pwc-archive/papers-with-abstracts huggingface dataset:
Synthetic Language Generation and Model Validation in BEAST2
Authors: Stuart Bradley
Date: 2016-07-27 00:00:00
Tasks: model, Text Generation

Generating synthetic languages aids in the testing and validation of future
computational linguistic models and methods. This thesis extends the BEAST2
phylogenetic framework to add linguistic sequence generation under multiple
models. The new plugin is then used to test the effects of the phenomena of
word borrowing on the inference process under two widely used phylolinguistic
models.
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
Authors: Shu Kong, Xiaohui Shen, Zhe Lin, Radomir Mech, Charless Fowlkes
Date: 2016-06-06 00:00:00
Tasks: Aesthetics Quality Assessment

Real-world applications could benefit from the ability to automatically
generate a fine-grained ranking of photo aesthetics. However, previous methods
for image aesthetics analysis have primarily focused on the coarse, binary
categorization of images into high- or low-aesthetic categories. In this work,
we propose to learn a deep convolutional neural network to rank photo
aesthetics in which the relative ranking of photo aesthetics are directly
modeled in the loss function. Our model incorporates joint learning of
meaningful photographic attributes and image content information which can help
regularize the complicated photo aesthetics rating problem.
  To train and analyze this model, we have assembled a new aesthetics and
attributes database (AADB) which contains aesthetic scores and meaningful
attributes assigned to each image by multiple human raters. Anonymized rater
identities are recorded across images allowing us to exploit intra-rater
consistency using a novel sampling strategy when computing the ranking loss of
training image pairs. We show the proposed sampling strategy is very effective
and robust in face of subjective judgement of image aesthetics by individuals
with different aesthetic tastes. Experiments demonstrate that our unified model
can generate aesthetic rankings that are more consistent with human ratings. To
further validate our model, we show that by simply thresholding the estimated
aesthetic scores, we are able to achieve state-or-the-art classification
performance on the existing AVA dataset benchmark.
Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization
Authors: Shervin Minaee, Yao Wang
Date: 2016-02-07 00:00:00
Tasks: Clustering, Denoising, image-classification, Image Classification, Image Denoising, Image Segmentation, Semantic Segmentation

Sparse decomposition has been widely used for different applications, such as
source separation, image classification, image denoising and more. This paper
presents a new algorithm for segmentation of an image into background and
foreground text and graphics using sparse decomposition and total variation
minimization. The proposed method is designed based on the assumption that the
background part of the image is smoothly varying and can be represented by a
linear combination of a few smoothly varying basis functions, while the
foreground text and graphics can be modeled with a sparse component overlaid on
the smooth background. The background and foreground are separated using a
sparse decomposition framework regularized with a few suitable regularization
terms which promotes the sparsity and connectivity of foreground pixels. This
algorithm has been tested on a dataset of images extracted from HEVC standard
test sequences for screen content coding, and is shown to have superior
performance over some prior methods, including least absolute deviation
fitting, k-means clustering based segmentation in DjVu and shape primitive
extraction and coding (SPEC) algorithm.
Learning Visual Storylines with Skipping Recurrent Neural Networks
Authors: Gunnar A. Sigurdsson, Xinlei Chen, Abhinav Gupta
Date: 2016-04-14 00:00:00

What does a typical visit to Paris look like? Do people first take photos of
the Louvre and then the Eiffel Tower? Can we visually model a temporal event
like "Paris Vacation" using current frameworks? In this paper, we explore how
we can automatically learn the temporal aspects, or storylines of visual
concepts from web data. Previous attempts focus on consecutive image-to-image
transitions and are unsuccessful at recovering the long-term underlying story.
Our novel Skipping Recurrent Neural Network (S-RNN) model does not attempt to
predict each and every data point in the sequence, like classic RNNs. Rather,
S-RNN uses a framework that skips through the images in the photo stream to
explore the space of all ordered subsets of the albums via an efficient
sampling procedure. This approach reduces the negative impact of strong
short-term correlations, and recovers the latent story more accurately. We show
how our learned storylines can be used to analyze, predict, and summarize photo
albums from Flickr. Our experimental results provide strong qualitative and
quantitative evidence that S-RNN is significantly better than other candidate
methods such as LSTMs on learning long-term correlations and recovering latent
storylines. Moreover, we show how storylines can help machines better
understand and summarize photo streams by inferring a brief personalized story
of each individual album.
Explaining Classification Models Built on High-Dimensional Sparse Data
Authors: Julie Moeyersoms, Brian d'Alessandro, Foster Provost, David Martens
Date: 2016-07-21 00:00:00
Tasks: Classification, General Classification, Vocal Bursts Intensity Prediction

