pwc-archive/papers-with-abstracts huggingface dataset:
Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions
Authors: Vijay Kumar B G, Gustavo Carneiro, Ian Reid
Date: 2015-12-31 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification, Triplet

Recent innovations in training deep convolutional neural network (ConvNet)
models have motivated the design of new methods to automatically learn local
image descriptors. The latest deep ConvNets proposed for this task consist of a
siamese network that is trained by penalising misclassification of pairs of
local image patches. Current results from machine learning show that replacing
this siamese by a triplet network can improve the classification accuracy in
several problems, but this has yet to be demonstrated for local image
descriptor learning. Moreover, current siamese and triplet networks have been
trained with stochastic gradient descent that computes the gradient from
individual pairs or triplets of local image patches, which can make them prone
to overfitting. In this paper, we first propose the use of triplet networks for
the problem of local image descriptor learning. Furthermore, we also propose
the use of a global loss that minimises the overall classification error in the
training set, which can improve the generalisation capability of the model.
Using the UBC benchmark dataset for comparing local image descriptors, we show
that the triplet network produces a more accurate embedding than the siamese
network in terms of the UBC dataset errors. Moreover, we also demonstrate that
a combination of the triplet and global losses produces the best embedding in
the field, using this triplet network. Finally, we also show that the use of
the central-surround siamese network trained with the global loss produces the
best result of the field on the UBC dataset. Pre-trained models are available
online at https://github.com/vijaykbg/deep-patchmatch
Video Summarization in a Multi-View Camera Network
Authors: Rameswar Panda, Abir Das, Amit K. Roy-Chowdhury
Date: 2016-08-01 00:00:00
Tasks: Video Summarization

While most existing video summarization approaches aim to extract an
informative summary of a single video, we propose a novel framework for
summarizing multi-view videos by exploiting both intra- and inter-view content
correlations in a joint embedding space. We learn the embedding by minimizing
an objective function that has two terms: one due to intra-view correlations
and another due to inter-view correlations across the multiple views. The
solution can be obtained directly by solving one Eigen-value problem that is
linear in the number of multi-view videos. We then employ a sparse
representative selection approach over the learned embedding space to summarize
the multi-view videos. Experimental results on several benchmark datasets
demonstrate that our proposed approach clearly outperforms the
state-of-the-art.
Linear Convergence of Proximal Gradient Algorithm with Extrapolation for a Class of Nonconvex Nonsmooth Minimization Problems
Authors: Bo Wen, Xiaojun Chen, Ting Kei Pong
Date: 2015-12-31 00:00:00

In this paper, we study the proximal gradient algorithm with extrapolation
for minimizing the sum of a Lipschitz differentiable function and a proper
closed convex function. Under the error bound condition used in [19] for
analyzing the convergence of the proximal gradient algorithm, we show that
there exists a threshold such that if the extrapolation coefficients are chosen
below this threshold, then the sequence generated converges $R$-linearly to a
stationary point of the problem. Moreover, the corresponding sequence of
objective values is also $R$-linearly convergent. In addition, the threshold
reduces to $1$ for convex problems and, as a consequence, we obtain the
$R$-linear convergence of the sequence generated by FISTA with fixed restart.
Finally, we present some numerical experiments to illustrate our results.
Fast Training of Triplet-based Deep Binary Embedding Networks
Authors: Bohan Zhuang, Guosheng Lin, Chunhua Shen, Ian Reid
Date: 2016-03-09 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Retrieval, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Retrieval, Triplet

