pwc-archive/papers-with-abstracts huggingface dataset:
Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
Authors: Zheng Shou, Dongang Wang, Shih-Fu Chang
Date: 2016-01-09 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Classification, Action Localization, Classification, General Classification, Temporal Action Localization, Temporal Localization

We address temporal action localization in untrimmed long videos. This is
important because videos in real applications are usually unconstrained and
contain multiple action instances plus video content of background scenes or
other activities. To address this challenging issue, we exploit the
effectiveness of deep networks in temporal action localization via three
segment-based 3D ConvNets: (1) a proposal network identifies candidate segments
in a long video that may contain actions; (2) a classification network learns
one-vs-all action classification model to serve as initialization for the
localization network; and (3) a localization network fine-tunes on the learned
classification network to localize each action instance. We propose a novel
loss function for the localization network to explicitly consider temporal
overlap and therefore achieve high temporal localization accuracy. Only the
proposal network and the localization network are used during prediction. On
two large-scale benchmarks, our approach achieves significantly superior
performances compared with other state-of-the-art systems: mAP increases from
1.7% to 7.4% on MEXaction2 and increases from 15.0% to 19.0% on THUMOS 2014,
when the overlap threshold for evaluation is set to 0.5.
State of the Art Control of Atari Games Using Shallow Reinforcement Learning
Authors: Yitao Liang, Marlos C. Machado, Erik Talvitie, Michael Bowling
Date: 2015-12-04 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The recently introduced Deep Q-Networks (DQN) algorithm has gained attention
as one of the first successful combinations of deep neural networks and
reinforcement learning. Its promise was demonstrated in the Arcade Learning
Environment (ALE), a challenging framework composed of dozens of Atari 2600
games used to evaluate general competency in AI. It achieved dramatically
better results than earlier approaches, showing that its ability to learn good
representations is quite robust and general. This paper attempts to understand
the principles that underlie DQN's impressive performance and to better
contextualize its success. We systematically evaluate the importance of key
representational biases encoded by DQN's network by proposing simple linear
representations that make use of these concepts. Incorporating these
characteristics, we obtain a computationally practical feature set that
achieves competitive performance to DQN in the ALE. Besides offering insight
into the strengths and weaknesses of DQN, we provide a generic representation
for the ALE, significantly reducing the burden of learning a representation for
each game. Moreover, we also provide a simple, reproducible benchmark for the
sake of comparison to future work in the ALE.
Efficient Training of Very Deep Neural Networks for Supervised Hashing
Authors: Ziming Zhang, Yu-Ting Chen, Venkatesh Saligrama
Date: 2015-11-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: Computational Efficiency, GPU

In this paper, we propose training very deep neural networks (DNNs) for
supervised learning of hash codes. Existing methods in this context train
relatively "shallow" networks limited by the issues arising in back propagation
(e.e. vanishing gradients) as well as computational efficiency. We propose a
novel and efficient training algorithm inspired by alternating direction method
of multipliers (ADMM) that overcomes some of these limitations. Our method
decomposes the training process into independent layer-wise local updates
through auxiliary variables. Empirically we observe that our training algorithm
always converges and its computational complexity is linearly proportional to
the number of edges in the networks. Empirically we manage to train DNNs with
64 hidden layers and 1024 nodes per layer for supervised hashing in about 3
hours using a single GPU. Our proposed very deep supervised hashing (VDSH)
method significantly outperforms the state-of-the-art on several benchmark
datasets.
Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples
Authors: Greg Ongie, Mathews Jacob
Date: 2015-10-01 00:00:00
Tasks: Image Super-Resolution, Relation, Super-Resolution

