pwc-archive/papers-with-abstracts huggingface dataset:
UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition
Authors: Asanka G Perera, Yee Wei Law, Javaan Chahl
Date: 2019-01-09 00:00:00
Tasks: Action Recognition, Gesture Recognition, Object Tracking, Temporal Action Localization

Current UAV-recorded datasets are mostly limited to action recognition and
object tracking, whereas the gesture signals datasets were mostly recorded in
indoor spaces. Currently, there is no outdoor recorded public video dataset for
UAV commanding signals. Gesture signals can be effectively used with UAVs by
leveraging the UAVs visual sensors and operational simplicity. To fill this gap
and enable research in wider application areas, we present a UAV gesture
signals dataset recorded in an outdoor setting. We selected 13 gestures
suitable for basic UAV navigation and command from general aircraft handling
and helicopter handling signals. We provide 119 high-definition video clips
consisting of 37151 frames. The overall baseline gesture recognition
performance computed using Pose-based Convolutional Neural Network (P-CNN) is
91.9 %. All the frames are annotated with body joints and gesture classes in
order to extend the dataset's applicability to a wider research area including
gesture recognition, action recognition, human pose recognition and situation
awareness.
D3TW: Discriminative Differentiable Dynamic Time Warping for Weakly Supervised Action Alignment and Segmentation
Authors: Chien-Yi Chang, De-An Huang, Yanan Sui, Li Fei-Fei, Juan Carlos Niebles
Date: 2019-01-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Dynamic Time Warping, Segmentation, Weakly Supervised Action Segmentation (Transcript)

We address weakly supervised action alignment and segmentation in videos,
where only the order of occurring actions is available during training. We
propose Discriminative Differentiable Dynamic Time Warping (D3TW), the first
discriminative model using weak ordering supervision. The key technical
challenge for discriminative modeling with weak supervision is that the loss
function of the ordering supervision is usually formulated using dynamic
programming and is thus not differentiable. We address this challenge with a
continuous relaxation of the min-operator in dynamic programming and extend the
alignment loss to be differentiable. The proposed D3TW innovatively solves
sequence alignment with discriminative modeling and end-to-end training, which
substantially improves the performance in weakly supervised action alignment
and segmentation tasks. We show that our model is able to bypass the
degenerated sequence problem usually encountered in previous work and
outperform the current state-of-the-art across three evaluation metrics in two
challenging datasets.
MSR: Multi-Scale Shape Regression for Scene Text Detection
Authors: Chuhui Xue, Shijian Lu, Wei zhang
Date: 2019-01-09 00:00:00
Tasks: regression, Scene Text Detection, Text Detection

State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes. This paper presents a novel multi-scale shape regression network (MSR) that is capable of locating text lines of different lengths, shapes and curvatures in scenes. The proposed MSR detects scene texts by predicting dense text boundary points that inherently capture the location and shape of text lines accurately and are also more tolerant to the variation of text line length as compared with the state of the arts using proposals or segmentation. Additionally, the multi-scale network extracts and fuses features at different scales which demonstrates superb tolerance to the text scale variation. Extensive experiments over several public datasets show that the proposed MSR obtains superior detection performance for both curved and straight text lines of different lengths and orientations.
Manipulation-skill Assessment from Videos with Spatial Attention Network
Authors: Zhenqiang Li, Yifei Huang, Minjie Cai, Yoichi Sato
Date: 2019-01-09 00:00:00

Recent advances in computer vision have made it possible to automatically
assess from videos the manipulation skills of humans in performing a task,
which breeds many important applications in domains such as health
rehabilitation and manufacturing. Previous methods of video-based skill
assessment did not consider the attention mechanism humans use in assessing
videos, limiting their performance as only a small part of video regions is
informative for skill assessment. Our motivation here is to estimate attention
in videos that helps to focus on critically important video regions for better
skill assessment. In particular, we propose a novel RNN-based spatial attention
model that considers accumulated attention state from previous frames as well
as high-level knowledge about the progress of an undergoing task. We evaluate
our approach on a newly collected dataset of infant grasping task and four
existing datasets of hand manipulation tasks. Experiment results demonstrate
that state-of-the-art performance can be achieved by considering attention in
automatic skill assessment.
Robust and Adaptive Planning under Model Uncertainty
Authors: Apoorva Sharma, James Harrison, Matthew Tsao, Marco Pavone
Date: 2019-01-09 00:00:00
Tasks: Computational Efficiency, Decision Making, model

