pwc-archive/papers-with-abstracts huggingface dataset:
Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system
Authors: Keelin Murphy, Shifa Salman Habib, Syed Mohammad Asad Zaidi, Saira Khowaja, Aamir Khan, Jaime Melendez, Ernst T. Scholten, Farhan Amad, Steven Schalekamp, Maurits Verhagen, Rick H. H. M. Philipsen, Annet Meijers, Bram van Ginneken
Date: 2019-03-08 00:00:00
Tasks: Sensitivity, Specificity

There is a growing interest in the automated analysis of chest X-Ray (CXR) as a sensitive and inexpensive means of screening susceptible populations for pulmonary tuberculosis. In this work we evaluate the latest version of CAD4TB, a commercial software platform designed for this purpose. Version 6 of CAD4TB was released in 2018 and is here tested on a fully independent dataset of 5565 CXR images with GeneXpert (Xpert) sputum test results available (854 Xpert positive subjects). A subset of 500 subjects (50% Xpert positive) was reviewed and annotated by 5 expert observers independently to obtain a radiological reference standard. The latest version of CAD4TB is found to outperform all previous versions in terms of area under receiver operating curve (ROC) with respect to both Xpert and radiological reference standards. Improvements with respect to Xpert are most apparent at high sensitivity levels with a specificity of 76% obtained at a fixed 90% sensitivity. When compared with the radiological reference standard, CAD4TB v6 also outperformed previous versions by a considerable margin and achieved 98% specificity at the 90% sensitivity setting. No substantial difference was found between the performance of CAD4TB v6 and any of the various expert observers against the Xpert reference standard. A cost and efficiency analysis on this dataset demonstrates that in a standard clinical situation, operating at 90% sensitivity, users of CAD4TB v6 can process 132 subjects per day at n average cost per screen of \$5.95 per subject, while users of version 3 process only 85 subjects per day at a cost of \$8.38 per subject. At all tested operating points version 6 is shown to be more efficient and cost effective than any other version.
Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network
Authors: Wei Shao, Flora D. Salim, Jeffrey Chan, Sean Morrison, Fabio Zambetta
Date: 2019-03-08 00:00:00
Tasks: Deep Learning, General Classification

Deep learning has been extended to a number of new domains with critical
success, though some traditional orienteering problems such as the Travelling
Salesman Problem (TSP) and its variants are not commonly solved using such
techniques. Deep neural networks (DNNs) are a potentially promising and
under-explored solution to solve these problems due to their powerful function
approximation abilities, and their fast feed-forward computation. In this
paper, we outline a method for converting an orienteering problem into a
classification problem, and design a customised multi-layer deep learning
network to approximate traditional optimisation solutions to this problem. We
test the performance of the network on a real-world parking violation dataset,
and conduct a generic study that empirically shows the critical architectural
components that affect network performance for this problem.
ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records
Authors: Rajkumar Saini, Derek Dobson, Jon Morrey, Marcus Liwicki, Foteini Simistira Liwicki
Date: 2019-03-08 00:00:00

We propose a Historical Document Reading Challenge on Large Chinese Structured Family Records, in short ICDAR2019 HDRC CHINESE. The objective of the proposed competition is to recognize and analyze the layout, and finally detect and recognize the textlines and characters of the large historical document collection containing more than 20 000 pages kindly provided by FamilySearch.
Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images
Authors: Mia Kokic, Danica Kragic, Jeannette Bohg
Date: 2019-03-08 00:00:00
Tasks: Image-to-Image Translation, Object, Translation

We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Neural Network (CNN) for Hand-held Object Pose and Shape estimation called HOPS-Net and utilize prior work to estimate the hand pose and configuration. We leverage the insight that information about the hand facilitates object pose and shape estimation by incorporating the hand into both training and inference of the object pose and shape as well as the refinement of the estimated pose. The network is trained on a large synthetic dataset of objects in interaction with a human hand. To bridge the gap between real and synthetic images, we employ an image-to-image translation model (Augmented CycleGAN) that generates realistically textured objects given a synthetic rendering. This provides a scalable way of generating annotated data for training HOPS-Net. Our quantitative experiments show that even noisy hand parameters significantly help object pose and shape estimation. The qualitative experiments show results of pose and shape estimation of objects held by a hand "in the wild".
Complex Valued Gated Auto-encoder for Video Frame Prediction
Authors: Niloofar Azizi, Nils Wandel, Sven Behnke
Date: 2019-03-08 00:00:00
Tasks: Prediction

