pwc-archive/papers-with-abstracts huggingface dataset:
A Neural Network Approach to Missing Marker Reconstruction in Human Motion Capture
Authors: Taras Kucherenko, Jonas Beskow, Hedvig Kjellström
Date: 2018-03-07 00:00:00
Tasks: 3D Reconstruction, Missing Markers Reconstruction

Optical motion capture systems have become a widely used technology in
various fields, such as augmented reality, robotics, movie production, etc.
Such systems use a large number of cameras to triangulate the position of
optical markers.The marker positions are estimated with high accuracy. However,
especially when tracking articulated bodies, a fraction of the markers in each
timestep is missing from the reconstruction. In this paper, we propose to use a
neural network approach to learn how human motion is temporally and spatially
correlated, and reconstruct missing markers positions through this model. We
experiment with two different models, one LSTM-based and one time-window-based.
Both methods produce state-of-the-art results, while working online, as opposed
to most of the alternative methods, which require the complete sequence to be
known. The implementation is publicly available at
https://github.com/Svito-zar/NN-for-Missing-Marker-Reconstruction .
Sketching for Principal Component Regression
Authors: Liron Mor-Yosef, Haim Avron
Date: 2018-03-07 00:00:00
Tasks: regression

Principal component regression (PCR) is a useful method for regularizing
linear regression. Although conceptually simple, straightforward
implementations of PCR have high computational costs and so are inappropriate
when learning with large scale data. In this paper, we propose efficient
algorithms for computing approximate PCR solutions that are, on one hand, high
quality approximations to the true PCR solutions (when viewed as minimizer of a
constrained optimization problem), and on the other hand entertain rigorous
risk bounds (when viewed as statistical estimators). In particular, we propose
an input sparsity time algorithms for approximate PCR. We also consider
computing an approximate PCR in the streaming model, and kernel PCR. Empirical
results demonstrate the excellent performance of our proposed methods.
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
Authors: Lichao Mou, Lorenzo Bruzzone, Xiao Xiang Zhu
Date: 2018-03-07 00:00:00
Tasks: Change Detection, Earth Observation

Change detection is one of the central problems in earth observation and was
extensively investigated over recent decades. In this paper, we propose a novel
recurrent convolutional neural network (ReCNN) architecture, which is trained
to learn a joint spectral-spatial-temporal feature representation in a unified
framework for change detection in multispectral images. To this end, we bring
together a convolutional neural network (CNN) and a recurrent neural network
(RNN) into one end-to-end network. The former is able to generate rich
spectral-spatial feature representations, while the latter effectively analyzes
temporal dependency in bi-temporal images. In comparison with previous
approaches to change detection, the proposed network architecture possesses
three distinctive properties: 1) It is end-to-end trainable, in contrast to
most existing methods whose components are separately trained or computed; 2)
it naturally harnesses spatial information that has been proven to be
beneficial to change detection task; 3) it is capable of adaptively learning
the temporal dependency between multitemporal images, unlike most of algorithms
that use fairly simple operation like image differencing or stacking. As far as
we know, this is the first time that a recurrent convolutional network
architecture has been proposed for multitemporal remote sensing image analysis.
The proposed network is validated on real multispectral data sets. Both visual
and quantitative analysis of experimental results demonstrates competitive
performance in the proposed mode.
An Exercise Fatigue Detection Model Based on Machine Learning Methods
Authors: Ming-Yen Wu, Chi-Hua Chen, Chi-Chun Lo
Date: 2018-03-07 00:00:00
Tasks: BIG-bench Machine Learning

This study proposes an exercise fatigue detection model based on real-time
clinical data which includes time domain analysis, frequency domain analysis,
detrended fluctuation analysis, approximate entropy, and sample entropy.
Furthermore, this study proposed a feature extraction method which is combined
with an analytical hierarchy process to analyze and extract critical features.
Finally, machine learning algorithms were adopted to analyze the data of each
feature for the detection of exercise fatigue. The practical experimental
results showed that the proposed exercise fatigue detection model and feature
extraction method could precisely detect the level of exercise fatigue, and the
accuracy of exercise fatigue detection could be improved up to 98.65%.
Boosting Variational Inference: an Optimization Perspective
Authors: Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
Date: 2017-08-05 00:00:00
Tasks: Variational Inference

