pwc-archive/papers-with-abstracts huggingface dataset:
Unified Perceptual Parsing for Scene Understanding
Authors: Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: 2D Semantic Segmentation, Scene Understanding, Semantic Segmentation

Humans recognize the visual world at multiple levels: we effortlessly
categorize scenes and detect objects inside, while also identifying the
textures and surfaces of the objects along with their different compositional
parts. In this paper, we study a new task called Unified Perceptual Parsing,
which requires the machine vision systems to recognize as many visual concepts
as possible from a given image. A multi-task framework called UPerNet and a
training strategy are developed to learn from heterogeneous image annotations.
We benchmark our framework on Unified Perceptual Parsing and show that it is
able to effectively segment a wide range of concepts from images. The trained
networks are further applied to discover visual knowledge in natural scenes.
Models are available at \url{https://github.com/CSAILVision/unifiedparsing}.
DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning
Authors: Jen-Tang Lu, Stefano Pedemonte, Bernardo Bizzo, Sean Doyle, Katherine P. Andriole, Mark H. Michalski, R. Gilberto Gonzalez, Stuart R. Pomerantz
Date: 2018-07-26 00:00:00

The high prevalence of spinal stenosis results in a large volume of MRI
imaging, yet interpretation can be time-consuming with high inter-reader
variability even among the most specialized radiologists. In this paper, we
develop an efficient methodology to leverage the subject-matter-expertise
stored in large-scale archival reporting and image data for a deep-learning
approach to fully-automated lumbar spinal stenosis grading. Specifically, we
introduce three major contributions: (1) a natural-language-processing scheme
to extract level-by-level ground-truth labels from free-text radiology reports
for the various types and grades of spinal stenosis (2) accurate vertebral
segmentation and disc-level localization using a U-Net architecture combined
with a spine-curve fitting method, and (3) a multi-input, multi-task, and
multi-class convolutional neural network to perform central canal and foraminal
stenosis grading on both axial and sagittal imaging series inputs with the
extracted report-derived labels applied to corresponding imaging level
segments. This study uses a large dataset of 22796 disc-levels extracted from
4075 patients. We achieve state-of-the-art performance on lumbar spinal
stenosis classification and expect the technique will increase both radiology
workflow efficiency and the perceived value of radiology reports for referring
clinicians and patients.
Robust Tracking via Weighted Online Extreme Learning Machine
Authors: Jing Zhang, Huibing Wang, Yong-Gong Ren
Date: 2018-07-26 00:00:00
Tasks: Classification, General Classification, imbalanced classification, Incremental Learning

The tracking method based on the extreme learning machine (ELM) is efficient
and effective. ELM randomly generates input weights and biases in the hidden
layer, and then calculates and computes the output weights by reducing the
iterative solution to the problem of linear equations. Therefore, ELM offers
the satisfying classification performance and fast training time than other
discriminative models in tracking. However, the original ELM method often
suffers from the problem of the imbalanced classification distribution, which
is caused by few target objects, leading to under-fitting and more background
samples leading to over-fitting. Worse still, it reduces the robustness of
tracking under special conditions including occlusion, illumination, etc. To
address above problems, in this paper, we present a robust tracking algorithm.
First, we introduce the local weight matrix that is the dynamic creation from
the data distribution at the current frame in the original ELM so as to balance
between the empirical and structure risk, and fully learn the target object to
enhance the classification performance. Second, we improve it to the
incremental learning method ensuring tracking real-time and efficient. Finally,
the forgetting factor is used to strengthen the robustness for changing of the
classification distribution with time. Meanwhile, we propose a novel optimized
method to obtain the optimal sample as the target object, which avoids tracking
drift resulting from noisy samples. Therefore, our tracking method can fully
learn both of the target object and background information to enhance the
tracking performance, and it is evaluated in 20 challenge image sequences with
different attributes including illumination, occlusion, deformation, etc.,
which achieves better performance than several state-of-the-art methods in
terms of effectiveness and robustness.
A Style-Aware Content Loss for Real-time HD Style Transfer
Authors: Artsiom Sanakoyeu, Dmytro Kotovenko, Sabine Lang, Björn Ommer
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Image Stylization, Style Transfer, Video Style Transfer

Recently, style transfer has received a lot of attention. While much of this
research has aimed at speeding up processing, the approaches are still lacking
from a principled, art historical standpoint: a style is more than just a
single image or an artist, but previous work is limited to only a single
instance of a style or shows no benefit from more images. Moreover, previous
work has relied on a direct comparison of art in the domain of RGB images or on
CNNs pre-trained on ImageNet, which requires millions of labeled object
bounding boxes and can introduce an extra bias, since it has been assembled
without artistic consideration. To circumvent these issues, we propose a
style-aware content loss, which is trained jointly with a deep encoder-decoder
network for real-time, high-resolution stylization of images and videos. We
propose a quantitative measure for evaluating the quality of a stylized image
and also have art historians rank patches from our approach against those from
previous work. These and our qualitative results ranging from small image
patches to megapixel stylistic images and videos show that our approach better
captures the subtle nature in which a style affects content.
Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation
Authors: Xiaohao Cai, Raymond Chan, Carola-Bibiane Schonlieb, Gabriele Steidl, Tieyong Zeng
Date: 2018-07-26 00:00:00
Tasks: Image Restoration, Image Segmentation, model, Segmentation, Semantic Segmentation, valid

