pwc-archive/papers-with-abstracts huggingface dataset:
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Authors: Justin Johnson, Alexandre Alahi, Li Fei-Fei
Date: 2016-03-27 00:00:00
Tasks: Image Super-Resolution, Nuclear Segmentation, Speech Enhancement, Style Transfer, Super-Resolution

We consider image transformation problems, where an input image is
transformed into an output image. Recent methods for such problems typically
train feed-forward convolutional neural networks using a \emph{per-pixel} loss
between the output and ground-truth images. Parallel work has shown that
high-quality images can be generated by defining and optimizing
\emph{perceptual} loss functions based on high-level features extracted from
pretrained networks. We combine the benefits of both approaches, and propose
the use of perceptual loss functions for training feed-forward networks for
image transformation tasks. We show results on image style transfer, where a
feed-forward network is trained to solve the optimization problem proposed by
Gatys et al in real-time. Compared to the optimization-based method, our
network gives similar qualitative results but is three orders of magnitude
faster. We also experiment with single-image super-resolution, where replacing
a per-pixel loss with a perceptual loss gives visually pleasing results.
Graph Cuts with Interacting Edge Costs - Examples, Approximations, and Algorithms
Authors: Stefanie Jegelka, Jeff Bilmes
Date: 2014-02-02 00:00:00
Tasks: BIG-bench Machine Learning

We study an extension of the classical graph cut problem, wherein we replace
the modular (sum of edge weights) cost function by a submodular set function
defined over graph edges. Special cases of this problem have appeared in
different applications in signal processing, machine learning, and computer
vision. In this paper, we connect these applications via the generic
formulation of "cooperative graph cuts", for which we study complexity,
algorithms, and connections to polymatroidal network flows. Finally, we compare
the proposed algorithms empirically.
Data-Driven Dynamic Decision Models
Authors: John J. Nay, Jonathan M. Gilligan
Date: 2016-03-26 00:00:00
Tasks: Decision Making

This article outlines a method for automatically generating models of dynamic
decision-making that both have strong predictive power and are interpretable in
human terms. This is useful for designing empirically grounded agent-based
simulations and for gaining direct insight into observed dynamic processes. We
use an efficient model representation and a genetic algorithm-based estimation
process to generate simple approximations that explain most of the structure of
complex stochastic processes. This method, implemented in C++ and R, scales
well to large data sets. We apply our methods to empirical data from human
subjects game experiments and international relations. We also demonstrate the
method's ability to recover known data-generating processes by simulating data
with agent-based models and correctly deriving the underlying decision models
for multiple agent models and degrees of stochasticity.
A Draft Memory Model on Spiking Neural Assemblies
Authors: João Ranhel, João H. Albuquerque, Bruno P. M. Azevedo, Nathalia M. Cunha, Pedro J. Ishimaru
Date: 2016-03-26 00:00:00
Tasks: Decoder, model

A draft memory model (DM) for neural networks with spike propagation delay
(SNNwD) is described. Novelty in this approach are that the DM learns
immediately, with stimuli presented once, without synaptic weight changes, and
without external learning algorithm. Basal on this model is to trap spikes
within neural loops. In order to construct the DM we developed two functional
blocks, also described herein. The decoder block receives input from a single
spikes source and connect it to one among many outputs. The selector block
operates in the opposite direction, receiving many spikes sources and
connecting one of them to a single output. We realized conceptual proofs by
testing the DM in the prime numbers classifying task. This activation-based
memory can be used as immediate and short-term memory.
Video Interpolation using Optical Flow and Laplacian Smoothness
Authors: Wenbin Li, Darren Cosker
Date: 2016-03-26 00:00:00
Tasks: Optical Flow Estimation

