pwc-archive/papers-with-abstracts huggingface dataset:
Complex Event Recognition from Images with Few Training Examples
Authors: Unaiza Ahsan, Chen Sun, James Hays, Irfan Essa
Date: 2017-01-17 00:00:00

We propose to leverage concept-level representations for complex event
recognition in photographs given limited training examples. We introduce a
novel framework to discover event concept attributes from the web and use that
to extract semantic features from images and classify them into social event
categories with few training examples. Discovered concepts include a variety of
objects, scenes, actions and event sub-types, leading to a discriminative and
compact representation for event images. Web images are obtained for each
discovered event concept and we use (pretrained) CNN features to train concept
classifiers. Extensive experiments on challenging event datasets demonstrate
that our proposed method outperforms several baselines using deep CNN features
directly in classifying images into events with limited training examples. We
also demonstrate that our method achieves the best overall accuracy on a
dataset with unseen event categories using a single training example.
3D Reconstruction of Simple Objects from A Single View Silhouette Image
Authors: Xinhan Di, Pengqian Yu
Date: 2017-01-17 00:00:00
Tasks: 3D Reconstruction

While recent deep neural networks have achieved promising results for 3D
reconstruction from a single-view image, these rely on the availability of RGB
textures in images and extra information as supervision. In this work, we
propose novel stacked hierarchical networks and an end to end training strategy
to tackle a more challenging task for the first time, 3D reconstruction from a
single-view 2D silhouette image. We demonstrate that our model is able to
conduct 3D reconstruction from a single-view silhouette image both
qualitatively and quantitatively. Evaluation is performed using Shapenet for
the single-view reconstruction and results are presented in comparison with a
single network, to highlight the improvements obtained with the proposed
stacked networks and the end to end training strategy. Furthermore, 3D re-
construction in forms of IoU is compared with the state of art 3D
reconstruction from a single-view RGB image, and the proposed model achieves
higher IoU than the state of art of reconstruction from a single view RGB
image.
Computing Egomotion with Local Loop Closures for Egocentric Videos
Authors: Suvam Patra, Himanshu Aggarwal, Himani Arora, Chetan Arora, Subhashis Banerjee
Date: 2017-01-17 00:00:00
Tasks: Camera Pose Estimation, Depth Estimation, Pose Estimation

Finding the camera pose is an important step in many egocentric video
applications. It has been widely reported that, state of the art SLAM
algorithms fail on egocentric videos. In this paper, we propose a robust method
for camera pose estimation, designed specifically for egocentric videos. In an
egocentric video, the camera views the same scene point multiple times as the
wearer's head sweeps back and forth. We use this specific motion profile to
perform short loop closures aligned with wearer's footsteps. For egocentric
videos, depth estimation is usually noisy. In an important departure, we use 2D
computations for rotation averaging which do not rely upon depth estimates. The
two modification results in much more stable algorithm as is evident from our
experiments on various egocentric video datasets for different egocentric
applications. The proposed algorithm resolves a long standing problem in
egocentric vision and unlocks new usage scenarios for future applications.
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
Authors: Rock Stevens, Octavian Suciu, Andrew Ruef, Sanghyun Hong, Michael Hicks, Tudor Dumitraş
Date: 2017-01-17 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Governments and businesses increasingly rely on data analytics and machine
learning (ML) for improving their competitive edge in areas such as consumer
satisfaction, threat intelligence, decision making, and product efficiency.
However, by cleverly corrupting a subset of data used as input to a target's ML
algorithms, an adversary can perturb outcomes and compromise the effectiveness
of ML technology. While prior work in the field of adversarial machine learning
has studied the impact of input manipulation on correct ML algorithms, we
consider the exploitation of bugs in ML implementations. In this paper, we
characterize the attack surface of ML programs, and we show that malicious
inputs exploiting implementation bugs enable strictly more powerful attacks
than the classic adversarial machine learning techniques. We propose a
semi-automated technique, called steered fuzzing, for exploring this attack
surface and for discovering exploitable bugs in machine learning programs, in
order to demonstrate the magnitude of this threat. As a result of our work, we
responsibly disclosed five vulnerabilities, established three new CVE-IDs, and
illuminated a common insecure practice across many machine learning systems.
Finally, we outline several research directions for further understanding and
mitigating this threat.
Visual-Inertial Monocular SLAM with Map Reuse
Authors: Raul Mur-Artal, Juan D. Tardos
Date: 2016-10-19 00:00:00
Tasks: Simultaneous Localization and Mapping

