pwc-archive/papers-with-abstracts huggingface dataset:
A generalizable approach for multi-view 3D human pose regression
Authors: Abdolrahim Kadkhodamohammadi, Nicolas Padoy
Date: 2018-04-27 00:00:00
Tasks: 3D Pose Estimation, Pose Estimation, regression

Despite the significant improvement in the performance of monocular pose estimation approaches and their ability to generalize to unseen environments, multi-view (MV) approaches are often lagging behind in terms of accuracy and are specific to certain datasets. This is mainly due to the fact that (1) contrary to real world single-view (SV) datasets, MV datasets are often captured in controlled environments to collect precise 3D annotations, which do not cover all real world challenges, and (2) the model parameters are learned for specific camera setups. To alleviate these problems, we propose a two-stage approach to detect and estimate 3D human poses, which separates SV pose detection from MV 3D pose estimation. This separation enables us to utilize each dataset for the right task, i.e. SV datasets for constructing robust pose detection models and MV datasets for constructing precise MV 3D regression models. In addition, our 3D regression approach only requires 3D pose data and its projections to the views for building the model, hence removing the need for collecting annotated data from the test setup. Our approach can therefore be easily generalized to a new environment by simply projecting 3D poses into 2D during training according to the camera setup used at test time. As 2D poses are collected at test time using a SV pose detector, which might generate inaccurate detections, we model its characteristics and incorporate this information during training. We demonstrate that incorporating the detector's characteristics is important to build a robust 3D regression model and that the resulting regression model generalizes well to new MV environments. Our evaluation results show that our approach achieves competitive results on the Human3.6M dataset and significantly improves results on a MV clinical dataset that is the first MV dataset generated from live surgery recordings.
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning
Authors: Haiyan Yin, Jianda Chen, Sinno Jialin Pan
Date: 2017-07-03 00:00:00
Tasks: Deep Reinforcement Learning, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In deep reinforcement learning (RL) tasks, an efficient exploration mechanism
should be able to encourage an agent to take actions that lead to less frequent
states which may yield higher accumulative future return. However, both knowing
about the future and evaluating the frequentness of states are non-trivial
tasks, especially for deep RL domains, where a state is represented by
high-dimensional image frames. In this paper, we propose a novel informed
exploration framework for deep RL, where we build the capability for an RL
agent to predict over the future transitions and evaluate the frequentness for
the predicted future frames in a meaningful manner. To this end, we train a
deep prediction model to predict future frames given a state-action pair, and a
convolutional autoencoder model to hash over the seen frames. In addition, to
utilize the counts derived from the seen frames to evaluate the frequentness
for the predicted frames, we tackle the challenge of matching the predicted
future frames and their corresponding seen frames at the latent feature level.
In this way, we derive a reliable metric for evaluating the novelty of the
future direction pointed by each action, and hence inform the agent to explore
the least frequent one.
Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filters
Authors: Andreas Meinel, Henrich Kolkhorst, Michael Tangermann
Date: 2018-04-27 00:00:00
Tasks: Brain Computer Interface

Data-driven spatial filtering algorithms optimize scores such as the contrast
between two conditions to extract oscillatory brain signal components. Most
machine learning approaches for filter estimation, however, disregard
within-trial temporal dynamics and are extremely sensitive to changes in
training data and involved hyperparameters. This leads to highly variable
solutions and impedes the selection of a suitable candidate for,
e.g.,~neurotechnological applications. Fostering component introspection, we
propose to embrace this variability by condensing the functional signatures of
a large set of oscillatory components into homogeneous clusters, each
representing specific within-trial envelope dynamics.
  The proposed method is exemplified by and evaluated on a complex hand force
task with a rich within-trial structure. Based on electroencephalography data
of 18 healthy subjects, we found that the components' distinct temporal
envelope dynamics are highly subject-specific. On average, we obtained seven
clusters per subject, which were strictly confined regarding their underlying
frequency bands. As the analysis method is not limited to a specific spatial
filtering algorithm, it could be utilized for a wide range of
neurotechnological applications, e.g., to select and monitor functionally
relevant features for brain-computer interface protocols in stroke
rehabilitation.
Bound and Conquer: Improving Triangulation by Enforcing Consistency
Authors: Adam Scholefield, Alireza Ghasemi, Martin Vetterli
Date: 2018-04-27 00:00:00

We study the accuracy of triangulation in multi-camera systems with respect
to the number of cameras. We show that, under certain conditions, the optimal
achievable reconstruction error decays quadratically as more cameras are added
to the system. Furthermore, we analyse the error decay-rate of major
state-of-the-art algorithms with respect to the number of cameras. To this end,
we introduce the notion of consistency for triangulation, and show that
consistent reconstruction algorithms achieve the optimal quadratic decay, which
is asymptotically faster than some other methods. Finally, we present
simulations results supporting our findings. Our simulations have been
implemented in MATLAB and the resulting code is available in the supplementary
material.
Generalized Logical Operations among Conditional Events
Authors: Angelo Gilio, Giuseppe Sanfilippo
Date: 2018-04-27 00:00:00
Tasks: Negation, valid