Variational inference is a popular technique to approximate a possibly
intractable Bayesian posterior with a more tractable one. Recently, boosting
variational inference has been proposed as a new paradigm to approximate the
posterior by a mixture of densities by greedily adding components to the
mixture. However, as is the case with many other variational inference
algorithms, its theoretical properties have not been studied. In the present
work, we study the convergence properties of this approach from a modern
optimization viewpoint by establishing connections to the classic Frank-Wolfe
algorithm. Our analyses yields novel theoretical insights regarding the
sufficient conditions for convergence, explicit rates, and algorithmic
simplifications. Since a lot of focus in previous works for variational
inference has been on tractability, our work is especially important as a much
needed attempt to bridge the gap between probabilistic models and their
corresponding theoretical properties.
Inferencing Based on Unsupervised Learning of Disentangled Representations
Authors: Tobias Hinz, Stefan Wermter
Date: 2018-03-07 00:00:00
Tasks: Descriptive, Representation Learning, Unsupervised Image Classification, Unsupervised MNIST

Combining Generative Adversarial Networks (GANs) with encoders that learn to
encode data points has shown promising results in learning data representations
in an unsupervised way. We propose a framework that combines an encoder and a
generator to learn disentangled representations which encode meaningful
information about the data distribution without the need for any labels. While
current approaches focus mostly on the generative aspects of GANs, our
framework can be used to perform inference on both real and generated data
points. Experiments on several data sets show that the encoder learns
interpretable, disentangled representations which encode descriptive properties
and can be used to sample images that exhibit specific characteristics.
TRLG: Fragile blind quad watermarking for image tamper detection and recovery by providing compact digests with quality optimized using LWT and GA
Authors: Behrouz Bolourian Haghighi, Amir Hossein Taherinia, Amir Hossein Mohajerzadeh
Date: 2018-03-07 00:00:00
Tasks: parameter estimation, SSIM

In this paper, an efficient fragile blind quad watermarking scheme for image
tamper detection and recovery based on lifting wavelet transform and genetic
algorithm is proposed. TRLG generates four compact digests with super quality
based on lifting wavelet transform and halftoning technique by distinguishing
the types of image blocks. In other words, for each 2*2 non-overlap blocks,
four chances for recovering destroyed blocks are considered. A special
parameter estimation technique based on genetic algorithm is performed to
improve and optimize the quality of digests and watermarked image. Furthermore,
CCS map is used to determine the mapping block for embedding information,
encrypting and confusing the embedded information. In order to improve the
recovery rate, Mirror-aside and Partner-block are proposed. The experiments
that have been conducted to evaluate the performance of TRLG proved the
superiority in terms of quality of the watermarked and recovered image, tamper
localization and security compared with state-of-the-art methods. The results
indicate that the PSNR and SSIM of the watermarked image are about 46 dB and
approximately one, respectively. Also, the mean of PSNR and SSIM of several
recovered images which has been destroyed about 90% is reached to 24 dB and
0.86, respectively.
Gaussian Process Latent Variable Alignment Learning
Authors: Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
Date: 2018-03-07 00:00:00

We present a model that can automatically learn alignments between
high-dimensional data in an unsupervised manner. Our proposed method casts
alignment learning in a framework where both alignment and data are modelled
simultaneously. Further, we automatically infer groupings of different types of
sequences within the same dataset. We derive a probabilistic model built on
non-parametric priors that allows for flexible warps while at the same time
providing means to specify interpretable constraints. We demonstrate the
efficacy of our approach with superior quantitative performance to the
state-of-the-art approaches and provide examples to illustrate the versatility
of our model in automatic inference of sequence groupings, absent from previous
approaches, as well as easy specification of high level priors for different
modalities of data.
Ising distribution as a latent variable model
Authors: Adrien Wohrer
Date: 2018-03-07 00:00:00
Tasks: model

