pwc-archive/papers-with-abstracts huggingface dataset:
Robust Guided Image Filtering
Authors: Wei Liu, Xiaogang Chen, Chunhua Shen, Jingyi Yu, Qiang Wu, Jie Yang
Date: 2017-03-28 00:00:00

The process of using one image to guide the filtering process of another one
is called Guided Image Filtering (GIF). The main challenge of GIF is the
structure inconsistency between the guidance image and the target image.
Besides, noise in the target image is also a challenging issue especially when
it is heavy. In this paper, we propose a general framework for Robust Guided
Image Filtering (RGIF), which contains a data term and a smoothness term, to
solve the two issues mentioned above. The data term makes our model
simultaneously denoise the target image and perform GIF which is robust against
the heavy noise. The smoothness term is able to make use of the property of
both the guidance image and the target image which is robust against the
structure inconsistency. While the resulting model is highly non-convex, it can
be solved through the proposed Iteratively Re-weighted Least Squares (IRLS) in
an efficient manner. For challenging applications such as guided depth map
upsampling, we further develop a data-driven parameter optimization scheme to
properly determine the parameter in our model. This optimization scheme can
help to preserve small structures and sharp depth edges even for a large
upsampling factor (8x for example). Moreover, the specially designed structure
of the data term and the smoothness term makes our model perform well in
edge-preserving smoothing for single-image tasks (i.e., the guidance image is
the target image itself). This paper is an extension of our previous work [1],
[2].
Discovery Radiomics for Pathologically-Proven Computed Tomography Lung Cancer Prediction
Authors: Devinder Kumar, Mohammad Javad Shafiee, Audrey G. Chung, Farzad Khalvati, Masoom A. Haider, Alexander Wong
Date: 2015-09-01 00:00:00
Tasks: Diagnostic, Specificity

Lung cancer is the leading cause for cancer related deaths. As such, there is
an urgent need for a streamlined process that can allow radiologists to provide
diagnosis with greater efficiency and accuracy. A powerful tool to do this is
radiomics: a high-dimension imaging feature set. In this study, we take the
idea of radiomics one step further by introducing the concept of discovery
radiomics for lung cancer prediction using CT imaging data. In this study, we
realize these custom radiomic sequencers as deep convolutional sequencers using
a deep convolutional neural network learning architecture. To illustrate the
prognostic power and effectiveness of the radiomic sequences produced by the
discovered sequencer, we perform cancer prediction between malignant and benign
lesions from 97 patients using the pathologically-proven diagnostic data from
the LIDC-IDRI dataset. Using the clinically provided pathologically-proven data
as ground truth, the proposed framework provided an average accuracy of 77.52%
via 10-fold cross-validation with a sensitivity of 79.06% and specificity of
76.11%, surpassing the state-of-the art method.
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Authors: Yu Guan, Thomas Ploetz
Date: 2017-03-28 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Recently, deep learning (DL) methods have been introduced very successfully
into human activity recognition (HAR) scenarios in ubiquitous and wearable
computing. Especially the prospect of overcoming the need for manual feature
design combined with superior classification capabilities render deep neural
networks very attractive for real-life HAR application. Even though DL-based
approaches now outperform the state-of-the-art in a number of recognitions
tasks of the field, yet substantial challenges remain. Most prominently, issues
with real-life datasets, typically including imbalanced datasets and
problematic data quality, still limit the effectiveness of activity recognition
using wearables. In this paper we tackle such challenges through Ensembles of
deep Long Short Term Memory (LSTM) networks. We have developed modified
training procedures for LSTM networks and combine sets of diverse LSTM learners
into classifier collectives. We demonstrate, both formally and empirically,
that Ensembles of deep LSTM learners outperform the individual LSTM networks.
Through an extensive experimental evaluation on three standard benchmarks
(Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition
capabilities of our approach and its potential for real-life applications of
human activity recognition.
Learning and inference in knowledge-based probabilistic model for medical diagnosis
Authors: Jingchi Jiang, Chao Zhao, Yi Guan, Qiubin Yu
Date: 2017-03-28 00:00:00
Tasks: Medical Diagnosis