Predictive modeling applications increasingly use data representing people's
behavior, opinions, and interactions. Fine-grained behavior data often has
different structure from traditional data, being very high-dimensional and
sparse. Models built from these data are quite difficult to interpret, since
they contain many thousands or even many millions of features. Listing features
with large model coefficients is not sufficient, because the model coefficients
do not incorporate information on feature presence, which is key when analysing
sparse data. In this paper we introduce two alternatives for explaining
predictive models by listing important features. We evaluate these alternatives
in terms of explanation "bang for the buck,", i.e., how many examples'
inferences are explained for a given number of features listed. The bottom
line: (i) The proposed alternatives have double the bang-for-the-buck as
compared to just listing the high-coefficient features, and (ii) interestingly,
although they come from different sources and motivations, the two new
alternatives provide strikingly similar rankings of important features.
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
Authors: Gunnar A. Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, Abhinav Gupta
Date: 2016-04-06 00:00:00
Tasks: Action Recognition, Diversity, Temporal Action Localization

Computer vision has a great potential to help our daily lives by searching
for lost keys, watering flowers or reminding us to take a pill. To succeed with
such tasks, computer vision methods need to be trained from real and diverse
examples of our daily dynamic scenes. While most of such scenes are not
particularly exciting, they typically do not appear on YouTube, in movies or TV
broadcasts. So how do we collect sufficiently many diverse but boring samples
representing our lives? We propose a novel Hollywood in Homes approach to
collect such data. Instead of shooting videos in the lab, we ensure diversity
by distributing and crowdsourcing the whole process of video creation from
script writing to video recording and annotation. Following this procedure we
collect a new dataset, Charades, with hundreds of people recording videos in
their own homes, acting out casual everyday activities. The dataset is composed
of 9,848 annotated videos with an average length of 30 seconds, showing
activities of 267 people from three continents. Each video is annotated by
multiple free-text descriptions, action labels, action intervals and classes of
interacted objects. In total, Charades provides 27,847 video descriptions,
66,500 temporally localized intervals for 157 action classes and 41,104 labels
for 46 object classes. Using this rich data, we evaluate and provide baseline
results for several tasks including action recognition and automatic
description generation. We believe that the realism, diversity, and casual
nature of this dataset will present unique challenges and new opportunities for
computer vision community.
Approximation and Parameterized Complexity of Minimax Approval Voting
Authors: Marek Cygan, Łukasz Kowalik, Arkadiusz Socała, Krzysztof Sornat
Date: 2016-07-26 00:00:00

We present three results on the complexity of Minimax Approval Voting. First,
we study Minimax Approval Voting parameterized by the Hamming distance $d$ from
the solution to the votes. We show Minimax Approval Voting admits no algorithm
running in time $\mathcal{O}^\star(2^{o(d\log d)})$, unless the Exponential
Time Hypothesis (ETH) fails. This means that the $\mathcal{O}^\star(d^{2d})$
algorithm of Misra et al. [AAMAS 2015] is essentially optimal. Motivated by
this, we then show a parameterized approximation scheme, running in time
$\mathcal{O}^\star(\left({3}/{\epsilon}\right)^{2d})$, which is essentially
tight assuming ETH. Finally, we get a new polynomial-time randomized
approximation scheme for Minimax Approval Voting, which runs in time
$n^{\mathcal{O}(1/\epsilon^2 \cdot \log(1/\epsilon))} \cdot \mathrm{poly}(m)$,
almost matching the running time of the fastest known PTAS for Closest String
due to Ma and Sun [SIAM J. Comp. 2009].
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
Authors: Muhammad Ghifary, David Balduzzi, W. Bastiaan Kleijn, Mengjie Zhang
Date: 2015-10-15 00:00:00
Tasks: Domain Adaptation, Domain Generalization, General Classification, Object Recognition, Representation Learning