Planning under model uncertainty is a fundamental problem across many
applications of decision making and learning. In this paper, we propose the
Robust Adaptive Monte Carlo Planning (RAMCP) algorithm, which allows
computation of risk-sensitive Bayes-adaptive policies that optimally trade off
exploration, exploitation, and robustness. RAMCP formulates the risk-sensitive
planning problem as a two-player zero-sum game, in which an adversary perturbs
the agent's belief over the models. We introduce two versions of the RAMCP
algorithm. The first, RAMCP-F, converges to an optimal risk-sensitive policy
without having to rebuild the search tree as the underlying belief over models
is perturbed. The second version, RAMCP-I, improves computational efficiency at
the cost of losing theoretical guarantees, but is shown to yield empirical
results comparable to RAMCP-F. RAMCP is demonstrated on an n-pull multi-armed
bandit problem, as well as a patient treatment scenario.
Interactive Image Segmentation using Label Propagation through Complex Networks
Authors: Fabricio Aparecido Breve
Date: 2019-01-09 00:00:00
Tasks: Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

Interactive image segmentation is a topic of many studies in image
processing. In a conventional approach, a user marks some pixels of the
object(s) of interest and background, and an algorithm propagates these labels
to the rest of the image. This paper presents a new graph-based method for
interactive segmentation with two stages. In the first stage, nodes
representing pixels are connected to their $k$-nearest neighbors to build a
complex network with the small-world property to propagate the labels quickly.
In the second stage, a regular network in a grid format is used to refine the
segmentation on the object borders. Despite its simplicity, the proposed method
can perform the task with high accuracy. Computer simulations are performed
using some real-world images to show its effectiveness in both two-classes and
multi-classes problems. It is also applied to all the images from the Microsoft
GrabCut dataset for comparison, and the segmentation accuracy is comparable to
those achieved by some state-of-the-art methods, while it is faster than them.
In particular, it outperforms some recent approaches when the user input is
composed only by a few "scribbles" draw over the objects. Its computational
complexity is only linear on the image size at the best-case scenario and
linearithmic in the worst case.
Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera
Authors: Chao Liu, Jinwei Gu, Kihwan Kim, Srinivasa Narasimhan, Jan Kautz
Date: 2019-01-09 00:00:00
Tasks: 3D Reconstruction, 3D Scene Reconstruction, Scene Understanding

Depth sensing is crucial for 3D reconstruction and scene understanding.
Active depth sensors provide dense metric measurements, but often suffer from
limitations such as restricted operating ranges, low spatial resolution, sensor
interference, and high power consumption. In this paper, we propose a deep
learning (DL) method to estimate per-pixel depth and its uncertainty
continuously from a monocular video stream, with the goal of effectively
turning an RGB camera into an RGB-D camera. Unlike prior DL-based methods, we
estimate a depth probability distribution for each pixel rather than a single
depth value, leading to an estimate of a 3D depth probability volume for each
input frame. These depth probability volumes are accumulated over time under a
Bayesian filtering framework as more incoming frames are processed
sequentially, which effectively reduces depth uncertainty and improves
accuracy, robustness, and temporal stability. Compared to prior work, the
proposed approach achieves more accurate and stable results, and generalizes
better to new datasets. Experimental results also show the output of our
approach can be directly fed into classical RGB-D based 3D scanning methods for
3D scene reconstruction.
High-Fidelity Vector Space Models of Structured Data
Authors: Maxwell Crouse, Achille Fokoue, Maria Chang, Pavan Kapanipathi, Ryan Musa, Constantine Nakos, Lingfei Wu, Kenneth Forbus, Michael Witbrock
Date: 2019-01-09 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

