pwc-archive/papers-with-abstracts huggingface dataset:
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images
Authors: David Dov, Shahar Ziv Kovalsky, Serge Assaad, Avani A. Pendse Jonathan Cohen, Danielle Elliott Range, Ricardo Henao, Lawrence Carin
Date: 2019-04-26 00:00:00
Tasks: Diagnostic, Informativeness, Multiple Instance Learning, Two-sample testing, whole slide images

We consider machine-learning-based thyroid-malignancy prediction from cytopathology whole-slide images (WSI). Multiple instance learning (MIL) approaches, typically used for the analysis of WSIs, divide the image (bag) into patches (instances), which are used to predict a single bag-level label. These approaches perform poorly in cytopathology slides due to a unique bag structure: sparsely located informative instances with varying characteristics of abnormality. We address these challenges by considering multiple types of labels: bag-level malignancy and ordered diagnostic scores, as well as instance-level informativeness and abnormality labels. We study their contribution beyond the MIL setting by proposing a maximum likelihood estimation (MLE) framework, from which we derive a two-stage deep-learning-based algorithm. The algorithm identifies informative instances and assigns them local malignancy scores that are incorporated into a global malignancy prediction. We derive a lower bound of the MLE, leading to an improved training strategy based on weak supervision, that we motivate through statistical analysis. The lower bound further allows us to extend the proposed algorithm to simultaneously predict multiple bag and instance-level labels from a single output of a neural network. Experimental results demonstrate that the proposed algorithm provides competitive performance compared to several competing methods, achieves (expert) human-level performance, and allows augmentation of human decisions.
Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
Authors: Maosen Li, Siheng Chen, Xu Chen, Ya zhang, Yan-Feng Wang, Qi Tian
Date: 2019-04-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Decoder, Pose Prediction, Skeleton Based Action Recognition, Temporal Action Localization

Action recognition with skeleton data has recently attracted much attention
in computer vision. Previous studies are mostly based on fixed skeleton graphs,
only capturing local physical dependencies among joints, which may miss
implicit joint correlations. To capture richer dependencies, we introduce an
encoder-decoder structure, called A-link inference module, to capture
action-specific latent dependencies, i.e. actional links, directly from
actions. We also extend the existing skeleton graphs to represent higher-order
dependencies, i.e. structural links. Combing the two types of links into a
generalized skeleton graph, we further propose the actional-structural graph
convolution network (AS-GCN), which stacks actional-structural graph
convolution and temporal convolution as a basic building block, to learn both
spatial and temporal features for action recognition. A future pose prediction
head is added in parallel to the recognition head to help capture more detailed
action patterns through self-supervision. We validate AS-GCN in action
recognition using two skeleton data sets, NTU-RGB+D and Kinetics. The proposed
AS-GCN achieves consistently large improvement compared to the state-of-the-art
methods. As a side product, AS-GCN also shows promising results for future pose
prediction.
Computational Approaches to Access Probabilistic Population Codes for Higher Cognition an Decision-Making
Authors: Kevin Jasberg, Sergej Sizov
Date: 2019-04-25 00:00:00
Tasks: Decision Making, Recommendation Systems

In recent years, research unveiled more and more evidence for the so-called
Bayesian Brain Paradigm, i.e. the human brain is interpreted as a probabilistic
inference machine and Bayesian modelling approaches are hence used
successfully. One of the many theories is that of Probabilistic Population
Codes (PPC). Although this model has so far only been considered as meaningful
and useful for sensory perception as well as motor control, it has always been
suggested that this mechanism also underlies higher cognition and
decision-making. However, the adequacy of PPC for this regard cannot be
confirmed by means of neurological standard measurement procedures.
  In this article we combine the parallel research branches of recommender
systems and predictive data mining with theoretical neuroscience. The nexus of
both fields is given by behavioural variability and resulting internal
distributions. We adopt latest experimental settings and measurement approaches
from predictive data mining to obtain these internal distributions, to inform
the theoretical PPC approach and to deduce medical correlates which can indeed
be measured in vivo. This is a strong hint for the applicability of the PPC
approach and the Bayesian Brain Paradigm for higher cognition and human
decision-making.
Winner-Take-All Computation in Spiking Neural Networks
Authors: Nancy Lynch, Cameron Musco, Merav Parter
Date: 2019-04-25 00:00:00
Tasks: All, Clustering