Non-rigid video interpolation is a common computer vision task. In this paper
we present an optical flow approach which adopts a Laplacian Cotangent Mesh
constraint to enhance the local smoothness. Similar to Li et al., our approach
adopts a mesh to the image with a resolution up to one vertex per pixel and
uses angle constraints to ensure sensible local deformations between image
pairs. The Laplacian Mesh constraints are expressed wholly inside the optical
flow optimization, and can be applied in a straightforward manner to a wide
range of image tracking and registration problems. We evaluate our approach by
testing on several benchmark datasets, including the Middlebury and Garg et al.
datasets. In addition, we show application of our method for constructing 3D
Morphable Facial Models from dynamic 3D data.
Fast and Provably Accurate Bilateral Filtering
Authors: Kunal. N. Chaudhury, Swapnil D. Dabhade
Date: 2016-03-26 00:00:00

The bilateral filter is a non-linear filter that uses a range filter along
with a spatial filter to perform edge-preserving smoothing of images. A direct
computation of the bilateral filter requires $O(S)$ operations per pixel, where
$S$ is the size of the support of the spatial filter. In this paper, we present
a fast and provably accurate algorithm for approximating the bilateral filter
when the range kernel is Gaussian. In particular, for box and Gaussian spatial
filters, the proposed algorithm can cut down the complexity to $O(1)$ per pixel
for any arbitrary $S$. The algorithm has a simple implementation involving
$N+1$ spatial filterings, where $N$ is the approximation order. We give a
detailed analysis of the filtering accuracy that can be achieved by the
proposed approximation in relation to the target bilateral filter. This allows
us to to estimate the order $N$ required to obtain a given accuracy. We also
present comprehensive numerical results to demonstrate that the proposed
algorithm is competitive with state-of-the-art methods in terms of speed and
accuracy.
Support Driven Wavelet Frame-based Image Deblurring
Authors: Liangtian He, Yi-Lun Wang, Zhaoyin Xiang
Date: 2016-03-26 00:00:00
Tasks: Deblurring, Image Deblurring, Image Restoration, Self-Learning

The wavelet frame systems have been playing an active role in image
restoration and many other image processing fields over the past decades, owing
to the good capability of sparsely approximating piece-wise smooth functions
such as images. In this paper, we propose a novel wavelet frame based sparse
recovery model called \textit{Support Driven Sparse Regularization} (SDSR) for
image deblurring, where the partial support information of frame coefficients
is attained via a self-learning strategy and exploited via the proposed
truncated $\ell_0$ regularization. Moreover, the state-of-the-art image
restoration methods can be naturally incorporated into our proposed wavelet
frame based sparse recovery framework. In particular, in order to achieve
reliable support estimation of the frame coefficients, we make use of the
state-of-the-art image restoration result such as that from the IDD-BM3D method
as the initial reference image for support estimation. Our extensive
experimental results have shown convincing improvements over existing
state-of-the-art deblurring methods.
Unsupervised Domain Adaptation in the Wild: Dealing with Asymmetric Label Sets
Authors: Ayush Mittal, Anant Raj, Vinay P. Namboodiri, Tinne Tuytelaars
Date: 2016-03-26 00:00:00
Tasks: Domain Adaptation, General Classification, Unsupervised Domain Adaptation

The goal of domain adaptation is to adapt models learned on a source domain
to a particular target domain. Most methods for unsupervised domain adaptation
proposed in the literature to date, assume that the set of classes present in
the target domain is identical to the set of classes present in the source
domain. This is a restrictive assumption that limits the practical
applicability of unsupervised domain adaptation techniques in real world
settings ("in the wild"). Therefore, we relax this constraint and propose a
technique that allows the set of target classes to be a subset of the source
classes. This way, large publicly available annotated datasets with a wide
variety of classes can be used as source, even if the actual set of classes in
target can be more limited and, maybe most importantly, unknown beforehand.
  To this end, we propose an algorithm that orders a set of source subspaces
that are relevant to the target classification problem. Our method then chooses
a restricted set from this ordered set of source subspaces. As an extension,
even starting from multiple source datasets with varied sets of categories,
this method automatically selects an appropriate subset of source categories
relevant to a target dataset. Empirical analysis on a number of source and
target domain datasets shows that restricting the source subspace to only a
subset of categories does indeed substantially improve the eventual target
classification accuracy over the baseline that considers all source classes.
Blind signal separation and identification of mixtures of images
Authors: Felipe P. do Carmo, Joaquim T. de Assis, Vania V. Estrela, Alessandra M. Coelho
Date: 2016-03-26 00:00:00

