pwc-archive/papers-with-abstracts huggingface dataset:
Using Word Embeddings for Visual Data Exploration with Ontodia and Wikidata
Authors: Gerhard Wohlgenannt, Nikolay Klimov, Dmitry Mouromtsev, Daniil Razdyakonov, Dmitry Pavlov, Yury Emelyanov
Date: 2019-03-04 00:00:00
Tasks: Word Embeddings

One of the big challenges in Linked Data consumption is to create visual and
natural language interfaces to the data usable for non-technical users. Ontodia
provides support for diagrammatic data exploration, showcased in this
publication in combination with the Wikidata dataset. We present improvements
to the natural language interface regarding exploring and querying Linked Data
entities. The method uses models of distributional semantics to find and rank
entity properties related to user input in Ontodia. Various word embedding
types and model settings are evaluated, and the results show that user
experience in visual data exploration benefits from the proposed approach.
Using Causal Analysis to Learn Specifications from Task Demonstrations
Authors: Daniel Angelov, Yordan Hristov, Subramanian Ramamoorthy
Date: 2019-03-04 00:00:00
Tasks: Clustering

Learning models of user behaviour is an important problem that is broadly
applicable across many application domains requiring human-robot interaction.
In this work we show that it is possible to learn a generative model for
distinct user behavioral types, extracted from human demonstrations, by
enforcing clustering of preferred task solutions within the latent space. We
use this model to differentiate between user types and to find cases with
overlapping solutions. Moreover, we can alter an initially guessed solution to
satisfy the preferences that constitute a particular user type by
backpropagating through the learned differentiable model. An advantage of
structuring generative models in this way is that it allows us to extract
causal relationships between symbols that might form part of the user's
specification of the task, as manifested in the demonstrations. We show that
the proposed method is capable of correctly distinguishing between three user
types, who differ in degrees of cautiousness in their motion, while performing
the task of moving objects with a kinesthetically driven robot in a tabletop
environment. Our method successfully identifies the correct type, within the
specified time, in 99% [97.8 - 99.8] of the cases, which outperforms an IRL
baseline. We also show that our proposed method correctly changes a default
trajectory to one satisfying a particular user specification even with unseen
objects. The resulting trajectory is shown to be directly implementable on a
PR2 humanoid robot completing the same task.
Towards Structured Evaluation of Deep Neural Network Supervisors
Authors: Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Cristofer Englund, Sankar Raman Sathyamoorthy, Stig Ursing
Date: 2019-03-04 00:00:00

Deep Neural Networks (DNN) have improved the quality of several non-safety
related products in the past years. However, before DNNs should be deployed to
safety-critical applications, their robustness needs to be systematically
analyzed. A common challenge for DNNs occurs when input is dissimilar to the
training set, which might lead to high confidence predictions despite proper
knowledge of the input. Several previous studies have proposed to complement
DNNs with a supervisor that detects when inputs are outside the scope of the
network. Most of these supervisors, however, are developed and tested for a
selected scenario using a specific performance metric. In this work, we
emphasize the need to assess and compare the performance of supervisors in a
structured way. We present a framework constituted by four datasets organized
in six test cases combined with seven evaluation metrics. The test cases
provide varying complexity and include data from publicly available sources as
well as a novel dataset consisting of images from simulated driving scenarios.
The latter we plan to make publicly available. Our framework can be used to
support DNN supervisor evaluation, which in turn could be used to motive
development, validation, and deployment of DNNs in safety-critical
applications.
Graph Neural Networks for Modelling Traffic Participant Interaction
Authors: Frederik Diehl, Thomas Brunner, Michael Truong Le, Alois Knoll
Date: 2019-03-04 00:00:00
Tasks: Graph Neural Network, Prediction, Traffic Prediction

By interpreting a traffic scene as a graph of interacting vehicles, we gain a flexible abstract representation which allows us to apply Graph Neural Network (GNN) models for traffic prediction. These naturally take interaction between traffic participants into account while being computationally efficient and providing large model capacity. We evaluate two state-of-the art GNN architectures and introduce several adaptations for our specific scenario. We show that prediction error in scenarios with much interaction decreases by 30% compared to a model that does not take interactions into account. This suggests that interaction is important, and shows that we can model it using graphs. This makes GNNs a worthwhile addition to traffic prediction systems.
Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model
Authors: Wenhui Cui, Yanlin Liu, Yuxing Li, Menghao Guo, Yiming Li, Xiuli Li, Tianle Wang, Xiangzhu Zeng, Chuyang Ye
Date: 2019-03-04 00:00:00
Tasks: image-classification, Image Classification, Ischemic Stroke Lesion Segmentation, Lesion Segmentation, Segmentation

