pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
Authors: Katsunori Ohnishi, Shohei Yamamoto, Yoshitaka Ushiku, Tatsuya Harada
Date: 2017-11-27 00:00:00
Tasks: Action Classification, Attribute, Optical Flow Estimation, Video Generation

Learning to represent and generate videos from unlabeled data is a very
challenging problem. To generate realistic videos, it is important not only to
ensure that the appearance of each frame is real, but also to ensure the
plausibility of a video motion and consistency of a video appearance in the
time direction. The process of video generation should be divided according to
these intrinsic difficulties. In this study, we focus on the motion and
appearance information as two important orthogonal components of a video, and
propose Flow-and-Texture-Generative Adversarial Networks (FTGAN) consisting of
FlowGAN and TextureGAN. In order to avoid a huge annotation cost, we have to
explore a way to learn from unlabeled data. Thus, we employ optical flow as
motion information to generate videos. FlowGAN generates optical flow, which
contains only the edge and motion of the videos to be begerated. On the other
hand, TextureGAN specializes in giving a texture to optical flow generated by
FlowGAN. This hierarchical approach brings more realistic videos with plausible
motion and appearance consistency. Our experiments show that our model
generates more plausible motion videos and also achieves significantly improved
performance for unsupervised action classification in comparison to previous
GAN works. In addition, because our model generates videos from two independent
information, our model can generate new combinations of motion and attribute
that are not seen in training data, such as a video in which a person is doing
sit-up in a baseball ground.
A double competitive strategy based learning automata algorithm
Authors: Chong Di
Date: 2017-12-01 00:00:00
Tasks: Reinforcement Learning

Learning Automata (LA) are considered as one of the most powerful tools in
the field of reinforcement learning. The family of estimator algorithms is
proposed to improve the convergence rate of LA and has made great achievements.
However, the estimators perform poorly on estimating the reward probabilities
of actions in the initial stage of the learning process of LA. In this
situation, a lot of rewards would be added to the probabilities of non-optimal
actions. Thus, a large number of extra iterations are needed to compensate for
these wrong rewards. In order to improve the speed of convergence, we propose a
new P-model absorbing learning automaton by utilizing a double competitive
strategy which is designed for updating the action probability vector. In this
way, the wrong rewards can be corrected instantly. Hence, the proposed Double
Competitive Algorithm overcomes the drawbacks of existing estimator algorithms.
A refined analysis is presented to show the $\epsilon-optimality$ of the
proposed scheme. The extensive experimental results in benchmark environments
demonstrate that our proposed learning automata perform more efficiently than
the most classic LA $SE_{RI}$ and the current fastest LA $DGCPA^{*}$.
Real-time Semantic Image Segmentation via Spatial Sparsity
Authors: Zifeng Wu, Chunhua Shen, Anton Van Den Hengel
Date: 2017-12-01 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We propose an approach to semantic (image) segmentation that reduces the
computational costs by a factor of 25 with limited impact on the quality of
results. Semantic segmentation has a number of practical applications, and for
most such applications the computational costs are critical. The method follows
a typical two-column network structure, where one column accepts an input
image, while the other accepts a half-resolution version of that image. By
identifying specific regions in the full-resolution image that can be safely
ignored, as well as carefully tailoring the network structure, we can process
approximately 15 highresolution Cityscapes images (1024x2048) per second using
a single GTX 980 video card, while achieving a mean intersection-over-union
score of 72.9% on the Cityscapes test set.
Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
Authors: Siyu Zhou, Weiqiang Zhao, Jiashi Feng, Hanjiang Lai, Yan Pan, Jian Yin, Shuicheng Yan
Date: 2017-11-26 00:00:00
Tasks: Human Aging