In this paper, a fresh procedure to handle image mixtures by means of blind
signal separation relying on a combination of second order and higher order
statistics techniques are introduced. The problem of blind signal separation is
reassigned to the wavelet domain. The key idea behind this method is that the
image mixture can be decomposed into the sum of uncorrelated and/or independent
sub-bands using wavelet transform. Initially, the observed image is
pre-whitened in the space domain. Afterwards, an initial separation matrix is
estimated from the second order statistics de-correlation model in the wavelet
domain. Later, this matrix will be used as an initial separation matrix for the
higher order statistics stage in order to find the best separation matrix. The
suggested algorithm was tested using natural images.Experiments have confirmed
that the use of the proposed process provides promising outcomes in identifying
an image from noisy mixtures of images.
Learning Hough Regression Models via Bridge Partial Least Squares for Object Detection
Authors: Jianyu Tang, Hanzi Wang, Yan Yan
Date: 2016-03-26 00:00:00
Tasks: Clustering, Object, object-detection, Object Detection, regression

Popular Hough Transform-based object detection approaches usually construct
an appearance codebook by clustering local image features. However, how to
choose appropriate values for the parameters used in the clustering step
remains an open problem. Moreover, some popular histogram features extracted
from overlapping image blocks may cause a high degree of redundancy and
multicollinearity. In this paper, we propose a novel Hough Transform-based
object detection approach. First, to address the above issues, we exploit a
Bridge Partial Least Squares (BPLS) technique to establish context-encoded
Hough Regression Models (HRMs), which are linear regression models that cast
probabilistic Hough votes to predict object locations. BPLS is an efficient
variant of Partial Least Squares (PLS). PLS-based regression techniques
(including BPLS) can reduce the redundancy and eliminate the multicollinearity
of a feature set. And the appropriate value of the only parameter used in PLS
(i.e., the number of latent components) can be determined by using a
cross-validation procedure. Second, to efficiently handle object scale changes,
we propose a novel multi-scale voting scheme. In this scheme, multiple Hough
images corresponding to multiple object scales can be obtained simultaneously.
Third, an object in a test image may correspond to multiple true and false
positive hypotheses at different scales. Based on the proposed multi-scale
voting scheme, a principled strategy is proposed to fuse hypotheses to reduce
false positives by evaluating normalized pointwise mutual information between
hypotheses. In the experiments, we also compare the proposed HRM approach with
its several variants to evaluate the influences of its components on its
performance. Experimental results show that the proposed HRM approach has
achieved desirable performances on popular benchmark datasets.
Measuring Book Impact Based on the Multi-granularity Online Review Mining
Authors: Qingqing Zhou, Chengzhi Zhang, Star X. Zhao, Bikun Chen
Date: 2016-03-26 00:00:00
Tasks: Articles

As with articles and journals, the customary methods for measuring books'
academic impact mainly involve citations, which is easy but limited to
interrogating traditional citation databases and scholarly book reviews,
Researchers have attempted to use other metrics, such as Google Books,
libcitation, and publisher prestige. However, these approaches lack
content-level information and cannot determine the citation intentions of
users. Meanwhile, the abundant online review resources concerning academic
books can be used to mine deeper information and content utilizing altmetric
perspectives. In this study, we measure the impacts of academic books by
multi-granularity mining online reviews, and we identify factors that affect a
book's impact. First, online reviews of a sample of academic books on Amazon.cn
are crawled and processed. Then, multi-granularity review mining is conducted
to identify review sentiment polarities and aspects' sentiment values. Lastly,
the numbers of positive reviews and negative reviews, aspect sentiment values,
star values, and information regarding helpfulness are integrated via the
entropy method, and lead to the calculation of the final book impact scores.
The results of a correlation analysis of book impact scores obtained via our
method versus traditional book citations show that, although there are
substantial differences between subject areas, online book reviews tend to
reflect the academic impact. Thus, we infer that online reviews represent a
promising source for mining book impact within the altmetric perspective and at
the multi-granularity content level. Moreover, our proposed method might also
be a means by which to measure other books besides academic publications.
Online shopping behavior study based on multi-granularity opinion mining: China vs. America
Authors: Qingqing Zhou, Rui Xia, Chengzhi Zhang
Date: 2016-03-26 00:00:00
Tasks: Opinion Mining, Question Generation, Question-Generation

