pwc-archive/papers-with-abstracts huggingface dataset:
A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI
Authors: Zhiwen Fan, Liyan Sun, Xinghao Ding, Yue Huang, Congbo Cai, John Paisley
Date: 2018-04-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Brain Segmentation, compressed sensing, Segmentation

Compressed sensing MRI is a classic inverse problem in the field of
computational imaging, accelerating the MR imaging by measuring less k-space
data. The deep neural network models provide the stronger representation
ability and faster reconstruction compared with "shallow" optimization-based
methods. However, in the existing deep-based CS-MRI models, the high-level
semantic supervision information from massive segmentation-labels in MRI
dataset is overlooked. In this paper, we proposed a segmentation-aware deep
fusion network called SADFN for compressed sensing MRI. The multilayer feature
aggregation (MLFA) method is introduced here to fuse all the features from
different layers in the segmentation network. Then, the aggregated feature maps
containing semantic information are provided to each layer in the
reconstruction network with a feature fusion strategy. This guarantees the
reconstruction network is aware of the different regions in the image it
reconstructs, simplifying the function mapping. We prove the utility of the
cross-layer and cross-task information fusion strategy by comparative study.
Extensive experiments on brain segmentation benchmark MRBrainS validated that
the proposed SADFN model achieves state-of-the-art accuracy in compressed
sensing MRI. This paper provides a novel approach to guide the low-level visual
task using the information from mid- or high-level task.
Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data
Authors: Zheng Xie, Guannan Liu, Junjie Wu, Yong Tan
Date: 2018-03-21 00:00:00

The prevalence of online media has attracted researchers from various domains
to explore human behavior and make interesting predictions. In this research,
we leverage heterogeneous social media data collected from various online
platforms to predict Taiwan's 2016 presidential election. In contrast to most
existing research, we take a "signal" view of heterogeneous information and
adopt the Kalman filter to fuse multiple signals into daily vote predictions
for the candidates. We also consider events that influenced the election in a
quantitative manner based on the so-called event study model that originated in
the field of financial research. We obtained the following interesting
findings. First, public opinions in online media dominate traditional polls in
Taiwan election prediction in terms of both predictive power and timeliness.
But offline polls can still function on alleviating the sample bias of online
opinions. Second, although online signals converge as election day approaches,
the simple Facebook "Like" is consistently the strongest indicator of the
election result. Third, most influential events have a strong connection to
cross-strait relations, and the Chou Tzu-yu flag incident followed by the
apology video one day before the election increased the vote share of Tsai
Ing-Wen by 3.66%. This research justifies the predictive power of online media
in politics and the advantages of information fusion. The combined use of the
Kalman filter and the event study method contributes to the data-driven
political analytics paradigm for both prediction and attribution purposes.
Generative Adversarial Perturbations
Authors: Omid Poursaeed, Isay Katsman, Bicheng Gao, Serge Belongie
Date: 2017-12-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, Semantic Segmentation

In this paper, we propose novel generative models for creating adversarial
examples, slightly perturbed images resembling natural images but maliciously
crafted to fool pre-trained models. We present trainable deep neural networks
for transforming images to adversarial perturbations. Our proposed models can
produce image-agnostic and image-dependent perturbations for both targeted and
non-targeted attacks. We also demonstrate that similar architectures can
achieve impressive results in fooling classification and semantic segmentation
models, obviating the need for hand-crafting attack methods for each task.
Using extensive experiments on challenging high-resolution datasets such as
ImageNet and Cityscapes, we show that our perturbations achieve high fooling
rates with small perturbation norms. Moreover, our attacks are considerably
faster than current iterative methods at inference time.
An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification
Authors: Yixiu Zhao, Xiangrui Zeng, Qiang Guo, Min Xu
Date: 2018-04-04 00:00:00
Tasks: General Classification

Motivation: Cellular Electron CryoTomography (CECT) is an emerging 3D imaging
technique that visualizes subcellular organization of single cells at
submolecular resolution and in near-native state. CECT captures large numbers
of macromolecular complexes of highly diverse structures and abundances.
However, the structural complexity and imaging limits complicate the systematic
de novo structural recovery and recognition of these macromolecular complexes.
Efficient and accurate reference-free subtomogram averaging and classification
represent the most critical tasks for such analysis. Existing subtomogram
alignment based methods are prone to the missing wedge effects and low
signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based
methods rely on integration operations, which are in principle computationally
infeasible for accurate calculation.
  Results: Built on existing works, we propose an integrated method, Fast
Alignment Maximum Likelihood method (FAML), which uses fast subtomogram
alignment to sample sub-optimal rigid transformations. The transformations are
then used to approximate integrals for maximum-likelihood update of subtomogram
averages through expectation-maximization algorithm. Our tests on simulated and
experimental subtomograms showed that, compared to our previously developed
fast alignment method (FA), FAML is significantly more robust to noise and
missing wedge effects with moderate increases of computation cost.Besides, FAML
performs well with significantly fewer input subtomograms when the FA method
fails. Therefore, FAML can serve as a key component for improved construction
of initial structural models from macromolecules captured by CECT.
Flipped-Adversarial AutoEncoders
Authors: Jiyi Zhang, Hung Dang, Hwee Kuan Lee, Ee-Chien Chang
Date: 2018-02-13 00:00:00

