pwc-archive/papers-with-abstracts huggingface dataset:
Public Sphere 2.0: Targeted Commenting in Online News Media
Authors: Ankan Mullick, Sayan Ghosh, Ritam Dutt, Avijit Ghosh, Abhijnan Chakraborty
Date: 2019-02-21 00:00:00
Tasks: Articles

With the increase in online news consumption, to maximize advertisement
revenue, news media websites try to attract and retain their readers on their
sites. One of the most effective tools for reader engagement is commenting,
where news readers post their views as comments against the news articles.
Traditionally, it has been assumed that the comments are mostly made against
the full article. In this work, we show that present commenting landscape is
far from this assumption. Because the readers lack the time to go over an
entire article, most of the comments are relevant to only particular sections
of an article. In this paper, we build a system which can automatically
classify comments against relevant sections of an article. To implement that,
we develop a deep neural network based mechanism to find comments relevant to
any section and a paragraph wise commenting interface to showcase them. We
believe that such a data driven commenting system can help news websites to
further increase reader engagement.
Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
Authors: Zeyu Cui, Zekun Li, Shu Wu, Xiao-Yu Zhang, Liang Wang
Date: 2019-02-21 00:00:00
Tasks: Recommendation Systems

With the rapid development of fashion market, the customers' demands of
customers for fashion recommendation are rising. In this paper, we aim to
investigate a practical problem of fashion recommendation by answering the
question "which item should we select to match with the given fashion items and
form a compatible outfit". The key to this problem is to estimate the outfit
compatibility. Previous works which focus on the compatibility of two items or
represent an outfit as a sequence fail to make full use of the complex
relations among items in an outfit. To remedy this, we propose to represent an
outfit as a graph. In particular, we construct a Fashion Graph, where each node
represents a category and each edge represents interaction between two
categories. Accordingly, each outfit can be represented as a subgraph by
putting items into their corresponding category nodes. To infer the outfit
compatibility from such a graph, we propose Node-wise Graph Neural Networks
(NGNN) which can better model node interactions and learn better node
representations. In NGNN, the node interaction on each edge is different, which
is determined by parameters correlated to the two connected nodes. An attention
mechanism is utilized to calculate the outfit compatibility score with learned
node representations. NGNN can not only be used to model outfit compatibility
from visual or textual modality but also from multiple modalities. We conduct
experiments on two tasks: (1) Fill-in-the-blank: suggesting an item that
matches with existing components of outfit; (2) Compatibility prediction:
predicting the compatibility scores of given outfits. Experimental results
demonstrate the great superiority of our proposed method over others.
Adversarial Augmentation for Enhancing Classification of Mammography Images
Authors: Lukas Jendele, Ondrej Skopek, Anton S. Becker, Ender Konukoglu
Date: 2019-02-20 00:00:00
Tasks: Cancer Classification, Classification, General Classification, Image Augmentation, image-classification, Image Classification

Supervised deep learning relies on the assumption that enough training data
is available, which presents a problem for its application to several fields,
like medical imaging. On the example of a binary image classification task
(breast cancer recognition), we show that pretraining a generative model for
meaningful image augmentation helps enhance the performance of the resulting
classifier. By augmenting the data, performance on downstream classification
tasks could be improved even with a relatively small training set. We show that
this "adversarial augmentation" yields promising results compared to classical
image augmentation on the example of breast cancer classification.
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods
Authors: Xingtong Liu, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Austin Reiter, Russell H. Taylor, Mathias Unberath
Date: 2019-02-20 00:00:00
Tasks: Anatomy, Computed Tomography (CT), Depth Estimation, Self-Supervised Learning