With the development of e-commerce, many products are now being sold
worldwide, and manufacturers are eager to obtain a better understanding of
customer behavior in various regions. To achieve this goal, most previous
efforts have focused mainly on questionnaires, which are time-consuming and
costly. The tremendous volume of product reviews on e-commerce websites has
seen a new trend emerge, whereby manufacturers attempt to understand user
preferences by analyzing online reviews. Following this trend, this paper
addresses the problem of studying customer behavior by exploiting recently
developed opinion mining techniques. This work is novel for three reasons.
First, questionnaire-based investigation is automatically enabled by employing
algorithms for template-based question generation and opinion mining-based
answer extraction. Using this system, manufacturers are able to obtain reports
of customer behavior featuring a much larger sample size, more direct
information, a higher degree of automation, and a lower cost. Second,
international customer behavior study is made easier by integrating tools for
multilingual opinion mining. Third, this is the first time an automatic
questionnaire investigation has been conducted to compare customer behavior in
China and America, where product reviews are written and read in Chinese and
English, respectively. Our study on digital cameras, smartphones, and tablet
computers yields three findings. First, Chinese customers follow the Doctrine
of the Mean, and often use euphemistic expressions, while American customers
express their opinions more directly. Second, Chinese customers care more about
general feelings, while American customers pay more attention to product
details. Third, Chinese customers focus on external features, while American
customers care more about the internal features of products.
On Fast Bilateral Filtering using Fourier Kernels
Authors: Sanjay Ghosh, Kunal. N. Chaudhury
Date: 2016-03-26 00:00:00

It was demonstrated in earlier work that, by approximating its range kernel
using shiftable functions, the non-linear bilateral filter can be computed
using a series of fast convolutions. Previous approaches based on shiftable
approximation have, however, been restricted to Gaussian range kernels. In this
work, we propose a novel approximation that can be applied to any range kernel,
provided it has a pointwise-convergent Fourier series. More specifically, we
propose to approximate the Gaussian range kernel of the bilateral filter using
a Fourier basis, where the coefficients of the basis are obtained by solving a
series of least-squares problems. The coefficients can be efficiently computed
using a recursive form of the QR decomposition. By controlling the cardinality
of the Fourier basis, we can obtain a good tradeoff between the run-time and
the filtering accuracy. In particular, we are able to guarantee sub-pixel
accuracy for the overall filtering, which is not provided by most existing
methods for fast bilateral filtering. We present simulation results to
demonstrate the speed and accuracy of the proposed algorithm.
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Authors: Yevgeni Berzak, Andrei Barbu, Daniel Harari, Boris Katz, Shimon Ullman
Date: 2016-03-26 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Sentence

Understanding language goes hand in hand with the ability to integrate
complex contextual information obtained via perception. In this work, we
present a novel task for grounded language understanding: disambiguating a
sentence given a visual scene which depicts one of the possible interpretations
of that sentence. To this end, we introduce a new multimodal corpus containing
ambiguous sentences, representing a wide range of syntactic, semantic and
discourse ambiguities, coupled with videos that visualize the different
interpretations for each sentence. We address this task by extending a vision
model which determines if a sentence is depicted by a video. We demonstrate how
such a model can be adjusted to recognize different interpretations of the same
underlying sentence, allowing to disambiguate sentences in a unified fashion
across the different ambiguity types.
Classification of Large-Scale Fundus Image Data Sets: A Cloud-Computing Framework
Authors: Sohini Roychowdhury
Date: 2016-03-26 00:00:00
Tasks: Classification, Cloud Computing, General Classification