Based on a weighted knowledge graph to represent first-order knowledge and
combining it with a probabilistic model, we propose a methodology for the
creation of a medical knowledge network (MKN) in medical diagnosis. When a set
of symptoms is activated for a specific patient, we can generate a ground
medical knowledge network composed of symptom nodes and potential disease
nodes. By Incorporating a Boltzmann machine into the potential function of a
Markov network, we investigated the joint probability distribution of the MKN.
In order to deal with numerical symptoms, a multivariate inference model is
presented that uses conditional probability. In addition, the weights for the
knowledge graph were efficiently learned from manually annotated Chinese
Electronic Medical Records (CEMRs). In our experiments, we found numerically
that the optimum choice of the quality of disease node and the expression of
symptom variable can improve the effectiveness of medical diagnosis. Our
experimental results comparing a Markov logic network and the logistic
regression algorithm on an actual CEMR database indicate that our method holds
promise and that MKN can facilitate studies of intelligent diagnosis.
De-noising, Stabilizing and Completing 3D Reconstructions On-the-go using Plane Priors
Authors: Maksym Dzitsiuk, Jürgen Sturm, Robert Maier, Lingni Ma, Daniel Cremers
Date: 2016-09-27 00:00:00
Tasks: 3D Reconstruction, Semantic Segmentation

Creating 3D maps on robots and other mobile devices has become a reality in
recent years. Online 3D reconstruction enables many exciting applications in
robotics and AR/VR gaming. However, the reconstructions are noisy and generally
incomplete. Moreover, during onine reconstruction, the surface changes with
every newly integrated depth image which poses a significant challenge for
physics engines and path planning algorithms. This paper presents a novel, fast
and robust method for obtaining and using information about planar surfaces,
such as walls, floors, and ceilings as a stage in 3D reconstruction based on
Signed Distance Fields. Our algorithm recovers clean and accurate surfaces,
reduces the movement of individual mesh vertices caused by noise during online
reconstruction and fills in the occluded and unobserved regions. We implemented
and evaluated two different strategies to generate plane candidates and two
strategies for merging them. Our implementation is optimized to run in
real-time on mobile devices such as the Tango tablet. In an extensive set of
experiments, we validated that our approach works well in a large number of
natural environments despite the presence of significant amount of occlusion,
clutter and noise, which occur frequently. We further show that plane fitting
enables in many cases a meaningful semantic segmentation of real-world scenes.
PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding
Authors: Chunhui Liu, Yueyu Hu, Yanghao Li, Sijie Song, Jiaying Liu
Date: 2017-03-22 00:00:00
Tasks: Action Detection, Action Recognition, Action Understanding, Temporal Action Localization

Despite the fact that many 3D human activity benchmarks being proposed, most
existing action datasets focus on the action recognition tasks for the
segmented videos. There is a lack of standard large-scale benchmarks,
especially for current popular data-hungry deep learning based methods. In this
paper, we introduce a new large scale benchmark (PKU-MMD) for continuous
multi-modality 3D human action understanding and cover a wide range of complex
human activities with well annotated information. PKU-MMD contains 1076 long
video sequences in 51 action categories, performed by 66 subjects in three
camera views. It contains almost 20,000 action instances and 5.4 million frames
in total. Our dataset also provides multi-modality data sources, including RGB,
depth, Infrared Radiation and Skeleton. With different modalities, we conduct
extensive experiments on our dataset in terms of two scenarios and evaluate
different methods by various metrics, including a new proposed evaluation
protocol 2D-AP. We believe this large-scale dataset will benefit future
researches on action detection for the community.
Graph Regularized Tensor Sparse Coding for Image Representation
Authors: Fei Jiang, Xiao-Yang Liu, Hongtao Lu, Ruimin Shen
Date: 2017-03-27 00:00:00
Tasks: Clustering, Image Clustering