In recent years, complex valued artificial neural networks have gained
increasing interest as they allow neural networks to learn richer
representations while potentially incorporating less parameters. Especially in
the domain of computer graphics, many traditional operations rely heavily on
computations in the complex domain, thus complex valued neural networks apply
naturally. In this paper, we perform frame predictions in video sequences using
a complex valued gated auto-encoder. First, our method is motivated showing how
the Fourier transform can be seen as the basis for translational operations.
Then, we present how a complex neural network can learn such transformations
and compare its performance and parameter efficiency to a real-valued gated
autoencoder. Furthermore, we show how extending both - the real and the complex
valued - neural networks by using convolutional units can significantly improve
prediction performance and parameter efficiency. The networks are assessed on a
moving noise and a bouncing ball dataset.
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
Authors: Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, Ambuj Singh
Date: 2019-03-08 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Combinatorial Optimization, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. While existing techniques have primarily focused on obtaining high-quality solutions, scalability to billion-sized graphs has not been adequately addressed. In addition, the impact of budget-constraint, which is necessary for many practical scenarios, remains to be studied. In this paper, we propose a framework called GCOMB to bridge these gaps. GCOMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. To further facilitate the combinatorial nature of the problem, GCOMB utilizes a Q-learning framework, which is made efficient through importance sampling. We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. Our results establish that GCOMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.
Knowledge-Embedded Routing Network for Scene Graph Generation
Authors: Tianshui Chen, Weihao Yu, Riquan Chen, Liang Lin
Date: 2019-03-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Generation, Scene Graph Generation

To understand a scene in depth not only involves locating/recognizing
individual objects, but also requires to infer the relationships and
interactions among them. However, since the distribution of real-world
relationships is seriously unbalanced, existing methods perform quite poorly
for the less frequent relationships. In this work, we find that the statistical
correlations between object pairs and their relationships can effectively
regularize semantic space and make prediction less ambiguous, and thus well
address the unbalanced distribution issue. To achieve this, we incorporate
these statistical correlations into deep neural networks to facilitate scene
graph generation by developing a Knowledge-Embedded Routing Network. More
specifically, we show that the statistical correlations between objects
appearing in images and their relationships, can be explicitly represented by a
structured knowledge graph, and a routing mechanism is learned to propagate
messages through the graph to explore their interactions. Extensive experiments
on the large-scale Visual Genome dataset demonstrate the superiority of the
proposed method over current state-of-the-art competitors.
Do we still need fuzzy classifiers for Small Data in the Era of Big Data?
Authors: Mikel Elkano, Humberto Bustince, Mikel Galar
Date: 2019-03-08 00:00:00
Tasks: Small Data Image Classification

The Era of Big Data has forced researchers to explore new distributed
solutions for building fuzzy classifiers, which often introduce approximation
errors or make strong assumptions to reduce computational and memory
requirements. As a result, Big Data classifiers might be expected to be
inferior to those designed for standard classification tasks (Small Data) in
terms of accuracy and model complexity. To our knowledge, however, there is no
empirical evidence to confirm such a conjecture yet. Here, we investigate the
extent to which state-of-the-art fuzzy classifiers for Big Data sacrifice
performance in favor of scalability. To this end, we carry out an empirical
study that compares these classifiers with some of the best performing
algorithms for Small Data. Assuming the latter were generally designed for
maximizing performance without considering scalability issues, the results of
this study provide some intuition around the tradeoff between performance and
scalability achieved by current Big Data solutions. Our findings show that,
although slightly inferior, Big Data classifiers are gradually catching up with
state-of-the-art classifiers for Small data, suggesting that a unified learning
algorithm for Big and Small Data might be possible.
3DN: 3D Deformation Network
Authors: Weiyue Wang, Duygu Ceylan, Radomir Mech, Ulrich Neumann
Date: 2019-03-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Shape Generation

