pwc-archive/papers-with-abstracts huggingface dataset:
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Authors: Subhamoy Mandal, Xosé Luís Deán-Ben, Daniel Razansky
Date: 2015-10-28 00:00:00
Tasks: Image Reconstruction, Segmentation

Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.
Reverse Engineering and Symbolic Knowledge Extraction on Łukasiewicz Fuzzy Logics using Linear Neural Networks
Authors: Carlos Leandro
Date: 2016-04-11 00:00:00
Tasks: Attribute, Binary Classification, Descriptive

This work describes a methodology to combine logic-based systems and
connectionist systems. Our approach uses finite truth valued {\L}ukasiewicz
logic, where we take advantage of fact what in this type of logics every
connective can be define by a neuron in an artificial network having by
activation function the identity truncated to zero and one. This allowed the
injection of first-order formulas in a network architecture, and also
simplified symbolic rule extraction.
  Our method trains a neural network using Levenderg-Marquardt algorithm, where
we restrict the knowledge dissemination in the network structure. We show how
this reduces neural networks plasticity without damage drastically the learning
performance. Making the descriptive power of produced neural networks similar
to the descriptive power of {\L}ukasiewicz logic language, simplifying the
translation between symbolic and connectionist structures.
  This method is used in the reverse engineering problem of finding the formula
used on generation of a truth table for a multi-valued {\L}ukasiewicz logic.
For real data sets the method is particularly useful for attribute selection,
on binary classification problems defined using nominal attribute. After
attribute selection and possible data set completion in the resulting
connectionist model: neurons are directly representable using a disjunctive or
conjunctive formulas, in the {\L}ukasiewicz logic, or neurons are
interpretations which can be approximated by symbolic rules. This fact is
exemplified, extracting symbolic knowledge from connectionist models generated
for the data set Mushroom from UCI Machine Learning Repository.
Generation and Comprehension of Unambiguous Object Descriptions
Authors: Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan Yuille, Kevin Murphy
Date: 2015-11-07 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Captioning, Object, Referring Expression

We propose a method that can generate an unambiguous description (known as a
referring expression) of a specific object or region in an image, and which can
also comprehend or interpret such an expression to infer which object is being
described. We show that our method outperforms previous methods that generate
descriptions of objects without taking into account other potentially ambiguous
objects in the scene. Our model is inspired by recent successes of deep
learning methods for image captioning, but while image captioning is difficult
to evaluate, our task allows for easy objective evaluation. We also present a
new large-scale dataset for referring expressions, based on MS-COCO. We have
released the dataset and a toolbox for visualization and evaluation, see
https://github.com/mjhucla/Google_Refexp_toolbox
Performance Trade-Offs in Multi-Processor Approximate Message Passing
Authors: Junan Zhu, Ahmad Beirami, Dror Baron
Date: 2016-04-10 00:00:00

We consider large-scale linear inverse problems in Bayesian settings. Our
general approach follows a recent line of work that applies the approximate
message passing (AMP) framework in multi-processor (MP) computational systems
by storing and processing a subset of rows of the measurement matrix along with
corresponding measurements at each MP node. In each MP-AMP iteration, nodes of
the MP system and its fusion center exchange lossily compressed messages
pertaining to their estimates of the input. There is a trade-off between the
physical costs of the reconstruction process including computation time,
communication loads, and the reconstruction quality, and it is impossible to
simultaneously minimize all the costs. We pose this minimization as a
multi-objective optimization problem (MOP), and study the properties of the
best trade-offs (Pareto optimality) in this MOP. We prove that the achievable
region of this MOP is convex, and conjecture how the combined cost of
computation and communication scales with the desired mean squared error. These
properties are verified numerically.
Unsupervised Learning of Edges
Authors: Yin Li, Manohar Paluri, James M. Rehg, Piotr Dollár
Date: 2015-11-13 00:00:00
Proceeding: CVPR 2016 6
Tasks: Edge Detection, Form, Motion Estimation, object-detection, Object Detection

Data-driven approaches for edge detection have proven effective and achieve
top results on modern benchmarks. However, all current data-driven edge
detectors require manual supervision for training in the form of hand-labeled
region segments or object boundaries. Specifically, human annotators mark
semantically meaningful edges which are subsequently used for training. Is this
form of strong, high-level supervision actually necessary to learn to
accurately detect edges? In this work we present a simple yet effective
approach for training edge detectors without human supervision. To this end we
utilize motion, and more specifically, the only input to our method is noisy
semi-dense matches between frames. We begin with only a rudimentary knowledge
of edges (in the form of image gradients), and alternate between improving
motion estimation and edge detection in turn. Using a large corpus of video
data, we show that edge detectors trained using our unsupervised scheme
approach the performance of the same methods trained with full supervision
(within 3-5%). Finally, we show that when using a deep network for the edge
detector, our approach provides a novel pre-training scheme for object
detection.
Evaluating the Performance of Offensive Linemen in the NFL
Authors: Nikhil Byanna, Diego Klabjan
Date: 2016-03-24 00:00:00
Tasks: Position