Face age progression, which aims to predict the future looks, is important
for various applications and has been received considerable attentions.
Existing methods and datasets are limited in exploring the effects of
occupations which may influence the personal appearances. In this paper, we
firstly introduce an occupational face aging dataset for studying the
influences of occupations on the appearances. It includes five occupations,
which enables the development of new algorithms for age progression and
facilitate future researches. Second, we propose a new occupational-aware
adversarial face aging network, which learns human aging process under
different occupations. Two factors are taken into consideration in our aging
process: personality-preserving and visually plausible texture change for
different occupations. We propose personalized network with personalized loss
in deep autoencoder network for keeping personalized facial characteristics,
and occupational-aware adversarial network with occupational-aware adversarial
loss for obtaining more realistic texture changes. Experimental results well
demonstrate the advantages of the proposed method by comparing with other
state-of-the-arts age progression methods.
Distributed Stratified Locality Sensitive Hashing for Critical Event Prediction in the Cloud
Authors: Alessandro De Palma, Erik Hemberg, Una-May O'Reilly
Date: 2017-12-01 00:00:00

The availability of massive healthcare data repositories calls for efficient
tools for data-driven medicine. We introduce a distributed system for
Stratified Locality Sensitive Hashing to perform fast similarity-based
prediction on large medical waveform datasets. Our implementation, for an ICU
use case, prioritizes latency over throughput and is targeted at a cloud
environment. We demonstrate our system on Acute Hypotensive Episode prediction
from Arterial Blood Pressure waveforms. On a dataset of $1.37$ million points,
we show scaling up to $40$ processors and a $21\times$ speedup in number of
comparisons to parallel exhaustive search at the price of a $10\%$ Matthews
correlation coefficient (MCC) loss. Furthermore, if additional MCC loss can be
tolerated, our system achieves speedups up to two orders of magnitude.
Tensors, Learning, and 'Kolmogorov Extension' for Finite-alphabet Random Vectors
Authors: Nikos Kargas, Nicholas D. Sidiropoulos, Xiao Fu
Date: 2017-12-01 00:00:00
Tasks: Movie Recommendation

Estimating the joint probability mass function (PMF) of a set of random
variables lies at the heart of statistical learning and signal processing.
Without structural assumptions, such as modeling the variables as a Markov
chain, tree, or other graphical model, joint PMF estimation is often considered
mission impossible - the number of unknowns grows exponentially with the number
of variables. But who gives us the structural model? Is there a generic,
`non-parametric' way to control joint PMF complexity without relying on a
priori structural assumptions regarding the underlying probability model? Is it
possible to discover the operational structure without biasing the analysis up
front? What if we only observe random subsets of the variables, can we still
reliably estimate the joint PMF of all? This paper shows, perhaps surprisingly,
that if the joint PMF of any three variables can be estimated, then the joint
PMF of all the variables can be provably recovered under relatively mild
conditions. The result is reminiscent of Kolmogorov's extension theorem -
consistent specification of lower-dimensional distributions induces a unique
probability measure for the entire process. The difference is that for
processes of limited complexity (rank of the high-dimensional PMF) it is
possible to obtain complete characterization from only three-dimensional
distributions. In fact not all three-dimensional PMFs are needed; and under
more stringent conditions even two-dimensional will do. Exploiting multilinear
algebra, this paper proves that such higher-dimensional PMF completion can be
guaranteed - several pertinent identifiability results are derived. It also
provides a practical and efficient algorithm to carry out the recovery task.
Judiciously designed simulations and real-data experiments on movie
recommendation and data classification are presented to showcase the
effectiveness of the approach.
InverseNet: Solving Inverse Problems with Splitting Networks
Authors: Kai Fan, Qi Wei, Wenlin Wang, Amit Chakraborty, Katherine Heller
Date: 2017-12-01 00:00:00
Tasks: Colorization, Deblurring, Denoising, Super-Resolution