We introduce a method to recover a continuous domain representation of a
piecewise constant two-dimensional image from few low-pass Fourier samples.
Assuming the edge set of the image is localized to the zero set of a
trigonometric polynomial, we show the Fourier coefficients of the partial
derivatives of the image satisfy a linear annihilation relation. We present
necessary and sufficient conditions for unique recovery of the image from
finite low-pass Fourier samples using the annihilation relation. We also
propose a practical two-stage recovery algorithm which is robust to
model-mismatch and noise. In the first stage we estimate a continuous domain
representation of the edge set of the image. In the second stage we perform an
extrapolation in Fourier domain by a least squares two-dimensional linear
prediction, which recovers the exact Fourier coefficients of the underlying
image. We demonstrate our algorithm on the super-resolution recovery of MRI
phantoms and real MRI data from low-pass Fourier samples, which shows benefits
over standard approaches for single-image super-resolution MRI.
Humans and deep networks largely agree on which kinds of variation make object recognition harder
Authors: Saeed Reza Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh, Timothée Masquelier
Date: 2016-04-21 00:00:00
Tasks: Object, Object Recognition, Template Matching

View-invariant object recognition is a challenging problem, which has
attracted much attention among the psychology, neuroscience, and computer
vision communities. Humans are notoriously good at it, even if some variations
are presumably more difficult to handle than others (e.g. 3D rotations). Humans
are thought to solve the problem through hierarchical processing along the
ventral stream, which progressively extracts more and more invariant visual
features. This feed-forward architecture has inspired a new generation of
bio-inspired computer vision systems called deep convolutional neural networks
(DCNN), which are currently the best algorithms for object recognition in
natural images. Here, for the first time, we systematically compared human
feed-forward vision and DCNNs at view-invariant object recognition using the
same images and controlling for both the kinds of transformation as well as
their magnitude. We used four object categories and images were rendered from
3D computer models. In total, 89 human subjects participated in 10 experiments
in which they had to discriminate between two or four categories after rapid
presentation with backward masking. We also tested two recent DCNNs on the same
tasks. We found that humans and DCNNs largely agreed on the relative
difficulties of each kind of variation: rotation in depth is by far the hardest
transformation to handle, followed by scale, then rotation in plane, and
finally position. This suggests that humans recognize objects mainly through 2D
template matching, rather than by constructing 3D object models, and that DCNNs
are not too unreasonable models of human feed-forward vision. Also, our results
show that the variation levels in rotation in depth and scale strongly modulate
both humans' and DCNNs' recognition performances. We thus argue that these
variations should be controlled in the image datasets used in vision research.
Understanding How Image Quality Affects Deep Neural Networks
Authors: Samuel Dodge, Lina Karam
Date: 2016-04-14 00:00:00
Tasks: General Classification, image-classification, Image Classification

Image quality is an important practical challenge that is often overlooked in
the design of machine vision systems. Commonly, machine vision systems are
trained and tested on high quality image datasets, yet in practical
applications the input images can not be assumed to be of high quality.
Recently, deep neural networks have obtained state-of-the-art performance on
many machine vision tasks. In this paper we provide an evaluation of 4
state-of-the-art deep neural network models for image classification under
quality distortions. We consider five types of quality distortions: blur,
noise, contrast, JPEG, and JPEG2000 compression. We show that the existing
networks are susceptible to these quality distortions, particularly to blur and
noise. These results enable future work in developing deep neural networks that
are more invariant to quality distortions.
Parallel Strategies Selection
Authors: Anthony Palmieri, Jean-Charles Régin, Pierre Schaus
Date: 2016-04-21 00:00:00