In this work we study biological neural networks from an algorithmic
perspective, focusing on understanding tradeoffs between computation time and
network complexity. Our goal is to abstract real neural networks in a way that,
while not capturing all interesting features, preserves high-level behavior and
allows us to make biologically relevant conclusions. Towards this goal, we
consider the implementation of algorithmic primitives in a simple yet
biologically plausible model of $stochastic\ spiking\ neural\ networks$. In
particular, we show how the stochastic behavior of neurons in this model can be
leveraged to solve a basic $symmetry-breaking\ task$ in which we are given
neurons with identical firing rates and want to select a distinguished one. In
computational neuroscience, this is known as the winner-take-all (WTA) problem,
and it is believed to serve as a basic building block in many tasks, e.g.,
learning, pattern recognition, and clustering. We provide efficient
constructions of WTA circuits in our stochastic spiking neural network model,
as well as lower bounds in terms of the number of auxiliary neurons required to
drive convergence to WTA in a given number of steps. These lower bounds
demonstrate that our constructions are near-optimal in some cases.
  This work covers and gives more in-depth proofs of a subset of results
originally published in [LMP17a]. It is adapted from the last chapter of C.
Musco's Ph.D. thesis [Mus18].
Twitter Sentiment Analysis using Distributed Word and Sentence Representation
Authors: Dwarampudi Mahidhar Reddy, Dr. N V Subba Reddy
Date: 2019-04-01 00:00:00
Tasks: Sentence, Sentiment Analysis, Twitter Sentiment Analysis

An important part of the information gathering and data analysis is to find
out what people think about, either a product or an entity. Twitter is an
opinion rich social networking site. The posts or tweets from this data can be
used for mining people's opinions. The recent surge of activity in this area
can be attributed to the computational treatment of data, which made opinion
extraction and sentiment analysis easier. This paper classifies tweets into
positive and negative sentiments, but instead of using traditional methods or
preprocessing text data here we use the distributed representations of words
and sentences to classify the tweets. We use Long Short Term Memory (LSTM)
Networks, Convolutional Neural Networks (CNNs) and Artificial Neural Networks.
The first two are used on Distributed Representation of words while the latter
is used on the distributed representation of sentences. This paper achieves
accuracies as high as 81%. It also suggests the best and optimal ways for
creating distributed representations of words for sentiment analysis, out of
the available methods.
BowTie - A deep learning feedforward neural network for sentiment analysis
Authors: Apostol Vassilev
Date: 2019-04-18 00:00:00
Tasks: Deep Learning, Sentiment Analysis

How to model and encode the semantics of human-written text and select the
type of neural network to process it are not settled issues in sentiment
analysis. Accuracy and transferability are critical issues in machine learning
in general. These properties are closely related to the loss estimates for the
trained model. I present a computationally-efficient and accurate feedforward
neural network for sentiment prediction capable of maintaining low losses. When
coupled with an effective semantics model of the text, it provides highly
accurate models with low losses. Experimental results on representative
benchmark datasets and comparisons to other methods show the advantages of the
new approach.
A Personalized Affective Memory Neural Model for Improving Emotion Recognition
Authors: Pablo Barros, German I. Parisi, Stefan Wermter
Date: 2019-04-23 00:00:00
Tasks: Emotion Recognition

Recent models of emotion recognition strongly rely on supervised deep learning solutions for the distinction of general emotion expressions. However, they are not reliable when recognizing online and personalized facial expressions, e.g., for person-specific affective understanding. In this paper, we present a neural model based on a conditional adversarial autoencoder to learn how to represent and edit general emotion expressions. We then propose Grow-When-Required networks as personalized affective memories to learn individualized aspects of emotion expressions. Our model achieves state-of-the-art performance on emotion recognition when evaluated on \textit{in-the-wild} datasets. Furthermore, our experiments include ablation studies and neural visualizations in order to explain the behavior of our model.
DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark
Authors: Jianwu Fang, Dingxin Yan, Jiahuan Qiao, Jianru Xue, He Wang, Sen Li
Date: 2019-04-23 00:00:00
Tasks: Driver Attention Monitoring