In recent years there have been excellent results in Visual-Inertial Odometry
techniques, which aim to compute the incremental motion of the sensor with high
accuracy and robustness. However these approaches lack the capability to close
loops, and trajectory estimation accumulates drift even if the sensor is
continually revisiting the same place. In this work we present a novel
tightly-coupled Visual-Inertial Simultaneous Localization and Mapping system
that is able to close loops and reuse its map to achieve zero-drift
localization in already mapped areas. While our approach can be applied to any
camera configuration, we address here the most general problem of a monocular
camera, with its well-known scale ambiguity. We also propose a novel IMU
initialization method, which computes the scale, the gravity direction, the
velocity, and gyroscope and accelerometer biases, in a few seconds with high
accuracy. We test our system in the 11 sequences of a recent micro-aerial
vehicle public dataset achieving a typical scale factor error of 1% and
centimeter precision. We compare to the state-of-the-art in visual-inertial
odometry in sequences with revisiting, proving the better accuracy of our
method due to map reuse and no drift accumulation.
Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition
Authors: Yi Xu, Qihang Lin, Tianbao Yang
Date: 2016-07-04 00:00:00
Tasks: Stochastic Optimization

In this paper, a new theory is developed for first-order stochastic convex optimization, showing that the global convergence rate is sufficiently quantified by a local growth rate of the objective function in a neighborhood of the optimal solutions. In particular, if the objective function $F(\mathbf w)$ in the $\epsilon$-sublevel set grows as fast as $\|\mathbf w - \mathbf w_*\|_2^{1/\theta}$, where $\mathbf w_*$ represents the closest optimal solution to $\mathbf w$ and $\theta\in(0,1]$ quantifies the local growth rate, the iteration complexity of first-order stochastic optimization for achieving an $\epsilon$-optimal solution can be $\widetilde O(1/\epsilon^{2(1-\theta)})$, which is optimal at most up to a logarithmic factor. To achieve the faster global convergence, we develop two different accelerated stochastic subgradient methods by iteratively solving the original problem approximately in a local region around a historical solution with the size of the local region gradually decreasing as the solution approaches the optimal set. Besides the theoretical improvements, this work also includes new contributions towards making the proposed algorithms practical: (i) we present practical variants of accelerated stochastic subgradient methods that can run without the knowledge of multiplicative growth constant and even the growth rate $\theta$; (ii) we consider a broad family of problems in machine learning to demonstrate that the proposed algorithms enjoy faster convergence than traditional stochastic subgradient method. We also characterize the complexity of the proposed algorithms for ensuring the gradient is small without the smoothness assumption.
Fully Convolutional Crowd Counting On Highly Congested Scenes
Authors: Mark Marsden, Kevin McGuinness, Suzanne Little, Noel E. O'Connor
Date: 2016-12-01 00:00:00
Tasks: Crowd Counting

In this paper we advance the state-of-the-art for crowd counting in high
density scenes by further exploring the idea of a fully convolutional crowd
counting model introduced by (Zhang et al., 2016). Producing an accurate and
robust crowd count estimator using computer vision techniques has attracted
significant research interest in recent years. Applications for crowd counting
systems exist in many diverse areas including city planning, retail, and of
course general public safety. Developing a highly generalised counting model
that can be deployed in any surveillance scenario with any camera perspective
is the key objective for research in this area. Techniques developed in the
past have generally performed poorly in highly congested scenes with several
thousands of people in frame (Rodriguez et al., 2011). Our approach, influenced
by the work of (Zhang et al., 2016), consists of the following contributions:
(1) A training set augmentation scheme that minimises redundancy among training
samples to improve model generalisation and overall counting performance; (2) a
deep, single column, fully convolutional network (FCN) architecture; (3) a
multi-scale averaging step during inference. The developed technique can
analyse images of any resolution or aspect ratio and achieves state-of-the-art
counting performance on the Shanghaitech Part B and UCF CC 50 datasets as well
as competitive performance on Shanghaitech Part A.
Decoding with Finite-State Transducers on GPUs
Authors: Arturo Argueta, David Chiang
Date: 2017-01-11 00:00:00
Proceeding: EACL 2017 4
Tasks: Chunking, GPU, Morphological Analysis, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, speech-recognition, Speech Recognition