We consider the problem of selecting the best variable-value strategy for
solving a given problem in constraint programming. We show that the recent
Embarrassingly Parallel Search method (EPS) can be used for this purpose. EPS
proposes to solve a problem by decomposing it in a lot of subproblems and to
give them on-demand to workers which run in parallel. Our method uses a part of
these subproblems as a simple sample as defined in statistics for comparing
some strategies in order to select the most promising one that will be used for
solving the remaining subproblems. For each subproblem of the sample, the
parallelism helps us to control the running time of the strategies because it
gives us the possibility to introduce timeouts by stopping a strategy when it
requires more than twice the time of the best one. Thus, we can deal with the
great disparity in solving times for the strategies. The selections we made are
based on the Wilcoxon signed rank tests because no assumption has to be made on
the distribution of the solving times and because these tests can deal with the
censored data that we obtain after introducing timeouts. The experiments we
performed on a set of classical benchmarks for satisfaction and optimization
problems show that our method obtain good performance by selecting almost all
the time the best variable-value strategy and by almost never choosing a
variable-value strategy which is dramatically slower than the best one. Our
method also outperforms the portfolio approach consisting in running some
strategies in parallel and is competitive with the multi armed bandit
framework.
Visual Congruent Ads for Image Search
Authors: Yannis Kalantidis, Ayman Farahat, Lyndon Kennedy, Ricardo Baeza-Yates, David A. Shamma
Date: 2016-04-21 00:00:00
Tasks: Image Retrieval

The quality of user experience online is affected by the relevance and
placement of advertisements. We propose a new system for selecting and
displaying visual advertisements in image search result sets. Our method
compares the visual similarity of candidate ads to the image search results and
selects the most visually similar ad to be displayed. The method further
selects an appropriate location in the displayed image grid to minimize the
perceptual visual differences between the ad and its neighbors. We conduct an
experiment with about 900 users and find that our proposed method provides
significant improvement in the users' overall satisfaction with the image
search experience, without diminishing the users' ability to see the ad or
recall the advertised brand.
On Detection and Structural Reconstruction of Small-World Random Networks
Authors: T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
Date: 2016-04-21 00:00:00
Tasks: Clustering

In this paper, we study detection and fast reconstruction of the celebrated
Watts-Strogatz (WS) small-world random graph model \citep{watts1998collective}
which aims to describe real-world complex networks that exhibit both high
clustering and short average length properties. The WS model with neighborhood
size $k$ and rewiring probability probability $\beta$ can be viewed as a
continuous interpolation between a deterministic ring lattice graph and the
Erd\H{o}s-R\'{e}nyi random graph. We study both the computational and
statistical aspects of detecting the deterministic ring lattice structure (or
local geographical links, strong ties) in the presence of random connections
(or long range links, weak ties), and for its recovery. The phase diagram in
terms of $(k,\beta)$ is partitioned into several regions according to the
difficulty of the problem. We propose distinct methods for the various regions.
Robust Estimators in High Dimensions without the Computational Intractability
Authors: Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
Date: 2016-04-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We study high-dimensional distribution learning in an agnostic setting where
an adversary is allowed to arbitrarily corrupt an $\varepsilon$-fraction of the
samples. Such questions have a rich history spanning statistics, machine
learning and theoretical computer science. Even in the most basic settings, the
only known approaches are either computationally inefficient or lose
dimension-dependent factors in their error guarantees. This raises the
following question:Is high-dimensional agnostic distribution learning even
possible, algorithmically?
  In this work, we obtain the first computationally efficient algorithms with
dimension-independent error guarantees for agnostically learning several
fundamental classes of high-dimensional distributions: (1) a single Gaussian,
(2) a product distribution on the hypercube, (3) mixtures of two product
distributions (under a natural balancedness condition), and (4) mixtures of
spherical Gaussians. Our algorithms achieve error that is independent of the
dimension, and in many cases scales nearly-linearly with the fraction of
adversarially corrupted samples. Moreover, we develop a general recipe for
detecting and correcting corruptions in high-dimensions, that may be applicable
to many other problems.
Learning Human Identity from Motion Patterns
Authors: Natalia Neverova, Christian Wolf, Griffin Lacey, Lex Fridman, Deepak Chandra, Brandon Barbello, Graham Taylor
Date: 2015-11-12 00:00:00