We propose a new method that uses deep learning techniques to solve the
inverse problems. The inverse problem is cast in the form of learning an
end-to-end mapping from observed data to the ground-truth. Inspired by the
splitting strategy widely used in regularized iterative algorithm to tackle
inverse problems, the mapping is decomposed into two networks, with one
handling the inversion of the physical forward model associated with the data
term and one handling the denoising of the output from the former network,
i.e., the inverted version, associated with the prior/regularization term. The
two networks are trained jointly to learn the end-to-end mapping, getting rid
of a two-step training. The training is annealing as the intermediate variable
between these two networks bridges the gap between the input (the degraded
version of output) and output and progressively approaches to the ground-truth.
The proposed network, referred to as InverseNet, is flexible in the sense that
most of the existing end-to-end network structure can be leveraged in the first
network and most of the existing denoising network structure can be used in the
second one. Extensive experiments on both synthetic data and real datasets on
the tasks, motion deblurring, super-resolution, and colorization, demonstrate
the efficiency and accuracy of the proposed method compared with other image
processing algorithms.
A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation
Authors: Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille
Date: 2017-12-01 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Pancreas Segmentation, Semantic Segmentation, Volumetric Medical Image Segmentation

In this paper, we adopt 3D Convolutional Neural Networks to segment
volumetric medical images. Although deep neural networks have been proven to be
very effective on many 2D vision tasks, it is still challenging to apply them
to 3D tasks due to the limited amount of annotated 3D data and limited
computational resources. We propose a novel 3D-based coarse-to-fine framework
to effectively and efficiently tackle these challenges. The proposed 3D-based
framework outperforms the 2D counterpart to a large margin since it can
leverage the rich spatial infor- mation along all three axes. We conduct
experiments on two datasets which include healthy and pathological pancreases
respectively, and achieve the current state-of-the-art in terms of
Dice-S{\o}rensen Coefficient (DSC). On the NIH pancreas segmentation dataset,
we outperform the previous best by an average of over 2%, and the worst case is
improved by 7% to reach almost 70%, which indicates the reliability of our
framework in clinical applications.
Minimax Filter: Learning to Preserve Privacy from Inference Attacks
Authors: Jihun Hamm
Date: 2016-10-12 00:00:00
Tasks: Classification, Emotion Classification, General Classification

Preserving privacy of continuous and/or high-dimensional data such as images,
videos and audios, can be challenging with syntactic anonymization methods
which are designed for discrete attributes. Differential privacy, which
provides a more formal definition of privacy, has shown more success in
sanitizing continuous data. However, both syntactic and differential privacy
are susceptible to inference attacks, i.e., an adversary can accurately infer
sensitive attributes from sanitized data. The paper proposes a novel
filter-based mechanism which preserves privacy of continuous and
high-dimensional attributes against inference attacks. Finding the optimal
utility-privacy tradeoff is formulated as a min-diff-max optimization problem.
The paper provides an ERM-like analysis of the generalization error and also a
practical algorithm to perform the optimization. In addition, the paper
proposes an extension that combines minimax filter and differentially-private
noisy mechanism. Advantages of the method over purely noisy mechanisms is
explained and demonstrated with examples. Experiments with several real-world
tasks including facial expression classification, speech emotion
classification, and activity classification from motion, show that the minimax
filter can simultaneously achieve similar or better target task accuracy and
lower inference accuracy, often significantly lower than previous methods.
3D Facial Action Units Recognition for Emotional Expression
Authors: N. Hussain, H. Ujir, I. Hipiny, J-L Minoi
Date: 2017-12-01 00:00:00
Tasks: General Classification

The muscular activities caused the activation of certain AUs for every facial
expression at the certain duration of time throughout the facial expression.
This paper presents the methods to recognise facial Action Unit (AU) using
facial distance of the facial features which activates the muscles. The seven
facial action units involved are AU1, AU4, AU6, AU12, AU15, AU17 and AU25 that
characterises happy and sad expression. The recognition is performed on each AU
according to rules defined based on the distance of each facial points. The
facial distances chosen are extracted from twelve facial features. Then the
facial distances are trained using Support Vector Machine (SVM) and Neural
Network (NN). Classification result using SVM is presented with several
different SVM kernels while result using NN is presented for each training,
validation and testing phase.
InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity
Authors: Hee Jung Ryu, Hartwig Adam, Margaret Mitchell
Date: 2017-12-01 00:00:00
Tasks: Attribute, Diversity