In this paper, we aim to learn a mapping (or embedding) from images to a
compact binary space in which Hamming distances correspond to a ranking measure
for the image retrieval task.
  We make use of a triplet loss because this has been shown to be most
effective for ranking problems.
  However, training in previous works can be prohibitively expensive due to the
fact that optimization is directly performed on the triplet space, where the
number of possible triplets for training is cubic in the number of training
examples.
  To address this issue, we propose to formulate high-order binary codes
learning as a multi-label classification problem by explicitly separating
learning into two interleaved stages.
  To solve the first stage, we design a large-scale high-order binary codes
inference algorithm to reduce the high-order objective to a standard binary
quadratic problem such that graph cuts can be used to efficiently infer the
binary code which serve as the label of each training datum.
  In the second stage we propose to map the original image to compact binary
codes via carefully designed deep convolutional neural networks (CNNs) and the
hashing function fitting can be solved by training binary CNN classifiers.
  An incremental/interleaved optimization strategy is proffered to ensure that
these two steps are interactive with each other during training for better
accuracy.
  We conduct experiments on several benchmark datasets, which demonstrate both
improved training time (by as much as two orders of magnitude) as well as
producing state-of-the-art hashing for various retrieval tasks.
Left-corner Methods for Syntactic Modeling with Universal Structural Constraints
Authors: Hiroshi Noji
Date: 2016-08-01 00:00:00
Tasks: Sentence

The primary goal in this thesis is to identify better syntactic constraint or
bias, that is language independent but also efficiently exploitable during
sentence processing. We focus on a particular syntactic construction called
center-embedding, which is well studied in psycholinguistics and noted to cause
particular difficulty for comprehension. Since people use language as a tool
for communication, one expects such complex constructions to be avoided for
communication efficiency. From a computational perspective, center-embedding is
closely relevant to a left-corner parsing algorithm, which can capture the
degree of center-embedding of a parse tree being constructed. This connection
suggests left-corner methods can be a tool to exploit the universal syntactic
constraint that people avoid generating center-embedded structures. We explore
such utilities of center-embedding as well as left-corner methods extensively
through several theoretical and empirical examinations.
  Our primary task is unsupervised grammar induction. In this task, the input
to the algorithm is a collection of sentences, from which the model tries to
extract the salient patterns on them as a grammar. This is a particularly hard
problem although we expect the universal constraint may help in improving the
performance since it can effectively restrict the possible search space for the
model. We build the model by extending the left-corner parsing algorithm for
efficiently tabulating the search space except those involving center-embedding
up to a specific degree. We examine the effectiveness of our approach on many
treebanks, and demonstrate that often our constraint leads to better parsing
performance. We thus conclude that left-corner methods are particularly useful
for syntax-oriented systems, as it can exploit efficiently the inherent
universal constraints in languages.
Neural shrinkage for wavelet-based SAR despeckling
Authors: Mario Mastriani, Alberto E. Giraldez
Date: 2016-07-31 00:00:00
Tasks: Denoising

The wavelet shrinkage denoising approach is able to maintain local regularity
of a signal while suppressing noise. However, the conventional wavelet
shrinkage based methods are not time-scale adaptive to track the local
time-scale variation. In this paper, a new type of Neural Shrinkage (NS) is
presented with a new class of shrinkage architecture for speckle reduction in
Synthetic Aperture Radar (SAR) images. The numerical results indicate that the
new method outperforms the standard filters, the standard wavelet shrinkage
despeckling method, and previous NS.
Fuzzy thresholding in wavelet domain for speckle reduction in Synthetic Aperture Radar images
Authors: Mario Mastriani
Date: 2016-07-31 00:00:00

The application of wavelet transforms to Synthetic Aperture Radar (SAR)
imagery has improved despeckling performance. To deduce the problem of
filtering the multiplicative noise to the case of an additive noise, the
wavelet decomposition is performed on the logarithm of the image gray levels.
The detail coefficients produced by the bidimensional discrete wavelet
transform (DWT-2D) needs to be thresholded to extract out the speckle in
highest subbands. An initial threshold value is estimated according to the
noise variance. In this paper, an additional fuzzy thresholding approach for
automatic determination of the rate threshold level around the traditional
wavelet noise thresholding (initial threshold) is applied, and used for the
soft or hard-threshold performed on all the high frequency subimages. The
filtered logarithmic image is then obtained by reconstruction from the
thresholded coefficients. This process is applied a single time, and
exclusively to the first level of decomposition. The exponential function of
this reconstructed image gives the final filtered image. Experimental results
on test images have demonstrated the effectiveness of this method compared to
the most of methods in use at the moment.
Denoising based on wavelets and deblurring via self-organizing map for Synthetic Aperture Radar images
Authors: Mario Mastriani
Date: 2016-07-31 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring

This work deals with unsupervised image deblurring. We present a new
deblurring procedure on images provided by low-resolution synthetic aperture
radar (SAR) or simply by multimedia in presence of multiplicative (speckle) or
additive noise, respectively. The method we propose is defined as a two-step
process. First, we use an original technique for noise reduction in wavelet
domain. Then, the learning of a Kohonen self-organizing map (SOM) is performed
directly on the denoised image to take out it the blur. This technique has been
successfully applied to real SAR images, and the simulation results are
presented to demonstrate the effectiveness of the proposed algorithms.
Kalman's shrinkage for wavelet-based despeckling of SAR images
Authors: Mario Mastriani, Alberto E. Giraldez
Date: 2016-07-31 00:00:00

In this paper, a new probability density function (pdf) is proposed to model
the statistics of wavelet coefficients, and a simple Kalman's filter is derived
from the new pdf using Bayesian estimation theory. Specifically, we decompose
the speckled image into wavelet subbands, we apply the Kalman's filter to the
high subbands, and reconstruct a despeckled image from the modified detail
coefficients. Experimental results demonstrate that our method compares
favorably to several other despeckling methods on test synthetic aperture radar
(SAR) images.
Image Segmentation Using Hierarchical Merge Tree
Authors: Ting Liu, Mojtaba Seyedhosseini, Tolga Tasdizen
Date: 2015-05-24 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

This paper investigates one of the most fundamental computer vision problems:
image segmentation. We propose a supervised hierarchical approach to
object-independent image segmentation. Starting with over-segmenting
superpixels, we use a tree structure to represent the hierarchy of region
merging, by which we reduce the problem of segmenting image regions to finding
a set of label assignment to tree nodes. We formulate the tree structure as a
constrained conditional model to associate region merging with likelihoods
predicted using an ensemble boundary classifier. Final segmentations can then
be inferred by finding globally optimal solutions to the model efficiently. We
also present an iterative training and testing algorithm that generates various
tree structures and combines them to emphasize accurate boundaries by
segmentation accumulation. Experiment results and comparisons with other very
recent methods on six public data sets demonstrate that our approach achieves
the state-of-the-art region accuracy and is very competitive in image
segmentation without semantic priors.
New wavelet-based superresolution algorithm for speckle reduction in SAR images
Authors: Mario Mastriani
Date: 2016-07-31 00:00:00
Tasks: Image Enhancement, Super-Resolution

This paper describes a novel projection algorithm, the Projection Onto Span
Algorithm (POSA) for wavelet-based superresolution and removing speckle (in
wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images.
Although the POSA is good as a new superresolution algorithm for image
enhancement, image metrology and biometric identification, here one will use it
like a tool of despeckling, being the first time that an algorithm of
super-resolution is used for despeckling of SAR images. Specifically, the
speckled SAR image is decomposed into wavelet subbands, POSA is applied to the
high subbands, and reconstruct a SAR image from the modified detail
coefficients. Experimental results demonstrate that the new method compares
favorably to several other despeckling methods on test SAR images.
Union is strength in lossy image compression
Authors: Mario Mastriani
Date: 2016-07-31 00:00:00
Tasks: Image Compression

In this work, we present a comparison between different techniques of image
compression. First, the image is divided in blocks which are organized
according to a certain scan. Later, several compression techniques are applied,
combined or alone. Such techniques are: wavelets (Haar's basis), Karhunen-Loeve
Transform, etc. Simulations show that the combined versions are the best, with
minor Mean Squared Error (MSE), and higher Peak Signal to Noise Ratio (PSNR)
and better image quality, even in the presence of noise.
Denoising and compression in wavelet domain via projection onto approximation coefficients
Authors: Mario Mastriani
Date: 2016-07-31 00:00:00
Tasks: Denoising, Image Denoising