Weighted finite automata and transducers (including hidden Markov models and
conditional random fields) are widely used in natural language processing (NLP)
to perform tasks such as morphological analysis, part-of-speech tagging,
chunking, named entity recognition, speech recognition, and others.
Parallelizing finite state algorithms on graphics processing units (GPUs) would
benefit many areas of NLP. Although researchers have implemented GPU versions
of basic graph algorithms, limited previous work, to our knowledge, has been
done on GPU algorithms for weighted finite automata. We introduce a GPU
implementation of the Viterbi and forward-backward algorithm, achieving
decoding speedups of up to 5.2x over our serial implementation running on
different computer architectures and 6093x over OpenFST.
Incremental Learning for Robot Perception through HRI
Authors: Sepehr Valipour, Camilo Perez, Martin Jagersand
Date: 2017-01-17 00:00:00
Tasks: Incremental Learning, Object, object-detection, Object Detection, Object Recognition, Scene Understanding

Scene understanding and object recognition is a difficult to achieve yet
crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have
shown success in this task. However, there is still a gap between their
performance on image datasets and real-world robotics scenarios. We present a
novel paradigm for incrementally improving a robot's visual perception through
active human interaction. In this paradigm, the user introduces novel objects
to the robot by means of pointing and voice commands. Given this information,
the robot visually explores the object and adds images from it to re-train the
perception module. Our base perception module is based on recent development in
object detection and recognition using deep learning. Our method leverages
state of the art CNNs from off-line batch learning, human guidance, robot
exploration and incremental on-line learning.
Human perception in computer vision
Authors: Ron Dekel
Date: 2017-01-17 00:00:00
Tasks: Sensitivity

Computer vision has made remarkable progress in recent years. Deep neural
network (DNN) models optimized to identify objects in images exhibit
unprecedented task-trained accuracy and, remarkably, some generalization
ability: new visual problems can now be solved more easily based on previous
learning. Biological vision (learned in life and through evolution) is also
accurate and general-purpose. Is it possible that these different learning
regimes converge to similar problem-dependent optimal computations? We
therefore asked whether the human system-level computation of visual perception
has DNN correlates and considered several anecdotal test cases. We found that
perceptual sensitivity to image changes has DNN mid-computation correlates,
while sensitivity to segmentation, crowding and shape has DNN end-computation
correlates. Our results quantify the applicability of using DNN computation to
estimate perceptual loss, and are consistent with the fascinating theoretical
view that properties of human perception are a consequence of
architecture-independent visual learning.
Intrinsically Motivated Acquisition of Modular Slow Features for Humanoids in Continuous and Non-Stationary Environments
Authors: Varun Raj Kompella, Laurenz Wiskott
Date: 2017-01-17 00:00:00

A compact information-rich representation of the environment, also called a
feature abstraction, can simplify a robot's task of mapping its raw sensory
inputs to useful action sequences. However, in environments that are
non-stationary and only partially observable, a single abstraction is probably
not sufficient to encode most variations. Therefore, learning multiple sets of
spatially or temporally local, modular abstractions of the inputs would be
beneficial. How can a robot learn these local abstractions without a teacher?
More specifically, how can it decide from where and when to start learning a
new abstraction? A recently proposed algorithm called Curious Dr. MISFA
addresses this problem. The algorithm is based on two underlying learning
principles called artificial curiosity and slowness. The former is used to make
the robot self-motivated to explore by rewarding itself whenever it makes
progress learning an abstraction; the later is used to update the abstraction
by extracting slowly varying components from raw sensory inputs. Curious Dr.
MISFA's application is, however, limited to discrete domains constrained by a
pre-defined state space and has design limitations that make it unstable in
certain situations. This paper presents a significant improvement that is
applicable to continuous environments, is computationally less expensive,
simpler to use with fewer hyper parameters, and stable in certain
non-stationary environments. We demonstrate the efficacy and stability of our
method in a vision-based robot simulator.
Community Question Answering Platforms vs. Twitter for Predicting Characteristics of Urban Neighbourhoods
Authors: Marzieh Saeidi, Alessandro Venerandi, Licia Capra, Sebastian Riedel
Date: 2017-01-17 00:00:00
Tasks: Community Question Answering, Question Answering