We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires monocular endoscopic videos and a multi-view stereo method, e.g., structure from motion, to supervise learning in a sparse manner. Consequently, our method requires neither manual labeling nor patient computed tomography (CT) scan in the training and application phases. In a cross-patient experiment using CT scans as groundtruth, the proposed method achieved submillimeter mean residual error. In a comparison study to recent self-supervised depth estimation methods designed for natural video on in vivo sinus endoscopy data, we demonstrate that the proposed approach outperforms the previous methods by a large margin. The source code for this work is publicly available online at https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch.
Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Authors: Diego Gragnaniello, Francesco Marra, Giovanni Poggi, Luisa Verdoliva
Date: 2019-02-20 00:00:00
Tasks: Face Recognition, General Classification, image-classification, Image Classification

Deep neural networks provide unprecedented performance in all image classification problems, taking advantage of huge amounts of data available for training. Recent studies, however, have shown their vulnerability to adversarial attacks, spawning an intense research effort in this field. With the aim of building better systems, new countermeasures and stronger attacks are proposed by the day. On the attacker's side, there is growing interest for the realistic black-box scenario, in which the user has no access to the neural network parameters. The problem is to design efficient attacks which mislead the neural network without compromising image quality. In this work, we propose to perform the black-box attack along a low-distortion path, so as to improve both the attack efficiency and the perceptual quality of the adversarial image. Numerical experiments on real-world systems prove the effectiveness of the proposed approach, both in benchmark classification tasks and in key applications in biometrics and forensics.
Class-independent sequential full image segmentation, using a convolutional net that finds a segment within an attention region, given a pointer pixel within this segment
Authors: Sagi Eppel
Date: 2019-02-20 00:00:00
Tasks: Image Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation

This work examines the use of a fully convolutional net (FCN) to find an
image segment, given a pixel within this segment region. The net receives an
image, a point in the image and a region of interest (RoI ) mask. The net
output is a binary mask of the segment in which the point is located. The
region where the segment can be found is contained within the input RoI mask.
Full image segmentation can be achieved by running this net sequentially,
region-by-region on the image, and stitching the output segments into a single
segmentation map. This simple method addresses two major challenges of image
segmentation: 1) Segmentation of unknown categories that were not included in
the training set. 2) Segmentation of both individual object instances (things)
and non-objects (stuff), such as sky and vegetation. Hence, if the pointer
pixel is located within a person in a group, the net will output a mask that
covers that individual person; if the pointer point is located within the sky
region, the net returns the region of the sky in the image. This is true even
if no example for sky or person appeared in the training set. The net was
tested and trained on the COCO panoptic dataset and achieved 67% IOU for
segmentation of familiar classes (that were part of the net training set) and
53% IOU for segmentation of unfamiliar classes (that were not included in the
training).
Cascade Feature Aggregation for Human Pose Estimation
Authors: Zhihui Su, Ming Ye, Guohui Zhang, Lei Dai, Jianda Sheng
Date: 2019-02-21 00:00:00
Tasks: Pose Estimation

Human pose estimation plays an important role in many computer vision tasks and has been studied for many decades. However, due to complex appearance variations from poses, illuminations, occlusions and low resolutions, it still remains a challenging problem. Taking the advantage of high-level semantic information from deep convolutional neural networks is an effective way to improve the accuracy of human pose estimation. In this paper, we propose a novel Cascade Feature Aggregation (CFA) method, which cascades several hourglass networks for robust human pose estimation. Features from different stages are aggregated to obtain abundant contextual information, leading to robustness to poses, partial occlusions and low resolution. Moreover, results from different stages are fused to further improve the localization accuracy. The extensive experiments on MPII datasets and LIP datasets demonstrate that our proposed CFA outperforms the state-of-the-art and achieves the best performance on the state-of-the-art benchmark MPII.
Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework
Authors: Lei Li, Fuping Wu, Guang Yang, Lingchao Xu, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Xiahai Zhuang
Date: 2019-02-21 00:00:00
Tasks: Prognosis