Automated brain lesion segmentation provides valuable information for the
analysis and intervention of patients. In particular, methods based on
convolutional neural networks (CNNs) have achieved state-of-the-art
segmentation performance. However, CNNs usually require a decent amount of
annotated data, which may be costly and time-consuming to obtain. Since
unannotated data is generally abundant, it is desirable to use unannotated data
to improve the segmentation performance for CNNs when limited annotated data is
available. In this work, we propose a semi-supervised learning (SSL) approach
to brain lesion segmentation, where unannotated data is incorporated into the
training of CNNs. We adapt the mean teacher model, which is originally
developed for SSL-based image classification, for brain lesion segmentation.
Assuming that the network should produce consistent outputs for similar inputs,
a loss of segmentation consistency is designed and integrated into a
self-ensembling framework. Specifically, we build a student model and a teacher
model, which share the same CNN architecture for segmentation. The student and
teacher models are updated alternately. At each step, the student model learns
from the teacher model by minimizing the weighted sum of the segmentation loss
computed from annotated data and the segmentation consistency loss between the
teacher and student models computed from unannotated data. Then, the teacher
model is updated by combining the updated student model with the historical
information of teacher models using an exponential moving average strategy. For
demonstration, the proposed approach was evaluated on ischemic stroke lesion
segmentation, where it improves stroke lesion segmentation with the
incorporation of unannotated data.
Attention-based Lane Change Prediction
Authors: Oliver Scheel, Naveen Shankar Nagaraja, Loren Schwarz, Nassir Navab, Federico Tombari
Date: 2019-03-04 00:00:00
Tasks: Prediction

Lane change prediction of surrounding vehicles is a key building block of
path planning. The focus has been on increasing the accuracy of prediction by
posing it purely as a function estimation problem at the cost of model
understandability. However, the efficacy of any lane change prediction model
can be improved when both corner and failure cases are humanly understandable.
We propose an attention-based recurrent model to tackle both understandability
and prediction quality. We also propose metrics which reflect the discomfort
felt by the driver. We show encouraging results on a publicly available dataset
and proprietary fleet data.
Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation
Authors: Aran Sena, Brendan Michael, Matthew Howard
Date: 2019-03-04 00:00:00

Learning from Demonstration depends on a robot learner generalising its
learned model to unseen conditions, as it is not feasible for a person to
provide a demonstration set that accounts for all possible variations in
non-trivial tasks. While there are many learning methods that can handle
interpolation of observed data effectively, extrapolation from observed data
offers a much greater challenge. To address this problem of generalisation,
this paper proposes a modified Task-Parameterised Gaussian Mixture Regression
method that considers the relevance of task parameters during trajectory
generation, as determined by variance in the data. The benefits of the proposed
method are first explored using a simulated reaching task data set. Here it is
shown that the proposed method offers far-reaching, low-error extrapolation
abilities that are different in nature to existing learning methods. Data
collected from novice users for a real-world manipulation task is then
considered, where it is shown that the proposed method is able to effectively
reduce grasping performance errors by ${\sim30\%}$ and extrapolate to unseen
grasp targets under real-world conditions. These results indicate the proposed
method serves to benefit novice users by placing less reliance on the user to
provide high quality demonstration data sets.
Understanding the Mechanism of Deep Learning Framework for Lesion Detection in Pathological Images with Breast Cancer
Authors: Wei-Wen Hsu, Chung-Hao Chen, Chang Hoa, Yu-Ling Hou, Xiang Gao, Yun Shao, Xueli Zhang, Jingjing Wang, Tao He, Yanghong Tai
Date: 2019-03-04 00:00:00
Tasks: Deep Learning, General Classification, Lesion Detection

The computer-aided detection (CADe) systems are developed to assist
pathologists in slide assessment, increasing diagnosis efficiency and reducing
missing inspections. Many studies have shown such a CADe system with deep
learning approaches outperforms the one using conventional methods that rely on
hand-crafted features based on field-knowledge. However, most developers who
adopted deep learning models directly focused on the efficacy of outcomes,
without providing comprehensive explanations on why their proposed frameworks
can work effectively. In this study, we designed four experiments to verify the
consecutive concepts, showing that the deep features learned from pathological
patches are interpretable by domain knowledge of pathology and enlightening for
clinical diagnosis in the task of lesion detection. The experimental results
show the activation features work as morphological descriptors for specific
cells or tissues, which agree with the clinical rules in classification. That
is, the deep learning framework not only detects the distribution of tumor
cells but also recognizes lymphocytes, collagen fibers, and some other non-cell
structural tissues. Most of the characteristics learned by the deep learning
models have summarized the detection rules that can be recognized by the
experienced pathologists, whereas there are still some features may not be
intuitive to domain experts but discriminative in classification for machines.
Those features are worthy to be further studied in order to find out the
reasonable correlations to pathological knowledge, from which pathological
experts may draw inspirations for exploring new characteristics in diagnosis.
Unsupervised Domain Adaptation Learning Algorithm for RGB-D Staircase Recognition
Authors: Jing Wang, Kuangen Zhang
Date: 2019-03-04 00:00:00
Tasks: Domain Adaptation, General Classification, Unsupervised Domain Adaptation