Sparse coding (SC) is an unsupervised learning scheme that has received an
increasing amount of interests in recent years. However, conventional SC
vectorizes the input images, which destructs the intrinsic spatial structures
of the images. In this paper, we propose a novel graph regularized tensor
sparse coding (GTSC) for image representation. GTSC preserves the local
proximity of elementary structures in the image by adopting the newly proposed
tubal-tensor representation. Simultaneously, it considers the intrinsic
geometric properties by imposing graph regularization that has been
successfully applied to uncover the geometric distribution for the image data.
Moreover, the returned sparse representations by GTSC have better physical
explanations as the key operation (i.e., circular convolution) in the
tubal-tensor model preserves the shifting invariance property. Experimental
results on image clustering demonstrate the effectiveness of the proposed
scheme.
Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos
Authors: Alejandro Betancourt, Natalia Díaz-Rodríguez, Emilia Barakova, Lucio Marcenaro, Matthias Rauterberg, Carlo Regazzoni
Date: 2016-03-30 00:00:00
Tasks: Hand Detection

Wearable cameras stand out as one of the most promising devices for the
upcoming years, and as a consequence, the demand of computer algorithms to
automatically understand the videos recorded with them is increasing quickly.
An automatic understanding of these videos is not an easy task, and its mobile
nature implies important challenges to be faced, such as the changing light
conditions and the unrestricted locations recorded. This paper proposes an
unsupervised strategy based on global features and manifold learning to endow
wearable cameras with contextual information regarding the light conditions and
the location captured. Results show that non-linear manifold methods can
capture contextual patterns from global features without compromising large
computational resources. The proposed strategy is used, as an application case,
as a switching mechanism to improve the hand-detection problem in egocentric
videos.
Blankets Joint Posterior score for learning Markov network structures
Authors: Federico Schlüter, Yanela Strappa, Diego H. Milone, Facundo Bromberg
Date: 2016-08-08 00:00:00

Markov networks are extensively used to model complex sequential, spatial,
and relational interactions in a wide range of fields. By learning the
structure of independences of a domain, more accurate joint probability
distributions can be obtained for inference tasks or, more directly, for
interpreting the most significant relations among the variables. Recently,
several researchers have investigated techniques for automatically learning the
structure from data by obtaining the probabilistic maximum-a-posteriori
structure given the available data. However, all the approximations proposed
decompose the posterior of the whole structure into local sub-problems, by
assuming that the posteriors of the Markov blankets of all the variables are
mutually independent. In this work, we propose a scoring function for relaxing
such assumption. The Blankets Joint Posterior score computes the joint
posterior of structures as a joint distribution of the collection of its Markov
blankets. Essentially, the whole posterior is obtained by computing the
posterior of the blanket of each variable as a conditional distribution that
takes into account information from other blankets in the network. We show in
our experimental results that the proposed approximation can improve the sample
complexity of state-of-the-art scores when learning complex networks, where the
independence assumption between blanket variables is clearly incorrect.
Femoral ROIs and Entropy for Texture-based Detection of Osteoarthritis from High-Resolution Knee Radiographs
Authors: Jiří Hladůvka, Bui Thi Mai Phuong, Richard Ljuhar, Davul Ljuhar, Ana M Rodrigues, Jaime C Branco, Helena Canhão
Date: 2017-03-27 00:00:00

The relationship between knee osteoarthritis progression and changes in
tibial bone structure has long been recognized and various texture descriptors
have been proposed to detect early osteoarthritis (OA) from radiographs. This
work aims to investigate (1) femoral textures as an OA indicator and (2) the
potential of entropy as a computationally efficient alternative to established
texture descriptors.
  We design a robust semi-automatically placed layout for regions of interest
(ROI), compute the Hurst coefficient and the entropy in each ROI, and employ
statistical and machine learning methods to evaluate feature combinations.
  Based on 153 high-resolution radiographs, our results identify medial femur
as an effective univariate descriptor, with significance comparable to medial
tibia. Entropy is shown to contribute to classification performance. A linear
five-feature classifier combining femur, entropic and standard texture
descriptors, achieves AUC of 0.85, outperforming the state-of-the-art by
roughly 0.1.
IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate
Authors: Aryan Mokhtari, Mark Eisen, Alejandro Ribeiro
Date: 2017-02-02 00:00:00