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to
be a promising alternative for scar assessment in patients with atrial
fibrillation (AF). Automating the quantification and analysis of atrial scars
can be challenging due to the low image quality. In this work, we propose a
fully automated method based on the graph-cuts framework, where the potentials
of the graph are learned on a surface mesh of the left atrium (LA) using a
multi-scale convolutional neural network (MS-CNN). For validation, we have
employed fifty-eight images with manual delineations. MS-CNN, which can
efficiently incorporate both the local and global texture information of the
images, has been shown to evidently improve the segmentation accuracy of the
proposed graph-cuts based method. The segmentation could be further improved
when the contribution between the t-link and n-link weights of the graph is
balanced. The proposed method achieves a mean accuracy of 0.856 +- 0.033 and
mean Dice score of 0.702 +- 0.071 for LA scar quantification. Compared with the
conventional methods, which are based on the manual delineation of LA for
initialization, our method is fully automatic and has demonstrated
significantly better Dice score and accuracy (p < 0.01). The method is
promising and can be useful in diagnosis and prognosis of AF.
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
Authors: Xiahai Zhuang, Lei LI, Christian Payer, Darko Stern, Martin Urschler, Mattias P. Heinrich, Julien Oster, Chunliang Wang, Orjan Smedby, Cheng Bian, Xin Yang, Pheng-Ann Heng, Aliasghar Mortazi, Ulas Bagci, Guanyu Yang, Chenchen Sun, Gaetan Galisot, Jean-Yves Ramel, Thierry Brouard, Qianqian Tong, Weixin Si, Xiangyun Liao, Guodong Zeng, Zenglin Shi, Guoyan Zheng, Chengjia Wang, Tom MacGillivray, David Newby, Kawal Rhode, Sebastien Ourselin, Raad Mohiaddin, Jennifer Keegan, David Firmin, Guang Yang
Date: 2019-02-21 00:00:00
Tasks: Anatomy, Benchmarking, Heart Segmentation, Segmentation

Knowledge of whole heart anatomy is a prerequisite for many clinical
applications. Whole heart segmentation (WHS), which delineates substructures of
the heart, can be very valuable for modeling and analysis of the anatomy and
functions of the heart. However, automating this segmentation can be arduous
due to the large variation of the heart shape, and different image qualities of
the clinical data. To achieve this goal, a set of training data is generally
needed for constructing priors or for training. In addition, it is difficult to
perform comparisons between different methods, largely due to differences in
the datasets and evaluation metrics used. This manuscript presents the
methodologies and evaluation results for the WHS algorithms selected from the
submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge,
in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional
cardiac images covering the whole heart, including 60 CT and 60 MRI volumes,
all acquired in clinical environments with manual delineation. Ten algorithms
for CT data and eleven algorithms for MRI data, submitted from twelve groups,
have been evaluated. The results show that many of the deep learning (DL) based
methods achieved high accuracy, even though the number of training datasets was
limited. A number of them also reported poor results in the blinded evaluation,
probably due to overfitting in their training. The conventional algorithms,
mainly based on multi-atlas segmentation, demonstrated robust and stable
performance, even though the accuracy is not as good as the best DL method in
CT segmentation. The challenge, including the provision of the annotated
training data and the blinded evaluation for submitted algorithms on the test
data, continues as an ongoing benchmarking resource via its homepage
(\url{www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/}).
Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices
Authors: Guilei Hu, Yang Xiao, Zhiguo Cao, Lubin Meng, Zhiwen Fang, Joey Tianyi Zhou, Junsong Yuan
Date: 2019-02-21 00:00:00
Tasks: Attribute, Deception Detection, Eyeblink detection, Face Anti-Spoofing

Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing, etc. Although numerous of efforts have already been paid, most of them focus on addressing the eyeblink detection problem under the constrained indoor conditions with the relative consistent subject and environment setup. Nevertheless, towards the practical applications eyeblink detection in the wild is more required, and of greater challenges. However, to our knowledge this has not been well studied before. In this paper, we shed the light to this research topic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673 eyeblink video samples (i.e., 381 positives, and 292 negatives) is first established by us. These samples are captured from the unconstrained movies, with the dramatic variation on human attribute, human pose, illumination condition, imaging configuration, etc. Then, we formulate eyeblink detection task as a spatial-temporal pattern recognition problem. After locating and tracking human eye using SeetaFace engine and KCF tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to execute eyeblink verification. A feature extraction approach that reveals appearance and motion characteristics simultaneously is also proposed. The experiments on HUST-LEBW reveal the superiority and efficiency of our approach. It also verifies that, the existing eyeblink detection methods cannot achieve satisfactory performance in the wild.
Long-Bone Fracture Detection using Artificial Neural Networks based on Contour Features of X-ray Images
Authors: Alice Yi Yang, Ling Cheng
Date: 2019-02-21 00:00:00
Tasks: Clustering, Fracture detection

The following paper proposes two contour-based fracture detection schemes.
The development of the contour-based fracture is based on the line-based
fracture detection schemes proposed in arXiv:1902.07458. Existing Computer
Aided Diagnosis (CAD) systems commonly employs Convolutional Neural Networks
(CNN), although the cost to obtain a high accuracy is the amount of training
data required. The purpose of the proposed schemes is to obtain a high
classification accuracy with a reduced number of training data through the use
of detected contours in X-ray images. There are two contour-based fracture
detection schemes. The first is the Standard Contour Histogram Feature-Based
(CHFB) and the second is the improved CHFB scheme. The difference between the
two schemes is the removal of the surrounding detected flesh contours from the
leg region in the improved CHFB scheme. The flesh contours are automatically
classified as non-fractures. The contours are further refined to give a precise
representation of the image edge objects. A total of 19 features are extracted
from each refined contour. 8 out of the 19 features are based on the number of
occurrences for particular detected gradients in the contour. Moreover, the
occurrence of the 0-degree gradient in the contours are employed for the
separation of the knee, leg and foot region. The features are a summary
representation of the contour, in which it is used as inputs into the
Artificial Neural Network (ANN). Both Standard CHFB and improved CHFB schemes
are evaluated with the same experimental set-ups. The average system accuracy
for the Standard CHFB scheme is 80.7%, whilst the improved CHFB scheme has an
average accuracy of 82.98%. Additionally, the hierarchical clustering technique
is adopted to highlight the fractured region within the X-ray image, using
extracted 0-degree gradients from fractured contours.
ComplexFace: a Multi-Representation Approach for Image Classification with Small Dataset
Authors: Guiying Zhang, Yuxin Cui, Yong Zhao, Jianjun Hu
Date: 2019-02-21 00:00:00
Tasks: Data Augmentation, Face Recognition, General Classification, image-classification, Image Classification

State-of-the-art face recognition algorithms are able to achieve good performance when sufficient training images are provided. Unfortunately, the number of facial images is limited in some real face recognition applications. In this paper, we propose ComplexFace, a novel and effective algorithm for face recognition with limited samples using complex number based data augmentation. The algorithm first generates new representations from original samples and then fuse both into complex numbers, which avoids the difficulty of weight setting in other fusion approaches. A test sample can then be expressed by the linear combination of all the training samples, which mapped the sample to the new representation space for classification by the kernel function. The collaborative representation based classifier is then built to make predictions. Extensive experiments on the Georgia Tech (GT) face database and the ORL face database show that our algorithm significantly outperforms existing methods: the average errors of previous approaches ranging from 31.66% to 41.75% are reduced to 14.54% over the GT database; the average errors of previous approaches ranging from 5.21% to 10.99% are reduced to 1.67% over the ORL database. In other words, our algorithm has decreased the average errors by up to 84.80% on the ORL database.
Deep Discriminative Representation Learning with Attention Map for Scene Classification
Authors: Jun Li, Daoyu Lin, Yang Wang, Guangluan Xu, Chibiao Ding
Date: 2019-02-21 00:00:00
Tasks: Classification, Face Recognition, General Classification, Representation Learning, Scene Classification