Machine learning systems regularly deal with structured data in real-world
applications. Unfortunately, such data has been difficult to faithfully
represent in a way that most machine learning techniques would expect, i.e. as
a real-valued vector of a fixed, pre-specified size. In this work, we introduce
a novel approach that compiles structured data into a satisfiability problem
which has in its set of solutions at least (and often only) the input data. The
satisfiability problem is constructed from constraints which are generated
automatically a priori from a given signature, thus trivially allowing for a
bag-of-words-esque vector representation of the input to be constructed. The
method is demonstrated in two areas, automated reasoning and natural language
processing, where it is shown to produce vector representations of
natural-language sentences and first-order logic clauses that can be precisely
translated back to their original, structured input forms.
Thinking Outside the Pool: Active Training Image Creation for Relative Attributes
Authors: Aron Yu, Kristen Grauman
Date: 2019-01-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Image Generation

Current wisdom suggests more labeled image data is always better, and
obtaining labels is the bottleneck. Yet curating a pool of sufficiently diverse
and informative images is itself a challenge. In particular, training image
curation is problematic for fine-grained attributes, where the subtle visual
differences of interest may be rare within traditional image sources. We
propose an active image generation approach to address this issue. The main
idea is to jointly learn the attribute ranking task while also learning to
generate novel realistic image samples that will benefit that task. We
introduce an end-to-end framework that dynamically "imagines" image pairs that
would confuse the current model, presents them to human annotators for
labeling, then improves the predictive model with the new examples. With
results on two datasets, we show that by thinking outside the pool of real
images, our approach gains generalization accuracy for challenging fine-grained
attribute comparisons.
Deep Neural Networks Predicting Oil Movement in a Development Unit
Authors: Pavel Temirchev, Maxim Simonov, Ruslan Kostoev, Evgeny Burnaev, Ivan Oseledets, Alexey Akhmetov, Andrey Margarit, Alexander Sitnikov, Dmitry Koroteev
Date: 2019-01-08 00:00:00

We present a novel technique for assessing the dynamics of multiphase fluid flow in the oil reservoir. We demonstrate an efficient workflow for handling the 3D reservoir simulation data in a way which is orders of magnitude faster than the conventional routine. The workflow (we call it "Metamodel") is based on a projection of the system dynamics into a latent variable space, using Variational Autoencoder model, where Recurrent Neural Network predicts the dynamics. We show that being trained on multiple results of the conventional reservoir modelling, the Metamodel does not compromise the accuracy of the reservoir dynamics reconstruction in a significant way. It allows forecasting not only the flow rates from the wells, but also the dynamics of pressure and fluid saturations within the reservoir. The results open a new perspective in the optimization of oilfield development as the scenario screening could be accelerated sufficiently.
NEARBY Platform: Algorithm for Automated Asteroids Detection in Astronomical Images
Authors: T. Stefanut, V. Bacu, C. Nandra, D. Balasz, D. Gorgan, O. Vaduvescu
Date: 2019-01-08 00:00:00

In the past two decades an increasing interest in discovering Near Earth
Objects has been noted in the astronomical community. Dedicated surveys have
been operated for data acquisition and processing, resulting in the present
discovery of over 18.000 objects that are closer than 30 million miles of
Earth. Nevertheless, recent events have shown that there still are many
undiscovered asteroids that can be on collision course to Earth. This article
presents an original NEO detection algorithm developed in the NEARBY research
object, that has been integrated into an automated MOPS processing pipeline
aimed at identifying moving space objects based on the blink method. Proposed
solution can be considered an approach of Big Data processing and analysis,
implementing visual analytics techniques for rapid human data validation.
Computational Register Analysis and Synthesis
Authors: Shlomo Engelson Argamon
Date: 2019-01-08 00:00:00

