pwc-archive/papers-with-abstracts huggingface dataset:
PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
Authors: Hanwang Zhang, Zawlin Kyaw, Jinyang Yu, Shih-Fu Chang
Date: 2017-08-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, object-detection, Object Detection, Relation, Weakly Supervised Object Detection

We aim to tackle a novel vision task called Weakly Supervised Visual Relation
Detection (WSVRD) to detect "subject-predicate-object" relations in an image
with object relation groundtruths available only at the image level. This is
motivated by the fact that it is extremely expensive to label the combinatorial
relations between objects at the instance level. Compared to the extensively
studied problem, Weakly Supervised Object Detection (WSOD), WSVRD is more
challenging as it needs to examine a large set of regions pairs, which is
computationally prohibitive and more likely stuck in a local optimal solution
such as those involving wrong spatial context. To this end, we present a
Parallel, Pairwise Region-based, Fully Convolutional Network (PPR-FCN) for
WSVRD. It uses a parallel FCN architecture that simultaneously performs pair
selection and classification of single regions and region pairs for object and
relation detection, while sharing almost all computation shared over the entire
image. In particular, we propose a novel position-role-sensitive score map with
pairwise RoI pooling to efficiently capture the crucial context associated with
a pair of objects. We demonstrate the superiority of PPR-FCN over all baselines
in solving the WSVRD challenge by using results of extensive experiments over
two visual relation benchmarks.
Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing
Authors: Wesley Tansey, Jesse Thomason, James G. Scott
Date: 2017-08-06 00:00:00
Tasks: Additive models, Denoising, regression

We consider the problem of estimating a regression function in the common
situation where the number of features is small, where interpretability of the
model is a high priority, and where simple linear or additive models fail to
provide adequate performance. To address this problem, we present Maximum
Variance Total Variation denoising (MVTV), an approach that is conceptually
related both to CART and to the more recent CRISP algorithm, a state-of-the-art
alternative method for interpretable nonlinear regression. MVTV divides the
feature space into blocks of constant value and fits the value of all blocks
jointly via a convex optimization routine. Our method is fully data-adaptive,
in that it incorporates highly robust routines for tuning all hyperparameters
automatically. We compare our approach against CART and CRISP via both a
complexity-accuracy tradeoff metric and a human study, demonstrating that that
MVTV is a more powerful and interpretable method.
Intensity Video Guided 4D Fusion for Improved Highly Dynamic 3D Reconstruction
Authors: Jie Zhang, Christos Maniatis, Luis Horna, Robert B. Fisher
Date: 2017-08-06 00:00:00
Tasks: 3D Reconstruction

The availability of high-speed 3D video sensors has greatly facilitated 3D
shape acquisition of dynamic and deformable objects, but high frame rate 3D
reconstruction is always degraded by spatial noise and temporal fluctuations.
This paper presents a simple yet powerful intensity video guided multi-frame 4D
fusion pipeline. Temporal tracking of intensity image points (of moving and
deforming objects) allows registration of the corresponding 3D data points,
whose 3D noise and fluctuations are then reduced by spatio-temporal multi-frame
4D fusion. We conducted simulated noise tests and real experiments on four 3D
objects using a 1000 fps 3D video sensor. The results demonstrate that the
proposed algorithm is effective at reducing 3D noise and is robust against
intensity noise. It outperforms existing algorithms with good scalability on
both stationary and dynamic objects.
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication
Authors: Miles E. Lopes, Shusen Wang, Michael W. Mahoney
Date: 2017-08-06 00:00:00
Tasks: Dimensionality Reduction