We generalize, by a progressive procedure, the notions of conjunction and
disjunction of two conditional events to the case of $n$ conditional events. In
our coherence-based approach, conjunctions and disjunctions are suitable
conditional random quantities. We define the notion of negation, by verifying
De Morgan's Laws. We also show that conjunction and disjunction satisfy the
associative and commutative properties, and a monotonicity property. Then, we
give some results on coherence of prevision assessments for some families of
compounded conditionals; in particular we examine the Fr\'echet-Hoeffding
bounds. Moreover, we study the reverse probabilistic inference from the
conjunction $\mathcal{C}_{n+1}$ of $n+1$ conditional events to the family
$\{\mathcal{C}_{n},E_{n+1}|H_{n+1}\}$. We consider the relation with the notion
of quasi-conjunction and we examine in detail the coherence of the prevision
assessments related with the conjunction of three conditional events. Based on
conjunction, we also give a characterization of p-consistency and of
p-entailment, with applications to several inference rules in probabilistic
nonmonotonic reasoning. Finally, we examine some non p-valid inference rules;
then, we illustrate by an example two methods which allow to suitably modify
non p-valid inference rules in order to get inferences which are p-valid.
Customized Image Narrative Generation via Interactive Visual Question Generation and Answering
Authors: Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada
Date: 2018-04-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Diversity, Image Description, Question Generation, Question-Generation

Image description task has been invariably examined in a static manner with
qualitative presumptions held to be universally applicable, regardless of the
scope or target of the description. In practice, however, different viewers may
pay attention to different aspects of the image, and yield different
descriptions or interpretations under various contexts. Such diversity in
perspectives is difficult to derive with conventional image description
techniques. In this paper, we propose a customized image narrative generation
task, in which the users are interactively engaged in the generation process by
providing answers to the questions. We further attempt to learn the user's
interest via repeating such interactive stages, and to automatically reflect
the interest in descriptions for new images. Experimental results demonstrate
that our model can generate a variety of descriptions from single image that
cover a wider range of topics than conventional models, while being
customizable to the target user of interaction.
How does the AI understand what's going on
Authors: Dimiter Dobrev
Date: 2018-04-27 00:00:00
Tasks: Articles

Most researchers regard AI as a static function without memory. This is one
of the few articles where AI is seen as a device with memory. When we have
memory, we can ask ourselves: "Where am I?", and "What is going on?" When we
have no memory, we have to assume that we are always in the same place and that
the world is always in the same state.
Routing Driverless Transport Vehicles in Car Assembly with Answer Set Programming
Authors: Martin Gebser, Philipp Obermeier, Michel Ratsch-Heitmann, Mario Runge, Torsten Schaub
Date: 2018-04-27 00:00:00
Tasks: Retrieval

Automated storage and retrieval systems are principal components of modern
production and warehouse facilities. In particular, automated guided vehicles
nowadays substitute human-operated pallet trucks in transporting production
materials between storage locations and assembly stations. While low-level
control systems take care of navigating such driverless vehicles along
programmed routes and avoid collisions even under unforeseen circumstances, in
the common case of multiple vehicles sharing the same operation area, the
problem remains how to set up routes such that a collection of transport tasks
is accomplished most effectively. We address this prevalent problem in the
context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale
producer of commercial vehicles, where routes for automated guided vehicles
used in the production process have traditionally been hand-coded by human
engineers. Such ad-hoc methods may suffice as long as a running production
process remains in place, while any change in the factory layout or production
targets necessitates tedious manual reconfiguration, not to mention the missing
portability between different production plants. Unlike this, we propose a
declarative approach based on Answer Set Programming to optimize the routes
taken by automated guided vehicles for accomplishing transport tasks. The
advantages include a transparent and executable problem formalization, provable
optimality of routes relative to objective criteria, as well as elaboration
tolerance towards particular factory layouts and production targets. Moreover,
we demonstrate that our approach is efficient enough to deal with the transport
tasks evolving in realistic production processes at the car factory of
Mercedes-Benz Ludwigsfelde GmbH.
Robust Classification of Graph-Based Data
Authors: Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
Date: 2016-12-21 00:00:00
Tasks: Classification, General Classification, regression, Robust classification