The piecewise constant Mumford-Shah (PCMS) model and the Rudin-Osher-Fatemi (ROF) model are two important variational models in image segmentation and image restoration, respectively. In this paper, we explore a linkage between these models. We prove that for the two-phase segmentation problem a partial minimizer of the PCMS model can be obtained by thresholding the minimizer of the ROF model. A similar linkage is still valid for multiphase segmentation under specific assumptions. Thus it opens a new segmentation paradigm: image segmentation can be done via image restoration plus thresholding. This new paradigm, which circumvents the innate non-convex property of the PCMS model, therefore improves the segmentation performance in both efficiency (much faster than state-of-the-art methods based on PCMS model, particularly when the phase number is high) and effectiveness (producing segmentation results with better quality) due to the flexibility of the ROF model in tackling degraded images, such as noisy images, blurry images or images with information loss. As a by-product of the new paradigm, we derive a novel segmentation method, called thresholded-ROF (T-ROF) method, to illustrate the virtue of managing image segmentation through image restoration techniques. The convergence of the T-ROF method is proved, and elaborate experimental results and comparisons are presented.
Superpixel Sampling Networks
Authors: Varun Jampani, Deqing Sun, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Segmentation, Superpixels

Superpixels provide an efficient low/mid-level representation of image data,
which greatly reduces the number of image primitives for subsequent vision
tasks. Existing superpixel algorithms are not differentiable, making them
difficult to integrate into otherwise end-to-end trainable deep neural
networks. We develop a new differentiable model for superpixel sampling that
leverages deep networks for learning superpixel segmentation. The resulting
"Superpixel Sampling Network" (SSN) is end-to-end trainable, which allows
learning task-specific superpixels with flexible loss functions and has fast
runtime. Extensive experimental analysis indicates that SSNs not only
outperform existing superpixel algorithms on traditional segmentation
benchmarks, but can also learn superpixels for other tasks. In addition, SSNs
can be easily integrated into downstream deep networks resulting in performance
improvements.
Differential Analysis of Directed Networks
Authors: Min Ren, Dabao Zhang
Date: 2018-07-26 00:00:00
Tasks: regression

We developed a novel statistical method to identify structural differences
between networks characterized by structural equation models. We propose to
reparameterize the model to separate the differential structures from common
structures, and then design an algorithm with calibration and construction
stages to identify these differential structures. The calibration stage serves
to obtain consistent prediction by building the L2 regularized regression of
each endogenous variables against pre-screened exogenous variables, correcting
for potential endogeneity issue. The construction stage consistently selects
and estimates both common and differential effects by undertaking L1
regularized regression of each endogenous variable against the predicts of
other endogenous variables as well as its anchoring exogenous variables. Our
method allows easy parallel computation at each stage. Theoretical results are
obtained to establish nonasymptotic error bounds of predictions and estimates
at both stages, as well as the consistency of identified common and
differential effects. Our studies on synthetic data demonstrated that our
proposed method performed much better than independently constructing the
networks. A real data set is analyzed to illustrate the applicability of our
method.
Rademacher Generalization Bounds for Classifier Chains
Authors: Moura Simon, Amini Massih-Reza, Louhichi Sana, Clausel Marianne
Date: 2018-07-26 00:00:00
Tasks: Generalization Bounds

In this paper, we propose a new framework to study the generalization
property of classifier chains trained over observations associated with
multiple and interdependent class labels. The results are based on large
deviation inequalities for Lipschitz functions of weakly dependent sequences
proposed by Rio in 2000. We believe that the resulting generalization error
bound brings many advantages and could be adapted to other frameworks that
consider interdependent outputs. First, it explicitly exhibits the dependencies
between class labels. Secondly, it provides insights of the effect of the order
of the chain on the algorithm generalization performances. Finally, the two
dependency coefficients that appear in the bound could also be used to design
new strategies to decide the order of the chain.
Fast and Accurate Intrinsic Symmetry Detection
Authors: Rajendra Nagar, Shanmuganathan Raman
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Symmetry Detection