This paper addresses classification tasks on a particular target domain in
which labeled training data are only available from source domains different
from (but related to) the target. Two closely related frameworks, domain
adaptation and domain generalization, are concerned with such tasks, where the
only difference between those frameworks is the availability of the unlabeled
target data: domain adaptation can leverage unlabeled target information, while
domain generalization cannot. We propose Scatter Component Analyis (SCA), a
fast representation learning algorithm that can be applied to both domain
adaptation and domain generalization. SCA is based on a simple geometrical
measure, i.e., scatter, which operates on reproducing kernel Hilbert space. SCA
finds a representation that trades between maximizing the separability of
classes, minimizing the mismatch between domains, and maximizing the
separability of data; each of which is quantified through scatter. The
optimization problem of SCA can be reduced to a generalized eigenvalue problem,
which results in a fast and exact solution. Comprehensive experiments on
benchmark cross-domain object recognition datasets verify that SCA performs
much faster than several state-of-the-art algorithms and also provides
state-of-the-art classification accuracy in both domain adaptation and domain
generalization. We also show that scatter can be used to establish a
theoretical generalization bound in the case of domain adaptation.
Product Offerings in Malicious Hacker Markets
Authors: Ericsson Marin, Ahmad Diab, Paulo Shakarian
Date: 2016-07-26 00:00:00
Tasks: Clustering

Marketplaces specializing in malicious hacking products - including malware
and exploits - have recently become more prominent on the darkweb and deepweb.
We scrape 17 such sites and collect information about such products in a
unified database schema. Using a combination of manual labeling and
unsupervised clustering, we examine a corpus of products in order to understand
their various categories and how they become specialized with respect to vendor
and marketplace. This initial study presents how we effectively employed
unsupervised techniques to this data as well as the types of insights we gained
on various categories of malicious hacking products.
Multi-scale Volumes for Deep Object Detection and Localization
Authors: Eshed Ohn-Bar, M. M. Trivedi
Date: 2015-05-14 00:00:00
Tasks: Object, object-detection, Object Detection

This study aims to analyze the benefits of improved multi-scale reasoning for
object detection and localization with deep convolutional neural networks. To
that end, an efficient and general object detection framework which operates on
scale volumes of a deep feature pyramid is proposed. In contrast to the
proposed approach, most current state-of-the-art object detectors operate on a
single-scale in training, while testing involves independent evaluation across
scales. One benefit of the proposed approach is in better capturing of
multi-scale contextual information, resulting in significant gains in both
detection performance and localization quality of objects on the PASCAL VOC
dataset and a multi-view highway vehicles dataset. The joint detection and
localization scale-specific models are shown to especially benefit detection of
challenging object categories which exhibit large scale variation as well as
detection of small objects.
Polling-systems-based Autonomous Vehicle Coordination in Traffic Intersections with No Traffic Signals
Authors: David Miculescu, Sertac Karaman
Date: 2016-07-26 00:00:00
Tasks: Autonomous Vehicles

The rapid development of autonomous vehicles spurred a careful investigation
of the potential benefits of all-autonomous transportation networks. Most
studies conclude that autonomous systems can enable drastic improvements in
performance. A widely studied concept is all-autonomous, collision-free
intersections, where vehicles arriving in a traffic intersection with no
traffic light adjust their speeds to cross safely through the intersection as
quickly as possible. In this paper, we propose a coordination control algorithm
for this problem, assuming stochastic models for the arrival times of the
vehicles. The proposed algorithm provides provable guarantees on safety and
performance. More precisely, it is shown that no collisions occur surely, and
moreover a rigorous upper bound is provided for the expected wait time. The
algorithm is also demonstrated in simulations. The proposed algorithms are
inspired by polling systems. In fact, the problem studied in this paper leads
to a new polling system where customers are subject to differential
constraints, which may be interesting in its own right.
Learning to Refine Object Segments
Authors: Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollàr
Date: 2016-03-29 00:00:00
Tasks: Object, Semantic Segmentation

