
Date: 2017-05-11 00:00:00
Tasks: Computed Tomography (CT), Denoising, Image Denoising

Image denoising techniques are essential to reducing noise levels and
enhancing diagnosis reliability in low-dose computed tomography (CT). Machine
learning based denoising methods have shown great potential in removing the
complex and spatial-variant noises in CT images. However, some residue
artifacts would appear in the denoised image due to complexity of noises. A
cascaded training network was proposed in this work, where the trained CNN was
applied on the training dataset to initiate new trainings and remove artifacts
induced by denoising. A cascades of convolutional neural networks (CNN) were
built iteratively to achieve better performance with simple CNN structures.
Experiments were carried out on 2016 Low-dose CT Grand Challenge datasets to
evaluate the method's performance.
Open-World Visual Recognition Using Knowledge Graphs
Authors: Vincent P. A. Lonij, Ambrish Rawat, Maria-Irina Nicolae
Date: 2017-08-28 00:00:00
Tasks: Knowledge Graphs

In a real-world setting, visual recognition systems can be brought to make
predictions for images belonging to previously unknown class labels. In order
to make semantically meaningful predictions for such inputs, we propose a
two-step approach that utilizes information from knowledge graphs. First, a
knowledge-graph representation is learned to embed a large set of entities into
a semantic space. Second, an image representation is learned to embed images
into the same space. Under this setup, we are able to predict structured
properties in the form of relationship triples for any open-world image. This
is true even when a set of labels has been omitted from the training protocols
of both the knowledge graph and image embeddings. Furthermore, we append this
learning framework with appropriate smoothness constraints and show how prior
knowledge can be incorporated into the model. Both these improvements combined
increase performance for visual recognition by a factor of six compared to our
baseline. Finally, we propose a new, extended dataset which we use for
experiments.
The Convergence of Machine Learning and Communications
Authors: Wojciech Samek, Slawomir Stanczak, Thomas Wiegand
Date: 2017-08-28 00:00:00
Tasks: BIG-bench Machine Learning, Management

The areas of machine learning and communication technology are converging.
Today's communications systems generate a huge amount of traffic data, which
can help to significantly enhance the design and management of networks and
communication components when combined with advanced machine learning methods.
Furthermore, recently developed end-to-end training procedures offer new ways
to jointly optimize the components of a communication system. Also in many
emerging application fields of communication technology, e.g., smart cities or
internet of things, machine learning methods are of central importance. This
paper gives an overview over the use of machine learning in different areas of
communications and discusses two exemplar applications in wireless networking.
Furthermore, it identifies promising future research topics and discusses their
potential impact.
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
Authors: Wojciech Samek, Thomas Wiegand, Klaus-Robert Müller
Date: 2017-08-28 00:00:00
Tasks: Deep Learning, Explainable artificial intelligence, General Classification, image-classification, Image Classification, Sentiment Analysis

With the availability of large databases and recent improvements in deep
learning methodology, the performance of AI systems is reaching or even
exceeding the human level on an increasing number of complex tasks. Impressive
examples of this development can be found in domains such as image
classification, sentiment analysis, speech understanding or strategic game
playing. However, because of their nested non-linear structure, these highly
successful machine learning and artificial intelligence models are usually
applied in a black box manner, i.e., no information is provided about what
exactly makes them arrive at their predictions. Since this lack of transparency
can be a major drawback, e.g., in medical applications, the development of
methods for visualizing, explaining and interpreting deep learning models has
recently attracted increasing attention. This paper summarizes recent
developments in this field and makes a plea for more interpretability in
artificial intelligence. Furthermore, it presents two approaches to explaining
predictions of deep learning models, one method which computes the sensitivity
of the prediction with respect to changes in the input and one approach which
meaningfully decomposes the decision in terms of the input variables. These
methods are evaluated on three classification tasks.
On Type-Aware Entity Retrieval
Authors: Darío Garigliotti, Krisztian Balog
Date: 2017-08-28 00:00:00
Tasks: Entity Retrieval, Retrieval, Vocal Bursts Type Prediction

Today, the practice of returning entities from a knowledge base in response
to search queries has become widespread. One of the distinctive characteristics
of entities is that they are typed, i.e., assigned to some hierarchically
organized type system (type taxonomy). The primary objective of this paper is
to gain a better understanding of how entity type information can be utilized
in entity retrieval. We perform this investigation in an idealized "oracle"
setting, assuming that we know the distribution of target types of the relevant
entities for a given query. We perform a thorough analysis of three main
aspects: (i) the choice of type taxonomy, (ii) the representation of
hierarchical type information, and (iii) the combination of type-based and
term-based similarity in the retrieval model. Using a standard entity search
test collection based on DBpedia, we find that type information proves most
useful when using large type taxonomies that provide very specific types. We
provide further insights on the extensional coverage of entities and on the
utility of target types.
Generating Query Suggestions to Support Task-Based Search
Authors: Darío Garigliotti, Krisztian Balog
Date: 2017-08-28 00:00:00

