pwc-archive/papers-with-abstracts huggingface dataset:
CAR-Net: Clairvoyant Attentive Recurrent Network
Authors: Amir Sadeghian, Ferdinand Legros, Maxime Voisin, Ricky Vesel, Alexandre Alahi, Silvio Savarese
Date: 2017-11-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: Prediction, Trajectory Forecasting

We present an interpretable framework for path prediction that leverages
dependencies between agents' behaviors and their spatial navigation
environment. We exploit two sources of information: the past motion trajectory
of the agent of interest and a wide top-view image of the navigation scene. We
propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where
to look in a large image of the scene when solving the path prediction task.
Our method can attend to any area, or combination of areas, within the raw
image (e.g., road intersections) when predicting the trajectory of the agent.
This allows us to visualize fine-grained semantic elements of navigation scenes
that influence the prediction of trajectories. To study the impact of space on
agents' trajectories, we build a new dataset made of top-view images of
hundreds of scenes (Formula One racing tracks) where agents' behaviors are
heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net
successfully attends to these salient regions. Additionally, CAR-Net reaches
state-of-the-art accuracy on the standard trajectory forecasting benchmark,
Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize
to unseen scenes.
Multimodal Biometric Authentication Using Choquet Integral and Genetic Algorithm
Authors: Anouar Ben Khalifa, Sami Gazzah, Najoua Essoukri Ben Amara
Date: 2018-03-27 00:00:00

The Choquet integral is a tool for the information fusion that is very
effective in the case where fuzzy measures associated with it are well chosen.
In this paper,we propose a new approach for calculating fuzzy measures
associated with the Choquet integral in a context of data fusion in multimodal
biometrics. The proposed approach is based on genetic algorithms. It has been
validated in two databases: the first base is relative to synthetic scores and
the second one is biometrically relating to the face, fingerprintand palmprint.
The results achieved attest the robustness of the proposed approach.
An Improved Evaluation Framework for Generative Adversarial Networks
Authors: Shaohui Liu, Yi Wei, Jiwen Lu, Jie zhou
Date: 2018-03-20 00:00:00

In this paper, we propose an improved quantitative evaluation framework for
Generative Adversarial Networks (GANs) on generating domain-specific images,
where we improve conventional evaluation methods on two levels: the feature
representation and the evaluation metric. Unlike most existing evaluation
frameworks which transfer the representation of ImageNet inception model to map
images onto the feature space, our framework uses a specialized encoder to
acquire fine-grained domain-specific representation. Moreover, for datasets
with multiple classes, we propose Class-Aware Frechet Distance (CAFD), which
employs a Gaussian mixture model on the feature space to better fit the
multi-manifold feature distribution. Experiments and analysis on both the
feature level and the image level were conducted to demonstrate improvements of
our proposed framework over the recently proposed state-of-the-art FID method.
To our best knowledge, we are the first to provide counter examples where FID
gives inconsistent results with human judgments. It is shown in the experiments
that our framework is able to overcome the shortness of FID and improves
robustness. Code will be made available.
HDM-Net: Monocular Non-Rigid 3D Reconstruction with Learned Deformation Model
Authors: Vladislav Golyanik, Soshi Shimada, Kiran varanasi, Didier Stricker
Date: 2018-03-27 00:00:00
Tasks: 3D Reconstruction