Object segmentation requires both object-level information and low-level
pixel data. This presents a challenge for feedforward networks: lower layers in
convolutional nets capture rich spatial information, while upper layers encode
object-level knowledge but are invariant to factors such as pose and
appearance. In this work we propose to augment feedforward nets for object
segmentation with a novel top-down refinement approach. The resulting
bottom-up/top-down architecture is capable of efficiently generating
high-fidelity object masks. Similarly to skip connections, our approach
leverages features at all layers of the net. Unlike skip connections, our
approach does not attempt to output independent predictions at each layer.
Instead, we first output a coarse `mask encoding' in a feedforward pass, then
refine this mask encoding in a top-down pass utilizing features at successively
lower layers. The approach is simple, fast, and effective. Building on the
recent DeepMask network for generating object proposals, we show accuracy
improvements of 10-20% in average recall for various setups. Additionally, by
optimizing the overall network architecture, our approach, which we call
SharpMask, is 50% faster than the original DeepMask network (under .8s per
image).
Quantifying mesoscale neuroanatomy using X-ray microtomography
Authors: Eva L. Dyer, William Gray Roncal, Hugo L. Fernandes, Doga Gürsoy, Vincent De Andrade, Rafael Vescovi, Kamel Fezzaa, Xianghui Xiao, Joshua T. Vogelstein, Chris Jacobsen, Konrad P. Körding, Narayanan Kasthuri
Date: 2016-04-13 00:00:00

Methods for resolving the 3D microstructure of the brain typically start by
thinly slicing and staining the brain, and then imaging each individual section
with visible light photons or electrons. In contrast, X-rays can be used to
image thick samples, providing a rapid approach for producing large 3D brain
maps without sectioning. Here we demonstrate the use of synchrotron X-ray
microtomography ($\mu$CT) for producing mesoscale $(1~\mu m^3)$ resolution
brain maps from millimeter-scale volumes of mouse brain. We introduce a
pipeline for $\mu$CT-based brain mapping that combines methods for sample
preparation, imaging, automated segmentation of image volumes into cells and
blood vessels, and statistical analysis of the resulting brain structures. Our
results demonstrate that X-ray tomography promises rapid quantification of
large brain volumes, complementing other brain mapping and connectomics
efforts.
Stacked Hourglass Networks for Human Pose Estimation
Authors: Alejandro Newell, Kaiyu Yang, Jia Deng
Date: 2016-03-22 00:00:00
Tasks: Pose Estimation

This work introduces a novel convolutional network architecture for the task
of human pose estimation. Features are processed across all scales and
consolidated to best capture the various spatial relationships associated with
the body. We show how repeated bottom-up, top-down processing used in
conjunction with intermediate supervision is critical to improving the
performance of the network. We refer to the architecture as a "stacked
hourglass" network based on the successive steps of pooling and upsampling that
are done to produce a final set of predictions. State-of-the-art results are
achieved on the FLIC and MPII benchmarks outcompeting all recent methods.
Technical Report: Giving Hints for Logic Programming Examples without Revealing Solutions
Authors: Gokhan Avci, Mustafa Mehuljic, Peter Schüller
Date: 2016-07-26 00:00:00