Driver attention prediction is currently becoming the focus in safe driving
research community, such as the DR(eye)VE project and newly emerged Berkeley
DeepDrive Attention (BDD-A) database in critical situations. In safe driving,
an essential task is to predict the incoming accidents as early as possible.
BDD-A was aware of this problem and collected the driver attention in
laboratory because of the rarity of such scenes. Nevertheless, BDD-A focuses
the critical situations which do not encounter actual accidents, and just faces
the driver attention prediction task, without a close step for accident
prediction. In contrast to this, we explore the view of drivers' eyes for
capturing multiple kinds of accidents, and construct a more diverse and larger
video benchmark than ever before with the driver attention and the driving
accident annotation simultaneously (named as DADA-2000), which has 2000 video
clips owning about 658,476 frames on 54 kinds of accidents. These clips are
crowd-sourced and captured in various occasions (highway, urban, rural, and
tunnel), weather (sunny, rainy and snowy) and light conditions (daytime and
nighttime). For the driver attention representation, we collect the maps of
fixations, saccade scan path and focusing time. The accidents are annotated by
their categories, the accident window in clips and spatial locations of the
crash-objects. Based on the analysis, we obtain a quantitative and positive
answer for the question in this paper.
Exploration of Self-Propelling Droplets Using a Curiosity Driven Robotic Assistant
Authors: Jonathan Grizou, Laurie J. Points, Abhishek Sharma, Leroy Cronin
Date: 2019-04-22 00:00:00

We describe a chemical robotic assistant equipped with a curiosity algorithm
(CA) that can efficiently explore the state a complex chemical system can
exhibit. The CA-robot is designed to explore formulations in an open-ended way
with no explicit optimization target. By applying the CA-robot to the study of
self-propelling multicomponent oil-in-water droplets, we are able to observe an
order of magnitude more variety of droplet behaviours than possible with a
random parameter search and given the same budget. We demonstrate that the
CA-robot enabled the discovery of a sudden and highly specific response of
droplets to slight temperature changes. Six modes of self-propelled droplets
motion were identified and classified using a time-temperature phase diagram
and probed using a variety of techniques including NMR. This work illustrates
how target free search can significantly increase the rate of unpredictable
observations leading to new discoveries with potential applications in
formulation chemistry.
Tracking as A Whole: Multi-Target Tracking by Modeling Group Behavior with Sequential Detection
Authors: Yuan Yuan, Yuwei Lu, Qi. Wang
Date: 2019-04-22 00:00:00
Tasks: vehicle detection

Video-based vehicle detection and tracking is one of the most important
components for Intelligent Transportation Systems (ITS). When it comes to road
junctions, the problem becomes even more difficult due to the occlusions and
complex interactions among vehicles. In order to get a precise detection and
tracking result, in this work we propose a novel tracking-by-detection
framework. In the detection stage, we present a sequential detection model to
deal with serious occlusions. In the tracking stage, we model group behavior to
treat complex interactions with overlaps and ambiguities. The main
contributions of this paper are twofold: 1) Shape prior is exploited in the
sequential detection model to tackle occlusions in crowded scene. 2) Traffic
force is defined in the traffic scene to model group behavior, and it can
assist to handle complex interactions among vehicles. We evaluate the proposed
approach on real surveillance videos at road junctions and the performance has
demonstrated the effectiveness of our method.
Forward Vehicle Collision Warning Based on Quick Camera Calibration
Authors: Yuwei Lu, Yuan Yuan, Qi. Wang
Date: 2019-04-22 00:00:00
Tasks: Autonomous Vehicles, Camera Calibration, vehicle detection