A graph-based classification method is proposed for semi-supervised learning
in the case of Euclidean data and for classification in the case of graph data.
Our manifold learning technique is based on a convex optimization problem
involving a convex quadratic regularization term and a concave quadratic loss
function with a trade-off parameter carefully chosen so that the objective
function remains convex. As shown empirically, the advantage of considering a
concave loss function is that the learning problem becomes more robust in the
presence of noisy labels. Furthermore, the loss function considered here is
then more similar to a classification loss while several other methods treat
graph-based classification problems as regression problems.
Variational Regularization of Inverse Problems for Manifold-Valued Data
Authors: Martin Storath, Andreas Weinmann
Date: 2018-04-27 00:00:00

In this paper, we consider the variational regularization of manifold-valued
data in the inverse problems setting. In particular, we consider TV and TGV
regularization for manifold-valued data with indirect measurement operators. We
provide results on the well-posedness and present algorithms for a numerical
realization of these models in the manifold setup. Further, we provide
experimental results for synthetic and real data to show the potential of the
proposed schemes for applications.
Open Set Domain Adaptation by Backpropagation
Authors: Kuniaki Saito, Shohei Yamamoto, Yoshitaka Ushiku, Tatsuya Harada
Date: 2018-04-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Domain Adaptation

Numerous algorithms have been proposed for transferring knowledge from a
label-rich domain (source) to a label-scarce domain (target). Almost all of
them are proposed for a closed-set scenario, where the source and the target
domain completely share the class of their samples. We call the shared class
the \doublequote{known class.} However, in practice, when samples in target
domain are not labeled, we cannot know whether the domains share the class. A
target domain can contain samples of classes that are not shared by the source
domain. We call such classes the \doublequote{unknown class} and algorithms
that work well in the open set situation are very practical. However, most
existing distribution matching methods for domain adaptation do not work well
in this setting because unknown target samples should not be aligned with the
source.
  In this paper, we propose a method for an open set domain adaptation scenario
which utilizes adversarial training. A classifier is trained to make a boundary
between the source and the target samples whereas a generator is trained to
make target samples far from the boundary. Thus, we assign two options to the
feature generator: aligning them with source known samples or rejecting them as
unknown target samples. This approach allows extracting features that separate
unknown target samples from known target samples. Our method was extensively
evaluated in domain adaptation setting and outperformed other methods with a
large margin in most settings.
Link and code: Fast indexing with graphs and compact regression codes
Authors: Matthijs Douze, Alexandre Sablayrolles, Hervé Jégou
Date: 2018-04-26 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Similarity Search, Quantization, regression

Similarity search approaches based on graph walks have recently attained
outstanding speed-accuracy trade-offs, taking aside the memory requirements. In
this paper, we revisit these approaches by considering, additionally, the
memory constraint required to index billions of images on a single server. This
leads us to propose a method based both on graph traversal and compact
representations. We encode the indexed vectors using quantization and exploit
the graph structure to refine the similarity estimation.
  In essence, our method takes the best of these two worlds: the search
strategy is based on nested graphs, thereby providing high precision with a
relatively small set of comparisons. At the same time it offers a significant
memory compression. As a result, our approach outperforms the state of the art
on operating points considering 64-128 bytes per vector, as demonstrated by our
results on two billion-scale public benchmarks.
Extracting Parallel Paragraphs from Common Crawl
Authors: Jakub Kúdela, Irena Holubová, Ondřej Bojar
Date: 2018-04-27 00:00:00

Most of the current methods for mining parallel texts from the web assume
that web pages of web sites share same structure across languages. We believe
that there still exists a non-negligible amount of parallel data spread across
sources not satisfying this assumption. We propose an approach based on a
combination of bivec (a bilingual extension of word2vec) and locality-sensitive
hashing which allows us to efficiently identify pairs of parallel segments
located anywhere on pages of a given web domain, regardless their structure. We
validate our method on realigning segments from a large parallel corpus.
Another experiment with real-world data provided by Common Crawl Foundation
confirms that our solution scales to hundreds of terabytes large set of
web-crawled data.
A Visual Distance for WordNet
Authors: Raquel Pérez-Arnal, Armand Vilalta, Dario Garcia-Gasulla, Ulises Cortés, Eduard Ayguadé, Jesus Labarta
Date: 2018-04-24 00:00:00

Measuring the distance between concepts is an important field of study of
Natural Language Processing, as it can be used to improve tasks related to the
interpretation of those same concepts. WordNet, which includes a wide variety
of concepts associated with words (i.e., synsets), is often used as a source
for computing those distances. In this paper, we explore a distance for WordNet
synsets based on visual features, instead of lexical ones. For this purpose, we
extract the graphic features generated within a deep convolutional neural
networks trained with ImageNet and use those features to generate a
representative of each synset. Based on those representatives, we define a
distance measure of synsets, which complements the traditional lexical
distances. Finally, we propose some experiments to evaluate its performance and
compare it with the current state-of-the-art.
CLBlast: A Tuned OpenCL BLAS Library
Authors: Cedric Nugteren
Date: 2017-05-12 00:00:00