We propose a flipped-Adversarial AutoEncoder (FAAE) that simultaneously
trains a generative model G that maps an arbitrary latent code distribution to
a data distribution and an encoder E that embodies an "inverse mapping" that
encodes a data sample into a latent code vector. Unlike previous hybrid
approaches that leverage adversarial training criterion in constructing
autoencoders, FAAE minimizes re-encoding errors in the latent space and
exploits adversarial criterion in the data space. Experimental evaluations
demonstrate that the proposed framework produces sharper reconstructed images
while at the same time enabling inference that captures rich semantic
representation of data.
Occlusion Aware Unsupervised Learning of Optical Flow
Authors: Yang Wang, Yi Yang, Zhenheng Yang, Liang Zhao, Peng Wang, Wei Xu
Date: 2017-11-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Optical Flow Estimation

It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit the current unsupervised learning of optical flow methods.
In this work we introduce a new method which models occlusion explicitly and a
new warping way that facilitates the learning of large motion. Our method shows
promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
Especially on KITTI dataset where abundant unlabeled samples exist, our
unsupervised method outperforms its counterpart trained with supervised
learning.
Contextual-based Image Inpainting: Infer, Match, and Translate
Authors: Yuhang Song, Chao Yang, Zhe Lin, Xiaofeng Liu, Qin Huang, Hao Li, C. -C. Jay Kuo
Date: 2017-11-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Inpainting, Translation

We study the task of image inpainting, which is to fill in the missing region
of an incomplete image with plausible contents. To this end, we propose a
learning-based approach to generate visually coherent completion given a
high-resolution image with missing components. In order to overcome the
difficulty to directly learn the distribution of high-dimensional image data,
we divide the task into inference and translation as two separate steps and
model each step with a deep neural network. We also use simple heuristics to
guide the propagation of local textures from the boundary to the hole. We show
that, by using such techniques, inpainting reduces to the problem of learning
two image-feature translation functions in much smaller space and hence easier
to train. We evaluate our method on several public datasets and show that we
generate results of better visual quality than previous state-of-the-art
methods.
Real-Time Prediction of the Duration of Distribution System Outages
Authors: Aaron Jaech, Baosen Zhang, Mari Ostendorf, Daniel S. Kirschen
Date: 2018-04-03 00:00:00

This paper addresses the problem of predicting duration of unplanned power
outages, using historical outage records to train a series of neural network
predictors. The initial duration prediction is made based on environmental
factors, and it is updated based on incoming field reports using natural
language processing to automatically analyze the text. Experiments using 15
years of outage records show good initial results and improved performance
leveraging text. Case studies show that the language processing identifies
phrases that point to outage causes and repair steps.
Hospital Readmission Prediction - Applying Hierarchical Sparsity Norms for Interpretable Models
Authors: Jialiang Jiang, Sharon Hewner, Varun Chandola
Date: 2018-04-03 00:00:00
Tasks: General Classification, Readmission Prediction

Hospital readmissions have become one of the key measures of healthcare
quality. Preventable readmissions have been identified as one of the primary
targets for reducing costs and improving healthcare delivery. However, most
data driven studies for understanding readmissions have produced black box
classification and predictive models with moderate performance, which precludes
them from being used effectively within the decision support systems in the
hospitals. In this paper we present an application of structured
sparsity-inducing norms for predicting readmission risk for patients based on
their disease history and demographics. Most existing studies have focused on
hospital utilization, test results, etc., to assign a readmission label to each
episode of hospitalization. However, we focus on assigning a readmission risk
label to a patient based on their disease history. Our emphasis is on
interpreting the models to improve the understanding of the readmission
problem. To achieve this, we exploit the domain induced hierarchical structure
available for the disease codes which are the features for the classification
algorithm. We use a tree based sparsity-inducing regularization strategy that
explicitly uses the domain hierarchy. The resulting model not only outperforms
standard regularization procedures but is also highly sparse and interpretable.
We analyze the model and identify several significant factors that have an
effect on readmission risk. Some of these factors conform to existing beliefs,
e.g., impact of surgical complications and infections during hospital stay.
Other factors, such as the impact of mental disorder and substance abuse on
readmission, provide empirical evidence for several pre-existing but unverified
hypotheses. The analysis also reveals previously undiscovered connections such
as the influence of socioeconomic factors like lack of housing and
malnutrition.
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
Authors: Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani
Date: 2018-04-03 00:00:00
Proceeding: ICLR 2018 1
Tasks: Program Synthesis