Applications in virtual and augmented reality create a demand for rapid
creation and easy access to large sets of 3D models. An effective way to
address this demand is to edit or deform existing 3D models based on a
reference, e.g., a 2D image which is very easy to acquire. Given such a source
3D model and a target which can be a 2D image, 3D model, or a point cloud
acquired as a depth scan, we introduce 3DN, an end-to-end network that deforms
the source model to resemble the target. Our method infers per-vertex offset
displacements while keeping the mesh connectivity of the source model fixed. We
present a training strategy which uses a novel differentiable operation, mesh
sampling operator, to generalize our method across source and target models
with varying mesh densities. Mesh sampling operator can be seamlessly
integrated into the network to handle meshes with different topologies.
Qualitative and quantitative results show that our method generates higher
quality results compared to the state-of-the art learning-based methods for 3D
shape generation. Code is available at github.com/laughtervv/3DN.
Provable Tensor Ring Completion
Authors: Huyan Huang, Yipeng Liu, Ce Zhu
Date: 2019-03-08 00:00:00
Tasks: Matrix Completion

Tensor completion recovers a multi-dimensional array from a limited number of measurements. Using the recently proposed tensor ring (TR) decomposition, in this paper we show that a d-order tensor of dimensional size n and TR rank r can be exactly recovered with high probability by solving a convex optimization program, given n^{d/2} r^2 ln^7(n^{d/2})samples. The proposed TR incoherence condition under which the result holds is similar to the matrix incoherence condition. The experiments on synthetic data verify the recovery guarantee for TR completion. Moreover, the experiments on real-world data show that our method improves the recovery performance compared with the state-of-the-art methods.
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
Authors: Yutong Xie, Jianpeng Zhang, Yong Xia, Chunhua Shen
Date: 2019-03-08 00:00:00
Tasks: Classification, General Classification, Lesion Classification, Lesion Segmentation, Segmentation

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.
Learning from Synthetic Data for Crowd Counting in the Wild
Authors: Qi. Wang, Junyu. Gao, Wei. Lin, Yuan Yuan
Date: 2019-03-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Crowd Counting, Domain Adaptation

Recently, counting the number of people for crowd scenes is a hot topic
because of its widespread applications (e.g. video surveillance, public
security). It is a difficult task in the wild: changeable environment,
large-range number of people cause the current methods can not work well. In
addition, due to the scarce data, many methods suffer from over-fitting to a
different extent. To remedy the above two problems, firstly, we develop a data
collector and labeler, which can generate the synthetic crowd scenes and
simultaneously annotate them without any manpower. Based on it, we build a
large-scale, diverse synthetic dataset. Secondly, we propose two schemes that
exploit the synthetic data to boost the performance of crowd counting in the
wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using
the real data, which significantly prompts the model's performance on real
data; 2) propose a crowd counting method via domain adaptation, which can free
humans from heavy data annotations. Extensive experiments show that the first
method achieves the state-of-the-art performance on four real datasets, and the
second outperforms our baselines. The dataset and source code are available at
https://gjy3035.github.io/GCC-CL/.
Should we Reload Time Series Classification Performance Evaluation ? (a position paper)
Authors: Dominique Gay, Vincent Lemaire
Date: 2019-03-08 00:00:00
Tasks: General Classification, Position, Time Series, Time Series Analysis, Time Series Classification

Since the introduction and the public availability of the \textsc{ucr} time
series benchmark data sets, numerous Time Series Classification (TSC) methods
has been designed, evaluated and compared to each others. We suggest a critical
view of TSC performance evaluation protocols put in place in recent TSC
literature. The main goal of this `position' paper is to stimulate discussion
and reflexion about performance evaluation in TSC literature.
You Only Recognize Once: Towards Fast Video Text Spotting
Authors: Zhanzhan Cheng, Jing Lu, Yi Niu, ShiLiang Pu, Fei Wu, Shuigeng Zhou
Date: 2019-03-08 00:00:00
Tasks: Text Detection, Text Spotting

Video text spotting is still an important research topic due to its various real-applications. Previous approaches usually fall into the four-staged pipeline: text detection in individual images, framewisely recognizing localized text regions, tracking text streams and generating final results with complicated post-processing skills, which might suffer from the huge computational cost as well as the interferences of low-quality text. In this paper, we propose a fast and robust video text spotting framework by only recognizing the localized text one-time instead of frame-wisely recognition. Specifically, we first obtain text regions in videos with a well-designed spatial-temporal detector. Then we concentrate on developing a novel text recommender for selecting the highest-quality text from text streams and only recognizing the selected ones. Here, the recommender assembles text tracking, quality scoring and recognition into an end-to-end trainable module, which not only avoids the interferences from low-quality text but also dramatically speeds up the video text spotting process. In addition, we collect a larger scale video text dataset (LSVTD) for promoting the video text spotting community, which contains 100 text videos from 22 different real-life scenarios. Extensive experiments on two public benchmarks show that our method greatly speeds up the recognition process averagely by 71 times compared with the frame-wise manner, and also achieves the remarkable state-of-the-art.
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Authors: Romero Morais, Vuong Le, Truyen Tran, Budhaditya Saha, Moussa Mansour, Svetha Venkatesh
Date: 2019-03-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Anomaly Detection, Decoder, Outlier Detection, Video Anomaly Detection

