pwc-archive/papers-with-abstracts huggingface dataset:
A 3D fully convolutional neural network and a random walker to segment the esophagus in CT
Authors: Tobias Fechter, Sonja Adebahr, Dimos Baltas, Ismail Ben Ayed, Christian Desrosiers, Jose Dolz
Date: 2017-04-21 00:00:00

Precise delineation of organs at risk (OAR) is a crucial task in radiotherapy
treatment planning, which aims at delivering high dose to the tumour while
sparing healthy tissues. In recent years algorithms showed high performance and
the possibility to automate this task for many OAR. However, for some OAR
precise delineation remains challenging. The esophagus with a versatile shape
and poor contrast is among these structures. To tackle these issues we propose
a 3D fully (convolutional neural network (CNN) driven random walk (RW) approach
to automatically segment the esophagus on CT. First, a soft probability map is
generated by the CNN. Then an active contour model (ACM) is fitted on the
probability map to get a first estimation of the center line. The outputs of
the CNN and ACM are then used in addition to CT Hounsfield values to drive the
RW. Evaluation and training was done on 50 CTs with peer reviewed esophagus
contours. Results were assessed regarding spatial overlap and shape
similarities.
  The generated contours showed a mean Dice coefficient of 0.76, an average
symmetric square distance of 1.36 mm and an average Hausdorff distance of 11.68
compared to the reference. These figures translate into a very good agreement
with the reference contours and an increase in accuracy compared to other
methods.
  We show that by employing a CNN accurate estimations of esophagus location
can be obtained and refined by a post processing RW step. One of the main
advantages compared to previous methods is that our network performs
convolutions in a 3D manner, fully exploiting the 3D spatial context and
performing an efficient and precise volume-wise prediction. The whole
segmentation process is fully automatic and yields esophagus delineations in
very good agreement with the used gold standard, showing that it can compete
with previously published methods.
Bandit Structured Prediction for Neural Sequence-to-Sequence Learning
Authors: Julia Kreutzer, Artem Sokolov, Stefan Riezler
Date: 2017-04-21 00:00:00
Proceeding: ACL 2017 7
Tasks: Domain Adaptation, Machine Translation, Stochastic Optimization, Structured Prediction, Translation

Bandit structured prediction describes a stochastic optimization framework
where learning is performed from partial feedback. This feedback is received in
the form of a task loss evaluation to a predicted output structure, without
having access to gold standard structures. We advance this framework by lifting
linear bandit learning to neural sequence-to-sequence learning problems using
attention-based recurrent neural networks. Furthermore, we show how to
incorporate control variates into our learning algorithms for variance
reduction and improved generalization. We present an evaluation on a neural
machine translation task that shows improvements of up to 5.89 BLEU points for
domain adaptation from simulated bandit feedback.
Recurrent Neural Networks to Correct Satellite Image Classification Maps
Authors: Emmanuel Maggiori, Guillaume Charpiat, Yuliya Tarabalka, Pierre Alliez
Date: 2016-08-11 00:00:00
Tasks: Classification, General Classification, Image Categorization, image-classification, Image Classification, Satellite Image Classification

While initially devised for image categorization, convolutional neural
networks (CNNs) are being increasingly used for the pixelwise semantic labeling
of images. However, the proper nature of the most common CNN architectures
makes them good at recognizing but poor at localizing objects precisely. This
problem is magnified in the context of aerial and satellite image labeling,
where a spatially fine object outlining is of paramount importance. Different
iterative enhancement algorithms have been presented in the literature to
progressively improve the coarse CNN outputs, seeking to sharpen object
boundaries around real image edges. However, one must carefully design, choose
and tune such algorithms. Instead, our goal is to directly learn the iterative
process itself. For this, we formulate a generic iterative enhancement process
inspired from partial differential equations, and observe that it can be
expressed as a recurrent neural network (RNN). Consequently, we train such a
network from manually labeled data for our enhancement task. In a series of
experiments we show that our RNN effectively learns an iterative process that
significantly improves the quality of satellite image classification maps.
A Domain Based Approach to Social Relation Recognition
Authors: Qianru Sun, Bernt Schiele, Mario Fritz
Date: 2017-04-21 00:00:00
Proceeding: CVPR 2017 7
Tasks: Attribute, Relation, Visual Social Relationship Recognition

