pwc-archive/papers-with-abstracts huggingface dataset:
Self-imitating Feedback Generation Using GAN for Computer-Assisted Pronunciation Training
Authors: Seung Hee Yang, Minhwa Chung
Date: 2019-04-20 00:00:00

Self-imitating feedback is an effective and learner-friendly method for
non-native learners in Computer-Assisted Pronunciation Training. Acoustic
characteristics in native utterances are extracted and transplanted onto
learner's own speech input, and given back to the learner as a corrective
feedback. Previous works focused on speech conversion using prosodic
transplantation techniques based on PSOLA algorithm. Motivated by the visual
differences found in spectrograms of native and non-native speeches, we
investigated applying GAN to generate self-imitating feedback by utilizing
generator's ability through adversarial training. Because this mapping is
highly under-constrained, we also adopt cycle consistency loss to encourage the
output to preserve the global structure, which is shared by native and
non-native utterances. Trained on 97,200 spectrogram images of short utterances
produced by native and non-native speakers of Korean, the generator is able to
successfully transform the non-native spectrogram input to a spectrogram with
properties of self-imitating feedback. Furthermore, the transformed spectrogram
shows segmental corrections that cannot be obtained by prosodic
transplantation. Perceptual test comparing the self-imitating and correcting
abilities of our method with the baseline PSOLA method shows that the
generative approach with cycle consistency loss is promising.
Context-Aware Zero-Shot Recognition
Authors: Ruotian Luo, Ning Zhang, Bohyung Han, Linjie Yang
Date: 2019-04-19 00:00:00
Tasks: Object Recognition, Zero-Shot Learning

We present a novel problem setting in zero-shot learning, zero-shot object
recognition and detection in the context. Contrary to the traditional zero-shot
learning methods, which simply infers unseen categories by transferring
knowledge from the objects belonging to semantically similar seen categories,
we aim to understand the identity of the novel objects in an image surrounded
by the known objects using the inter-object relation prior. Specifically, we
leverage the visual context and the geometric relationships between all pairs
of objects in a single image, and capture the information useful to infer
unseen categories. We integrate our context-aware zero-shot learning framework
into the traditional zero-shot learning techniques seamlessly using a
Conditional Random Field (CRF). The proposed algorithm is evaluated on both
zero-shot region classification and zero-shot detection tasks. The results on
Visual Genome (VG) dataset show that our model significantly boosts performance
with the additional visual context compared to traditional methods.
Compact Scene Graphs for Layout Composition and Patch Retrieval
Authors: Subarna Tripathi, Sharath Nittur Sridhar, Sairam Sundaresan, Hanlin Tang
Date: 2019-04-19 00:00:00
Tasks: Image Generation, Retrieval

Structured representations such as scene graphs serve as an efficient and
compact representation that can be used for downstream rendering or retrieval
tasks. However, existing efforts to generate realistic images from scene graphs
perform poorly on scene composition for cluttered or complex scenes. We propose
two contributions to improve the scene composition. First, we enhance the scene
graph representation with heuristic-based relations, which add minimal storage
overhead. Second, we use extreme points representation to supervise the
learning of the scene composition network. These methods achieve significantly
higher performance over existing work (69.0% vs 51.2% in relation score
metric). We additionally demonstrate how scene graphs can be used to retrieve
pose-constrained image patches that are semantically similar to the source
query. Improving structured scene graph representations for rendering or
retrieval is an important step towards realistic image generation.
FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition
Authors: Qingqing Wang, Wenjing Jia, Xiangjian He, Yue Lu, Michael Blumenstein, Ye Huang
Date: 2019-04-20 00:00:00
Tasks: Prediction, Scene Text Recognition

Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role. Due to the limitation of FC-LSTM, existing methods have to convert 2-D feature maps into 1-D sequential feature vectors, resulting in severe damages of the valuable spatial and structural information of text images. In this paper, we argue that scene text recognition is essentially a spatiotemporal prediction problem for its 2-D image inputs, and propose a convolution LSTM (ConvLSTM)-based scene text recognizer, namely, FACLSTM, i.e., Focused Attention ConvLSTM, where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM. Particularly, the attention mechanism is properly incorporated into an efficient ConvLSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas. The experimental results on benchmark datasets IIIT5K, SVT and CUTE demonstrate that our proposed FACLSTM performs competitively on the regular, low-resolution and noisy text images, and outperforms the state-of-the-art approaches on the curved text with large margins.
Funnel Transform for Straight Line Detection
Authors: QianRu Wei, Da-Zheng Feng, Weixing Zheng
Date: 2019-04-20 00:00:00
Tasks: Line Detection

Most of the classical approaches to straight line detection only deal with a
binary edge image and need to use 2D interpolation operation. This paper
proposes a new transform method figuratively named as funnel transform which
can efficiently and rapidly detect straight lines. The funnel transform
consists of three 1D Fourier transforms and one nonlinear variable-metric
transform (NVMT). It only needs to exploit 1D interpolation operation for
achieving its NVMT, and can directly handle grayscale images by using its
high-pass filter property, which significantly improves the performance of the
closely-related approaches. Based on the slope-intercept line equation, the
funnel transform can more uniformly turn the straight lines formed by
ridge-typical and step-typical edges into the local maximum points (peaks). The
parameters of each line can be uniquely extracted from its corresponding peak
coordinates. Additionally, each peak can be theoretically specified by a 2D
delta function, which makes the peaks and lines more easily identified and
detected, respectively. Theoretical analysis and experimental results
demonstrate that the funnel transform has advantages including smaller
computational complexity, lower hardware cost, higher detection probability,
greater location precision, better parallelization properties, stronger
anti-occlusion and noise robustness.
LEARNet Dynamic Imaging Network for Micro Expression Recognition
Authors: Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh, Subrahmanyam Murala
Date: 2019-04-20 00:00:00
Tasks: Micro Expression Recognition, Micro-Expression Recognition

Unlike prevalent facial expressions, micro expressions have subtle,
involuntary muscle movements which are short-lived in nature. These minute
muscle movements reflect true emotions of a person. Due to the short duration
and low intensity, these micro-expressions are very difficult to perceive and
interpret correctly. In this paper, we propose the dynamic representation of
micro-expressions to preserve facial movement information of a video in a
single frame. We also propose a Lateral Accretive Hybrid Network (LEARNet) to
capture micro-level features of an expression in the facial region. The LEARNet
refines the salient expression features in accretive manner by incorporating
accretion layers (AL) in the network. The response of the AL holds the hybrid
feature maps generated by prior laterally connected convolution layers.
Moreover, LEARNet architecture incorporates the cross decoupled relationship
between convolution layers which helps in preserving the tiny but influential
facial muscle change information. The visual responses of the proposed LEARNet
depict the effectiveness of the system by preserving both high- and micro-level
edge features of facial expression. The effectiveness of the proposed LEARNet
is evaluated on four benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and SMIC.
The experimental results after investigation show a significant improvement of
4.03%, 1.90%, 1.79% and 2.82% as compared with ResNet on CASME-I, CASME-II,
CAS(ME)^2 and SMIC datasets respectively.
Cubic LSTMs for Video Prediction
Authors: Hehe Fan, Linchao Zhu, Yi Yang
Date: 2019-04-20 00:00:00
Tasks: motion prediction, Prediction, Video Prediction