During the past decades, the Ising distribution has attracted interest in many applied disciplines, as the maximum entropy distribution associated to any set of correlated binary (`spin') variables with observed means and covariances. However, numerically speaking, the Ising distribution is unpractical, so alternative models are often preferred to handle correlated binary data. One popular alternative, especially in life sciences, is the Cox distribution (or the closely related dichotomized Gaussian distribution and log-normal Cox point process), where the spins are generated independently conditioned on the drawing of a latent variable with a multivariate normal distribution. This article explores the conditions for a principled replacement of the Ising distribution by a Cox distribution. It shows that the Ising distribution itself can be treated as a latent variable model, and it explores when this latent variable has a quasi-normal distribution. A variational approach to this question reveals a formal link with classic mean-field methods, especially Opper and Winther's adaptive TAP approximation. This link is confirmed by weak coupling (Plefka) expansions of the different approximations and then by numerical tests. Overall, this study suggests that an Ising distribution can be replaced by a Cox distribution in practical applications, precisely when its parameters lie in the `mean-field domain'.
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain
Authors: Yu-Xiang Wang
Date: 2018-03-07 00:00:00
Tasks: regression

We revisit the problem of linear regression under a differential privacy
constraint. By consolidating existing pieces in the literature, we clarify the
correct dependence of the feature, label and coefficient domains in the
optimization error and estimation error, hence revealing the delicate price of
differential privacy in statistical estimation and statistical learning.
Moreover, we propose simple modifications of two existing DP algorithms: (a)
posterior sampling, (b) sufficient statistics perturbation, and show that they
can be upgraded into **adaptive** algorithms that are able to exploit
data-dependent quantities and behave nearly optimally **for every instance**.
Extensive experiments are conducted on both simulated data and real data, which
conclude that both AdaOPS and AdaSSP outperform the existing techniques on
nearly all 36 data sets that we test on.
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
Authors: Wenyu Du, Shuai Yu, Min Yang, Qiang Qu, Jia Zhu
Date: 2018-03-07 00:00:00
Tasks: Clustering, General Classification, Network Embedding, Node Classification

In this paper, we propose GPSP, a novel Graph Partition and Space Projection
based approach, to learn the representation of a heterogeneous network that
consists of multiple types of nodes and links. Concretely, we first partition
the heterogeneous network into homogeneous and bipartite subnetworks. Then, the
projective relations hidden in bipartite subnetworks are extracted by learning
the projective embedding vectors. Finally, we concatenate the projective
vectors from bipartite subnetworks with the ones learned from homogeneous
subnetworks to form the final representation of the heterogeneous network.
Extensive experiments are conducted on a real-life dataset. The results
demonstrate that GPSP outperforms the state-of-the-art baselines in two key
network mining tasks: node classification and clustering.
Detect to Track and Track to Detect
Authors: Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
Date: 2017-10-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, object-detection, Object Detection

Recent approaches for high accuracy detection and tracking of object
categories in video consist of complex multistage solutions that become more
cumbersome each year. In this paper we propose a ConvNet architecture that
jointly performs detection and tracking, solving the task in a simple and
effective way. Our contributions are threefold: (i) we set up a ConvNet
architecture for simultaneous detection and tracking, using a multi-task
objective for frame-based object detection and across-frame track regression;
(ii) we introduce correlation features that represent object co-occurrences
across time to aid the ConvNet during tracking; and (iii) we link the frame
level detections based on our across-frame tracklets to produce high accuracy
detections at the video level. Our ConvNet architecture for spatiotemporal
object detection is evaluated on the large-scale ImageNet VID dataset where it
achieves state-of-the-art results. Our approach provides better single model
performance than the winning method of the last ImageNet challenge while being
conceptually much simpler. Finally, we show that by increasing the temporal
stride we can dramatically increase the tracker speed.
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Authors: Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus Gross
Date: 2017-11-16 00:00:00
Proceeding: ICLR 2018 1
Tasks: text-classification, Text Classification