Synthesizing user-intended programs from a small number of input-output
examples is a challenging problem with several important applications like
spreadsheet manipulation, data wrangling and code refactoring. Existing
synthesis systems either completely rely on deductive logic techniques that are
extensively hand-engineered or on purely statistical models that need massive
amounts of data, and in general fail to provide real-time synthesis on
challenging benchmarks. In this work, we propose Neural Guided Deductive Search
(NGDS), a hybrid synthesis technique that combines the best of both symbolic
logic techniques and statistical models. Thus, it produces programs that
satisfy the provided specifications by construction and generalize well on
unseen examples, similar to data-driven systems. Our technique effectively
utilizes the deductive search framework to reduce the learning problem of the
neural component to a simple supervised learning setup. Further, this allows us
to both train on sparingly available real-world data and still leverage
powerful recurrent neural network encoders. We demonstrate the effectiveness of
our method by evaluating on real-world customer scenarios by synthesizing
accurate programs with up to 12x speed-up compared to state-of-the-art systems.
Stochastic variance reduced multiplicative update for nonnegative matrix factorization
Authors: Hiroyuki Kasai
Date: 2017-10-30 00:00:00
Tasks: Dimensionality Reduction

Nonnegative matrix factorization (NMF), a dimensionality reduction and factor
analysis method, is a special case in which factor matrices have low-rank
nonnegative constraints. Considering the stochastic learning in NMF, we
specifically address the multiplicative update (MU) rule, which is the most
popular, but which has slow convergence property. This present paper introduces
on the stochastic MU rule a variance-reduced technique of stochastic gradient.
Numerical comparisons suggest that our proposed algorithms robustly outperform
state-of-the-art algorithms across different synthetic and real-world datasets.
Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fields
Authors: Kevan Yuen, Mohan M. Trivedi
Date: 2018-04-03 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

In the context of autonomous driving, where humans may need to take over in
the event where the computer may issue a takeover request, a key step towards
driving safety is the monitoring of the hands to ensure the driver is ready for
such a request. This work, focuses on the first step of this process, which is
to locate the hands. Such a system must work in real-time and under varying
harsh lighting conditions. This paper introduces a fast ConvNet approach, based
on the work of original work of OpenPose for full body joint estimation. The
network is modified with fewer parameters and retrained using our own day-time
naturalistic autonomous driving dataset to estimate joint and affinity heatmaps
for driver & passenger's wrist and elbows, for a total of 8 joint classes and
part affinity fields between each wrist-elbow pair. The approach runs real-time
on real-world data at 40 fps on multiple drivers and passengers. The system is
extensively evaluated both quantitatively and qualitatively, showing at least
95% detection performance on joint localization and arm-angle estimation.
Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images
Authors: Srujana Gattupalli, Ashwin Ramesh Babu, James Robert Brady, Fillia Makedon, Vassilis Athitsos
Date: 2018-04-03 00:00:00
Tasks: Keypoint Detection, Pose Estimation

Hand keypoints detection and pose estimation has numerous applications in
computer vision, but it is still an unsolved problem in many aspects. An
application of hand keypoints detection is in performing cognitive assessments
of a subject by observing the performance of that subject in physical tasks
involving rapid finger motion. As a part of this work, we introduce a novel
hand key-points benchmark dataset that consists of hand gestures recorded
specifically for cognitive behavior monitoring. We explore the state of the art
methods in hand keypoint detection and we provide quantitative evaluations for
the performance of these methods on our dataset. In future, these results and
our dataset can serve as a useful benchmark for hand keypoint recognition for
rapid finger movements.
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Authors: Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian
Date: 2017-12-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Point Cloud Registration

Unlike on images, semantic learning on 3D point clouds using a deep network
is challenging due to the naturally unordered data structure. Among existing
works, PointNet has achieved promising results by directly learning on point
sets. However, it does not take full advantage of a point's local neighborhood
that contains fine-grained structural information which turns out to be helpful
towards better semantic learning. In this regard, we present two new operations
to improve PointNet with a more efficient exploitation of local structures. The
first one focuses on local 3D geometric structures. In analogy to a convolution
kernel for images, we define a point-set kernel as a set of learnable 3D points
that jointly respond to a set of neighboring data points according to their
geometric affinities measured by kernel correlation, adapted from a similar
technique for point cloud registration. The second one exploits local
high-dimensional feature structures by recursive feature aggregation on a
nearest-neighbor-graph computed from 3D positions. Experiments show that our
network can efficiently capture local information and robustly achieve better
performances on major datasets. Our code is available at
http://www.merl.com/research/license#KCNet
Structured Set Matching Networks for One-Shot Part Labeling
Authors: Jonghyun Choi, Jayant Krishnamurthy, Aniruddha Kembhavi, Ali Farhadi
Date: 2017-12-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: set matching, Structured Prediction