We describe a new filtering approach in the wavelet domain for image
denoising and compression, based on the projections of details subbands
coefficients (resultants of the splitting procedure, typical in wavelet domain)
onto the approximation subband coefficients (much less noisy). The new
algorithm is called Projection Onto Approximation Coefficients (POAC). As a
result of this approach, only the approximation subband coefficients and three
scalars are stored and/or transmitted to the channel. Besides, with the
elimination of the details subbands coefficients, we obtain a bigger
compression rate. Experimental results demonstrate that our approach compares
favorably to more typical methods of denoising and compression in wavelet
domain.
Identification of repeats in DNA sequences using nucleotide distribution uniformity
Authors: Changchuan Yin
Date: 2016-07-31 00:00:00

Repetitive elements are important in genomic structures, functions and
regulations, yet effective methods in precisely identifying repetitive elements
in DNA sequences are not fully accessible, and the relationship between
repetitive elements and periodicities of genomes is not clearly understood. We
present an $\textit{ab initio}$ method to quantitatively detect repetitive
elements and infer the consensus repeat pattern in repetitive elements. The
method uses the measure of the distribution uniformity of nucleotides at
periodic positions in DNA sequences or genomes. It can identify periodicities,
consensus repeat patterns, copy numbers and perfect levels of repetitive
elements. The results of using the method on different DNA sequences and
genomes demonstrate efficacy and accuracy in identifying repeat patterns and
periodicities. The complexity of the method is linear with respect to the
lengths of the analyzed sequences.
On Regularization Parameter Estimation under Covariate Shift
Authors: Wouter M. Kouw, Marco Loog
Date: 2016-07-31 00:00:00
Tasks: Domain Adaptation, L2 Regularization, parameter estimation

This paper identifies a problem with the usual procedure for
L2-regularization parameter estimation in a domain adaptation setting. In such
a setting, there are differences between the distributions generating the
training data (source domain) and the test data (target domain). The usual
cross-validation procedure requires validation data, which can not be obtained
from the unlabeled target data. The problem is that if one decides to use
source validation data, the regularization parameter is underestimated. One
possible solution is to scale the source validation data through importance
weighting, but we show that this correction is not sufficient. We conclude the
paper with an empirical analysis of the effect of several importance weight
estimators on the estimation of the regularization parameter.
Similarity Registration Problems for 2D/3D Ultrasound Calibration
Authors: Francisco Vasconcelos, Donald Peebles, Sebastien Ourselin, Danail Stoyanov
Date: 2016-07-31 00:00:00
Tasks: Pose Tracking

We propose a minimal solution for the similarity registration (rigid pose and
scale) between two sets of 3D lines, and also between a set of co-planar points
and a set of 3D lines. The first problem is solved up to 8 discrete solutions
with a minimum of 2 line-line correspondences, while the second is solved up to
4 discrete solutions using 4 point-line correspondences. We use these
algorithms to perform the extrinsic calibration between a pose tracking sensor
and a 2D/3D ultrasound (US) curvilinear probe using a tracked needle as
calibration target. The needle is tracked as a 3D line, and is scanned by the
ultrasound as either a 3D line (3D US) or as a 2D point (2D US). Since the
scale factor that converts US scan units to metric coordinates is unknown, the
calibration is formulated as a similarity registration problem. We present
results with both synthetic and real data and show that the minimum solutions
outperform the correspondent non-minimal linear formulations.
Learning Robust Features using Deep Learning for Automatic Seizure Detection
Authors: Pierre Thodoroff, Joelle Pineau, Andrew Lim
Date: 2016-07-31 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG), Seizure Detection, Sensitivity

