pwc-archive/papers-with-abstracts huggingface dataset:
Radial Line Fourier Descriptor for Historical Handwritten Text Representation
Authors: Anders Hast, Ekta Vats
Date: 2017-09-06 00:00:00
Tasks: Information Retrieval, Retrieval

Automatic recognition of historical handwritten manuscripts is a daunting
task due to paper degradation over time. Recognition-free retrieval or word
spotting is popularly used for information retrieval and digitization of the
historical handwritten documents. However, the performance of word spotting
algorithms depends heavily on feature detection and representation methods.
Although there exist popular feature descriptors such as Scale Invariant
Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant
properties of these descriptors amplify the noise in the degraded document
images, rendering them more sensitive to noise and complex characteristics of
historical manuscripts. Therefore, an efficient and relaxed feature descriptor
is required as handwritten words across different documents are indeed similar,
but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor
for handwritten word representation, with a short feature vector of 32
dimensions. A segmentation-free and training-free handwritten word spotting
method is studied herein that relies on the proposed RLF descriptor, takes into
account different keypoint representations and uses a simple
preconditioner-based feature matching algorithm. The effectiveness of the RLF
descriptor for segmentation-free handwritten word spotting is empirically
evaluated on well-known historical handwritten datasets using standard
evaluation measures.
Natural Gradient Deep Q-learning
Authors: Ethan Knight, Osher Lerner
Date: 2018-03-20 00:00:00
Tasks: Deep Reinforcement Learning, Hyperparameter Optimization, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a novel algorithm to train a deep Q-learning agent using
natural-gradient techniques. We compare the original deep Q-network (DQN)
algorithm to its natural-gradient counterpart, which we refer to as NGDQN, on a
collection of classic control domains. Without employing target networks, NGDQN
significantly outperforms DQN without target networks, and performs no worse
than DQN with target networks, suggesting that NGDQN stabilizes training and
can help reduce the need for additional hyperparameter tuning. We also find
that NGDQN is less sensitive to hyperparameter optimization relative to DQN.
Together these results suggest that natural-gradient techniques can improve
value-function optimization in deep reinforcement learning.
MAGSAC: marginalizing sample consensus
Authors: Daniel Barath, Jana Noskova, Jiri Matas
Date: 2018-03-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: Homography Estimation

A method called, sigma-consensus, is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC. Instead of estimating the noise sigma, it is marginalized over a range of noise scales. The optimized model is obtained by weighted least-squares fitting where the weights come from the marginalization over sigma of the point likelihoods of being inliers. A new quality function is proposed not requiring sigma and, thus, a set of inliers to determine the model quality. Also, a new termination criterion for RANSAC is built on the proposed marginalization approach. Applying sigma-consensus, MAGSAC is proposed with no need for a user-defined sigma and improving the accuracy of robust estimation significantly. It is superior to the state-of-the-art in terms of geometric accuracy on publicly available real-world datasets for epipolar geometry (F and E) and homography estimation. In addition, applying sigma-consensus only once as a post-processing step to the RANSAC output always improved the model quality on a wide range of vision problems without noticeable deterioration in processing time, adding a few milliseconds. The source code is at https://github.com/danini/magsac.
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions
Authors: Qing Li, Qingyi Tao, Shafiq Joty, Jianfei Cai, Jiebo Luo
Date: 2018-03-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: Explanatory Visual Question Answering, Multi-Task Learning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Most existing works in visual question answering (VQA) are dedicated to
improving the accuracy of predicted answers, while disregarding the
explanations. We argue that the explanation for an answer is of the same or
even more importance compared with the answer itself, since it makes the
question and answering process more understandable and traceable. To this end,
we propose a new task of VQA-E (VQA with Explanation), where the computational
models are required to generate an explanation with the predicted answer. We
first construct a new dataset, and then frame the VQA-E problem in a multi-task
learning architecture. Our VQA-E dataset is automatically derived from the VQA
v2 dataset by intelligently exploiting the available captions. We have
conducted a user study to validate the quality of explanations synthesized by
our method. We quantitatively show that the additional supervision from
explanations can not only produce insightful textual sentences to justify the
answers, but also improve the performance of answer prediction. Our model
outperforms the state-of-the-art methods by a clear margin on the VQA v2
dataset.
Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks
Authors: Seyed Ali Jalalifar, Hosein Hasani, Hamid Aghajan
Date: 2018-03-20 00:00:00
Tasks: Constrained Lip-synchronization

We present a novel approach to generating photo-realistic images of a face
with accurate lip sync, given an audio input. By using a recurrent neural
network, we achieved mouth landmarks based on audio features. We exploited the
power of conditional generative adversarial networks to produce
highly-realistic face conditioned on a set of landmarks. These two networks
together are capable of producing a sequence of natural faces in sync with an
input audio track.
Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression
Authors: Julien Gout, Markus Quade, Kamran Shafi, Robert K. Niven, Markus Abel
Date: 2016-12-15 00:00:00
Tasks: regression, Symbolic Regression