We address the problem of generating query suggestions to support users in
completing their underlying tasks (which motivated them to search in the first
place). Given an initial query, these query suggestions should provide a
coverage of possible subtasks the user might be looking for. We propose a
probabilistic modeling framework that obtains keyphrases from multiple sources
and generates query suggestions from these keyphrases. Using the test suites of
the TREC Tasks track, we evaluate and analyze each component of our model.
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
Authors: Mathilde Ménoret, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon
Date: 2017-03-06 00:00:00
Tasks: Dimensionality Reduction, Functional Connectivity, General Classification, Graph Sampling

Graph Signal Processing (GSP) is a promising framework to analyze
multi-dimensional neuroimaging datasets, while taking into account both the
spatial and functional dependencies between brain signals. In the present work,
we apply dimensionality reduction techniques based on graph representations of
the brain to decode brain activity from real and simulated fMRI datasets. We
introduce seven graphs obtained from a) geometric structure and/or b)
functional connectivity between brain areas at rest, and compare them when
performing dimension reduction for classification. We show that mixed graphs
using both a) and b) offer the best performance. We also show that graph
sampling methods perform better than classical dimension reduction including
Principal Component Analysis (PCA) and Independent Component Analysis (ICA).
Stylizing Face Images via Multiple Exemplars
Authors: Yibing Song, Linchao Bao, Shengfeng He, Qingxiong Yang, Ming-Hsuan Yang
Date: 2017-08-28 00:00:00

We address the problem of transferring the style of a headshot photo to face
images. Existing methods using a single exemplar lead to inaccurate results
when the exemplar does not contain sufficient stylized facial components for a
given photo. In this work, we propose an algorithm to stylize face images using
multiple exemplars containing different subjects in the same style. Patch
correspondences between an input photo and multiple exemplars are established
using a Markov Random Field (MRF), which enables accurate local energy transfer
via Laplacian stacks. As image patches from multiple exemplars are used, the
boundaries of facial components on the target image are inevitably
inconsistent. The artifacts are removed by a post-processing step using an
edge-preserving filter. Experimental results show that the proposed algorithm
consistently produces visually pleasing results.
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
Authors: Mizuho Nishio, Mitsuo Nishizawa, Osamu Sugiyama, Ryosuke Kojima, Masahiro Yakami, Tomohiro Kuroda, Kaori Togashi
Date: 2017-08-19 00:00:00
Tasks: Bayesian Optimization, Lung Nodule Classification

We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule
classification focusing on (i) usefulness of gradient tree boosting (XGBoost)
and (ii) effectiveness of parameter optimization using Bayesian optimization
(Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung
cancers and 37 benign lung nodules) were included from public databases of CT
images. A variant of local binary pattern was used for calculating feature
vectors. Support vector machine (SVM) or XGBoost was trained using the feature
vectors and their labels. TPE or random search was used for parameter
optimization of SVM and XGBoost. Leave-one-out cross-validation was used for
optimizing and evaluating the performance of our CADx system. Performance was
evaluated using area under the curve (AUC) of receiver operating characteristic
analysis. AUC was calculated 10 times, and its average was obtained. The best
averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were
obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters
for achieving high AUC were obtained with fewer numbers of trials when using
TPE, compared with random search. In conclusion, XGBoost was better than SVM
for classifying lung nodules. TPE was more efficient than random search for
parameter optimization.
Attention-Set based Metric Learning for Video Face Recognition
Authors: Yibo Hu, Xiang Wu, Ran He
Date: 2017-04-12 00:00:00
Tasks: Face Recognition, Metric Learning

Face recognition has made great progress with the development of deep
learning. However, video face recognition (VFR) is still an ongoing task due to
various illumination, low-resolution, pose variations and motion blur. Most
existing CNN-based VFR methods only obtain a feature vector from a single image
and simply aggregate the features in a video, which less consider the
correlations of face images in one video. In this paper, we propose a novel
Attention-Set based Metric Learning (ASML) method to measure the statistical
characteristics of image sets. It is a promising and generalized extension of
Maximum Mean Discrepancy with memory attention weighting. First, we define an
effective distance metric on image sets, which explicitly minimizes the
intra-set distance and maximizes the inter-set distance simultaneously. Second,
inspired by Neural Turing Machine, a Memory Attention Weighting is proposed to
adapt set-aware global contents. Then ASML is naturally integrated into CNNs,
resulting in an end-to-end learning scheme. Our method achieves
state-of-the-art performance for the task of video face recognition on the
three widely used benchmarks including YouTubeFace, YouTube Celebrities and
Celebrity-1000.
Proximal Policy Optimization Algorithms
Authors: John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
Date: 2017-07-20 00:00:00
Tasks: Continuous Control, Dota 2, Multi-Goal Reinforcement Learning, Neural Architecture Search, OpenAI Gym, Policy Gradient Methods, Reinforcement Learning, Speech Enhancement