In computer vision and graphics, various types of symmetries are extensively
studied since symmetry present in objects is a fundamental cue for
understanding the shape and the structure of objects. In this work, we detect
the intrinsic reflective symmetry in triangle meshes where we have to find the
intrinsically symmetric point for each point of the shape. We establish
correspondences between functions defined on the shapes by extending the
functional map framework and then recover the point-to-point correspondences.
Previous approaches using the functional map for this task find the functional
correspondences matrix by solving a non-linear optimization problem which makes
them slow. In this work, we propose a closed form solution for this matrix
which makes our approach faster. We find the closed-form solution based on our
following results. If the given shape is intrinsically symmetric, then the
shortest length geodesic between two intrinsically symmetric points is also
intrinsically symmetric. If an eigenfunction of the Laplace-Beltrami operator
for the given shape is an even (odd) function, then its restriction on the
shortest length geodesic between two intrinsically symmetric points is also an
even (odd) function. The sign of a low-frequency eigenfunction is the same on
the neighboring points. Our method is invariant to the ordering of the
eigenfunctions and has the least time complexity. We achieve the best
performance on the SCAPE dataset and comparable performance with the
state-of-the-art methods on the TOSCA dataset.
Adaptively Transforming Graph Matching
Authors: Fu-Dong Wang, Nan Xue, Yi-Peng Zhang, Xiang Bai, Gui-Song Xia
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Domain Adaptation, Graph Matching

Recently, many graph matching methods that incorporate pairwise constraint
and that can be formulated as a quadratic assignment problem (QAP) have been
proposed. Although these methods demonstrate promising results for the graph
matching problem, they have high complexity in space or time. In this paper, we
introduce an adaptively transforming graph matching (ATGM) method from the
perspective of functional representation. More precisely, under a
transformation formulation, we aim to match two graphs by minimizing the
discrepancy between the original graph and the transformed graph. With a linear
representation map of the transformation, the pairwise edge attributes of
graphs are explicitly represented by unary node attributes, which enables us to
reduce the space and time complexity significantly. Due to an efficient
Frank-Wolfe method-based optimization strategy, we can handle graphs with
hundreds and thousands of nodes within an acceptable amount of time. Meanwhile,
because transformation map can preserve graph structures, a domain
adaptation-based strategy is proposed to remove the outliers. The experimental
results demonstrate that our proposed method outperforms the state-of-the-art
graph matching algorithms.
A Benchmark of Selected Algorithmic Differentiation Tools on Some Problems in Computer Vision and Machine Learning
Authors: Filip Šrajer, Zuzana Kukelova, Andrew Fitzgibbon
Date: 2018-07-26 00:00:00
Tasks: BIG-bench Machine Learning

Algorithmic differentiation (AD) allows exact computation of derivatives
given only an implementation of an objective function. Although many AD tools
are available, a proper and efficient implementation of AD methods is not
straightforward. The existing tools are often too different to allow for a
general test suite. In this paper, we compare fifteen ways of computing
derivatives including eleven automatic differentiation tools implementing
various methods and written in various languages (C++, F#, MATLAB, Julia and
Python), two symbolic differentiation tools, finite differences, and
hand-derived computation.
  We look at three objective functions from computer vision and machine
learning. These objectives are for the most part simple, in the sense that no
iterative loops are involved, and conditional statements are encapsulated in
functions such as {\tt abs} or {\tt logsumexp}. However, it is important for
the success of algorithmic differentiation that such `simple' objective
functions are handled efficiently, as so many problems in computer vision and
machine learning are of this form.
  Of course, our results depend on programmer skill, and familiarity with the
tools. However, we contend that this paper presents an important datapoint: a
skilled programmer devoting roughly a week to each tool produced the timings we
present. We have made our implementations available as open source to allow the
community to replicate and update these benchmarks.
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks
Authors: Yuzhe Ma, Ran Chen, Wei Li, Fanhua Shang, Wenjian Yu, Minsik Cho, Bei Yu
Date: 2018-07-26 00:00:00
Tasks: General Classification, image-classification, Image Classification, Model Compression

Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference. Both low-rankness and sparsity are appealing properties for the network approximation. In this paper we propose a unified framework to compress the convolutional neural networks (CNNs) by combining these two properties, while taking the nonlinear activation into consideration. Each layer in the network is approximated by the sum of a structured sparse component and a low-rank component, which is formulated as an optimization problem. Then, an extended version of alternating direction method of multipliers (ADMM) with guaranteed convergence is presented to solve the relaxed optimization problem. Experiments are carried out on VGG-16, AlexNet and GoogLeNet with large image classification datasets. The results outperform previous work in terms of accuracy degradation, compression rate and speedup ratio. The proposed method is able to remarkably compress the model (with up to 4.9x reduction of parameters) at a cost of little loss or without loss on accuracy.
Effectiveness of Scaled Exponentially-Regularized Linear Units (SERLUs)
Authors: G. Zhang, H. Li
Date: 2018-07-26 00:00:00