Networks of coupled dynamical systems provide a powerful way to model systems
with enormously complex dynamics, such as the human brain. Control of
synchronization in such networked systems has far reaching applications in many
domains, including engineering and medicine. In this paper, we formulate the
synchronization control in dynamical systems as an optimization problem and
present a multi-objective genetic programming-based approach to infer optimal
control functions that drive the system from a synchronized to a
non-synchronized state and vice-versa. The genetic programming-based controller
allows learning optimal control functions in an interpretable symbolic form.
The effectiveness of the proposed approach is demonstrated in controlling
synchronization in coupled oscillator systems linked in networks of increasing
order complexity, ranging from a simple coupled oscillator system to a
hierarchical network of coupled oscillators. The results show that the proposed
method can learn highly-effective and interpretable control functions for such
systems.
Confounder Detection in High Dimensional Linear Models using First Moments of Spectral Measures
Authors: Furui Liu, Laiwan Chan
Date: 2018-03-19 00:00:00
Tasks: regression

In this paper, we study the confounder detection problem in the linear model,
where the target variable $Y$ is predicted using its $n$ potential causes
$X_n=(x_1,...,x_n)^T$. Based on an assumption of rotation invariant generating
process of the model, recent study shows that the spectral measure induced by
the regression coefficient vector with respect to the covariance matrix of
$X_n$ is close to a uniform measure in purely causal cases, but it differs from
a uniform measure characteristically in the presence of a scalar confounder.
Then, analyzing spectral measure pattern could help to detect confounding. In
this paper, we propose to use the first moment of the spectral measure for
confounder detection. We calculate the first moment of the regression vector
induced spectral measure, and compare it with the first moment of a uniform
spectral measure, both defined with respect to the covariance matrix of $X_n$.
The two moments coincide in non-confounding cases, and differ from each other
in the presence of confounding. This statistical causal-confounding asymmetry
can be used for confounder detection. Without the need of analyzing the
spectral measure pattern, our method does avoid the difficulty of metric choice
and multiple parameter optimization. Experiments on synthetic and real data
show the performance of this method.
MLtuner: System Support for Automatic Machine Learning Tuning
Authors: Henggang Cui, Gregory R. Ganger, Phillip B. Gibbons
Date: 2018-03-20 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, image-classification, Image Classification, Video Classification

MLtuner automatically tunes settings for training tunables (such as the
learning rate, the momentum, the mini-batch size, and the data staleness bound)
that have a significant impact on large-scale machine learning (ML)
performance. Traditionally, these tunables are set manually, which is
unsurprisingly error-prone and difficult to do without extensive domain
knowledge. MLtuner uses efficient snapshotting, branching, and
optimization-guided online trial-and-error to find good initial settings as
well as to re-tune settings during execution. Experiments show that MLtuner can
robustly find and re-tune tunable settings for a variety of ML applications,
including image classification (for 3 models and 2 datasets), video
classification, and matrix factorization. Compared to state-of-the-art ML
auto-tuning approaches, MLtuner is more robust for large problems and over an
order of magnitude faster.
Ontology-Based Reasoning about the Trustworthiness of Cyber-Physical Systems
Authors: Marcello Balduccini, Edward Griffor, Michael Huth, Claire Vishik, Martin Burns, David Wollman
Date: 2018-03-20 00:00:00

It has been challenging for the technical and regulatory communities to
formulate requirements for trustworthiness of the cyber-physical systems (CPS)
due to the complexity of the issues associated with their design, deployment,
and operations. The US National Institute of Standards and Technology (NIST),
through a public working group, has released a CPS Framework that adopts a
broad and integrated view of CPS and positions trustworthiness among other
aspects of CPS. This paper takes the model created by the CPS Framework and its
further developments one step further, by applying ontological approaches and
reasoning techniques in order to achieve greater understanding of CPS. The
example analyzed in the paper demonstrates the enrichment of the original CPS
model obtained through ontology and reasoning and its ability to deliver
additional insights to the developers and operators of CPS.
A Distance Oriented Kalman Filter Particle Swarm Optimizer Applied to Multi-Modality Image Registration
Authors: Chengjia Wang, Keith A. Goatman, James Boardman, Erin Beveridge, David Newby, Scott Semple
Date: 2018-03-20 00:00:00
Tasks: Benchmarking, Image Registration, Medical Image Registration