Monocular dense 3D reconstruction of deformable objects is a hard ill-posed problem in computer vision. Current techniques either require dense correspondences and rely on motion and deformation cues, or assume a highly accurate reconstruction (referred to as a template) of at least a single frame given in advance and operate in the manner of non-rigid tracking. Accurate computation of dense point tracks often requires multiple frames and might be computationally expensive. Availability of a template is a very strong prior which restricts system operation to a pre-defined environment and scenarios. In this work, we propose a new hybrid approach for monocular non-rigid reconstruction which we call Hybrid Deformation Model Network (HDM-Net). In our approach, deformation model is learned by a deep neural network, with a combination of domain-specific loss functions. We train the network with multiple states of a non-rigidly deforming structure with a known shape at rest. HDM-Net learns different reconstruction cues including texture-dependent surface deformations, shading and contours. We show generalisability of HDM-Net to states not presented in the training dataset, with unseen textures and under new illumination conditions. Experiments with noisy data and a comparison with other methods demonstrate robustness and accuracy of the proposed approach and suggest possible application scenarios of the new technique in interventional diagnostics and augmented reality.
Pixels to Graphs by Associative Embedding
Authors: Alejandro Newell, Jia Deng
Date: 2017-06-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Graph Generation, Scene Graph Generation

Graphs are a useful abstraction of image content. Not only can graphs
represent details about individual objects in a scene but they can capture the
interactions between pairs of objects. We present a method for training a
convolutional neural network such that it takes in an input image and produces
a full graph definition. This is done end-to-end in a single stage with the use
of associative embeddings. The network learns to simultaneously identify all of
the elements that make up a graph and piece them together. We benchmark on the
Visual Genome dataset, and demonstrate state-of-the-art performance on the
challenging task of scene graph generation.
Band Target Entropy Minimization and Target Partial Least Squares for Spectral Recovery and Calibration
Authors: Casey Kneale, Steven D. Brown
Date: 2018-02-11 00:00:00

The resolution and calibration of pure spectra of minority components in
measurements of chemical mixtures without prior knowledge of the mixture is a
challenging problem. In this work, a combination of band target entropy
minimization (BTEM) and target partial least squares (T-PLS) was used to obtain
estimates for single pure component spectra and to calibrate those estimates in
a true, one-at-a-time fashion. This approach allows for minor components to be
targeted and their relative amounts estimated in the presence of other varying
components in spectral data. The use of T-PLS estimation is an improvement to
the BTEM method because it overcomes the need to identify all of the pure
components prior to estimation. Estimated amounts from this combination were
found to be similar to those obtained from a standard method, multivariate
curve resolution-alternating least squares (MCR-ALS), on a simple, three
component mixture dataset. Studies from two experimental datasets demonstrate
where the combination of BTEM and T-PLS could model the pure component spectra
and obtain concentration profiles of minor components but MCR-ALS could not.
A Neuronal Planar Modeling for Handwriting Signature based on Automatic Segmentation
Authors: Imen Abroug Ben Abdelghani, Najwa Essoukri Ben Amara
Date: 2018-03-27 00:00:00

This paper deals with offline handwriting signature verification.We propose a
planar neuronal model of signature image. Planarmodelsare generally based on
delimiting homogenous zones ofimages; we propose in this paper an automatic
segmentationapproach into bands of signature images. Signature image ismodeled
by a planar neuronal model with horizontal secondarymodels and a
verticalprincipal model. The proposed methodhas been tested on two databases.
The first is the one we havecollected; it includes 6000 signaturescorresponding
to 60writers. The second is the public GPDS-300 database including16200
signature corresponding to 300 persons. The achievedresults are promising.
Distributed Adaptive Sampling for Kernel Matrix Approximation
Authors: Daniele Calandriello, Alessandro Lazaric, Michal Valko
Date: 2018-03-27 00:00:00
Tasks: Clustering