We demonstrate an approach to face attribute detection that retains or
improves attribute detection accuracy across gender and race subgroups by
learning demographic information prior to learning the attribute detection
task. The system, which we call InclusiveFaceNet, detects face attributes by
transferring race and gender representations learned from a held-out dataset of
public race and gender identities. Leveraging learned demographic
representations while withholding demographic inference from the downstream
face attribute detection task preserves potential users' demographic privacy
while resulting in some of the best reported numbers to date on attribute
detection in the Faces of the World and CelebA datasets.
Delineation of Skin Strata in Reflectance Confocal Microscopy Images using Recurrent Convolutional Networks with Toeplitz Attention
Authors: Alican Bozkurt, Kivanc Kose, Jaume Coll-Font, Christi Alessi-Fox, Dana H. Brooks, Jennifer G. Dy, Milind Rajadhyaksha
Date: 2017-12-01 00:00:00
Tasks: General Classification

Reflectance confocal microscopy (RCM) is an effective, non-invasive
pre-screening tool for skin cancer diagnosis, but it requires extensive
training and experience to assess accurately. There are few quantitative tools
available to standardize image acquisition and analysis, and the ones that are
available are not interpretable. In this study, we use a recurrent neural
network with attention on convolutional network features. We apply it to
delineate skin strata in vertically-oriented stacks of transverse RCM image
slices in an interpretable manner. We introduce a new attention mechanism
called Toeplitz attention, which constrains the attention map to have a
Toeplitz structure. Testing our model on an expert labeled dataset of 504 RCM
stacks, we achieve 88.17% image-wise classification accuracy, which is the
current state-of-art.
Rank of Experts: Detection Network Ensemble
Authors: Seung-Hwan Bae, Youngwan Lee, Youngjoo Jo, Yuseok Bae, Joong-won Hwang
Date: 2017-12-01 00:00:00
Tasks: Object, object-detection, Object Detection

The recent advances of convolutional detectors show impressive performance
improvement for large scale object detection. However, in general, the
detection performance usually decreases as the object classes to be detected
increases, and it is a practically challenging problem to train a dominant
model for all classes due to the limitations of detection models and datasets.
In most cases, therefore, there are distinct performance differences of the
modern convolutional detectors for each object class detection. In this paper,
in order to build an ensemble detector for large scale object detection, we
present a conceptually simple but very effective class-wise ensemble detection
which is named as Rank of Experts. We first decompose an intractable problem of
finding the best detections for all object classes into small subproblems of
finding the best ones for each object class. We then solve the detection
problem by ranking detectors in order of the average precision rate for each
class, and then aggregate the responses of the top ranked detectors (i.e.
experts) for class-wise ensemble detection. The main benefit of our method is
easy to implement and does not require any joint training of experts for
ensemble. Based on the proposed Rank of Experts, we won the 2nd place in the
ILSVRC 2017 object detection competition.
Inertial-aided Rolling Shutter Relative Pose Estimation
Authors: Chang-Ryeol Lee, Kuk-Jin Yoon
Date: 2017-12-01 00:00:00
Tasks: Pose Estimation