Recently, self-normalizing neural networks (SNNs) have been proposed with the
intention to avoid batch or weight normalization. The key step in SNNs is to
properly scale the exponential linear unit (referred to as SELU) to inherently
incorporate normalization based on central limit theory. SELU is a
monotonically increasing function, where it has an approximately constant
negative output for large negative input. In this work, we propose a new
activation function to break the monotonicity property of SELU while still
preserving the self-normalizing property. Differently from SELU, the new
function introduces a bump-shaped function in the region of negative input by
regularizing a linear function with a scaled exponential function, which is
referred to as a scaled exponentially-regularized linear unit (SERLU). The
bump-shaped function has approximately zero response to large negative input
while being able to push the output of SERLU towards zero mean statistically.
To effectively combat over-fitting, we develop a so-called shift-dropout for
SERLU, which includes standard dropout as a special case. Experimental results
on MNIST, CIFAR10 and CIFAR100 show that SERLU-based neural networks provide
consistently promising results in comparison to other 5 activation functions
including ELU, SELU, Swish, Leakly ReLU and ReLU.
MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net For Multi-Modal Alzheimer's Classification
Authors: Apoorva Sikka, Skand Vishwanath Peri, Deepti. R. Bathula
Date: 2018-07-26 00:00:00
Tasks: General Classification, SSIM

Recent studies suggest that combined analysis of Magnetic resonance
imaging~(MRI) that measures brain atrophy and positron emission
tomography~(PET) that quantifies hypo-metabolism provides improved accuracy in
diagnosing Alzheimer's disease. However, such techniques are limited by the
availability of corresponding scans of each modality. Current work focuses on a
cross-modal approach to estimate FDG-PET scans for the given MR scans using a
3D U-Net architecture. The use of the complete MR image instead of a local
patch based approach helps in capturing non-local and non-linear correlations
between MRI and PET modalities. The quality of the estimated PET scans is
measured using quantitative metrics such as MAE, PSNR and SSIM. The efficacy of
the proposed method is evaluated in the context of Alzheimer's disease
classification. The accuracy using only MRI is 70.18% while joint
classification using synthesized PET and MRI is 74.43% with a p-value of
$0.06$. The significant improvement in diagnosis demonstrates the utility of
the synthesized PET scans for multi-modal analysis.
ToriLLE: Learning Environment for Hand-to-Hand Combat
Authors: Anssi Kanervisto, Ville Hautamäki
Date: 2018-07-26 00:00:00
Tasks: BIG-bench Machine Learning, MuJoCo

We present Toribash Learning Environment (ToriLLE), a learning environment for machine learning agents based on the video game Toribash. Toribash is a MuJoCo-like environment of two humanoid character fighting each other hand-to-hand, controlled by changing actuation modes of the joints. Competitive nature of Toribash as well its focused domain provide a platform for evaluating self-play methods, and evaluating machine learning agents against human players. In this paper we describe the environment with ToriLLE's capabilities and limitations, and experimentally show its applicability as a learning environment. The source code of the environment and conducted experiments can be found at https://github.com/Miffyli/ToriLLE.
Effects of Degradations on Deep Neural Network Architectures
Authors: Prasun Roy, Subhankar Ghosh, Saumik Bhattacharya, Umapada Pal
Date: 2018-07-26 00:00:00
Tasks: image-classification, Image Classification

Deep convolutional neural networks (CNN) have massively influenced recent advances in large-scale image classification. More recently, a dynamic routing algorithm with capsules (groups of neurons) has shown state-of-the-art recognition performance. However, the behavior of such networks in the presence of a degrading signal (noise) is mostly unexplored. An analytical study on different network architectures toward noise robustness is essential for selecting the appropriate model in a specific application scenario. This paper presents an extensive performance analysis of six deep architectures for image classification on six most common image degradation models. In this study, we have compared VGG-16, VGG-19, ResNet-50, Inception-v3, MobileNet and CapsuleNet architectures on Gaussian white, Gaussian color, salt-and-pepper, Gaussian blur, motion blur and JPEG compression noise models.
Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
Authors: Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan, Yoav Goldberg, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
Date: 2018-07-26 00:00:00

We present SetExpander, a corpus-based system for expanding a seed set of
terms into a more complete set of terms that belong to the same semantic class.
SetExpander implements an iterative end-to end workflow for term set expansion.
It enables users to easily select a seed set of terms, expand it, view the
expanded set, validate it, re-expand the validated set and store it, thus
simplifying the extraction of domain-specific fine-grained semantic classes.
SetExpander has been used for solving real-life use cases including integration
in an automated recruitment system and an issues and defects resolution system.
A video demo of SetExpander is available at
https://drive.google.com/open?id=1e545bB87Autsch36DjnJHmq3HWfSd1Rv (some images
were blurred for privacy reasons).