Most kernel-based methods, such as kernel or Gaussian process regression,
kernel PCA, ICA, or $k$-means clustering, do not scale to large datasets,
because constructing and storing the kernel matrix $\mathbf{K}_n$ requires at
least $\mathcal{O}(n^2)$ time and space for $n$ samples. Recent works show that
sampling points with replacement according to their ridge leverage scores (RLS)
generates small dictionaries of relevant points with strong spectral
approximation guarantees for $\mathbf{K}_n$. The drawback of RLS-based methods
is that computing exact RLS requires constructing and storing the whole kernel
matrix. In this paper, we introduce SQUEAK, a new algorithm for kernel
approximation based on RLS sampling that sequentially processes the dataset,
storing a dictionary which creates accurate kernel matrix approximations with a
number of points that only depends on the effective dimension $d_{eff}(\gamma)$
of the dataset. Moreover since all the RLS estimations are efficiently
performed using only the small dictionary, SQUEAK is the first RLS sampling
algorithm that never constructs the whole matrix $\mathbf{K}_n$, runs in linear
time $\widetilde{\mathcal{O}}(nd_{eff}(\gamma)^3)$ w.r.t. $n$, and requires
only a single pass over the dataset. We also propose a parallel and distributed
version of SQUEAK that linearly scales across multiple machines, achieving
similar accuracy in as little as
$\widetilde{\mathcal{O}}(\log(n)d_{eff}(\gamma)^3)$ time.
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
Authors: Simon Hecker, Dengxin Dai, Luc van Gool
Date: 2018-03-27 00:00:00
Proceeding: ECCV 2018 9

For human drivers, having rear and side-view mirrors is vital for safe
driving. They deliver a more complete view of what is happening around the car.
Human drivers also heavily exploit their mental map for navigation.
Nonetheless, several methods have been published that learn driving models with
only a front-facing camera and without a route planner. This lack of
information renders the self-driving task quite intractable. We investigate the
problem in a more realistic setting, which consists of a surround-view camera
system with eight cameras, a route planner, and a CAN bus reader. In
particular, we develop a sensor setup that provides data for a 360-degree view
of the area surrounding the vehicle, the driving route to the destination, and
low-level driving maneuvers (e.g. steering angle and speed) by human drivers.
With such a sensor setup we collect a new driving dataset, covering diverse
driving scenarios and varying weather/illumination conditions. Finally, we
learn a novel driving model by integrating information from the surround-view
cameras and the route planner. Two route planners are exploited: 1) by
representing the planned routes on OpenStreetMap as a stack of GPS coordinates,
and 2) by rendering the planned routes on TomTom Go Mobile and recording the
progression into a video. Our experiments show that: 1) 360-degree
surround-view cameras help avoid failures made with a single front-view camera,
in particular for city driving and intersection scenarios; and 2) route
planners help the driving task significantly, especially for steering angle
prediction.
Incremental Training of Deep Convolutional Neural Networks
Authors: Roxana Istrate, Adelmo Cristiano Innocenza Malossi, Costas Bekas, Dimitrios Nikolopoulos
Date: 2018-03-27 00:00:00

We propose an incremental training method that partitions the original
network into sub-networks, which are then gradually incorporated in the running
network during the training process. To allow for a smooth dynamic growth of
the network, we introduce a look-ahead initialization that outperforms the
random initialization. We demonstrate that our incremental approach reaches the
reference network baseline accuracy. Additionally, it allows to identify
smaller partitions of the original state-of-the-art network, that deliver the
same final accuracy, by using only a fraction of the global number of
parameters. This allows for a potential speedup of the training time of several
factors. We report training results on CIFAR-10 for ResNet and VGGNet.
Safe end-to-end imitation learning for model predictive control
Authors: Keuntaek Lee, Kamil Saigol, Evangelos A. Theodorou
Date: 2018-03-27 00:00:00
Tasks: Autonomous Driving, Imitation Learning, Model Predictive Control, Reinforcement Learning

We propose the use of Bayesian networks, which provide both a mean value and
an uncertainty estimate as output, to enhance the safety of learned control
policies under circumstances in which a test-time input differs significantly
from the training set. Our algorithm combines reinforcement learning and
end-to-end imitation learning to simultaneously learn a control policy as well
as a threshold over the predictive uncertainty of the learned model, with no
hand-tuning required. Corrective action, such as a return of control to the
model predictive controller or human expert, is taken when the uncertainty
threshold is exceeded. We validate our method on fully-observable and
vision-based partially-observable systems using cart-pole and autonomous
driving simulations using deep convolutional Bayesian neural networks. We
demonstrate that our method is robust to uncertainty resulting from varying
system dynamics as well as from partial state observability.
Comprehending Real Numbers: Development of Bengali Real Number Speech Corpus
Authors: Md Mahadi Hasan Nahid, Md. Ashraful Islam, Bishwajit Purkaystha, Md. Saiful Islam
Date: 2018-03-27 00:00:00
Tasks: speech-recognition, Speech Recognition