We propose a new family of policy gradient methods for reinforcement
learning, which alternate between sampling data through interaction with the
environment, and optimizing a "surrogate" objective function using stochastic
gradient ascent. Whereas standard policy gradient methods perform one gradient
update per data sample, we propose a novel objective function that enables
multiple epochs of minibatch updates. The new methods, which we call proximal
policy optimization (PPO), have some of the benefits of trust region policy
optimization (TRPO), but they are much simpler to implement, more general, and
have better sample complexity (empirically). Our experiments test PPO on a
collection of benchmark tasks, including simulated robotic locomotion and Atari
game playing, and we show that PPO outperforms other online policy gradient
methods, and overall strikes a favorable balance between sample complexity,
simplicity, and wall-time.
Efficient Decision Trees for Multi-class Support Vector Machines Using Entropy and Generalization Error Estimation
Authors: Pittipol Kantavat, Boonserm Kijsirikul, Patoomsiri Songsiri, Ken-ichi Fukui, Masayuki Numao
Date: 2017-08-28 00:00:00
Tasks: General Classification

We propose new methods for Support Vector Machines (SVMs) using tree
architecture for multi-class classi- fication. In each node of the tree, we
select an appropriate binary classifier using entropy and generalization error
estimation, then group the examples into positive and negative classes based on
the selected classi- fier and train a new classifier for use in the
classification phase. The proposed methods can work in time complexity between
O(log2N) to O(N) where N is the number of classes. We compared the performance
of our proposed methods to the traditional techniques on the UCI machine
learning repository using 10-fold cross-validation. The experimental results
show that our proposed methods are very useful for the problems that need fast
classification time or problems with a large number of classes as the proposed
methods run much faster than the traditional techniques but still provide
comparable accuracy.
Joint Discovery of Object States and Manipulation Actions
Authors: Jean-Baptiste Alayrac, Josev Sivic, Ivan Laptev, Simon Lacoste-Julien
Date: 2017-02-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Recognition, Clustering, Object, Temporal Action Localization

Many human activities involve object manipulations aiming to modify the
object state. Examples of common state changes include full/empty bottle,
open/closed door, and attached/detached car wheel. In this work, we seek to
automatically discover the states of objects and the associated manipulation
actions. Given a set of videos for a particular task, we propose a joint model
that learns to identify object states and to localize state-modifying actions.
Our model is formulated as a discriminative clustering cost with constraints.
We assume a consistent temporal order for the changes in object states and
manipulation actions, and introduce new optimization techniques to learn model
parameters without additional supervision. We demonstrate successful discovery
of seven manipulation actions and corresponding object states on a new dataset
of videos depicting real-life object manipulations. We show that our joint
formulation results in an improvement of object state discovery by action
recognition and vice versa.
Show and Recall: Learning What Makes Videos Memorable
Authors: Sumit Shekhar, Dhruv Singal, Harvineet Singh, Manav Kedia, Akhil Shetty
Date: 2017-07-17 00:00:00
Tasks: Video Summarization

With the explosion of video content on the Internet, there is a need for
research on methods for video analysis which take human cognition into account.
One such cognitive measure is memorability, or the ability to recall visual
content after watching it. Prior research has looked into image memorability
and shown that it is intrinsic to visual content, but the problem of modeling
video memorability has not been addressed sufficiently. In this work, we
develop a prediction model for video memorability, including complexities of
video content in it. Detailed feature analysis reveals that the proposed method
correlates well with existing findings on memorability. We also describe a
novel experiment of predicting video sub-shot memorability and show that our
approach improves over current memorability methods in this task. Experiments
on standard datasets demonstrate that the proposed metric can achieve results
on par or better than the state-of-the art methods for video summarization.
Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
Authors: Tianyue Zheng, Weihong Deng, Jiani Hu
Date: 2017-08-28 00:00:00
Tasks: Attribute, Face Recognition, Face Verification, Metric Learning

Labeled Faces in the Wild (LFW) database has been widely utilized as the
benchmark of unconstrained face verification and due to big data driven machine
learning methods, the performance on the database approaches nearly 100%.
However, we argue that this accuracy may be too optimistic because of some
limiting factors. Besides different poses, illuminations, occlusions and
expressions, cross-age face is another challenge in face recognition. Different
ages of the same person result in large intra-class variations and aging
process is unavoidable in real world face verification. However, LFW does not
pay much attention on it. Thereby we construct a Cross-Age LFW (CALFW) which
deliberately searches and selects 3,000 positive face pairs with age gaps to
add aging process intra-class variance. Negative pairs with same gender and
race are also selected to reduce the influence of attribute difference between
positive/negative pairs and achieve face verification instead of attributes
classification. We evaluate several metric learning and deep learning methods
on the new database. Compared to the accuracy on LFW, the accuracy drops about
10%-17% on CALFW.