In this paper we describe improvements to the particle swarm optimizer (PSO)
made by inclusion of an unscented Kalman filter to guide particle motion. We
demonstrate the effectiveness of the unscented Kalman filter PSO by comparing
it with the original PSO algorithm and its variants designed to improve
performance. The PSOs were tested firstly on a number of common synthetic
benchmarking functions, and secondly applied to a practical three-dimensional
image registration problem. The proposed methods displayed better performances
for 4 out of 8 benchmark functions, and reduced the target registration errors
by at least 2mm when registering down-sampled benchmark brain images. Our
methods also demonstrated an ability to align images featuring motion related
artefacts which all other methods failed to register. These new PSO methods
provide a novel, efficient mechanism to integrate prior knowledge into each
iteration of the optimization process, which can enhance the accuracy and speed
of convergence in the application of medical image registration.
Patch-Based Image Inpainting with Generative Adversarial Networks
Authors: Ugur Demir, Gozde Unal
Date: 2018-03-20 00:00:00
Tasks: Generative Adversarial Network, Image Inpainting

Area of image inpainting over relatively large missing regions recently
advanced substantially through adaptation of dedicated deep neural networks.
However, current network solutions still introduce undesired artifacts and
noise to the repaired regions. We present an image inpainting method that is
based on the celebrated generative adversarial network (GAN) framework. The
proposed PGGAN method includes a discriminator network that combines a global
GAN (G-GAN) architecture with a patchGAN approach. PGGAN first shares network
layers between G-GAN and patchGAN, then splits paths to produce two adversarial
losses that feed the generator network in order to capture both local
continuity of image texture and pervasive global features in images. The
proposed framework is evaluated extensively, and the results including
comparison to recent state-of-the-art demonstrate that it achieves considerable
improvements on both visual and quantitative evaluations.
Residual Codean Autoencoder for Facial Attribute Analysis
Authors: Akshay Sethi, Maneet Singh, Richa Singh, Mayank Vatsa
Date: 2018-03-20 00:00:00
Tasks: Attribute, Marketing

Facial attributes can provide rich ancillary information which can be
utilized for different applications such as targeted marketing, human computer
interaction, and law enforcement. This research focuses on facial attribute
prediction using a novel deep learning formulation, termed as R-Codean
autoencoder. The paper first presents Cosine similarity based loss function in
an autoencoder which is then incorporated into the Euclidean distance based
autoencoder to formulate R-Codean. The proposed loss function thus aims to
incorporate both magnitude and direction of image vectors during feature
learning. Further, inspired by the utility of shortcut connections in deep
models to facilitate learning of optimal parameters, without incurring the
problem of vanishing gradient, the proposed formulation is extended to
incorporate shortcut connections in the architecture. The proposed R-Codean
autoencoder is utilized in facial attribute prediction framework which
incorporates patch-based weighting mechanism for assigning higher weights to
relevant patches for each attribute. The experimental results on publicly
available CelebA and LFWA datasets demonstrate the efficacy of the proposed
approach in addressing this challenging problem.
Are you eligible? Predicting adulthood from face images via class specific mean autoencoder
Authors: Maneet Singh, Shruti Nagpal, Mayank Vatsa, Richa Singh
Date: 2018-03-20 00:00:00
Tasks: General Classification

Predicting if a person is an adult or a minor has several applications such
as inspecting underage driving, preventing purchase of alcohol and tobacco by
minors, and granting restricted access. The challenging nature of this problem
arises due to the complex and unique physiological changes that are observed
with age progression. This paper presents a novel deep learning based
formulation, termed as Class Specific Mean Autoencoder, to learn the
intra-class similarity and extract class-specific features. We propose that the
feature of a particular class if brought similar/closer to the mean feature of
that class can help in learning class-specific representations. The proposed
formulation is applied for the task of adulthood classification which predicts
whether the given face image is of an adult or not. Experiments are performed
on two large databases and the results show that the proposed algorithm yields
higher classification accuracy compared to existing algorithms and a
Commercial-Off-The-Shelf system.
Time Limits in Reinforcement Learning
Authors: Fabio Pardo, Arash Tavakoli, Vitaly Levdik, Petar Kormushev
Date: 2017-12-01 00:00:00
Proceeding: ICML 2018 7
Tasks: General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning, it is common to let an agent interact for a fixed amount of time with its environment before resetting it and repeating the process in a series of episodes. The task that the agent has to learn can either be to maximize its performance over (i) that fixed period, or (ii) an indefinite period where time limits are only used during training to diversify experience. In this paper, we provide a formal account for how time limits could effectively be handled in each of the two cases and explain why not doing so can cause state aliasing and invalidation of experience replay, leading to suboptimal policies and training instability. In case (i), we argue that the terminations due to time limits are in fact part of the environment, and thus a notion of the remaining time should be included as part of the agent's input to avoid violation of the Markov property. In case (ii), the time limits are not part of the environment and are only used to facilitate learning. We argue that this insight should be incorporated by bootstrapping from the value of the state at the end of each partial episode. For both cases, we illustrate empirically the significance of our considerations in improving the performance and stability of existing reinforcement learning algorithms, showing state-of-the-art results on several control tasks.
DARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition
Authors: Louis Lettry, Kenneth Vanhoey, Luc van Gool
Date: 2016-12-23 00:00:00
Tasks: Intrinsic Image Decomposition, valid