Appearance features have been widely used in video anomaly detection even
though they contain complex entangled factors. We propose a new method to model
the normal patterns of human movements in surveillance video for anomaly
detection using dynamic skeleton features. We decompose the skeletal movements
into two sub-components: global body movement and local body posture. We model
the dynamics and interaction of the coupled features in our novel
Message-Passing Encoder-Decoder Recurrent Network. We observed that the
decoupled features collaboratively interact in our spatio-temporal model to
accurately identify human-related irregular events from surveillance video
sequences. Compared to traditional appearance-based models, our method achieves
superior outlier detection performance. Our model also offers "open-box"
examination and decision explanation made possible by the semantically
understandable features and a network architecture supporting interpretability.
Let's Play Mahjong!
Authors: Sanjiang Li, Xueqing Yan
Date: 2019-03-08 00:00:00

Mahjong is a very popular tile-based game commonly played by four players.
Each player begins with a hand of 13 tiles and, in turn, players draw and
discard (i.e., change) tiles until they complete a legal hand using a 14th
tile. In this paper, we initiate a mathematical and AI study of the Mahjong
game and try to answer two fundamental questions: how bad is a hand of 14
tiles? and which tile should I discard? We define and characterise the notion
of deficiency and present an optimal policy to discard a tile in order to
increase the chance of completing a legal hand within $k$ tile changes for each
$k\geq 1$.
Towards Time-Aware Distant Supervision for Relation Extraction
Authors: Tianwen Jiang, Sendong Zhao, Jing Liu, Jin-Ge Yao, Ming Liu, Bing Qin, Ting Liu, Chin-Yew Lin
Date: 2019-03-08 00:00:00
Tasks: Relation, Relation Extraction, Time Series, Time Series Analysis

Distant supervision for relation extraction heavily suffers from the wrong
labeling problem. To alleviate this issue in news data with the timestamp, we
take a new factor time into consideration and propose a novel time-aware
distant supervision framework (Time-DS). Time-DS is composed of a time series
instance-popularity and two strategies. Instance-popularity is to encode the
strong relevance of time and true relation mention. Therefore,
instance-popularity would be an effective clue to reduce the noises generated
through distant supervision labeling. The two strategies, i.e., hard filter and
curriculum learning are both ways to implement instance-popularity for better
relation extraction in the manner of Time-DS. The curriculum learning is a more
sophisticated and flexible way to exploit instance-popularity to eliminate the
bad effects of noises, thus get better relation extraction performance.
Experiments on our collected multi-source news corpus show that Time-DS
achieves significant improvements for relation extraction.
Attribute Acquisition in Ontology based on Representation Learning of Hierarchical Classes and Attributes
Authors: Tianwen Jiang, Ming Liu, Bing Qin, Ting Liu
Date: 2019-03-08 00:00:00
Tasks: Attribute, Relation, Representation Learning

Attribute acquisition for classes is a key step in ontology construction,
which is often achieved by community members manually. This paper investigates
an attention-based automatic paradigm called TransATT for attribute
acquisition, by learning the representation of hierarchical classes and
attributes in Chinese ontology. The attributes of an entity can be acquired by
merely inspecting its classes, because the entity can be regard as the instance
of its classes and inherit their attributes. For explicitly describing of the
class of an entity unambiguously, we propose class-path to represent the
hierarchical classes in ontology, instead of the terminal class word of the
hypernym-hyponym relation (i.e., is-a relation) based hierarchy. The high
performance of TransATT on attribute acquisition indicates the promising
ability of the learned representation of class-paths and attributes. Moreover,
we construct a dataset named \textbf{BigCilin11k}. To the best of our
knowledge, this is the first Chinese dataset with abundant hierarchical classes
and entities with attributes.