We introduce a framework for supporting learning to program in the paradigm
of Answer Set Programming (ASP), which is a declarative logic programming
formalism. Based on the idea of teaching by asking the student to complete
small example ASP programs, we introduce a three-stage method for giving hints
to the student without revealing the correct solution of an example. We
categorize mistakes into (i) syntactic mistakes, (ii) unexpected but
syntactically correct input, and (iii) semantic mistakes, describe mathematical
definitions of these mistakes, and show how to compute hints from these
definitions.
Risk Bounds for High-dimensional Ridge Function Combinations Including Neural Networks
Authors: Jason M. Klusowski, Andrew R. Barron
Date: 2016-07-05 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Let $ f^{\star} $ be a function on $ \mathbb{R}^d $ with an assumption of a
spectral norm $ v_{f^{\star}} $. For various noise settings, we show that $
\mathbb{E}\|\hat{f} - f^{\star} \|^2 \leq \left(v^4_{f^{\star}}\frac{\log
d}{n}\right)^{1/3} $, where $ n $ is the sample size and $ \hat{f} $ is either
a penalized least squares estimator or a greedily obtained version of such
using linear combinations of sinusoidal, sigmoidal, ramp, ramp-squared or other
smooth ridge functions. The candidate fits may be chosen from a continuum of
functions, thus avoiding the rigidity of discretizations of the parameter
space. On the other hand, if the candidate fits are chosen from a
discretization, we show that $ \mathbb{E}\|\hat{f} - f^{\star} \|^2 \leq
\left(v^3_{f^{\star}}\frac{\log d}{n}\right)^{2/5} $. This work bridges
non-linear and non-parametric function estimation and includes single-hidden
layer nets. Unlike past theory for such settings, our bound shows that the risk
is small even when the input dimension $ d $ of an infinite-dimensional
parameterized dictionary is much larger than the available sample size. When
the dimension is larger than the cube root of the sample size, this quantity is
seen to improve the more familiar risk bound of $
v_{f^{\star}}\left(\frac{d\log (n/d)}{n}\right)^{1/2} $, also investigated
here.
Shuffle and Learn: Unsupervised Learning using Temporal Order Verification
Authors: Ishan Misra, C. Lawrence Zitnick, Martial Hebert
Date: 2016-03-28 00:00:00
Tasks: Action Recognition, Pose Estimation, Self-Supervised Action Recognition, Temporal Action Localization, Video Alignment

In this paper, we present an approach for learning a visual representation
from the raw spatiotemporal signals in videos. Our representation is learned
without supervision from semantic labels. We formulate our method as an
unsupervised sequential verification task, i.e., we determine whether a
sequence of frames from a video is in the correct temporal order. With this
simple task and no semantic labels, we learn a powerful visual representation
using a Convolutional Neural Network (CNN). The representation contains
complementary information to that learned from supervised image datasets like
ImageNet. Qualitative results show that our method captures information that is
temporally varying, such as human pose. When used as pre-training for action
recognition, our method gives significant gains over learning without external
data on benchmark datasets like UCF101 and HMDB51. To demonstrate its
sensitivity to human pose, we show results for pose estimation on the FLIC and
MPII datasets that are competitive, or better than approaches using
significantly more supervision. Our method can be combined with supervised
representations to provide an additional boost in accuracy.
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling
Authors: Zhen Li, Yukang Gan, Xiaodan Liang, Yizhou Yu, Hui Cheng, Liang Lin
Date: 2016-04-18 00:00:00
Tasks: Scene Labeling

Semantic labeling of RGB-D scenes is crucial to many intelligent applications
including perceptual robotics. It generates pixelwise and fine-grained label
maps from simultaneously sensed photometric (RGB) and depth channels. This
paper addresses this problem by i) developing a novel Long Short-Term Memorized
Context Fusion (LSTM-CF) Model that captures and fuses contextual information
from multiple channels of photometric and depth data, and ii) incorporating
this model into deep convolutional neural networks (CNNs) for end-to-end
training. Specifically, contexts in photometric and depth channels are,
respectively, captured by stacking several convolutional layers and a long
short-term memory layer; the memory layer encodes both short-range and
long-range spatial dependencies in an image along the vertical direction.
Another long short-term memorized fusion layer is set up to integrate the
contexts along the vertical direction from different channels, and perform
bi-directional propagation of the fused vertical contexts along the horizontal
direction to obtain true 2D global contexts. At last, the fused contextual
representation is concatenated with the convolutional features extracted from
the photometric channels in order to improve the accuracy of fine-scale
semantic labeling. Our proposed model has set a new state of the art, i.e.,
48.1% and 49.4% average class accuracy over 37 categories (2.2% and 5.4%
improvement) on the large-scale SUNRGBD dataset and the NYUDv2dataset,
respectively.