We present and evaluate the capacity of a deep neural network to learn robust
features from EEG to automatically detect seizures. This is a challenging
problem because seizure manifestations on EEG are extremely variable both
inter- and intra-patient. By simultaneously capturing spectral, temporal and
spatial information our recurrent convolutional neural network learns a general
spatially invariant representation of a seizure. The proposed approach exceeds
significantly previous results obtained on cross-patient classifiers both in
terms of sensitivity and false positive rate. Furthermore, our model proves to
be robust to missing channel and variable electrode montage.
Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training
Authors: Ilija Ilievski, Jiashi Feng
Date: 2016-07-31 00:00:00
Tasks: Hyperparameter Optimization, Transfer Learning

Recently, several optimization methods have been successfully applied to the
hyperparameter optimization of deep neural networks (DNNs). The methods work by
modeling the joint distribution of hyperparameter values and corresponding
error. Those methods become less practical when applied to modern DNNs whose
training may take a few days and thus one cannot collect sufficient
observations to accurately model the distribution. To address this challenging
issue, we propose a method that learns to transfer optimal hyperparameter
values for a small source dataset to hyperparameter values with comparable
performance on a dataset of interest. As opposed to existing transfer learning
methods, our proposed method does not use hand-designed features. Instead, it
uses surrogates to model the hyperparameter-error distributions of the two
datasets and trains a neural network to learn the transfer function. Extensive
experiments on three CV benchmark datasets clearly demonstrate the efficiency
of our method.
Learning deep representation from coarse to fine for face alignment
Authors: Zhiwen Shao, Shouhong Ding, Yiru Zhao, Qinchuan Zhang, Lizhuang Ma
Date: 2016-07-31 00:00:00
Tasks: Face Alignment

In this paper, we propose a novel face alignment method that trains deep
convolutional network from coarse to fine. It divides given landmarks into
principal subset and elaborate subset. We firstly keep a large weight for
principal subset to make our network primarily predict their locations while
slightly take elaborate subset into account. Next the weight of principal
subset is gradually decreased until two subsets have equivalent weights. This
process contributes to learn a good initial model and search the optimal model
smoothly to avoid missing fairly good intermediate models in subsequent
procedures. On the challenging COFW dataset [1], our method achieves 6.33% mean
error with a reduction of 21.37% compared with the best previous result [2].
Lifting Object Detection Datasets into 3D
Authors: Joao Carreira, Sara Vicente, Lourdes Agapito, Jorge Batista
Date: 2015-03-22 00:00:00
Tasks: 3D Reconstruction, Object, object-detection, Object Detection, Object Recognition, Object Reconstruction

While data has certainly taken the center stage in computer vision in recent
years, it can still be difficult to obtain in certain scenarios. In particular,
acquiring ground truth 3D shapes of objects pictured in 2D images remains a
challenging feat and this has hampered progress in recognition-based object
reconstruction from a single image. Here we propose to bypass previous
solutions such as 3D scanning or manual design, that scale poorly, and instead
populate object category detection datasets semi-automatically with dense,
per-object 3D reconstructions, bootstrapped from:(i) class labels, (ii) ground
truth figure-ground segmentations and (iii) a small set of keypoint
annotations. Our proposed algorithm first estimates camera viewpoint using
rigid structure-from-motion and then reconstructs object shapes by optimizing
over visual hull proposals guided by loose within-class shape similarity
assumptions. The visual hull sampling process attempts to intersect an object's
projection cone with the cones of minimal subsets of other similar objects
among those pictured from certain vantage points. We show that our method is
able to produce convincing per-object 3D reconstructions and to accurately
estimate cameras viewpoints on one of the most challenging existing
object-category detection datasets, PASCAL VOC. We hope that our results will
re-stimulate interest on joint object recognition and 3D reconstruction from a
single image.
Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks
Authors: Balint Antal
Date: 2016-07-31 00:00:00

In this paper, an automatic approach to predict 3D coordinates from stereo
laparoscopic images is presented. The approach maps a vector of pixel
intensities to 3D coordinates through training a six layer deep neural network.
The architectural aspects of the approach is presented and in detail and the
method is evaluated on a publicly available dataset with promising results.