Learning powerful discriminative features for remote sensing image scene
classification is a challenging computer vision problem. In the past, most
classification approaches were based on handcrafted features. However, most
recent approaches to remote sensing scene classification are based on
Convolutional Neural Networks (CNNs). The de facto practice when learning these
CNN models is only to use original RGB patches as input with training performed
on large amounts of labeled data (ImageNet). In this paper, we show class
activation map (CAM) encoded CNN models, codenamed DDRL-AM, trained using
original RGB patches and attention map based class information provide
complementary information to the standard RGB deep models. To the best of our
knowledge, we are the first to investigate attention information encoded CNNs.
Additionally, to enhance the discriminability, we further employ a recently
developed object function called "center loss," which has proved to be very
useful in face recognition. Finally, our framework provides attention guidance
to the model in an end-to-end fashion. Extensive experiments on two benchmark
datasets show that our approach matches or exceeds the performance of other
methods.
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation
Authors: Nadja Gruber, Stephan Antholzer, Werner Jaschke, Christian Kremser, Markus Haltmeier
Date: 2019-02-21 00:00:00
Tasks: Prognosis, Segmentation, Tumor Segmentation

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis. The segmentation of liver lesions in CT images allows assessment of tumor load, treatment planning, prognosis and monitoring of treatment response. Manual segmentation is a very time-consuming task and in many cases, prone to inaccuracies and automatic tools for tumor detection and segmentation are desirable. In this paper, we compare two network architectures, one that is composed of one neural network and manages the segmentation task in one step and one that consists of two consecutive fully convolutional neural networks. The first network segments the liver whereas the second network segments the actual tumor inside the liver. Our networks are trained on a subset of the LiTS (Liver Tumor Segmentation) Challenge and evaluated on data.
Multiple-image encryption and hiding with an optical diffractive neural network
Authors: Yang Gao, Shuming Jiao, Juncheng Fang, Ting Lei, Zhenwei Xie, Xiaocong Yuan
Date: 2019-02-21 00:00:00
Tasks: 10-shot image generation

A cascaded phase-only mask architecture (or an optical diffractive neural network) can be employed for different optical information processing tasks such as pattern recognition, orbital angular momentum (OAM) mode conversion, image salience detection and image encryption. However, for optical encryption and watermarking applications, such a system usually cannot process multiple pairs of input images and output images simultaneously. In our proposed scheme, multiple input images can be simultaneously fed to an optical diffractive neural network (DNN) system and each corresponding output image will be displayed in a non-overlap sub-region in the output imaging plane. Each input image undergoes a different optical transform in an independent channel within the same system. The multiple cascaded phase masks in the system can be effectively optimized by a wavefront matching algorithm. Similar to recent optical pattern recognition and mode conversion works, the orthogonality property is employed to design a multiplexed DNN.
GSLAM: A General SLAM Framework and Benchmark
Authors: Yong Zhao, Shibiao Xu, Shuhui Bu, Hongkai Jiang, Pengcheng Han
Date: 2019-02-21 00:00:00
Proceeding: ICCV 2019 10
Tasks: Simultaneous Localization and Mapping

SLAM technology has recently seen many successes and attracted the attention
of high-technological companies. However, how to unify the interface of
existing or emerging algorithms, and effectively perform benchmark about the
speed, robustness and portability are still problems. In this paper, we propose
a novel SLAM platform named GSLAM, which not only provides evaluation
functionality, but also supplies useful toolkit for researchers to quickly
develop their own SLAM systems. The core contribution of GSLAM is an universal,
cross-platform and full open-source SLAM interface for both research and
commercial usage, which is aimed to handle interactions with input dataset,
SLAM implementation, visualization and applications in an unified framework.
Through this platform, users can implement their own functions for better
performance with plugin form and further boost the application to practical
usage of the SLAM.