Social relations are the foundation of human daily life. Developing
techniques to analyze such relations from visual data bears great potential to
build machines that better understand us and are capable of interacting with us
at a social level. Previous investigations have remained partial due to the
overwhelming diversity and complexity of the topic and consequently have only
focused on a handful of social relations. In this paper, we argue that the
domain-based theory from social psychology is a great starting point to
systematically approach this problem. The theory provides coverage of all
aspects of social relations and equally is concrete and predictive about the
visual attributes and behaviors defining the relations included in each domain.
We provide the first dataset built on this holistic conceptualization of social
life that is composed of a hierarchical label space of social domains and
social relations. We also contribute the first models to recognize such domains
and relations and find superior performance for attribute based features.
Beyond the encouraging performance of the attribute based approach, we also
find interpretable features that are in accordance with the predictions from
social psychology literature. Beyond our findings, we believe that our
contributions more tightly interleave visual recognition and social psychology
theory that has the potential to complement the theoretical work in the area
with empirical and data-driven models of social life.
UmUTracker: A versatile MATLAB program for automated particle tracking of 2D light microscopy or 3D digital holography data
Authors: Hanqing Zhang, Tim Stangner, Krister Wiklund, Alvaro Rodriguez, Magnus Andersson
Date: 2017-01-27 00:00:00
Tasks: Position

We present a versatile and fast MATLAB program (UmUTracker) that
automatically detects and tracks particles by analyzing video sequences
acquired by either light microscopy or digital in-line holographic microscopy.
Our program detects the 2D lateral positions of particles with an algorithm
based on the isosceles triangle transform, and reconstructs their 3D axial
positions by a fast implementation of the Rayleigh-Sommerfeld model using a
radial intensity profile. To validate the accuracy and performance of our
program, we first track the 2D position of polystyrene particles using bright
field and digital holographic microscopy. Second, we determine the 3D particle
position by analyzing synthetic and experimentally acquired holograms. Finally,
to highlight the full program features, we profile the microfluidic flow in a
100 micrometer high flow chamber. This result agrees with computational fluid
dynamic simulations. On a regular desktop computer UmUTracker can detect,
analyze, and track multiple particles at 5 frames per second for a template
size of 201 x 201 in a 1024 x 1024 image. To enhance usability and to make it
easy to implement new functions we used object-oriented programming. UmUTracker
is suitable for studies related to: particle dynamics, cell localization,
colloids and microfluidic flow measurement.
Weakly-Supervised Semantic Segmentation using Motion Cues
Authors: Pavel Tokmakov, Karteek Alahari, Cordelia Schmid
Date: 2016-03-23 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Fully convolutional neural networks (FCNNs) trained on a large number of
images with strong pixel-level annotations have become the new state of the art
for the semantic segmentation task. While there have been recent attempts to
learn FCNNs from image-level weak annotations, they need additional
constraints, such as the size of an object, to obtain reasonable performance.
To address this issue, we present motion-CNN (M-CNN), a novel FCNN framework
which incorporates motion cues and is learned from video-level weak
annotations. Our learning scheme to train the network uses motion segments as
soft constraints, thereby handling noisy motion information. When trained on
weakly-annotated videos, our method outperforms the state-of-the-art EM-Adapt
approach on the PASCAL VOC 2012 image segmentation benchmark. We also
demonstrate that the performance of M-CNN learned with 150 weak video
annotations is on par with state-of-the-art weakly-supervised methods trained
with thousands of images. Finally, M-CNN substantially outperforms recent
approaches in a related task of video co-localization on the YouTube-Objects
dataset.
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Authors: Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann Lecun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, Riccardo Zecchina
Date: 2016-11-06 00:00:00

This paper proposes a new optimization algorithm called Entropy-SGD for
training deep neural networks that is motivated by the local geometry of the
energy landscape. Local extrema with low generalization error have a large
proportion of almost-zero eigenvalues in the Hessian with very few positive or
negative eigenvalues. We leverage upon this observation to construct a
local-entropy-based objective function that favors well-generalizable solutions
lying in large flat regions of the energy landscape, while avoiding
poorly-generalizable solutions located in the sharp valleys. Conceptually, our
algorithm resembles two nested loops of SGD where we use Langevin dynamics in
the inner loop to compute the gradient of the local entropy before each update
of the weights. We show that the new objective has a smoother energy landscape
and show improved generalization over SGD using uniform stability, under
certain assumptions. Our experiments on convolutional and recurrent networks
demonstrate that Entropy-SGD compares favorably to state-of-the-art techniques
in terms of generalization error and training time.
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition
Authors: Sebastien C. Wong, Victor Stamatescu, Adam Gatt, David Kearney, Ivan Lee, Mark D. McDonnell
Date: 2017-04-21 00:00:00
Tasks: General Classification, Multi-Object Tracking, Object, Object Recognition, Object Tracking, Online Multi-Object Tracking, Visual Tracking