Predicting future frames in videos has become a promising direction of
research for both computer vision and robot learning communities. The core of
this problem involves moving object capture and future motion prediction. While
object capture specifies which objects are moving in videos, motion prediction
describes their future dynamics. Motivated by this analysis, we propose a Cubic
Long Short-Term Memory (CubicLSTM) unit for video prediction. CubicLSTM
consists of three branches, i.e., a spatial branch for capturing moving
objects, a temporal branch for processing motions, and an output branch for
combining the first two branches to generate predicted frames. Stacking
multiple CubicLSTM units along the spatial branch and output branch, and then
evolving along the temporal branch can form a cubic recurrent neural network
(CubicRNN). Experiment shows that CubicRNN produces more accurate video
predictions than prior methods on both synthetic and real-world datasets.
GestARLite: An On-Device Pointing Finger Based Gestural Interface for Smartphones and Video See-Through Head-Mounts
Authors: Varun Jain, Gaurav Garg, Ramakrishna Perla, Ramya Hebbalaguppe
Date: 2019-04-19 00:00:00
Tasks: Deep Learning, General Classification, Gesture Recognition, GPU, Hand Gesture Recognition, Hand-Gesture Recognition, Mixed Reality

Hand gestures form an intuitive means of interaction in Mixed Reality (MR)
applications. However, accurate gesture recognition can be achieved only
through state-of-the-art deep learning models or with the use of expensive
sensors. Despite the robustness of these deep learning models, they are
generally computationally expensive and obtaining real-time performance
on-device is still a challenge. To this end, we propose a novel lightweight
hand gesture recognition framework that works in First Person View for wearable
devices. The models are trained on a GPU machine and ported on an Android
smartphone for its use with frugal wearable devices such as the Google
Cardboard and VR Box. The proposed hand gesture recognition framework is driven
by a cascade of state-of-the-art deep learning models: MobileNetV2 for hand
localisation, our custom fingertip regression architecture followed by a
Bi-LSTM model for gesture classification. We extensively evaluate the framework
on our EgoGestAR dataset. The overall framework works in real-time on mobile
devices and achieves a classification accuracy of 80% on EgoGestAR video
dataset with an average latency of only 0.12 s.
Assessing Architectural Similarity in Populations of Deep Neural Networks
Authors: Audrey Chung, Paul Fieguth, Alexander Wong
Date: 2019-04-19 00:00:00

Evolutionary deep intelligence has recently shown great promise for producing
small, powerful deep neural network models via the synthesis of increasingly
efficient architectures over successive generations. Despite recent research
showing the efficacy of multi-parent evolutionary synthesis, little has been
done to directly assess architectural similarity between networks during the
synthesis process for improved parent network selection. In this work, we
present a preliminary study into quantifying architectural similarity via the
percentage overlap of architectural clusters. Results show that networks
synthesized using architectural alignment (via gene tagging) maintain higher
architectural similarities within each generation, potentially restricting the
search space of highly efficient network architectures.
Evaluation Uncertainty in Data-Driven Self-Driving Testing
Authors: Zhiyuan Huang, Mansur Arief, Henry Lam, Ding Zhao
Date: 2019-04-19 00:00:00
Tasks: Autonomous Vehicles, Diagnostic

Safety evaluation of self-driving technologies has been extensively studied. One recent approach uses Monte Carlo based evaluation to estimate the occurrence probabilities of safety-critical events as safety measures. These Monte Carlo samples are generated from stochastic input models constructed based on real-world data. In this paper, we propose an approach to assess the impact on the probability estimates from the evaluation procedures due to the estimation error caused by data variability. Our proposed method merges the classical bootstrap method for estimating input uncertainty with a likelihood ratio based scheme to reuse experiment outputs. This approach is economical and efficient in terms of implementation costs in assessing input uncertainty for the evaluation of self-driving technology. We use an example in autonomous vehicle (AV) safety evaluation to demonstrate the proposed approach as a diagnostic tool for the quality of the fitted input model.
Efficient functional estimation and the super-oracle phenomenon
Authors: Thomas B. Berrett, Richard J. Samworth
Date: 2019-04-18 00:00:00
Tasks: valid

We consider the estimation of two-sample integral functionals, of the type that occur naturally, for example, when the object of interest is a divergence between unknown probability densities. Our first main result is that, in wide generality, a weighted nearest neighbour estimator is efficient, in the sense of achieving the local asymptotic minimax lower bound. Moreover, we also prove a corresponding central limit theorem, which facilitates the construction of asymptotically valid confidence intervals for the functional, having asymptotically minimal width. One interesting consequence of our results is the discovery that, for certain functionals, the worst-case performance of our estimator may improve on that of the natural `oracle' estimator, which is given access to the values of the unknown densities at the observations.
Learning Sparse Dynamical Systems from a Single Sample Trajectory
Authors: Salar Fattahi, Nikolai Matni, Somayeh Sojoudi
Date: 2019-04-20 00:00:00