This work introduces CLBlast, an open-source BLAS library providing optimized
OpenCL routines to accelerate dense linear algebra for a wide variety of
devices. It is targeted at machine learning and HPC applications and thus
provides a fast matrix-multiplication routine (GEMM) to accelerate the core of
many applications (e.g. deep learning, iterative solvers, astrophysics,
computational fluid dynamics, quantum chemistry). CLBlast has five main
advantages over other OpenCL BLAS libraries: 1) it is optimized for and tested
on a large variety of OpenCL devices including less commonly used devices such
as embedded and low-power GPUs, 2) it can be explicitly tuned for specific
problem-sizes on specific hardware platforms, 3) it can perform operations in
half-precision floating-point FP16 saving bandwidth, time and energy, 4) it has
an optional CUDA back-end, 5) and it can combine multiple operations in a
single batched routine, accelerating smaller problems significantly. This paper
describes the library and demonstrates the advantages of CLBlast experimentally
for different use-cases on a wide variety of OpenCL hardware.
TextBoxes++: A Single-Shot Oriented Scene Text Detector
Authors: Minghui Liao, Baoguang Shi, Xiang Bai
Date: 2018-01-09 00:00:00
Tasks: object-detection, Object Detection, Scene Text Detection, Scene Text Recognition, Text Detection

Scene text detection is an important step of scene text recognition system
and also a challenging problem. Different from general object detection, the
main challenges of scene text detection lie on arbitrary orientations, small
sizes, and significantly variant aspect ratios of text in natural images. In
this paper, we present an end-to-end trainable fast scene text detector, named
TextBoxes++, which detects arbitrary-oriented scene text with both high
accuracy and efficiency in a single network forward pass. No post-processing
other than an efficient non-maximum suppression is involved. We have evaluated
the proposed TextBoxes++ on four public datasets. In all experiments,
TextBoxes++ outperforms competing methods in terms of text localization
accuracy and runtime. More specifically, TextBoxes++ achieves an f-measure of
0.817 at 11.6fps for 1024*1024 ICDAR 2015 Incidental text images, and an
f-measure of 0.5591 at 19.8fps for 768*768 COCO-Text images. Furthermore,
combined with a text recognizer, TextBoxes++ significantly outperforms the
state-of-the-art approaches for word spotting and end-to-end text recognition
tasks on popular benchmarks. Code is available at:
https://github.com/MhLiao/TextBoxes_plusplus
An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation
Authors: Shabnam Sadeghi Esfahlani, Silvia Cirstea, Alireza Sanaei, George Wilson
Date: 2018-04-27 00:00:00

Rehabilitation robotics combined with video game technology provides a means
of assisting in the rehabilitation of patients with neuromuscular disorders by
performing various facilitation movements. The current work presents ReHabGame,
a serious game using a fusion of implemented technologies that can be easily
used by patients and therapists to assess and enhance sensorimotor performance
and also increase the activities in the daily lives of patients. The game
allows a player to control avatar movements through a Kinect Xbox, Myo armband
and rudder foot pedal, and involves a series of reach-grasp-collect tasks whose
difficulty levels are learnt by a fuzzy interface. The orientation, angular
velocity, head and spine tilts and other data generated by the player are
monitored and saved, whilst the task completion is calculated by solving an
inverse kinematics algorithm which orientates the upper limb joints of the
avatar. The different values in upper body quantities of movement provide fuzzy
input from which crisp output is determined and used to generate an appropriate
subsequent rehabilitation game level. The system can thus provide personalised,
autonomously-learnt rehabilitation programmes for patients with neuromuscular
disorders with superior predictions to guide the development of improved
clinical protocols compared to traditional theraputic activities.
Automatic classification of trees using a UAV onboard camera and deep learning
Authors: Masanori Onishi, Takeshi Ise
Date: 2018-04-27 00:00:00
Tasks: Deep Learning, General Classification

Automatic classification of trees using remotely sensed data has been a dream
of many scientists and land use managers. Recently, Unmanned aerial vehicles
(UAV) has been expected to be an easy-to-use, cost-effective tool for remote
sensing of forests, and deep learning has attracted attention for its ability
concerning machine vision. In this study, using a commercially available UAV
and a publicly available package for deep learning, we constructed a machine
vision system for the automatic classification of trees. In our method, we
segmented a UAV photography image of forest into individual tree crowns and
carried out object-based deep learning. As a result, the system was able to
classify 7 tree types at 89.0% accuracy. This performance is notable because we
only used basic RGB images from a standard UAV. In contrast, most of previous
studies used expensive hardware such as multispectral imagers to improve the
performance. This result means that our method has the potential to classify
individual trees in a cost-effective manner. This can be a usable tool for many
forest researchers and managements.