The problem of minimizing an objective that can be written as the sum of a
set of $n$ smooth and strongly convex functions is considered. The Incremental
Quasi-Newton (IQN) method proposed here belongs to the family of stochastic and
incremental methods that have a cost per iteration independent of $n$. IQN
iterations are a stochastic version of BFGS iterations that use memory to
reduce the variance of stochastic approximations. The convergence properties of
IQN bridge a gap between deterministic and stochastic quasi-Newton methods.
Deterministic quasi-Newton methods exploit the possibility of approximating the
Newton step using objective gradient differences. They are appealing because
they have a smaller computational cost per iteration relative to Newton's
method and achieve a superlinear convergence rate under customary regularity
assumptions. Stochastic quasi-Newton methods utilize stochastic gradient
differences in lieu of actual gradient differences. This makes their
computational cost per iteration independent of the number of objective
functions $n$. However, existing stochastic quasi-Newton methods have sublinear
or linear convergence at best. IQN is the first stochastic quasi-Newton method
proven to converge superlinearly in a local neighborhood of the optimal
solution. IQN differs from state-of-the-art incremental quasi-Newton methods in
three aspects: (i) The use of aggregated information of variables, gradients,
and quasi-Newton Hessian approximation matrices to reduce the noise of gradient
and Hessian approximations. (ii) The approximation of each individual function
by its Taylor's expansion in which the linear and quadratic terms are evaluated
with respect to the same iterate. (iii) The use of a cyclic scheme to update
the functions in lieu of a random selection routine. We use these fundamental
properties of IQN to establish its local superlinear convergence rate.
CommAI: Evaluating the first steps towards a useful general AI
Authors: Marco Baroni, Armand Joulin, Allan Jabri, Germàn Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas Mikolov
Date: 2017-01-31 00:00:00
Tasks: BIG-bench Machine Learning, Continual Learning, General Classification, image-classification, Translation

With machine learning successfully applied to new daunting problems almost
every day, general AI starts looking like an attainable goal. However, most
current research focuses instead on important but narrow applications, such as
image classification or machine translation. We believe this to be largely due
to the lack of objective ways to measure progress towards broad machine
intelligence. In order to fill this gap, we propose here a set of concrete
desiderata for general AI, together with a platform to test machines on how
well they satisfy such desiderata, while keeping all further complexities to a
minimum.
Discriminative Transfer Learning for General Image Restoration
Authors: Lei Xiao, Felix Heide, Wolfgang Heidrich, Bernhard Schölkopf, Michael Hirsch
Date: 2017-03-27 00:00:00
Tasks: Computational Efficiency, Deblurring, Demosaicking, Denoising, Image Restoration, Transfer Learning

Recently, several discriminative learning approaches have been proposed for
effective image restoration, achieving convincing trade-off between image
quality and computational efficiency. However, these methods require separate
training for each restoration task (e.g., denoising, deblurring, demosaicing)
and problem condition (e.g., noise level of input images). This makes it
time-consuming and difficult to encompass all tasks and conditions during
training. In this paper, we propose a discriminative transfer learning method
that incorporates formal proximal optimization and discriminative learning for
general image restoration. The method requires a single-pass training and
allows for reuse across various problems and conditions while achieving an
efficiency comparable to previous discriminative approaches. Furthermore, after
being trained, our model can be easily transferred to new likelihood terms to
solve untrained tasks, or be combined with existing priors to further improve
image restoration quality.
Adversarial Source Identification Game with Corrupted Training
Authors: Mauro Barni, Benedetta Tondi
Date: 2017-03-27 00:00:00
Tasks: LEMMA