The study of register in computational language research has historically
been divided into register analysis, seeking to determine the registerial
character of a text or corpus, and register synthesis, seeking to generate a
text in a desired register. This article surveys the different approaches to
these disparate tasks. Register synthesis has tended to use more theoretically
articulated notions of register and genre than analysis work, which often seeks
to categorize on the basis of intuitive and somewhat incoherent notions of
prelabeled 'text types'. I argue that an integration of computational register
analysis and synthesis will benefit register studies as a whole, by enabling a
new large-scale research program in register studies. It will enable
comprehensive global mapping of functional language varieties in multiple
languages, including the relationships between them. Furthermore, computational
methods together with high coverage systematically collected and analyzed data
will thus enable rigorous empirical validation and refinement of different
theories of register, which will have also implications for our understanding
of linguistic variation in general.
Asteroids Detection Technique: Classic "Blink" An Automated Approch
Authors: D. Copandean, C. Nandra, D. Gorgan, O. Vaduvescu
Date: 2019-01-08 00:00:00

Asteroids detection is a very important research field that received
increased attention in the last couple of decades. Some major surveys have
their own dedicated people, equipment and detection applications, so they are
discovering Near Earth Asteroids (NEAs) daily. The interest in asteroids is not
limited to those major surveys, it is shared by amateurs and mini-surveys too.
A couple of them are using the few existent software solutions, most of which
are developed by amateurs. The rest obtain their results in a visual manner:
they "blink" a sequence of reduced images of the same field, taken at a
specific time interval, and they try to detect a real moving object in the
resulting animation. Such a technique becomes harder with the increase in size
of the CCD cameras. Aiming to replace manual detection, we propose an automated
"blink" technique for asteroids detection.
Supervised Transfer Learning for Product Information Question Answering
Authors: Tuan Manh Lai, Trung Bui, Nedim Lipka, Sheng Li
Date: 2019-01-08 00:00:00
Tasks: Community Question Answering, Question Answering, Transfer Learning

Popular e-commerce websites such as Amazon offer community question answering
systems for users to pose product related questions and experienced customers
may provide answers voluntarily. In this paper, we show that the large volume
of existing community question answering data can be beneficial when building a
system for answering questions related to product facts and specifications. Our
experimental results demonstrate that the performance of a model for answering
questions related to products listed in the Home Depot website can be improved
by a large margin via a simple transfer learning technique from an existing
large-scale Amazon community question answering dataset. Transfer learning can
result in an increase of about 10% in accuracy in the experimental setting
where we restrict the size of the data of the target task used for training. As
an application of this work, we integrate the best performing model trained in
this work into a mobile-based shopping assistant and show its usefulness.
Collaborative Execution of Deep Neural Networks on Internet of Things Devices
Authors: Ramyad Hadidi, Jiashen Cao, Micheal S. Ryoo, Hyesoon Kim
Date: 2019-01-08 00:00:00

With recent advancements in deep neural networks (DNNs), we are able to solve
traditionally challenging problems. Since DNNs are compute intensive,
consumers, to deploy a service, need to rely on expensive and scarce compute
resources in the cloud. This approach, in addition to its dependability on
high-quality network infrastructure and data centers, raises new privacy
concerns. These challenges may limit DNN-based applications, so many
researchers have tried optimize DNNs for local and in-edge execution. However,
inadequate power and computing resources of edge devices along with small
number of requests limits current optimizations applicability, such as batch
processing. In this paper, we propose an approach that utilizes aggregated
existing computing power of Internet of Things (IoT) devices surrounding an
environment by creating a collaborative network. In this approach, IoT devices
cooperate to conduct single-batch inferencing in real time. While exploiting
several new model-parallelism methods and their distribution characteristics,
our approach enhances the collaborative network by creating a balanced and
distributed processing pipeline. We have illustrated our work using many
Raspberry Pis with studying DNN models such as AlexNet, VGG16, Xception, and
C3D.
Team Papelo: Transformer Networks at FEVER
Authors: Christopher Malon
Date: 2019-01-08 00:00:00
Proceeding: WS 2018 11
Tasks: Articles, Language Modeling, Language Modelling