This paper addresses the problem of identifying sparse linear time-invariant
(LTI) systems from a single sample trajectory generated by the system dynamics.
We introduce a Lasso-like estimator for the parameters of the system, taking
into account their sparse nature. Assuming that the system is stable, or that
it is equipped with an initial stabilizing controller, we provide sharp
finite-time guarantees on the accurate recovery of both the sparsity structure
and the parameter values of the system. In particular, we show that the
proposed estimator can correctly identify the sparsity pattern of the system
matrices with high probability, provided that the length of the sample
trajectory exceeds a threshold. Furthermore, we show that this threshold scales
polynomially in the number of nonzero elements in the system matrices, but
logarithmically in the system dimensions --- this improves on existing sample
complexity bounds for the sparse system identification problem. We further
extend these results to obtain sharp bounds on the $\ell_{\infty}$-norm of the
estimation error and show how different properties of the system---such as its
stability level and \textit{mutual incoherency}---affect this bound. Finally,
an extensive case study on power systems is presented to illustrate the
performance of the proposed estimation method.
DeepMoD: Deep learning for Model Discovery in noisy data
Authors: Gert-Jan Both, Subham Choudhury, Pierre Sens, Remy Kusters
Date: 2019-04-20 00:00:00
Tasks: Deep Learning, Model Discovery, regression, Small Data Image Classification, Time Series, Time Series Analysis

We introduce DeepMoD, a Deep learning based Model Discovery algorithm. DeepMoD discovers the partial differential equation underlying a spatio-temporal data set using sparse regression on a library of possible functions and their derivatives. A neural network approximates the data and constructs the function library, but it also performs the sparse regression. This construction makes it extremely robust to noise, applicable to small data sets, and, contrary to other deep learning methods, does not require a training set. We benchmark our approach on several physical problems such as the Burgers', Korteweg-de Vries and Keller-Segel equations, and find that it requires as few as $\mathcal{O}(10^2)$ samples and works at noise levels up to $75\%$. Motivated by these results, we apply DeepMoD directly on noisy experimental time-series data from a gel electrophoresis experiment and find that it discovers the advection-diffusion equation describing this system.
Bayesian inversion for nanowire field-effect sensors
Authors: Amirreza Khodadadian, Benjamin Stadlbauer, Clemens Heitzinger
Date: 2019-04-12 00:00:00

Nanowire field-effect sensors have recently been developed for label-free detection of biomolecules. In this work, we introduce a computational technique based on Bayesian estimation to determine the physical parameters of the sensor and, more importantly, the properties of the analyte molecules. To that end, we first propose a PDE based model to simulate the device charge transport and electrochemical behavior. Then, the adaptive Metropolis algorithm with delayed rejection (DRAM) is applied to estimate the posterior distribution of unknown parameters, namely molecule charge density, molecule density, doping concentration, and electron and hole mobilities. We determine the device and molecules properties simultaneously, and we also calculate the molecule density as the only parameter after having determined the device parameters. This approach makes it possible not only to determine unknown parameters, but it also shows how well each parameter can be determined by yielding the probability density function (pdf).
Continual Learning with Self-Organizing Maps
Authors: Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer, Yuhai Tu
Date: 2019-04-19 00:00:00
Tasks: Continual Learning