We study a variant of the source identification game with training data in
which part of the training data is corrupted by an attacker. In the addressed
scenario, the defender aims at deciding whether a test sequence has been drawn
according to a discrete memoryless source $X \sim P_X$, whose statistics are
known to him through the observation of a training sequence generated by $X$.
In order to undermine the correct decision under the alternative hypothesis
that the test sequence has not been drawn from $X$, the attacker can modify a
sequence produced by a source $Y \sim P_Y$ up to a certain distortion, and
corrupt the training sequence either by adding some fake samples or by
replacing some samples with fake ones. We derive the unique rationalizable
equilibrium of the two versions of the game in the asymptotic regime and by
assuming that the defender bases its decision by relying only on the first
order statistics of the test and the training sequences. By mimicking Stein's
lemma, we derive the best achievable performance for the defender when the
first type error probability is required to tend to zero exponentially fast
with an arbitrarily small, yet positive, error exponent. We then use such a
result to analyze the ultimate distinguishability of any two sources as a
function of the allowed distortion and the fraction of corrupted samples
injected into the training sequence.
Biologically inspired protection of deep networks from adversarial attacks
Authors: Aran Nayebi, Surya Ganguli
Date: 2017-03-27 00:00:00
Tasks: Adversarial Attack, Second-order methods

Inspired by biophysical principles underlying nonlinear dendritic computation
in neural circuits, we develop a scheme to train deep neural networks to make
them robust to adversarial attacks. Our scheme generates highly nonlinear,
saturated neural networks that achieve state of the art performance on gradient
based adversarial examples on MNIST, despite never being exposed to
adversarially chosen examples during training. Moreover, these networks exhibit
unprecedented robustness to targeted, iterative schemes for generating
adversarial examples, including second-order methods. We further identify
principles governing how these networks achieve their robustness, drawing on
methods from information geometry. We find these networks progressively create
highly flat and compressed internal representations that are sensitive to very
few input dimensions, while still solving the task. Moreover, they employ
highly kurtotic weight distributions, also found in the brain, and we
demonstrate how such kurtosis can protect even linear classifiers from
adversarial attack.
The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation
Authors: Yuanhan Mo, Fangde Liu, Douglas McIlwraith, Guang Yang, Jingqing Zhang, Taigang He, Yike Guo
Date: 2017-03-27 00:00:00
Tasks: Left Ventricle Segmentation, LV Segmentation, Segmentation

Precise segmentation of the left ventricle (LV) within cardiac MRI images is
a prerequisite for the quantitative measurement of heart function. However,
this task is challenging due to the limited availability of labeled data and
motion artifacts from cardiac imaging. In this work, we present an iterative
segmentation algorithm for LV delineation. By coupling deep learning with a
novel dynamic-based labeling scheme, we present a new methodology where a
policy model is learned to guide an agent to travel over the the image, tracing
out a boundary of the ROI -- using the magnitude difference of the Poincar\'e
map as a stopping criterion. Our method is evaluated on two datasets, namely
the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV
segmentation challenge. Our method outperforms the previous research over many
metrics. In order to demonstrate the transferability of our method we present
encouraging results over the STACOM 2011 data, when using a model trained on
the SCD dataset.
Introduction To The Monogenic Signal
Authors: Christopher P. Bridge
Date: 2017-03-27 00:00:00
Tasks: Math

The monogenic signal is an image analysis methodology that was introduced by
Felsberg and Sommer in 2001 and has been employed for a variety of purposes in
image processing and computer vision research. In particular, it has been found
to be useful in the analysis of ultrasound imagery in several research
scenarios mostly in work done within the BioMedIA lab at Oxford. However, the
literature on the monogenic signal can be difficult to penetrate due to the
lack of a single resource to explain the various principles from basics. The
purpose of this document is therefore to introduce the principles, purpose,
applications, and limitations of the methodology. It assumes some background
knowledge from the fields of image and signal processing, in particular a good
knowledge of Fourier transforms as applied to signals and images. We will not
attempt to provide a thorough math- ematical description or derivation of the
monogenic signal, but rather focus on developing an intuition for understanding
and using the methodology and refer the reader elsewhere for a more
mathematical treatment.
Deep Architectures for Modulation Recognition
Authors: Nathan E West, Timothy J. O'Shea
Date: 2017-03-27 00:00:00
Tasks: BIG-bench Machine Learning, Survey

We survey the latest advances in machine learning with deep neural networks
by applying them to the task of radio modulation recognition. Results show that
radio modulation recognition is not limited by network depth and further work
should focus on improving learned synchronization and equalization. Advances in
these areas will likely come from novel architectures designed for these tasks
or through novel training methods.