Detection and recognition of staircase as upstairs, downstairs and negative
(e.g., ladder) are the fundamental of assisting the visually impaired to travel
independently in unfamiliar environments. Previous researches have focused on
using massive amounts of RGB-D scene data to train traditional machine learning
(ML) based models to detect and recognize the staircase. However, the
performance of traditional ML techniques is limited by the amount of labeled
RGB-D staircase data. In this paper, we apply an unsupervised domain adaptation
approach in deep architectures to transfer knowledge learned from the labeled
RGB-D stationary staircase dataset to the unlabeled RGB-D escalator dataset. By
utilizing the domain adaptation method, our feedforward convolutional neural
networks (CNN) based feature extractor with 5 convolution layers can achieve
100% classification accuracy on testing the labeled stationary staircase data
and 80.6% classification accuracy on testing the unlabeled escalator data. We
demonstrate the success of the approach for classifying staircase on two
domains with a limited amount of data. To further demonstrate the effectiveness
of the approach, we also validate the same CNN model without domain adaptation
and compare its results with those of our proposed architecture.
STEFANN: Scene Text Editor using Font Adaptive Neural Network
Authors: Prasun Roy, Saumik Bhattacharya, Subhankar Ghosh, Umapada Pal
Date: 2019-03-04 00:00:00
Proceeding: CVPR 2020 6
Tasks: Scene Text Editing

Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advantages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. Next, we replace the source character with the generated character maintaining both geometric and visual consistency with neighboring characters. Our method works as a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR datasets both qualitatively and quantitatively.
Complement Objective Training
Authors: Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Date: 2019-03-04 00:00:00
Proceeding: ICLR 2019 5
Tasks: Natural Language Understanding

Learning with a primary objective, such as softmax cross entropy for
classification and sequence generation, has been the norm for training deep
neural networks for years. Although being a widely-adopted approach, using
cross entropy as the primary objective exploits mostly the information from the
ground-truth class for maximizing data likelihood, and largely ignores
information from the complement (incorrect) classes. We argue that, in addition
to the primary objective, training also using a complement objective that
leverages information from the complement classes can be effective in improving
model performance. This motivates us to study a new training paradigm that
maximizes the likelihood of the groundtruth class while neutralizing the
probabilities of the complement classes. We conduct extensive experiments on
multiple tasks ranging from computer vision to natural language understanding.
The experimental results confirm that, compared to the conventional training
with just one primary objective, training also with the complement objective
further improves the performance of the state-of-the-art models across all
tasks. In addition to the accuracy improvement, we also show that models
trained with both primary and complement objectives are more robust to
single-step adversarial attacks.
Evolving Spiking Neural Networks for Nonlinear Control Problems
Authors: Huanneng Qiu, Matthew Garratt, David Howard, Sreenatha Anavatti
Date: 2019-03-04 00:00:00

Spiking Neural Networks are powerful computational modelling tools that have
attracted much interest because of the bioinspired modelling of synaptic
interactions between neurons. Most of the research employing spiking neurons
has been non-behavioural and discontinuous. Comparatively, this paper presents
a recurrent spiking controller that is capable of solving nonlinear control
problems in continuous domains using a popular topology evolution algorithm as
the learning mechanism. We propose two mechanisms necessary to the decoding of
continuous signals from discrete spike transmission: (i) a background current
component to maintain frequency sufficiency for spike rate decoding, and (ii) a
general network structure that derives strength from topology evolution. We
demonstrate that the proposed spiking controller can learn significantly faster
to discover functional solutions than sigmoidal neural networks in solving a
classic nonlinear control problem.
PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things
Authors: Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji
Date: 2019-03-04 00:00:00
Tasks: 3D Instance Segmentation, Instance Segmentation, Panoptic Segmentation, Semantic Segmentation