In recent years, randomized methods for numerical linear algebra have
received growing interest as a general approach to large-scale problems.
Typically, the essential ingredient of these methods is some form of randomized
dimension reduction, which accelerates computations, but also creates random
approximation error. In this way, the dimension reduction step encodes a
tradeoff between cost and accuracy. However, the exact numerical relationship
between cost and accuracy is typically unknown, and consequently, it may be
difficult for the user to precisely know (1) how accurate a given solution is,
or (2) how much computation is needed to achieve a given level of accuracy. In
the current paper, we study randomized matrix multiplication (sketching) as a
prototype setting for addressing these general problems. As a solution, we
develop a bootstrap method for \emph{directly estimating} the accuracy as a
function of the reduced dimension (as opposed to deriving worst-case bounds on
the accuracy in terms of the reduced dimension). From a computational
standpoint, the proposed method does not substantially increase the cost of
standard sketching methods, and this is made possible by an "extrapolation"
technique. In addition, we provide both theoretical and empirical results to
demonstrate the effectiveness of the proposed method.
Rookie: A unique approach for exploring news archives
Authors: Abram Handler, Brendan O'Connor
Date: 2017-08-06 00:00:00

News archives are an invaluable primary source for placing current events in
historical context. But current search engine tools do a poor job at uncovering
broad themes and narratives across documents. We present Rookie: a practical
software system which uses natural language processing (NLP) to help readers,
reporters and editors uncover broad stories in news archives. Unlike prior
work, Rookie's design emerged from 18 months of iterative development in
consultation with editors and computational journalists. This process lead to a
dramatically different approach from previous academic systems with similar
goals. Our efforts offer a generalizable case study for others building
real-world journalism software using NLP.
First-Person Activity Forecasting with Online Inverse Reinforcement Learning
Authors: Nicholas Rhinehart, Kris M. Kitani
Date: 2016-12-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Trajectory Forecasting

We address the problem of incrementally modeling and forecasting long-term
goals of a first-person camera wearer: what the user will do, where they will
go, and what goal they seek. In contrast to prior work in trajectory
forecasting, our algorithm, DARKO, goes further to reason about semantic states
(will I pick up an object?), and future goal states that are far in terms of
both space and time. DARKO learns and forecasts from first-person visual
observations of the user's daily behaviors via an Online Inverse Reinforcement
Learning (IRL) approach. Classical IRL discovers only the rewards in a batch
setting, whereas DARKO discovers the states, transitions, rewards, and goals of
a user from streaming data. Among other results, we show DARKO forecasts goals
better than competing methods in both noisy and ideal settings, and our
approach is theoretically and empirically no-regret.
A Framework for Visually Realistic Multi-robot Simulation in Natural Environment
Authors: Ori Ganoni, Ramakrishnan Mukundan
Date: 2017-08-06 00:00:00
Tasks: Position

This paper presents a generalized framework for the simulation of multiple
robots and drones in highly realistic models of natural environments. The
proposed simulation architecture uses the Unreal Engine4 for generating both
optical and depth sensor outputs from any position and orientation within the
environment and provides several key domain specific simulation capabilities.
Various components and functionalities of the system have been discussed in
detail. The simulation engine also allows users to test and validate a wide
range of computer vision algorithms involving different drone configurations
under many types of environmental effects such as wind gusts. The paper
demonstrates the effectiveness of the system by giving experimental results for
a test scenario where one drone tracks the simulated motion of another in a
complex natural environment.
Face Parsing via Recurrent Propagation
Authors: Sifei Liu, Jianping Shi, Ji Liang, Ming-Hsuan Yang
Date: 2017-08-06 00:00:00
Tasks: Face Parsing, GPU

Face parsing is an important problem in computer vision that finds numerous
applications including recognition and editing. Recently, deep convolutional
neural networks (CNNs) have been applied to image parsing and segmentation with
the state-of-the-art performance. In this paper, we propose a face parsing
algorithm that combines hierarchical representations learned by a CNN, and
accurate label propagations achieved by a spatially variant recurrent neural
network (RNN). The RNN-based propagation approach enables efficient inference
over a global space with the guidance of semantic edges generated by a local
convolutional model. Since the convolutional architecture can be shallow and
the spatial RNN can have few parameters, the framework is much faster and more
light-weighted than the state-of-the-art CNNs for the same task. We apply the
proposed model to coarse-grained and fine-grained face parsing. For
fine-grained face parsing, we develop a two-stage approach by first identifying
the main regions and then segmenting the detail components, which achieves
better performance in terms of accuracy and efficiency. With a single GPU, the
proposed algorithm parses face images accurately at 300 frames per second,
which facilitates real-time applications.
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning
Authors: Harish RaviPrakash, Milena Korostenskaja, Eduardo Castillo, Ki Lee, James Baumgartner, Ulas Bagci
Date: 2017-05-26 00:00:00
Tasks: BIG-bench Machine Learning, Time Series Analysis