Understanding the flow of information in Deep Neural Networks (DNNs) is a
challenging problem that has gain increasing attention over the last few years.
While several methods have been proposed to explain network predictions, there
have been only a few attempts to compare them from a theoretical perspective.
What is more, no exhaustive empirical comparison has been performed in the
past. In this work, we analyze four gradient-based attribution methods and
formally prove conditions of equivalence and approximation between them. By
reformulating two of these methods, we construct a unified framework which
enables a direct comparison, as well as an easier implementation. Finally, we
propose a novel evaluation metric, called Sensitivity-n and test the
gradient-based attribution methods alongside with a simple perturbation-based
attribution method on several datasets in the domains of image and text
classification, using various network architectures.
Single-Perspective Warps in Natural Image Stitching
Authors: Tianli Liao, Nan Li
Date: 2018-02-13 00:00:00
Tasks: Image Stitching

Results of image stitching can be perceptually divided into
single-perspective and multiple-perspective. Compared to the
multiple-perspective result, the single-perspective result excels in
perspective consistency but suffers from projective distortion. In this paper,
we propose two single-perspective warps for natural image stitching. The first
one is a parametric warp, which is a combination of the
as-projective-as-possible warp and the quasi-homography warp via dual-feature.
The second one is a mesh-based warp, which is determined by optimizing a total
energy function that simultaneously emphasizes different characteristics of the
single-perspective warp, including alignment, naturalness, distortion and
saliency. A comprehensive evaluation demonstrates that the proposed warp
outperforms some state-of-the-art warps, including homography, APAP,
AutoStitch, SPHP and GSP.
Neural-Network Quantum States, String-Bond States, and Chiral Topological States
Authors: Ivan Glasser, Nicola Pancotti, Moritz August, Ivan D. Rodriguez, J. Ignacio Cirac
Date: 2017-10-11 00:00:00
Tasks: Tensor Networks

Neural-Network Quantum States have been recently introduced as an Ansatz for
describing the wave function of quantum many-body systems. We show that there
are strong connections between Neural-Network Quantum States in the form of
Restricted Boltzmann Machines and some classes of Tensor-Network states in
arbitrary dimensions. In particular we demonstrate that short-range Restricted
Boltzmann Machines are Entangled Plaquette States, while fully connected
Restricted Boltzmann Machines are String-Bond States with a nonlocal geometry
and low bond dimension. These results shed light on the underlying architecture
of Restricted Boltzmann Machines and their efficiency at representing many-body
quantum states. String-Bond States also provide a generic way of enhancing the
power of Neural-Network Quantum States and a natural generalization to systems
with larger local Hilbert space. We compare the advantages and drawbacks of
these different classes of states and present a method to combine them
together. This allows us to benefit from both the entanglement structure of
Tensor Networks and the efficiency of Neural-Network Quantum States into a
single Ansatz capable of targeting the wave function of strongly correlated
systems. While it remains a challenge to describe states with chiral
topological order using traditional Tensor Networks, we show that
Neural-Network Quantum States and their String-Bond States extension can
describe a lattice Fractional Quantum Hall state exactly. In addition, we
provide numerical evidence that Neural-Network Quantum States can approximate a
chiral spin liquid with better accuracy than Entangled Plaquette States and
local String-Bond States. Our results demonstrate the efficiency of neural
networks to describe complex quantum wave functions and pave the way towards
the use of String-Bond States as a tool in more traditional machine-learning
applications.
Active Image-based Modeling with a Toy Drone
Authors: Rui Huang, Danping Zou, Richard Vaughan, Ping Tan
Date: 2017-05-02 00:00:00