Forward Vehicle Collision Warning (FCW) is one of the most important
functions for autonomous vehicles. In this procedure, vehicle detection and
distance measurement are core components, requiring accurate localization and
estimation. In this paper, we propose a simple but efficient forward vehicle
collision warning framework by aggregating monocular distance measurement and
precise vehicle detection. In order to obtain forward vehicle distance, a quick
camera calibration method which only needs three physical points to calibrate
related camera parameters is utilized. As for the forward vehicle detection, a
multi-scale detection algorithm that regards the result of calibration as
distance priori is proposed to improve the precision. Intensive experiments are
conducted in our established real scene dataset and the results have
demonstrated the effectiveness of the proposed framework.
X-Ray CT Reconstruction of Additively Manufactured Parts using 2.5D Deep Learning MBIR
Authors: Amirkoushyar Ziabari, Michael Kirka, Vincent Paquit, Philip Bingham, Singanallur Venkatakrishnan
Date: 2019-04-02 00:00:00
Tasks: CT Reconstruction

In this paper, we present a deep learning algorithm to rapidly obtain high quality CT reconstructions for AM parts. In particular, we propose to use CAD models of the parts that are to be manufactured, introduce typical defects and simulate XCT measurements. These simulated measurements were processed using FBP (computationally simple but result in noisy images) and the MBIR technique. We then train a 2.5D deep convolutional neural network [4], deemed 2.5D Deep Learning MBIR (2.5D DL-MBIR), on these pairs of noisy and high-quality 3D volumes to learn a fast, non-linear mapping function. The 2.5D DL-MBIR reconstructs a 3D volume in a 2.5D scheme where each slice is reconstructed from multiple inputs slices of the FBP input. Given this trained system, we can take a small set of measurements on an actual part, process it using a combination of FBP followed by 2.5D DL-MBIR. Both steps can be rapidly performed using GPUs, resulting in a real-time algorithm that achieves the high-quality of MBIR as fast as standard techniques. Intuitively, since CAD models are typically available for parts to be manufactured, this provides a strong constraint "prior" which can be leveraged to improve the reconstruction.
Robust object extraction from remote sensing data
Authors: Sophie Crommelinck, Mila Koeva, Michael Ying Yang, George Vosselman
Date: 2019-04-03 00:00:00
Tasks: Object

The extraction of object outlines has been a research topic during the last
decades. In spite of advances in photogrammetry, remote sensing and computer
vision, this task remains challenging due to object and data complexity. The
development of object extraction approaches is promoted through publically
available benchmark datasets and evaluation frameworks. Many aspects of
performance evaluation have already been studied. This study collects the best
practices from literature, puts the various aspects in one evaluation
framework, and demonstrates its usefulness to a case study on mapping object
outlines. The evaluation framework includes five dimensions: the robustness to
changes in resolution, input, location, parameters, and application. Examples
for investigating these dimensions are provided, as well as accuracy measures
for their qualitative analysis. The measures consist of time efficiency and a
procedure for line-based accuracy assessment regarding quantitative
completeness and spatial correctness. The delineation approach to which the
evaluation framework is applied, was previously introduced and is substantially
improved in this study.
An Ensemble of Neural Networks for Non-Linear Segmentation of Overlapped Cursive Script
Authors: Amjad Rehman
Date: 2019-04-07 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Segmentation, valid