How does one objectively measure the performance of an individual offensive
lineman in the NFL? The existing literature proposes various measures that rely
on subjective assessments of game film, but has yet to develop an objective
methodology to evaluate performance. Using a variety of statistics related to
an offensive lineman's performance, we develop a framework to objectively
analyze the overall performance of an individual offensive lineman and
determine specific linemen who are overvalued or undervalued relative to their
salary. We identify eight players across the 2013-2014 and 2014-2015 NFL
seasons that are considered to be overvalued or undervalued and corroborate the
results with existing metrics that are based on subjective evaluation. To the
best of our knowledge, the techniques set forth in this work have not been
utilized in previous works to evaluate the performance of NFL players at any
position, including offensive linemen.
Soccer Field Localization from a Single Image
Authors: Namdar Homayounfar, Sanja Fidler, Raquel Urtasun
Date: 2016-04-10 00:00:00

In this work, we propose a novel way of efficiently localizing a soccer field
from a single broadcast image of the game. Related work in this area relies on
manually annotating a few key frames and extending the localization to similar
images, or installing fixed specialized cameras in the stadium from which the
layout of the field can be obtained. In contrast, we formulate this problem as
a branch and bound inference in a Markov random field where an energy function
is defined in terms of field cues such as grass, lines and circles. Moreover,
our approach is fully automatic and depends only on single images from the
broadcast video of the game. We demonstrate the effectiveness of our method by
applying it to various games and obtain promising results. Finally, we posit
that our approach can be applied easily to other sports such as hockey and
basketball.
PatchBatch: a Batch Augmented Loss for Optical Flow
Authors: David Gadot, Lior Wolf
Date: 2015-12-06 00:00:00
Proceeding: CVPR 2016 6
Tasks: Optical Flow Estimation, Patch Matching

We propose a new pipeline for optical flow computation, based on Deep
Learning techniques. We suggest using a Siamese CNN to independently, and in
parallel, compute the descriptors of both images. The learned descriptors are
then compared efficiently using the L2 norm and do not require network
processing of patch pairs. The success of the method is based on an innovative
loss function that computes higher moments of the loss distributions for each
training batch. Combined with an Approximate Nearest Neighbor patch matching
method and a flow interpolation technique, state of the art performance is
obtained on the most challenging and competitive optical flow benchmarks.
Stability and Structural Properties of Gene Regulation Networks with Coregulation Rules
Authors: Jonathan H. Warrell, Musa M. Mhlanga
Date: 2016-02-28 00:00:00

Coregulation of the expression of groups of genes has been extensively
demonstrated empirically in bacterial and eukaryotic systems. Such coregulation
can arise through the use of shared regulatory motifs, which allow the
coordinated expression of modules (and module groups) of functionally related
genes across the genome. Coregulation can also arise through the physical
association of multi-gene complexes through chromosomal looping, which are then
transcribed together. We present a general formalism for modeling coregulation
rules in the framework of Random Boolean Networks (RBN), and develop specific
models for transcription factor networks with modular structure (including
module groups, and multi-input modules (MIM) with autoregulation) and
multi-gene complexes (including hierarchical differentiation between multi-gene
complex members). We develop a mean-field approach to analyse the stability of
large networks incorporating coregulation, and show that autoregulated MIM and
hierarchical gene-complex models can achieve greater stability than networks
without coregulation whose rules have matching activation frequency. We provide
further analysis of the stability of small networks of both kinds through
simulations. We also characterize several general properties of the transients
and attractors in the hierarchical coregulation model, and show using
simulations that the steady-state distribution factorizes hierarchically as a
Bayesian network in a Markov Jump Process analogue of the RBN model.
Generalized Statistical Tests for mRNA and Protein Subcellular Spatial Patterning against Complete Spatial Randomness
Authors: Jonathan H. Warrell, Anca F. Savulescu, Robyn Brackin, Musa M. Mhlanga
Date: 2016-02-20 00:00:00
Tasks: Clustering, Point Processes