Image-based modeling techniques can now generate photo-realistic 3D models
from images. But it is up to users to provide high quality images with good
coverage and view overlap, which makes the data capturing process tedious and
time consuming. We seek to automate data capturing for image-based modeling.
The core of our system is an iterative linear method to solve the multi-view
stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our
fast MVS algorithm enables online model reconstruction and quality assessment
to determine the NBVs on the fly. We test our system with a toy unmanned aerial
vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that
our system improves the efficiency of data acquisition and ensures the
completeness of the final model.
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation
Authors: Tianyi Zhang, Guosheng Lin, Jianfei Cai, Tong Shen, Chunhua Shen, Alex C. Kot
Date: 2018-03-07 00:00:00
Tasks: Image Captioning, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation receives much research attention
since it alleviates the need to obtain a large amount of dense pixel-wise
ground-truth annotations for the training images. Compared with other forms of
weak supervision, image labels are quite efficient to obtain. In our work, we
focus on the weakly supervised semantic segmentation with image label
annotations. Recent progress for this task has been largely dependent on the
quality of generated pseudo-annotations. In this work, inspired by spatial
neural-attention for image captioning, we propose a decoupled spatial neural
attention network for generating pseudo-annotations. Our decoupled attention
structure could simultaneously identify the object regions and localize the
discriminative parts which generates high-quality pseudo-annotations in one
forward path. The generated pseudo-annotations lead to the segmentation results
which achieve the state-of-the-art in weakly-supervised semantic segmentation.
Multi-Channel Pyramid Person Matching Network for Person Re-Identification
Authors: Chaojie Mao, Yingming Li, Yaqing Zhang, Zhongfei Zhang, Xi Li
Date: 2018-03-07 00:00:00
Tasks: Person Re-Identification

In this work, we present a Multi-Channel deep convolutional Pyramid Person
Matching Network (MC-PPMN) based on the combination of the semantic-components
and the color-texture distributions to address the problem of person
re-identification. In particular, we learn separate deep representations for
semantic-components and color-texture distributions from two person images and
then employ pyramid person matching network (PPMN) to obtain correspondence
representations. These correspondence representations are fused to perform the
re-identification task. Further, the proposed framework is optimized via a
unified end-to-end deep learning scheme. Extensive experiments on several
benchmark datasets demonstrate the effectiveness of our approach against the
state-of-the-art literature, especially on the rank-1 recognition rate.
Automating Reading Comprehension by Generating Question and Answer Pairs
Authors: Vishwajeet Kumar, Kireeti Boorla, Yogesh Meena, Ganesh Ramakrishnan, Yuan-Fang Li
Date: 2018-03-07 00:00:00
Tasks: Answer Generation, Question Generation, Question-Generation, Reading Comprehension, Sentence

Neural network-based methods represent the state-of-the-art in question
generation from text. Existing work focuses on generating only questions from
text without concerning itself with answer generation. Moreover, our analysis
shows that handling rare words and generating the most appropriate question
given a candidate answer are still challenges facing existing approaches. We
present a novel two-stage process to generate question-answer pairs from the
text. For the first stage, we present alternatives for encoding the span of the
pivotal answer in the sentence using Pointer Networks. In our second stage, we
employ sequence to sequence models for question generation, enhanced with rich
linguistic features. Finally, global attention and answer encoding are used for
generating the question most relevant to the answer. We motivate and
linguistically analyze the role of each component in our framework and consider
compositions of these. This analysis is supported by extensive experimental
evaluations. Using standard evaluation metrics as well as human evaluations,
our experimental results validate the significant improvement in the quality of
questions generated by our framework over the state-of-the-art. The technique
presented here represents another step towards more automated reading
comprehension assessment. We also present a live system \footnote{Demo of the
system is available at
\url{https://www.cse.iitb.ac.in/~vishwajeet/autoqg.html}.} to demonstrate the
effectiveness of our approach.