We propose PanopticFusion, a novel online volumetric semantic mapping system at the level of stuff and things. In contrast to previous semantic mapping systems, PanopticFusion is able to densely predict class labels of a background region (stuff) and individually segment arbitrary foreground objects (things). In addition, our system has the capability to reconstruct a large-scale scene and extract a labeled mesh thanks to its use of a spatially hashed volumetric map representation. Our system first predicts pixel-wise panoptic labels (class labels for stuff regions and instance IDs for thing regions) for incoming RGB frames by fusing 2D semantic and instance segmentation outputs. The predicted panoptic labels are integrated into the volumetric map together with depth measurements while keeping the consistency of the instance IDs, which could vary frame to frame, by referring to the 3D map at that moment. In addition, we construct a fully connected conditional random field (CRF) model with respect to panoptic labels for map regularization. For online CRF inference, we propose a novel unary potential approximation and a map division strategy. We evaluated the performance of our system on the ScanNet (v2) dataset. PanopticFusion outperformed or compared with state-of-the-art offline 3D DNN methods in both semantic and instance segmentation benchmarks. Also, we demonstrate a promising augmented reality application using a 3D panoptic map generated by the proposed system.
Binary Classifier Inspired by Quantum Theory
Authors: Prayag Tiwari, Massimo Melucci
Date: 2019-03-04 00:00:00
Tasks: General Classification

\ac{ML} helps us to recognize patterns from raw data. \ac{ML} is used in
numerous domains i.e. biomedical, agricultural, food technology, etc. Despite
recent technological advancements, there is still room for substantial
improvement in prediction. Current \ac{ML} models are based on classical
theories of probability and statistics, which can now be replaced by \ac{QT}
with the aim of improving the effectiveness of \ac{ML}. In this paper, we
propose the \ac{BCIQT} model, which outperforms the state of the art
classification in terms of recall for every category.
Attacking Power Indices by Manipulating Player Reliability
Authors: Gabriel Istrate, Cosmin Bonchiş, Alin Brînduşescu
Date: 2019-03-04 00:00:00

We investigate the manipulation of power indices in TU-cooperative games by stimulating (subject to a budget constraint) changes in the propensity of other players to participate to the game. We display several algorithms that show that the problem is often tractable for so-called network centrality games and influence attribution games, as well as an example when optimal manipulation is intractable, even though computing power indices is feasible.
Learning STRIPS Action Models with Classical Planning
Authors: Diego Aineto, Sergio Jiménez, Eva Onaindia
Date: 2019-03-04 00:00:00
Tasks: Inductive Learning

This paper presents a novel approach for learning STRIPS action models from
examples that compiles this inductive learning task into a classical planning
task. Interestingly, the compilation approach is flexible to different amounts
of available input knowledge; the learning examples can range from a set of
plans (with their corresponding initial and final states) to just a pair of
initial and final states (no intermediate action or state is given). Moreover,
the compilation accepts partially specified action models and it can be used to
validate whether the observation of a plan execution follows a given STRIPS
action model, even if this model is not fully specified.
Differentiable Causal Computations via Delayed Trace
Authors: David Sprunger, Shin-ya Katsumata
Date: 2019-03-04 00:00:00
Tasks: Rolling Shutter Correction

We investigate causal computations taking sequences of inputs to sequences of
outputs where the $n$th output depends on the first $n$ inputs only. We model
these in category theory via a construction taking a Cartesian category $C$ to
another category $St(C)$ with a novel trace-like operation called "delayed
trace", which misses yanking and dinaturality axioms of the usual trace. The
delayed trace operation provides a feedback mechanism in $St(C)$ with an
implicit guardedness guarantee.
  When $C$ is equipped with a Cartesian differential operator, we construct a
differential operator for $St(C)$ using an abstract version of backpropagation
through time, a technique from machine learning based on unrolling of
functions. This obtains a swath of properties for backpropagation through time,
including a chain rule and Schwartz theorem. Our differential operator is also
able to compute the derivative of a stateful network without requiring the
network to be unrolled.
Zero-Shot Task Transfer
Authors: Arghya Pal, Vineeth N. Balasubramanian
Date: 2019-03-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Camera Pose Estimation, Meta-Learning, Pose Estimation, Transfer Learning, Zero-Shot Learning

In this work, we present a novel meta-learning algorithm, i.e. TTNet, that
regresses model parameters for novel tasks for which no ground truth is
available (zero-shot tasks). In order to adapt to novel zero-shot tasks, our
meta-learner learns from the model parameters of known tasks (with ground
truth) and the correlation of known tasks to zero-shot tasks. Such intuition
finds its foothold in cognitive science, where a subject (human baby) can adapt
to a novel-concept (depth understanding) by correlating it with old concepts
(hand movement or self-motion), without receiving explicit supervision. We
evaluated our model on the Taskonomy dataset, with four tasks as zero-shot:
surface-normal, room layout, depth, and camera pose estimation. These tasks
were chosen based on the data acquisition complexity and the complexity
associated with the learning process using a deep network. Our proposed
methodology out-performs state-of-the-art models (which use ground truth)on
each of our zero-shot tasks, showing promise on zero-shot task transfer. We
also conducted extensive experiments to study the various choices of our
methodology, as well as showed how the proposed method can also be used in
transfer learning. To the best of our knowledge, this is the firstsuch effort
on zero-shot learning in the task space.