Large medical image data sets with high dimensionality require substantial
amount of computation time for data creation and data processing. This paper
presents a novel generalized method that finds optimal image-based feature sets
that reduce computational time complexity while maximizing overall
classification accuracy for detection of diabetic retinopathy (DR). First,
region-based and pixel-based features are extracted from fundus images for
classification of DR lesions and vessel-like structures. Next, feature ranking
strategies are used to distinguish the optimal classification feature sets. DR
lesion and vessel classification accuracies are computed using the boosted
decision tree and decision forest classifiers in the Microsoft Azure Machine
Learning Studio platform, respectively. For images from the DIARETDB1 data set,
40 of its highest-ranked features are used to classify four DR lesion types
with an average classification accuracy of 90.1% in 792 seconds. Also, for
classification of red lesion regions and hemorrhages from microaneurysms,
accuracies of 85% and 72% are observed, respectively. For images from STARE
data set, 40 high-ranked features can classify minor blood vessels with an
accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis
systems can significantly enhance the borderline classification performances in
automated screening systems.
A generalized flow for multi-class and binary classification tasks: An Azure ML approach
Authors: Matthew Bihis, Sohini Roychowdhury
Date: 2016-03-26 00:00:00
Tasks: Binary Classification, Classification, Dimensionality Reduction, General Classification, Multi-class Classification

The constant growth in the present day real-world databases pose
computational challenges for a single computer. Cloud-based platforms, on the
other hand, are capable of handling large volumes of information manipulation
tasks, thereby necessitating their use for large real-world data set
computations. This work focuses on creating a novel Generalized Flow within the
cloud-based computing platform: Microsoft Azure Machine Learning Studio (MAMLS)
that accepts multi-class and binary classification data sets alike and
processes them to maximize the overall classification accuracy. First, each
data set is split into training and testing data sets, respectively. Then,
linear and nonlinear classification model parameters are estimated using the
training data set. Data dimensionality reduction is then performed to maximize
classification accuracy. For multi-class data sets, data centric information is
used to further improve overall classification accuracy by reducing the
multi-class classification to a series of hierarchical binary classification
tasks. Finally, the performance of optimized classification model thus achieved
is evaluated and scored on the testing data set. The classification
characteristics of the proposed flow are comparatively evaluated on 3 public
data sets and a local data set with respect to existing state-of-the-art
methods. On the 3 public data sets, the proposed flow achieves 78-97.5%
classification accuracy. Also, the local data set, created using the
information regarding presence of Diabetic Retinopathy lesions in fundus
images, results in 85.3-95.7% average classification accuracy, which is higher
than the existing methods. Thus, the proposed generalized flow can be useful
for a wide range of application-oriented "big data sets".
Recognizing Car Fluents from Video
Authors: Bo Li, Tianfu Wu, Caiming Xiong, Song-Chun Zhu
Date: 2016-03-26 00:00:00
Proceeding: CVPR 2016 6

Physical fluents, a term originally used by Newton [40], refers to
time-varying object states in dynamic scenes. In this paper, we are interested
in inferring the fluents of vehicles from video. For example, a door (hood,
trunk) is open or closed through various actions, light is blinking to turn.
Recognizing these fluents has broad applications, yet have received scant
attention in the computer vision literature. Car fluent recognition entails a
unified framework for car detection, car part localization and part status
recognition, which is made difficult by large structural and appearance
variations, low resolutions and occlusions. This paper learns a
spatial-temporal And-Or hierarchical model to represent car fluents. The
learning of this model is formulated under the latent structural SVM framework.
Since there are no publicly related dataset, we collect and annotate a car
fluent dataset consisting of car videos with diverse fluents. In experiments,
the proposed method outperforms several highly related baseline methods in
terms of car fluent recognition and car part localization.