Speech recognition has received a less attention in Bengali literature due to
the lack of a comprehensive dataset. In this paper, we describe the development
process of the first comprehensive Bengali speech dataset on real numbers. It
comprehends all the possible words that may arise in uttering any Bengali real
number. The corpus has ten speakers from the different regions of Bengali
native people. It comprises of more than two thousands of speech samples in a
total duration of closed to four hours. We also provide a deep analysis of our
corpus, highlight some of the notable features of it, and finally evaluate the
performances of two of the notable Bengali speech recognizers on it.
Task Agnostic Continual Learning Using Online Variational Bayes
Authors: Chen Zeno, Itay Golan, Elad Hoffer, Daniel Soudry
Date: 2018-03-27 00:00:00
Tasks: Continual Learning

Catastrophic forgetting is the notorious vulnerability of neural networks to
the change of the data distribution while learning. This phenomenon has long
been considered a major obstacle for allowing the use of learning agents in
realistic continual learning settings. A large body of continual learning
research assumes that task boundaries are known during training. However,
research for scenarios in which task boundaries are unknown during training has
been lacking. In this paper we present, for the first time, a method for
preventing catastrophic forgetting (BGD) for scenarios with task boundaries
that are unknown during training --- task-agnostic continual learning. Code of
our algorithm is available at https://github.com/igolan/bgd.
Privacy Preserving Machine Learning: Threats and Solutions
Authors: Mohammad Al-Rubaie, J. Morris Chang
Date: 2018-03-27 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

For privacy concerns to be addressed adequately in current machine learning
systems, the knowledge gap between the machine learning and privacy communities
must be bridged. This article aims to provide an introduction to the
intersection of both fields with special emphasis on the techniques used to
protect the data.
Dynamic Zoom-in Network for Fast Object Detection in Large Images
Authors: Mingfei Gao, Ruichi Yu, Ang Li, Vlad I. Morariu, Larry S. Davis
Date: 2017-11-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: object-detection, Object Detection, Real-Time Object Detection, Reinforcement Learning

We introduce a generic framework that reduces the computational cost of
object detection while retaining accuracy for scenarios where objects with
varied sizes appear in high resolution images. Detection progresses in a
coarse-to-fine manner, first on a down-sampled version of the image and then on
a sequence of higher resolution regions identified as likely to improve the
detection accuracy. Built upon reinforcement learning, our approach consists of
a model (R-net) that uses coarse detection results to predict the potential
accuracy gain for analyzing a region at a higher resolution and another model
(Q-net) that sequentially selects regions to zoom in. Experiments on the
Caltech Pedestrians dataset show that our approach reduces the number of
processed pixels by over 50% without a drop in detection accuracy. The merits
of our approach become more significant on a high resolution test set collected
from YFCC100M dataset, where our approach maintains high detection performance
while reducing the number of processed pixels by about 70% and the detection
time by over 50%.
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline
Authors: Szu-Jui Chen, Aswin Shanmugam Subramanian, Hainan Xu, Shinji Watanabe
Date: 2018-03-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Distant Speech Recognition, Language Modeling, Language Modelling, Noisy Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