We derive generalized estimators for a number of spatial statistics that have
been used in the analysis of spatially resolved omics data, such as Ripley's K,
H and L functions, clustering index, and degree of clustering, which allow
these statistics to be calculated on data modelled by arbitrary random measures
(RMs). Our estimators generalize those typically used to calculate these
statistics on point process data, allowing them to be calculated on RMs which
assign continuous values to spatial regions, for instance to model protein
intensity. The clustering index (H*) compares Ripley's H function calculated
empirically to its distribution under complete spatial randomness (CSR),
leading us to consider CSR null hypotheses for RMs which are not
point-processes when generalizing this statistic. We thus consider restricted
classes of completely random measures which can be simulated directly (Gamma
processes and Marked Poisson Processes), as well as the general class of all
CSR RMs, for which we derive an exact permutation-based H* estimator. We
establish several properties of the estimators, including bounds on the
accuracy of our general Ripley K estimator, its relationship to a previous
estimator for the cross-correlation measure, and the relationship of our
generalized H* estimator to previous statistics. To test the ability of our
approach to identify spatial patterning, we use Fluorescent In Situ
Hybridization (FISH) and Immunofluorescence (IF) data to probe for mRNA and
protein subcellular localization patterns respectively in polarizing mouse
fibroblasts on micropattened cells. We observe correlated patterns of
clustering over time for corresponding mRNAs and proteins, suggesting a
deterministic effect of mRNA localization on protein localization for several
pairs tested, including one case in which spatial patterning at the mRNA level
has not been previously demonstrated.
DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
Authors: Hao Chen, Xiaojuan Qi, Lequan Yu, Pheng-Ann Heng
Date: 2016-04-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: Multi-Task Learning, Segmentation

The morphology of glands has been used routinely by pathologists to assess
the malignancy degree of adenocarcinomas. Accurate segmentation of glands from
histology images is a crucial step to obtain reliable morphological statistics
for quantitative diagnosis. In this paper, we proposed an efficient deep
contour-aware network (DCAN) to solve this challenging problem under a unified
multi-task learning framework. In the proposed network, multi-level contextual
features from the hierarchical architecture are explored with auxiliary
supervision for accurate gland segmentation. When incorporated with multi-task
regularization during the training, the discriminative capability of
intermediate features can be further improved. Moreover, our network can not
only output accurate probability maps of glands, but also depict clear contours
simultaneously for separating clustered objects, which further boosts the gland
segmentation performance. This unified framework can be efficient when applied
to large-scale histopathological data without resorting to additional steps to
generate contours based on low-level cues for post-separating. Our method won
the 2015 MICCAI Gland Segmentation Challenge out of 13 competitive teams,
surpassing all the other methods by a significant margin.
Distance for Functional Data Clustering Based on Smoothing Parameter Commutation
Authors: ShengLi Tzeng, Christian Hennig, Yu-Fen Li, Chien-Ju Lin
Date: 2016-04-10 00:00:00
Tasks: Clustering, Missing Values, Numerical Integration, Outlier Detection

We propose a novel method to determine the dissimilarity between subjects for
functional data clustering. Spline smoothing or interpolation is common to deal
with data of such type. Instead of estimating the best-representing curve for
each subject as fixed during clustering, we measure the dissimilarity between
subjects based on varying curve estimates with commutation of smoothing
parameters pair-by-pair (of subjects). The intuitions are that smoothing
parameters of smoothing splines reflect inverse signal-to-noise ratios and that
applying an identical smoothing parameter the smoothed curves for two similar
subjects are expected to be close. The effectiveness of our proposal is shown
through simulations comparing to other dissimilarity measures. It also has
several pragmatic advantages. First, missing values or irregular time points
can be handled directly, thanks to the nature of smoothing splines. Second,
conventional clustering method based on dissimilarity can be employed
straightforward, and the dissimilarity also serves as a useful tool for outlier
detection. Third, the implementation is almost handy since subroutines for
smoothing splines and numerical integration are widely available. Fourth, the
computational complexity does not increase and is parallel with that in
calculating Euclidean distance between curves estimated by smoothing splines.
Latent Embeddings for Zero-shot Classification
Authors: Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, Bernt Schiele
Date: 2016-03-29 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, General Classification, zero-shot-classification, Zero-Shot Learning

We present a novel latent embedding model for learning a compatibility
function between image and class embeddings, in the context of zero-shot
classification. The proposed method augments the state-of-the-art bilinear
compatibility model by incorporating latent variables. Instead of learning a
single bilinear map, it learns a collection of maps with the selection, of
which map to use, being a latent variable for the current image-class pair. We
train the model with a ranking based objective function which penalizes
incorrect rankings of the true class for a given image. We empirically
demonstrate that our model improves the state-of-the-art for various class
embeddings consistently on three challenging publicly available datasets for
the zero-shot setting. Moreover, our method leads to visually highly
interpretable results with clear clusters of different fine-grained object
properties that correspond to different latent variable maps.
Direction matters: hand pose estimation from local surface normals
Authors: Chengde Wan, Angela Yao, Luc van Gool
Date: 2016-04-10 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation, regression