Despite remarkable successes achieved by modern neural networks in a wide
range of applications, these networks perform best in domain-specific
stationary environments where they are trained only once on large-scale
controlled data repositories. When exposed to non-stationary learning
environments, current neural networks tend to forget what they had previously
learned, a phenomena known as catastrophic forgetting. Most previous approaches
to this problem rely on memory replay buffers which store samples from
previously learned tasks, and use them to regularize the learning on new ones.
This approach suffers from the important disadvantage of not scaling well to
real-life problems in which the memory requirements become enormous. We propose
a memoryless method that combines standard supervised neural networks with
self-organizing maps to solve the continual learning problem. The role of the
self-organizing map is to adaptively cluster the inputs into appropriate task
contexts - without explicit labels - and allocate network resources
accordingly. Thus, it selectively routes the inputs in accord with previous
experience, ensuring that past learning is maintained and does not interfere
with current learning. Out method is intuitive, memoryless, and performs on par
with current state-of-the-art approaches on standard benchmarks.
Donkey and Smuggler Optimization Algorithm: A Collaborative Working Approach to Path Finding
Authors: Ahmed S. Shamsaldin, Tarik A. Rashid, Rawan A. Al-Rashid Agha, Nawzad K. Al-Salihi, Mokhtar Mohammadi
Date: 2019-04-19 00:00:00
Tasks: Metaheuristic Optimization, Traveling Salesman Problem

Swarm Intelligence is a metaheuristic optimization approach that has become
very predominant over the last few decades. These algorithms are inspired by
animals' physical behaviors and their evolutionary perceptions. The simplicity
of these algorithms allows researchers to simulate different natural phenomena
to solve various real-world problems. This paper suggests a novel algorithm
called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by
the searching behavior of donkeys. The algorithm imitates transportation
behavior such as searching and selecting routes for movement by donkeys in the
actual world. Two modes are established for implementing the search behavior
and route-selection in this algorithm. These are the Smuggler and Donkeys. In
the Smuggler mode, all the possible paths are discovered and the shortest path
is then found. In the Donkeys mode, several donkey behaviors are utilized such
as Run, Face & Suicide, and Face & Support. Real world data and applications
are used to test the algorithm. The experimental results consisted of two
parts, firstly, we used the standard benchmark test functions to evaluate the
performance of the algorithm in respect to the most popular and the state of
the art algorithms. Secondly, the DSO is adapted and implemented on three
real-world applications namely; traveling salesman problem, packet routing, and
ambulance routing. The experimental results of DSO on these real-world problems
are very promising. The results exhibit that the suggested DSO is appropriate
to tackle other unfamiliar search spaces and complex problems.
Latent Variable Algorithms for Multimodal Learning and Sensor Fusion
Authors: Lijiang Guo
Date: 2019-04-23 00:00:00
Tasks: Activity Recognition, Decision Making, Event Detection, Sensor Fusion, Sequential Decision Making, Speech Synthesis

Multimodal learning has been lacking principled ways of combining information
from different modalities and learning a low-dimensional manifold of meaningful
representations. We study multimodal learning and sensor fusion from a latent
variable perspective. We first present a regularized recurrent attention filter
for sensor fusion. This algorithm can dynamically combine information from
different types of sensors in a sequential decision making task. Each sensor is
bonded with a modular neural network to maximize utility of its own
information. A gating modular neural network dynamically generates a set of
mixing weights for outputs from sensor networks by balancing utility of all
sensors' information. We design a co-learning mechanism to encourage
co-adaption and independent learning of each sensor at the same time, and
propose a regularization based co-learning method. In the second part, we focus
on recovering the manifold of latent representation. We propose a co-learning
approach using probabilistic graphical model which imposes a structural prior
on the generative model: multimodal variational RNN (MVRNN) model, and derive a
variational lower bound for its objective functions. In the third part, we
extend the siamese structure to sensor fusion for robust acoustic event
detection. We perform experiments to investigate the latent representations
that are extracted; works will be done in the following months. Our experiments
show that the recurrent attention filter can dynamically combine different
sensor inputs according to the information carried in the inputs. We consider
MVRNN can identify latent representations that are useful for many downstream
tasks such as speech synthesis, activity recognition, and control and planning.
Both algorithms are general frameworks which can be applied to other tasks
where different types of sensors are jointly used for decision making.