We present a large-scale study exploring the capability of temporal deep
neural networks to interpret natural human kinematics and introduce the first
method for active biometric authentication with mobile inertial sensors. At
Google, we have created a first-of-its-kind dataset of human movements,
passively collected by 1500 volunteers using their smartphones daily over
several months. We (1) compare several neural architectures for efficient
learning of temporal multi-modal data representations, (2) propose an optimized
shift-invariant dense convolutional mechanism (DCWRNN), and (3) incorporate the
discriminatively-trained dynamic features in a probabilistic generative
framework taking into account temporal characteristics. Our results demonstrate
that human kinematics convey important information about user identity and can
serve as a valuable component of multi-modal authentication systems.
Row-less Universal Schema
Authors: Patrick Verga, Andrew McCallum
Date: 2016-04-21 00:00:00
Proceeding: WS 2016 6
Tasks: Knowledge Base Construction, Relation

Universal schema jointly embeds knowledge bases and textual patterns to
reason about entities and relations for automatic knowledge base construction
and information extraction. In the past, entity pairs and relations were
represented as learned vectors with compatibility determined by a scoring
function, limiting generalization to unseen text patterns and entities.
Recently, 'column-less' versions of Universal Schema have used compositional
pattern encoders to generalize to all text patterns. In this work we take the
next step and propose a 'row-less' model of universal schema, removing explicit
entity pair representations. Instead of learning vector representations for
each entity pair in our training set, we treat an entity pair as a function of
its relation types. In experimental results on the FB15k-237 benchmark we
demonstrate that we can match the performance of a comparable model with
explicit entity pair representations using a model of attention over relation
types. We further demonstrate that the model per- forms with nearly the same
accuracy on entity pairs never seen during training.
Markov models for ocular fixation locations in the presence and absence of colour
Authors: Adam B. Kashlak, Eoin Devane, Helge Dietert, Henry Jackson
Date: 2016-04-21 00:00:00
Tasks: Clustering, Model Selection

We propose to model the fixation locations of the human eye when observing a
still image by a Markovian point process in R 2 . Our approach is data driven
using k-means clustering of the fixation locations to identify distinct salient
regions of the image, which in turn correspond to the states of our Markov
chain. Bayes factors are computed as model selection criterion to determine the
number of clusters. Furthermore, we demonstrate that the behaviour of the human
eye differs from this model when colour information is removed from the given
image.
Non-contact hemodynamic imaging reveals the jugular venous pulse waveform
Authors: Robert Amelard, Richard L Hughson, Danielle K Greaves, Kaylen J. Pfisterer, Jason Leung, David A. Clausi, Alexander Wong
Date: 2016-04-15 00:00:00

Cardiovascular monitoring is important to prevent diseases from progressing.
The jugular venous pulse (JVP) waveform offers important clinical information
about cardiac health, but is not routinely examined due to its invasive
catheterisation procedure. Here, we demonstrate for the first time that the JVP
can be consistently observed in a non-contact manner using a novel light-based
photoplethysmographic imaging system, coded hemodynamic imaging (CHI). While
traditional monitoring methods measure the JVP at a single location, CHI's
wide-field imaging capabilities were able to observe the jugular venous pulse's
spatial flow profile for the first time. The important inflection points in the
JVP were observed, meaning that cardiac abnormalities can be assessed through
JVP distortions. CHI provides a new way to assess cardiac health through
non-contact light-based JVP monitoring, and can be used in non-surgical
environments for cardiac assessment.
A Novel Approach to Dropped Pronoun Translation
Authors: Long-Yue Wang, Zhaopeng Tu, Xiaojun Zhang, Hang Li, Andy Way, Qun Liu
Date: 2016-04-21 00:00:00
Proceeding: NAACL 2016 6
Tasks: Machine Translation, Translation