We present a new deep supervised learning method for intrinsic decomposition
of a single image into its albedo and shading components. Our contributions are
based on a new fully convolutional neural network that estimates absolute
albedo and shading jointly. Our solution relies on a single end-to-end deep
sequence of residual blocks and a perceptually-motivated metric formed by two
adversarially trained discriminators. As opposed to classical intrinsic image
decomposition work, it is fully data-driven, hence does not require any
physical priors like shading smoothness or albedo sparsity, nor does it rely on
geometric information such as depth. Compared to recent deep learning
techniques, we simplify the architecture, making it easier to build and train,
and constrain it to generate a valid and reversible decomposition. We rediscuss
and augment the set of quantitative metrics so as to account for the more
challenging recovery of non scale-invariant quantities. We train and
demonstrate our architecture on the publicly available MPI Sintel dataset and
its intrinsic image decomposition, show attenuated overfitting issues and
discuss generalizability to other data. Results show that our work outperforms
the state of the art deep algorithms both on the qualitative and quantitative
aspect.
Adaptive Co-weighting Deep Convolutional Features For Object Retrieval
Authors: Jiaxing Wang, Jihua Zhu, Shanmin Pang, Zhongyu Li, Yaochen Li, Xueming Qian
Date: 2018-03-20 00:00:00
Tasks: Image Retrieval, Object, Retrieval

Aggregating deep convolutional features into a global image vector has
attracted sustained attention in image retrieval. In this paper, we propose an
efficient unsupervised aggregation method that uses an adaptive Gaussian filter
and an elementvalue sensitive vector to co-weight deep features. Specifically,
the Gaussian filter assigns large weights to features of region-of-interests
(RoI) by adaptively determining the RoI's center, while the element-value
sensitive channel vector suppresses burstiness phenomenon by assigning small
weights to feature maps with large sum values of all locations. Experimental
results on benchmark datasets validate the proposed two weighting schemes both
effectively improve the discrimination power of image vectors. Furthermore,
with the same experimental setting, our method outperforms other very recent
aggregation approaches by a considerable margin.
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
Authors: Beilun Wang, Ji Gao, Yanjun Qi
Date: 2017-02-09 00:00:00
Tasks: Computational Efficiency

Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many
related tasks (large $K$) under a high-dimensional (large $p$) situation is an
important task. Most previous studies for the joint estimation of multiple
sGGMs rely on penalized log-likelihood estimators that involve expensive and
difficult non-smooth optimizations. We propose a novel approach, FASJEM for
\underline{fa}st and \underline{s}calable \underline{j}oint
structure-\underline{e}stimation of \underline{m}ultiple sGGMs at a large
scale. As the first study of joint sGGM using the Elementary Estimator
framework, our work has three major contributions: (1) We solve FASJEM through
an entry-wise manner which is parallelizable. (2) We choose a proximal
algorithm to optimize FASJEM. This improves the computational efficiency from
$O(Kp^3)$ to $O(Kp^2)$ and reduces the memory requirement from $O(Kp^2)$ to
$O(K)$. (3) We theoretically prove that FASJEM achieves a consistent estimation
with a convergence rate of $O(\log(Kp)/n_{tot})$. On several synthetic and four
real-world datasets, FASJEM shows significant improvements over baselines on
accuracy, computational complexity, and memory costs.
Discrete Potts Model for Generating Superpixels on Noisy Images
Authors: Ruobing Shen, Xiaoyu Chen, Xiangrui Zheng, Gerhard Reinelt
Date: 2018-03-20 00:00:00
Tasks: Denoising, Object Recognition, Segmentation, Superpixels

Many computer vision applications, such as object recognition and
segmentation, increasingly build on superpixels. However, there have been so
far few superpixel algorithms that systematically deal with noisy images. We
propose to first decompose the image into equal-sized rectangular patches,
which also sets the maximum superpixel size. Within each patch, a Potts model
for simultaneous segmentation and denoising is applied, that guarantees
connected and non-overlapping superpixels and also produces a denoised image.
The corresponding optimization problem is formulated as a mixed integer linear
program (MILP), and solved by a commercial solver. Extensive experiments on the
BSDS500 dataset images with noises are compared with other state-of-the-art
superpixel methods. Our method achieves the best result in terms of a combined
score (OP) composed of the under-segmentation error, boundary recall and
compactness.