This paper addresses the problem of online tracking and classification of
multiple objects in an image sequence. Our proposed solution is to first track
all objects in the scene without relying on object-specific prior knowledge,
which in other systems can take the form of hand-crafted features or user-based
track initialization. We then classify the tracked objects with a fast-learning
image classifier that is based on a shallow convolutional neural network
architecture and demonstrate that object recognition improves when this is
combined with object state information from the tracking algorithm. We argue
that by transferring the use of prior knowledge from the detection and tracking
stages to the classification stage we can design a robust, general purpose
object recognition system with the ability to detect and track a variety of
object types. We describe our biologically inspired implementation, which
adaptively learns the shape and motion of tracked objects, and apply it to the
Neovision2 Tower benchmark data set, which contains multiple object types. An
experimental evaluation demonstrates that our approach is competitive with
state-of-the-art video object recognition systems that do make use of
object-specific prior knowledge in detection and tracking, while providing
additional practical advantages by virtue of its generality.
Neural System Combination for Machine Translation
Authors: Long Zhou, Wenpeng Hu, Jiajun Zhang, Cheng-qing Zong
Date: 2017-04-21 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) becomes a new approach to machine
translation and generates much more fluent results compared to statistical
machine translation (SMT).
  However, SMT is usually better than NMT in translation adequacy. It is
therefore a promising direction to combine the advantages of both NMT and SMT.
  In this paper, we propose a neural system combination framework leveraging
multi-source NMT, which takes as input the outputs of NMT and SMT systems and
produces the final translation.
  Extensive experiments on the Chinese-to-English translation task show that
our model archives significant improvement by 5.3 BLEU points over the best
single system output and 3.4 BLEU points over the state-of-the-art traditional
system combination methods.
Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation
Authors: Mohamed Elawady, Olivier Alata, Christophe Ducottet, Cecile Barat, Philippe Colantoni
Date: 2017-04-21 00:00:00
Tasks: Density Estimation, Symmetry Detection

Symmetry is an important composition feature by investigating similar sides
inside an image plane. It has a crucial effect to recognize man-made or nature
objects within the universe. Recent symmetry detection approaches used a
smoothing kernel over different voting maps in the polar coordinate system to
detect symmetry peaks, which split the regions of symmetry axis candidates in
inefficient way. We propose a reliable voting representation based on weighted
linear-directional kernel density estimation, to detect multiple symmetries
over challenging real-world and synthetic images. Experimental evaluation on
two public datasets demonstrates the superior performance of the proposed
algorithm to detect global symmetry axes respect to the major image shapes.
Hierarchical 3D fully convolutional networks for multi-organ segmentation
Authors: Holger R. Roth, Hirohisa ODA, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Michitaka Fujiwara, Kazunari Misawa, Kensaku MORI
Date: 2017-04-21 00:00:00
Tasks: Organ Segmentation

Recent advances in 3D fully convolutional networks (FCN) have made it
feasible to produce dense voxel-wise predictions of full volumetric images. In
this work, we show that a multi-class 3D FCN trained on manually labeled CT
scans of seven abdominal structures (artery, vein, liver, spleen, stomach,
gallbladder, and pancreas) can achieve competitive segmentation results, while
avoiding the need for handcrafting features or training organ-specific models.
To this end, we propose a two-stage, coarse-to-fine approach that trains an FCN
model to roughly delineate the organs of interest in the first stage (seeing
$\sim$40% of the voxels within a simple, automatically generated binary mask of
the patient's body). We then use these predictions of the first-stage FCN to
define a candidate region that will be used to train a second FCN. This step
reduces the number of voxels the FCN has to classify to $\sim$10% while
maintaining a recall high of $>$99%. This second-stage FCN can now focus on
more detailed segmentation of the organs. We respectively utilize training and
validation sets consisting of 281 and 50 clinical CT images. Our hierarchical
approach provides an improved Dice score of 7.5 percentage points per organ on
average in our validation set. We furthermore test our models on a completely
unseen data collection acquired at a different hospital that includes 150 CT
scans with three anatomical labels (liver, spleen, and pancreas). In such
challenging organs as the pancreas, our hierarchical approach improves the mean
Dice score from 68.5 to 82.2%, achieving the highest reported average score on
this dataset.
Joint Space Neural Probabilistic Language Model for Statistical Machine Translation
Authors: Tsuyoshi Okita
Date: 2013-01-16 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

A neural probabilistic language model (NPLM) provides an idea to achieve the
better perplexity than n-gram language model and their smoothed language
models. This paper investigates application area in bilingual NLP, specifically
Statistical Machine Translation (SMT). We focus on the perspectives that NPLM
has potential to open the possibility to complement potentially `huge'
monolingual resources into the `resource-constraint' bilingual resources. We
introduce an ngram-HMM language model as NPLM using the non-parametric Bayesian
construction. In order to facilitate the application to various tasks, we
propose the joint space model of ngram-HMM language model. We show an
experiment of system combination in the area of SMT. One discovery was that our
treatment of noise improved the results 0.20 BLEU points if NPLM is trained in
relatively small corpus, in our case 500,000 sentence pairs, which is often the
case due to the long training time of NPLM.
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The $\ell_0$ Method
Authors: Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A. Fessler
Date: 2015-11-27 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