This paper describes a new baseline system for automatic speech recognition
(ASR) in the CHiME-4 challenge to promote the development of noisy ASR in
speech processing communities by providing 1) state-of-the-art system with a
simplified single system comparable to the complicated top systems in the
challenge, 2) publicly available and reproducible recipe through the main
repository in the Kaldi speech recognition toolkit. The proposed system adopts
generalized eigenvalue beamforming with bidirectional long short-term memory
(LSTM) mask estimation. We also propose to use a time delay neural network
(TDNN) based on the lattice-free version of the maximum mutual information
(LF-MMI) trained with augmented all six microphones plus the enhanced data
after beamforming. Finally, we use a LSTM language model for lattice and n-best
re-scoring. The final system achieved 2.74\% WER for the real test set in the
6-channel track, which corresponds to the 2nd place in the challenge. In
addition, the proposed baseline recipe includes four different speech
enhancement measures, short-time objective intelligibility measure (STOI),
extended STOI (eSTOI), perceptual evaluation of speech quality (PESQ) and
speech distortion ratio (SDR) for the simulation test set. Thus, the recipe
also provides an experimental platform for speech enhancement studies with
these performance measures.
Event-based Face Detection and Tracking in the Blink of an Eye
Authors: Gregor Lenz, Sio-Hoi Ieng, Ryad Benosman
Date: 2018-03-27 00:00:00
Tasks: Face Detection, Position

We present the first purely event-based method for face detection using the
high temporal resolution of an event-based camera. We will rely on a new
feature that has never been used for such a task that relies on detecting eye
blinks. Eye blinks are a unique natural dynamic signature of human faces that
is captured well by event-based sensors that rely on relative changes of
luminance. Although an eye blink can be captured with conventional cameras, we
will show that the dynamics of eye blinks combined with the fact that two eyes
act simultaneously allows to derive a robust methodology for face detection at
a low computational cost and high temporal resolution. We show that eye blinks
have a unique temporal signature over time that can be easily detected by
correlating the acquired local activity with a generic temporal model of eye
blinks that has been generated from a wide population of users. We furthermore
show that once the face is reliably detected it is possible to apply a
probabilistic framework to track the spatial position of a face for each
incoming event while updating the position of trackers. Results are shown for
several indoor and outdoor experiments. We will also release an annotated data
set that can be used for future work on the topic.
A Fast Face Detection Method via Convolutional Neural Network
Authors: Guanjun Guo, Hanzi Wang, Yan Yan, Jin Zheng, Bo Li
Date: 2018-03-27 00:00:00
Tasks: Face Detection, object-detection, Object Detection

Current face or object detection methods via convolutional neural network
(such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features
based on an image pyramid. However, such a strategy increases the computational
burden for face detection. In this paper, we propose a fast face detection
method based on discriminative complete features (DCFs) extracted by an
elaborately designed convolutional neural network, where face detection is
directly performed on the complete feature maps. DCFs have shown the ability of
scale invariance, which is beneficial for face detection with high speed and
promising performance. Therefore, extracting multi-scale features on an image
pyramid employed in the conventional methods is not required in the proposed
method, which can greatly improve its efficiency for face detection.
Experimental results on several popular face detection datasets show the
efficiency and the effectiveness of the proposed method for face detection.
A Cluster Elastic Net for Multivariate Regression
Authors: Bradley S. Price, Ben Sherwood
Date: 2017-07-12 00:00:00
Tasks: Clustering, regression, Variable Selection

We propose a method for estimating coefficients in multivariate regression
when there is a clustering structure to the response variables. The proposed
method includes a fusion penalty, to shrink the difference in fitted values
from responses in the same cluster, and an L1 penalty for simultaneous variable
selection and estimation. The method can be used when the grouping structure of
the response variables is known or unknown. When the clustering structure is
unknown the method will simultaneously estimate the clusters of the response
and the regression coefficients. Theoretical results are presented for the
penalized least squares case, including asymptotic results allowing for p >> n.
We extend our method to the setting where the responses are binomial variables.
We propose a coordinate descent algorithm for both the normal and binomial
likelihood, which can easily be extended to other generalized linear model
(GLM) settings. Simulations and data examples from business operations and
genomics are presented to show the merits of both the least squares and
binomial methods.