We present a hierarchical regression framework for estimating hand joint
positions from single depth images based on local surface normals. The
hierarchical regression follows the tree structured topology of hand from wrist
to finger tips. We propose a conditional regression forest, i.e., the Frame
Conditioned Regression Forest (FCRF) which uses a new normal difference
feature. At each stage of the regression, the frame of reference is established
from either the local surface normal or previously estimated hand joints. By
making the regression with respect to the local frame, the pose estimation is
more robust to rigid transformations. We also introduce a new efficient
approximation to estimate surface normals. We verify the effectiveness of our
method by conducting experiments on two challenging real-world datasets and
show consistent improvements over previous discriminative pose estimation
methods.
Real-Time Facial Segmentation and Performance Capture from RGB Input
Authors: Shunsuke Saito, Tianye Li, Hao Li
Date: 2016-04-10 00:00:00
Tasks: Data Augmentation, Segmentation, Semantic Segmentation

We introduce the concept of unconstrained real-time 3D facial performance
capture through explicit semantic segmentation in the RGB input. To ensure
robustness, cutting edge supervised learning approaches rely on large training
datasets of face images captured in the wild. While impressive tracking quality
has been demonstrated for faces that are largely visible, any occlusion due to
hair, accessories, or hand-to-face gestures would result in significant visual
artifacts and loss of tracking accuracy. The modeling of occlusions has been
mostly avoided due to its immense space of appearance variability. To address
this curse of high dimensionality, we perform tracking in unconstrained images
assuming non-face regions can be fully masked out. Along with recent
breakthroughs in deep learning, we demonstrate that pixel-level facial
segmentation is possible in real-time by repurposing convolutional neural
networks designed originally for general semantic segmentation. We develop an
efficient architecture based on a two-stream deconvolution network with
complementary characteristics, and introduce carefully designed training
samples and data augmentation strategies for improved segmentation accuracy and
robustness. We adopt a state-of-the-art regression-based facial tracking
framework with segmented face images as training, and demonstrate accurate and
uninterrupted facial performance capture in the presence of extreme occlusion
and even side views. Furthermore, the resulting segmentation can be directly
used to composite partial 3D face models on the input images and enable
seamless facial manipulation tasks, such as virtual make-up or face
replacement.
Grid Based Nonlinear Filtering Revisited: Recursive Estimation & Asymptotic Optimality
Authors: Dionysios S. Kalogerias, Athina P. Petropulu
Date: 2016-04-10 00:00:00
Tasks: Quantization

We revisit the development of grid based recursive approximate filtering of
general Markov processes in discrete time, partially observed in conditionally
Gaussian noise. The grid based filters considered rely on two types of state
quantization: The \textit{Markovian} type and the \textit{marginal} type. We
propose a set of novel, relaxed sufficient conditions, ensuring strong and
fully characterized pathwise convergence of these filters to the respective
MMSE state estimator. In particular, for marginal state quantizations, we
introduce the notion of \textit{conditional regularity of stochastic kernels},
which, to the best of our knowledge, constitutes the most relaxed condition
proposed, under which asymptotic optimality of the respective grid based
filters is guaranteed. Further, we extend our convergence results, including
filtering of bounded and continuous functionals of the state, as well as
recursive approximate state prediction. For both Markovian and marginal
quantizations, the whole development of the respective grid based filters
relies more on linear-algebraic techniques and less on measure theoretic
arguments, making the presentation considerably shorter and technically
simpler.
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos
Authors: Moisés H. R. Pereira, Flávio L. C. Pádua, Adriano C. M. Pereira, Fabrício Benevenuto, Daniel H. Dalip
Date: 2016-04-09 00:00:00
Tasks: 3D Human Pose Estimation, Emotion Recognition, Sentiment Analysis

This paper presents a novel approach to perform sentiment analysis of news
videos, based on the fusion of audio, textual and visual clues extracted from
their contents. The proposed approach aims at contributing to the
semiodiscoursive study regarding the construction of the ethos (identity) of
this media universe, which has become a central part of the modern-day lives of
millions of people. To achieve this goal, we apply state-of-the-art
computational methods for (1) automatic emotion recognition from facial
expressions, (2) extraction of modulations in the participants' speeches and
(3) sentiment analysis from the closed caption associated to the videos of
interest. More specifically, we compute features, such as, visual intensities
of recognized emotions, field sizes of participants, voicing probability, sound
loudness, speech fundamental frequencies and the sentiment scores (polarities)
from text sentences in the closed caption. Experimental results with a dataset
containing 520 annotated news videos from three Brazilian and one American
popular TV newscasts show that our approach achieves an accuracy of up to 84%
in the sentiments (tension levels) classification task, thus demonstrating its
high potential to be used by media analysts in several applications,
especially, in the journalistic domain.