The sparsity of natural signals and images in a transform domain or
dictionary has been extensively exploited in several applications such as
compression, denoising and inverse problems. More recently, data-driven
adaptation of synthesis dictionaries has shown promise in many applications
compared to fixed or analytical dictionary models. However, dictionary learning
problems are typically non-convex and NP-hard, and the usual alternating
minimization approaches for these problems are often computationally expensive,
with the computations dominated by the NP-hard synthesis sparse coding step. In
this work, we investigate an efficient method for $\ell_{0}$ "norm"-based
dictionary learning by first approximating the training data set with a sum of
sparse rank-one matrices and then using a block coordinate descent approach to
estimate the unknowns. The proposed block coordinate descent algorithm involves
efficient closed-form solutions. In particular, the sparse coding step involves
a simple form of thresholding. We provide a convergence analysis for the
proposed block coordinate descent approach. Our numerical experiments show the
promising performance and significant speed-ups provided by our method over the
classical K-SVD scheme in sparse signal representation and image denoising.
Improving Context Aware Language Models
Authors: Aaron Jaech, Mari Ostendorf
Date: 2017-04-21 00:00:00
Tasks: General Classification, Language Modeling, Language Modelling

Increased adaptability of RNN language models leads to improved predictions
that benefit many applications. However, current methods do not take full
advantage of the RNN structure. We show that the most widely-used approach to
adaptation (concatenating the context with the word embedding at the input to
the recurrent layer) is outperformed by a model that has some low-cost
improvements: adaptation of both the hidden and output layers. and a feature
hashing bias term to capture context idiosyncrasies. Experiments on language
modeling and classification tasks using three different corpora demonstrate the
advantages of the proposed techniques.
A data set for evaluating the performance of multi-class multi-object video tracking
Authors: Avishek Chakraborty, Victor Stamatescu, Sebastien C. Wong, Grant Wigley, David Kearney
Date: 2017-04-21 00:00:00
Tasks: Classification, General Classification, Multi-Object Tracking, Object, Object Tracking

One of the challenges in evaluating multi-object video detection, tracking
and classification systems is having publically available data sets with which
to compare different systems. However, the measures of performance for tracking
and classification are different. Data sets that are suitable for evaluating
tracking systems may not be appropriate for classification. Tracking video data
sets typically only have ground truth track IDs, while classification video
data sets only have ground truth class-label IDs. The former identifies the
same object over multiple frames, while the latter identifies the type of
object in individual frames. This paper describes an advancement of the ground
truth meta-data for the DARPA Neovision2 Tower data set to allow both the
evaluation of tracking and classification. The ground truth data sets presented
in this paper contain unique object IDs across 5 different classes of object
(Car, Bus, Truck, Person, Cyclist) for 24 videos of 871 image frames each. In
addition to the object IDs and class labels, the ground truth data also
contains the original bounding box coordinates together with new bounding boxes
in instances where un-annotated objects were present. The unique IDs are
maintained during occlusions between multiple objects or when objects re-enter
the field of view. This will provide: a solid foundation for evaluating the
performance of multi-object tracking of different types of objects, a
straightforward comparison of tracking system performance using the standard
Multi Object Tracking (MOT) framework, and classification performance using the
Neovision2 metrics. These data have been hosted publically.
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems
Authors: Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A. Fessler
Date: 2015-11-19 00:00:00
Tasks: compressed sensing, Denoising, Dictionary Learning, Image Reconstruction

The sparsity of signals in a transform domain or dictionary has been
exploited in applications such as compression, denoising and inverse problems.
More recently, data-driven adaptation of synthesis dictionaries has shown
promise compared to analytical dictionary models. However, dictionary learning
problems are typically non-convex and NP-hard, and the usual alternating
minimization approaches for these problems are often computationally expensive,
with the computations dominated by the NP-hard synthesis sparse coding step.
This paper exploits the ideas that drive algorithms such as K-SVD, and
investigates in detail efficient methods for aggregate sparsity penalized
dictionary learning by first approximating the data with a sum of sparse
rank-one matrices (outer products) and then using a block coordinate descent
approach to estimate the unknowns. The resulting block coordinate descent
algorithms involve efficient closed-form solutions. Furthermore, we consider
the problem of dictionary-blind image reconstruction, and propose novel and
efficient algorithms for adaptive image reconstruction using block coordinate
descent and sum of outer products methodologies. We provide a convergence study
of the algorithms for dictionary learning and dictionary-blind image
reconstruction. Our numerical experiments show the promising performance and
speed-ups provided by the proposed methods over previous schemes in sparse data
representation and compressed sensing-based image reconstruction.