We develop a system for the FEVER fact extraction and verification challenge
that uses a high precision entailment classifier based on transformer networks
pretrained with language modeling, to classify a broad set of potential
evidence. The precision of the entailment classifier allows us to enhance
recall by considering every statement from several articles to decide upon each
claim. We include not only the articles best matching the claim text by TFIDF
score, but read additional articles whose titles match named entities and
capitalized expressions occurring in the claim text. The entailment module
evaluates potential evidence one statement at a time, together with the title
of the page the evidence came from (providing a hint about possible pronoun
antecedents). In preliminary evaluation, the system achieves .5736 FEVER score,
.6108 label accuracy, and .6485 evidence F1 on the FEVER shared task test set.
Fast 3D Line Segment Detection From Unorganized Point Cloud
Authors: Xiaohu Lu, Yahui Liu, Kai Li
Date: 2019-01-08 00:00:00
Tasks: Line Detection, Line Segment Detection, Point Cloud Segmentation

This paper presents a very simple but efficient algorithm for 3D line segment
detection from large scale unorganized point cloud. Unlike traditional methods
which usually extract 3D edge points first and then link them to fit for 3D
line segments, we propose a very simple 3D line segment detection algorithm
based on point cloud segmentation and 2D line detection. Given the input
unorganized point cloud, three steps are performed to detect 3D line segments.
Firstly, the point cloud is segmented into 3D planes via region growing and
region merging. Secondly, for each 3D plane, all the points belonging to it are
projected onto the plane itself to form a 2D image, which is followed by 2D
contour extraction and Least Square Fitting to get the 2D line segments. Those
2D line segments are then re-projected onto the 3D plane to get the
corresponding 3D line segments. Finally, a post-processing procedure is
proposed to eliminate outliers and merge adjacent 3D line segments. Experiments
on several public datasets demonstrate the efficiency and robustness of our
method. More results and the C++ source code of the proposed algorithm are
publicly available at https://github.com/xiaohulugo/3DLineDetection.
A Spatial-temporal 3D Human Pose Reconstruction Framework
Authors: X. T. Nguyen, T. D. Ngo, T. H. Le
Date: 2019-01-08 00:00:00
Tasks: 3D Reconstruction

3D human pose reconstruction from single-view camera is a difficult and
challenging topic. Many approaches have been proposed, but almost focusing on
frame-by-frame independently while inter-frames are highly correlated in a pose
sequence. In contrast, we introduce a novel spatial-temporal 3D reconstruction
framework that leverages both intra and inter frame relationships in
consecutive 2D pose sequences. Orthogonal Matching Pursuit (OMP) algorithm,
pre-trained Pose-angle Limits and Temporal Models have been implemented. We
quantitatively compare our framework versus recent works on CMU motion capture
dataset and Vietnamese traditional dance sequences. Our method outperforms
others with 10 percent lower of Euclidean reconstruction error and robustness
against Gaussian noise. Additionally, it is also important to mention that our
reconstructed 3D pose sequences are smoother and more natural than others.
Robust Change Captioning
Authors: Dong Huk Park, Trevor Darrell, Anna Rohrbach
Date: 2019-01-08 00:00:00
Proceeding: ICCV 2019 10
Tasks: Natural Language Visual Grounding

Describing what has changed in a scene can be useful to a user, but only if
generated text focuses on what is semantically relevant. It is thus important
to distinguish distractors (e.g. a viewpoint change) from relevant changes
(e.g. an object has moved). We present a novel Dual Dynamic Attention Model
(DUDA) to perform robust Change Captioning. Our model learns to distinguish
distractors from semantic changes, localize the changes via Dual Attention over
"before" and "after" images, and accurately describe them in natural language
via Dynamic Speaker, by adaptively focusing on the necessary visual inputs
(e.g. "before" or "after" image). To study the problem in depth, we collect a
CLEVR-Change dataset, built off the CLEVR engine, with 5 types of scene
changes. We benchmark a number of baselines on our dataset, and systematically
study different change types and robustness to distractors. We show the
superiority of our DUDA model in terms of both change captioning and
localization. We also show that our approach is general, obtaining
state-of-the-art results on the recent realistic Spot-the-Diff dataset which
has no distractors.