Dropped Pronouns (DP) in which pronouns are frequently dropped in the source
language but should be retained in the target language are challenge in machine
translation. In response to this problem, we propose a semi-supervised approach
to recall possibly missing pronouns in the translation. Firstly, we build
training data for DP generation in which the DPs are automatically labelled
according to the alignment information from a parallel corpus. Secondly, we
build a deep learning-based DP generator for input sentences in decoding when
no corresponding references exist. More specifically, the generation is
two-phase: (1) DP position detection, which is modeled as a sequential
labelling task with recurrent neural networks; and (2) DP prediction, which
employs a multilayer perceptron with rich features. Finally, we integrate the
above outputs into our translation system to recall missing pronouns by both
extracting rules from the DP-labelled training data and translating the
DP-generated input sentences. Experimental results show that our approach
achieves a significant improvement of 1.58 BLEU points in translation
performance with 66% F-score for DP generation accuracy.
Automatic 3D Reconstruction of Manifold Meshes via Delaunay Triangulation and Mesh Sweeping
Authors: Andrea Romanoni, Amaël Delaunoy, Marc Pollefeys, Matteo Matteucci
Date: 2016-04-21 00:00:00
Tasks: 3D Reconstruction

In this paper we propose a new approach to incrementally initialize a
manifold surface for automatic 3D reconstruction from images. More precisely we
focus on the automatic initialization of a 3D mesh as close as possible to the
final solution; indeed many approaches require a good initial solution for
further refinement via multi-view stereo techniques. Our novel algorithm
automatically estimates an initial manifold mesh for surface evolving
multi-view stereo algorithms, where the manifold property needs to be enforced.
It bootstraps from 3D points extracted via Structure from Motion, then iterates
between a state-of-the-art manifold reconstruction step and a novel mesh
sweeping algorithm that looks for new 3D points in the neighborhood of the
reconstructed manifold to be added in the manifold reconstruction. The
experimental results show quantitatively that the mesh sweeping improves the
resolution and the accuracy of the manifold reconstruction, allowing a better
convergence of state-of-the-art surface evolution multi-view stereo algorithms.
Sparse group factor analysis for biclustering of multiple data sources
Authors: Kerstin Bunte, Eemeli Leppäaho, Inka Saarinen, Samuel Kaski
Date: 2015-12-29 00:00:00
Tasks: Functional Connectivity, Sensitivity

Motivation: Modelling methods that find structure in data are necessary with
the current large volumes of genomic data, and there have been various efforts
to find subsets of genes exhibiting consistent patterns over subsets of
treatments. These biclustering techniques have focused on one data source,
often gene expression data. We present a Bayesian approach for joint
biclustering of multiple data sources, extending a recent method Group Factor
Analysis (GFA) to have a biclustering interpretation with additional sparsity
assumptions. The resulting method enables data-driven detection of linear
structure present in parts of the data sources. Results: Our simulation studies
show that the proposed method reliably infers bi-clusters from heterogeneous
data sources. We tested the method on data from the NCI-DREAM drug sensitivity
prediction challenge, resulting in an excellent prediction accuracy. Moreover,
the predictions are based on several biclusters which provide insight into the
data sources, in this case on gene expression, DNA methylation, protein
abundance, exome sequence, functional connectivity fingerprints and drug
sensitivity.
Evaluation of the Effect of Improper Segmentation on Word Spotting
Authors: Sounak Dey, Anguelos Nicolaou, Josep Llados, Umapada Pal
Date: 2016-04-21 00:00:00
Tasks: Segmentation

Word spotting is an important recognition task in historical document
analysis. In most cases methods are developed and evaluated assuming perfect
word segmentations. In this paper we propose an experimental framework to
quantify the effect of goodness of word segmentation has on the performance
achieved by word spotting methods in identical unbiased conditions. The
framework consists of generating systematic distortions on segmentation and
retrieving the original queries from the distorted dataset. We apply the
framework on the George Washington and Barcelona Marriage Dataset and on
several established and state-of-the-art methods. The experiments allow for an
estimate of the end-to-end performance of word spotting methods.