Relative pose estimation is a fundamental problem in computer vision and it
has been studied for conventional global shutter cameras for decades. However,
recently, a rolling shutter camera has been widely used due to its low cost
imaging capability and, since the rolling shutter camera captures the image
line-by-line, the relative pose estimation of a rolling shutter camera is more
difficult than that of a global shutter camera. In this paper, we propose to
exploit inertial measurements (gravity and angular velocity) for the rolling
shutter relative pose estimation problem. The inertial measurements provide
information about the partial relative rotation between two views (cameras) and
the instantaneous motion that causes the rolling shutter distortion. Based on
this information, we simplify the rolling shutter relative pose estimation
problem and propose effective methods to solve it. Unlike the previous methods,
which require 44 (linear) or 17 (nonlinear) points with the uniform rolling
shutter camera model, the proposed methods require at most 9 or 11 points to
estimate the relative pose between the rolling shutter cameras. Experimental
results on synthetic data and the public PennCOSYVIO dataset show that the
proposed methods outperform the existing methods.
Learning Depth from Monocular Videos using Direct Methods
Authors: Chaoyang Wang, Jose Miguel Buenaposada, Rui Zhu, Simon Lucey
Date: 2017-12-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth And Camera Motion, Visual Odometry

The ability to predict depth from a single image - using recent advances in
CNNs - is of increasing interest to the vision community. Unsupervised
strategies to learning are particularly appealing as they can utilize much
larger and varied monocular video datasets during learning without the need for
ground truth depth or stereo. In previous works, separate pose and depth CNN
predictors had to be determined such that their joint outputs minimized the
photometric error. Inspired by recent advances in direct visual odometry (DVO),
we argue that the depth CNN predictor can be learned without a pose CNN
predictor. Further, we demonstrate empirically that incorporation of a
differentiable implementation of DVO, along with a novel depth normalization
strategy - substantially improves performance over state of the art that use
monocular videos for training.
Rapid point-of-care Hemoglobin measurement through low-cost optics and Convolutional Neural Network based validation
Authors: Chris Wu, Tanay Tandon
Date: 2017-12-01 00:00:00

A low-cost, robust, and simple mechanism to measure hemoglobin would play a
critical role in the modern health infrastructure. Consistent sample
acquisition has been a long-standing technical hurdle for photometer-based
portable hemoglobin detectors which rely on micro cuvettes and dry chemistry.
Any particulates (e.g. intact red blood cells (RBCs), microbubbles, etc.) in a
cuvette's sensing area drastically impact optical absorption profile, and
commercial hemoglobinometers lack the ability to automatically detect faulty
samples. We present the ground-up development of a portable, low-cost and open
platform with equivalent accuracy to medical-grade devices, with the addition
of CNN-based image processing for rapid sample viability prechecks. The
developed platform has demonstrated precision to the nearest $0.18[g/dL]$ of
hemoglobin, an R^2 = 0.945 correlation to hemoglobin absorption curves reported
in literature, and a 97% detection accuracy of poorly-prepared samples. We see
the developed hemoglobin device/ML platform having massive implications in
rural medicine, and consider it an excellent springboard for robust deep
learning optical spectroscopy: a currently untapped source of data for
detection of countless analytes.
Identifying Most Walkable Direction for Navigation in an Outdoor Environment
Authors: Sachin Mehta, Hannaneh Hajishirzi, Linda Shapiro
Date: 2017-11-21 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation

We present an approach for identifying the most walkable direction for
navigation using a hand-held camera. Our approach extracts semantically rich
contextual information from the scene using a custom encoder-decoder
architecture for semantic segmentation and models the spatial and temporal
behavior of objects in the scene using a spatio-temporal graph. The system
learns to minimize a cost function over the spatial and temporal object
attributes to identify the most walkable direction. We construct a new
annotated navigation dataset collected using a hand-held mobile camera in an
unconstrained outdoor environment, which includes challenging settings such as
highly dynamic scenes, occlusion between objects, and distortions. Our system
achieves an accuracy of 84% on predicting a safe direction. We also show that
our custom segmentation network is both fast and accurate, achieving mIOU (mean
intersection over union) scores of 81 and 44.7 on the PASCAL VOC and the PASCAL
Context datasets, respectively, while running at about 21 frames per second.