Precise character segmentation is the only solution towards higher Optical
Character Recognition (OCR) accuracy. In cursive script, overlapped characters
are serious issue in the process of character segmentations as characters are
deprived from their discriminative parts using conventional linear segmentation
strategy. Hence, non-linear segmentation is an utmost need to avoid loss of
characters parts and to enhance character/script recognition accuracy. This
paper presents an improved approach for non-linear segmentation of the
overlapped characters in handwritten roman script. The proposed technique is
composed of a sequence of heuristic rules based on geometrical features of
characters to locate possible non-linear character boundaries in a cursive
script word. However, to enhance efficiency, heuristic approach is integrated
with trained ensemble neural network validation strategy for verification of
character boundaries. Accordingly, correct boundaries are retained and
incorrect are removed based on ensemble neural networks vote. Finally, based on
verified valid segmentation points, characters are segmented non-linearly. For
fair comparison CEDAR benchmark database is experimented. The experimental
results are much better than conventional linear character segmentation
techniques reported in the state of art. Ensemble neural network play vital
role to enhance character segmentation accuracy as compared to individual
neural networks.
Region homogeneity in the Logarithmic Image Processing framework: application to region growing algorithms
Authors: Guillaume Noyel, Michel Jourlin
Date: 2019-04-17 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In order to create an image segmentation method robust to lighting changes,
two novel homogeneity criteria of an image region were studied. Both were
defined using the Logarithmic Image Processing (LIP) framework whose laws model
lighting changes. The first criterion estimates the LIP-additive homogeneity
and is based on the LIP-additive law. It is theoretically insensitive to
lighting changes caused by variations of the camera exposure-time or source
intensity. The second, the LIP-multiplicative homogeneity criterion, is based
on the LIP-multiplicative law and is insensitive to changes due to variations
of the object thickness or opacity. Each criterion is then applied in Revol and
Jourlin's (1997) region growing method which is based on the homogeneity of an
image region. The region growing method becomes therefore robust to the
lighting changes specific to each criterion. Experiments on simulated and on
real images presenting lighting variations prove the robustness of the criteria
to those variations. Compared to a state-of the art method based on the image
component-tree, ours is more robust. These results open the way to numerous
applications where the lighting is uncontrolled or partially controlled.
Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences
Authors: Sascha Wirges, Johannes Gräter, Qiuhao Zhang, Christoph Stiller
Date: 2019-04-17 00:00:00
Tasks: Optical Flow Estimation, Scene Flow Estimation

We present a self-supervised approach to estimate flow in camera image and
top-view grid map sequences using fully convolutional neural networks in the
domain of automated driving. We extend existing approaches for self-supervised
optical flow estimation by adding a regularizer expressing motion consistency
assuming a static environment. However, as this assumption is violated for
other moving traffic participants we also estimate a mask to scale this
regularization. Adding a regularization towards motion consistency improves
convergence and flow estimation accuracy. Furthermore, we scale the errors due
to spatial flow inconsistency by a mask that we derive from the motion mask.
This improves accuracy in regions where the flow drastically changes due to a
better separation between static and dynamic environment. We apply our approach
to optical flow estimation from camera image sequences, validate on odometry
estimation and suggest a method to iteratively increase optical flow estimation
accuracy using the generated motion masks. Finally, we provide quantitative and
qualitative results based on the KITTI odometry and tracking benchmark for
scene flow estimation based on grid map sequences. We show that we can improve
accuracy and convergence when applying motion and spatial consistency
regularization.
EV-Action: Electromyography-Vision Multi-Modal Action Dataset
Authors: Lichen Wang, Bin Sun, Joseph Robinson, Taotao Jing, Yun Fu
Date: 2019-04-20 00:00:00
Tasks: Action Analysis, Action Recognition, Electromyography (EMG), Multimodal Activity Recognition, Temporal Action Localization

Multi-modal human action analysis is a critical and attractive research topic. However, the majority of the existing datasets only provide visual modalities (i.e., RGB, depth and skeleton). To make up this, we introduce a new, large-scale EV-Action dataset in this work, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities. Compared with the conventional datasets, EV-Action dataset has two major improvements: (1) we deploy a motion capturing system to obtain high quality skeleton modality, which provides more comprehensive motion information including skeleton, trajectory, acceleration with higher accuracy, sampling frequency, and more skeleton markers. (2) we introduce an EMG modality which is usually used as an effective indicator in the biomechanics area, also it has yet to be well explored in motion related research. To the best of our knowledge, this is the first action dataset with EMG modality. The details of EV-Action dataset are clarified, meanwhile, a simple yet effective framework for EMG-based action recognition is proposed. Moreover, state-of-the-art baselines are applied to evaluate the effectiveness of all the modalities. The obtained result clearly shows the validity of EMG modality in human action analysis tasks. We hope this dataset can make significant contributions to human motion analysis, computer vision, machine learning, biomechanics, and other interdisciplinary fields.