Diagrams often depict complex phenomena and serve as a good test bed for
visual and textual reasoning. However, understanding diagrams using natural
image understanding approaches requires large training datasets of diagrams,
which are very hard to obtain. Instead, this can be addressed as a matching
problem either between labeled diagrams, images or both. This problem is very
challenging since the absence of significant color and texture renders local
cues ambiguous and requires global reasoning. We consider the problem of
one-shot part labeling: labeling multiple parts of an object in a target image
given only a single source image of that category. For this set-to-set matching
problem, we introduce the Structured Set Matching Network (SSMN), a structured
prediction model that incorporates convolutional neural networks. The SSMN is
trained using global normalization to maximize local match scores between
corresponding elements and a global consistency score among all matched
elements, while also enforcing a matching constraint between the two sets. The
SSMN significantly outperforms several strong baselines on three label transfer
scenarios: diagram-to-diagram, evaluated on a new diagram dataset of over 200
categories; image-to-image, evaluated on a dataset built on top of the Pascal
Part Dataset; and image-to-diagram, evaluated on transferring labels across
these datasets.
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
Authors: Rajeev Ranjan, Ankan Bansal, Hongyu Xu, Swami Sankaranarayanan, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa
Date: 2018-04-03 00:00:00
Tasks: Face Verification

In recent years, the performance of face verification and recognition systems
based on deep convolutional neural networks (DCNNs) has significantly improved.
A typical pipeline for face verification includes training a deep network for
subject classification with softmax loss, using the penultimate layer output as
the feature descriptor, and generating a cosine similarity score given a pair
of face images or videos. The softmax loss function does not optimize the
features to have higher similarity score for positive pairs and lower
similarity score for negative pairs, which leads to a performance gap. In this
paper, we propose a new loss function, called Crystal Loss, that restricts the
features to lie on a hypersphere of a fixed radius. The loss can be easily
implemented using existing deep learning frameworks. We show that integrating
this simple step in the training pipeline significantly improves the
performance of face verification and recognition systems. We achieve
state-of-the-art performance for face verification and recognition on
challenging LFW, IJB-A, IJB-B and IJB-C datasets over a large range of false
alarm rates (10-1 to 10-7).
Socioeconomic Dependencies of Linguistic Patterns in Twitter: A Multivariate Analysis
Authors: Jacob Levy Abitbol, Márton Karsai, Jean-Philippe Magué, Jean-Pierre Chevrot, Eric Fleury
Date: 2018-04-03 00:00:00

Our usage of language is not solely reliant on cognition but is arguably
determined by myriad external factors leading to a global variability of
linguistic patterns. This issue, which lies at the core of sociolinguistics and
is backed by many small-scale studies on face-to-face communication, is
addressed here by constructing a dataset combining the largest French Twitter
corpus to date with detailed socioeconomic maps obtained from national census
in France. We show how key linguistic variables measured in individual Twitter
streams depend on factors like socioeconomic status, location, time, and the
social network of individuals. We found that (i) people of higher socioeconomic
status, active to a greater degree during the daytime, use a more standard
language; (ii) the southern part of the country is more prone to use more
standard language than the northern one, while locally the used variety or
dialect is determined by the spatial distribution of socioeconomic status; and
(iii) individuals connected in the social network are closer linguistically
than disconnected ones, even after the effects of status homophily have been
removed. Our results inform sociolinguistic theory and may inspire novel
learning methods for the inference of socioeconomic status of people from the
way they tweet.
Stochastic model-based minimization of weakly convex functions
Authors: Damek Davis, Dmitriy Drusvyatskiy
Date: 2018-03-17 00:00:00
Tasks: model

We consider a family of algorithms that successively sample and minimize
simple stochastic models of the objective function. We show that under
reasonable conditions on approximation quality and regularity of the models,
any such algorithm drives a natural stationarity measure to zero at the rate
$O(k^{-1/4})$. As a consequence, we obtain the first complexity guarantees for
the stochastic proximal point, proximal subgradient, and regularized
Gauss-Newton methods for minimizing compositions of convex functions with
smooth maps. The guiding principle, underlying the complexity guarantees, is
that all algorithms under consideration can be interpreted as approximate
descent methods on an implicit smoothing of the problem, given by the Moreau
envelope. Specializing to classical circumstances, we obtain the long-sought
convergence rate of the stochastic projected gradient method, without batching,
for minimizing a smooth function on a closed convex set.