Accurate localization of brain regions responsible for language and cognitive
functions in Epilepsy patients should be carefully determined prior to surgery.
Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been
shown to be a safer alternative to the electrical cortical stimulation mapping
(ESM), which is currently the clinical/gold standard. Conventional methods for
analyzing RTFM signals are based on statistical comparison of signal power at
certain frequency bands. Compared to gold standard (ESM), they have limited
accuracies when assessing channel responses.
  In this study, we address the accuracy limitation of the current RTFM signal
estimation methods by analyzing the full frequency spectrum of the signal and
replacing signal power estimation methods with machine learning algorithms,
specifically random forest (RF), as a proof of concept. We train RF with power
spectral density of the time-series RTFM signal in supervised learning
framework where ground truth labels are obtained from the ESM. Results obtained
from RTFM of six adult patients in a strictly controlled experimental setup
reveal the state of the art detection accuracy of $\approx 78\%$ for the
language comprehension task, an improvement of $23\%$ over the conventional
RTFM estimation method. To the best of our knowledge, this is the first study
exploring the use of machine learning approaches for determining RTFM signal
characteristics, and using the whole-frequency band for better region
localization. Our results demonstrate the feasibility of machine learning based
RTFM signal analysis method over the full spectrum to be a clinical routine in
the near future.
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
Authors: Faisal Riaz, Muaz A. Niazi
Date: 2017-08-06 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance

Background Road collisions and casualties pose a serious threat to commuters
around the globe. Autonomous Vehicles (AVs) aim to make the use of technology
to reduce the road accidents. However, the most of research work in the context
of collision avoidance has been performed to address, separately, the rear end,
front end and lateral collisions in less congested and with high
inter-vehicular distances. Purpose The goal of this paper is to introduce the
concept of a social agent, which interact with other AVs in social manners like
humans are social having the capability of predicting intentions, i.e.
mentalizing and copying the actions of each other, i.e. mirroring. The proposed
social agent is based on a human-brain inspired mentalizing and mirroring
capabilities and has been modelled for collision detection and avoidance under
congested urban road traffic.
  Method We designed our social agent having the capabilities of mentalizing
and mirroring and for this purpose we utilized Exploratory Agent Based Modeling
(EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by
Niazi and Hussain.
  Results Our simulation and practical experiments reveal that by embedding
Richardson's arms race model within AVs, collisions can be avoided while
travelling on congested urban roads in a flock like topologies. The performance
of the proposed social agent has been compared at two different levels.
Enhanced Emotion Enabled Cognitive Agent Based Rear End Collision Avoidance Controller for Autonomous Vehicles
Authors: Faisal Riaz, Muaz A. Niazi
Date: 2017-08-06 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance

Rear end collisions are deadliest in nature and cause most of traffic
casualties and injuries. In the existing research, many rear end collision
avoidance solutions have been proposed. However, the problem with these
proposed solutions is that they are highly dependent on precise mathematical
models. Whereas, the real road driving is influenced by non-linear factors such
as road surface situations, driver reaction time, pedestrian flow and vehicle
dynamics, hence obtaining the accurate mathematical model of the vehicle
control system is challenging. This problem with precise control based rear end
collision avoidance schemes has been addressed using fuzzy logic, but the
excessive number of fuzzy rules straightforwardly prejudice their efficiency.
Furthermore, these fuzzy logic based controllers have been proposed without
using proper agent based modeling that helps in mimicking the functions of an
artificial human driver executing these fuzzy rules. Keeping in view these
limitations, we have proposed an Enhanced Emotion Enabled Cognitive Agent
(EEEC_Agent) based controller that helps the Autonomous Vehicles (AVs) to
perform rear end collision avoidance with less number of rules, designed after
fear emotion, and high efficiency. To introduce a fear emotion generation
mechanism in EEEC_Agent, Orton, Clore & Collins (OCC) model has been employed.
The fear generation mechanism of EEEC_Agent has been verified using NetLogo
simulation. Furthermore, practical validation of EEEC_Agent functions has been
performed using specially built prototype AV platform. Eventually, the
qualitative comparative study with existing state of the art research works
reflect that proposed model outperforms recent research.
Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation
Authors: Manu Goyal, Neil D. Reeves, Satyan Rajbhandari, Jennifer Spragg, Moi Hoon Yap
Date: 2017-08-06 00:00:00
Tasks: Transfer Learning

Diabetic Foot Ulcer (DFU) is a major complication of Diabetes, which if not
managed properly can lead to amputation. DFU can appear anywhere on the foot
and can vary in size, colour, and contrast depending on various pathologies.
Current clinical approaches to DFU treatment rely on patients and clinician
vigilance, which has significant limitations such as the high cost involved in
the diagnosis, treatment and lengthy care of the DFU. We introduce a dataset of
705 foot images. We provide the ground truth of ulcer region and the
surrounding skin that is an important indicator for clinicians to assess the
progress of ulcer. Then, we propose a two-tier transfer learning from bigger
datasets to train the Fully Convolutional Networks (FCNs) to automatically
segment the ulcer and surrounding skin. Using 5-fold cross-validation, the
proposed two-tier transfer learning FCN Models achieve a Dice Similarity
Coefficient of 0.794 ($\pm$0.104) for ulcer region, 0.851 ($\pm$0.148) for
surrounding skin region, and 0.899 ($\pm$0.072) for the combination of both
regions. This demonstrates the potential of FCNs in DFU segmentation, which can
be further improved with a larger dataset.
Emotion Controlled Spectrum Mobility Scheme for Efficient Syntactic Interoperability In Cognitive Radio Based Internet of Vehicles
Authors: Faisal Riaz, Muaz A. Niazi
Date: 2017-08-06 00:00:00

Blind spots are one of the causes of road accidents in the hilly and flat
areas. These blind spot accidents can be decreased by establishing an Internet
of Vehicles (IoV) using Vehicle-2-Vehicle (V2V) and Vehicle-2-Infrastrtructure
(V2I) communication systems. But the problem with these IoV is that most of
them are using DSRC or single Radio Access Technology (RAT) as a wireless
technology, which has been proven to be failed for efficient communication
between vehicles. Recently, Cognitive Radio (CR) based IoV have to be proven
best wireless communication systems for vehicular networks. However, the
spectrum mobility is a challenging task to keep CR based vehicular networks
interoperable and has not been addressed sufficiently in existing research. In
our previous research work, the Cognitive Radio Site (CR-Site) has been
proposed as in-vehicle CR-device, which can be utilized to establish efficient
IoV systems. H In this paper, we have introduced the Emotions Inspired
Cognitive Agent (EIC_Agent) based spectrum mobility mechanism in CR-Site and
proposed a novel emotions controlled spectrum mobility scheme for efficient
syntactic interoperability between vehicles. For this purpose, a probabilistic
deterministic finite automaton using fear factor is proposed to perform
efficient spectrum mobility using fuzzy logic. In addition, the quantitative
computation of different fear intensity levels has been performed with the help
of fuzzy logic. The system has been tested using active data from different GSM
service providers on Mangla-Mirpur road. This is supplemented by extensive
simulation experiments which validate the proposed scheme for CR based
high-speed vehicular networks. The qualitative comparison with the
existing-state-of the-art has proven the superiority of the proposed emotions
controlled syntactic interoperable spectrum mobility scheme within cognitive
radio based IoV systems.
Designing Autonomous Vehicles: Evaluating the Role of Human Emotions and Social Norms
Authors: Faisal Riaz, Muaz A. Niazi
Date: 2017-08-06 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance

Humans are going to delegate the rights of driving to the autonomous vehicles
in near future. However, to fulfill this complicated task, there is a need for
a mechanism, which enforces the autonomous vehicles to obey the road and social
rules that have been practiced by well-behaved drivers. This task can be
achieved by introducing social norms compliance mechanism in the autonomous
vehicles. This research paper is proposing an artificial society of autonomous
vehicles as an analogy of human social society. Each AV has been assigned a
social personality having different social influence. Social norms have been
introduced which help the AVs in making the decisions, influenced by emotions,
regarding road collision avoidance. Furthermore, social norms compliance
mechanism, by artificial social AVs, has been proposed using prospect based
emotion i.e. fear, which is conceived from OCC model. Fuzzy logic has been
employed to compute the emotions quantitatively. Then, using SimConnect
approach, fuzzy values of fear has been provided to the Netlogo simulation
environment to simulate artificial society of AVs. Extensive testing has been
performed using the behavior space tool to find out the performance of the
proposed approach in terms of the number of collisions. For comparison, the
random-walk model based artificial society of AVs has been proposed as well. A
comparative study with a random walk, prove that proposed approach provides a
better option to tailor the autopilots of future AVS, Which will be more
socially acceptable and trustworthy by their riders in terms of safe road
travel.
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
Authors: Maurilio Di Cicco, Ciro Potena, Giorgio Grisetti, Alberto Pretto
Date: 2016-12-09 00:00:00
Tasks: Dataset Generation, General Classification, Image Segmentation, Semantic Segmentation

Selective weeding is one of the key challenges in the field of agriculture
robotics. To accomplish this task, a farm robot should be able to accurately
detect plants and to distinguish them between crop and weeds. Most of the
promising state-of-the-art approaches make use of appearance-based models
trained on large annotated datasets. Unfortunately, creating large agricultural
datasets with pixel-level annotations is an extremely time consuming task,
actually penalizing the usage of data-driven techniques. In this paper, we face
this problem by proposing a novel and effective approach that aims to
dramatically minimize the human intervention needed to train the detection and
classification algorithms. The idea is to procedurally generate large synthetic
training datasets randomizing the key features of the target environment (i.e.,
crop and weed species, type of soil, light conditions). More specifically, by
tuning these model parameters, and exploiting a few real-world textures, it is
possible to render a large amount of realistic views of an artificial
agricultural scenario with no effort. The generated data can be directly used
to train the model or to supplement real-world images. We validate the proposed
methodology by using as testbed a modern deep learning based image segmentation
architecture. We compare the classification results obtained using both real
and synthetic images as training data. The reported results confirm the
effectiveness and the potentiality of our approach.
Training of Deep Neural Networks based on Distance Measures using RMSProp
Authors: Thomas Kurbiel, Shahrzad Khaleghian
Date: 2017-08-06 00:00:00

The vanishing gradient problem was a major obstacle for the success of deep
learning. In recent years it was gradually alleviated through multiple
different techniques. However the problem was not really overcome in a
fundamental way, since it is inherent to neural networks with activation
functions based on dot products. In a series of papers, we are going to analyze
alternative neural network structures which are not based on dot products. In
this first paper, we revisit neural networks built up of layers based on
distance measures and Gaussian activation functions. These kinds of networks
were only sparsely used in the past since they are hard to train when using
plain stochastic gradient descent methods. We show that by using Root Mean
Square Propagation (RMSProp) it is possible to efficiently learn multi-layer
neural networks. Furthermore we show that when appropriately initialized these
kinds of neural networks suffer much less from the vanishing and exploding
gradient problem than traditional neural networks even for deep networks.