In this paper, we investigate whether text from a Community Question
Answering (QA) platform can be used to predict and describe real-world
attributes. We experiment with predicting a wide range of 62 demographic
attributes for neighbourhoods of London. We use the text from QA platform of
Yahoo! Answers and compare our results to the ones obtained from Twitter
microblogs. Outcomes show that the correlation between the predicted
demographic attributes using text from Yahoo! Answers discussions and the
observed demographic attributes can reach an average Pearson correlation
coefficient of \r{ho} = 0.54, slightly higher than the predictions obtained
using Twitter data. Our qualitative analysis indicates that there is semantic
relatedness between the highest correlated terms extracted from both datasets
and their relative demographic attributes. Furthermore, the correlations
highlight the different natures of the information contained in Yahoo! Answers
and Twitter. While the former seems to offer a more encyclopedic content, the
latter provides information related to the current sociocultural aspects or
phenomena.
Une mesure d'expertise pour le crowdsourcing
Authors: Hosna Ouni, Arnaud Martin, Laetitia Gros, Mouloud Kharoune, Zoltan Miklos
Date: 2017-01-17 00:00:00

Crowdsourcing, a major economic issue, is the fact that the firm outsources
internal task to the crowd. It is a form of digital subcontracting for the
general public. The evaluation of the participants work quality is a major
issue in crowdsourcing. Indeed, contributions must be controlled to ensure the
effectiveness and relevance of the campaign. We are particularly interested in
small, fast and not automatable tasks. Several methods have been proposed to
solve this problem, but they are applicable when the "golden truth" is not
always known. This work has the particularity to propose a method for
calculating the degree of expertise in the presence of gold data in
crowdsourcing. This method is based on the belief function theory and proposes
a structuring of data using graphs. The proposed approach will be assessed and
applied to the data.
Hierarchical Salient Object Detection for Assisted Grasping
Authors: Dominik Alexander Klein, Boris Illing, Bastian Gaspers, Dirk Schulz, Armin Bernd Cremers
Date: 2017-01-16 00:00:00
Tasks: Clustering, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Visual scene decomposition into semantic entities is one of the major
challenges when creating a reliable object grasping system. Recently, we
introduced a bottom-up hierarchical clustering approach which is able to
segment objects and parts in a scene. In this paper, we introduce a transform
from such a segmentation into a corresponding, hierarchical saliency function.
In comprehensive experiments we demonstrate its ability to detect salient
objects in a scene. Furthermore, this hierarchical saliency defines a most
salient corresponding region (scale) for every point in an image. Based on
this, an easy-to-use pick and place manipulation system was developed and
tested exemplarily.
Automagically encoding Adverse Drug Reactions in MedDRA
Authors: Carlo Combi, Riccardo Lora, Ugo Moretti, Marco Pagliarini, Margherita Zorzi
Date: 2015-06-26 00:00:00
Tasks: Pharmacovigilance

Pharmacovigilance is the field of science devoted to the collection, analysis
and prevention of Adverse Drug Reactions (ADRs). Efficient strategies for the
extraction of information about ADRs from free text resources are essential to
support the work of experts, employed in the crucial task of detecting and
classifying unexpected pathologies possibly related to drug assumptions.
Narrative ADR descriptions may be collected in several way, e.g. by monitoring
social networks or through the so called spontaneous reporting, the main method
pharmacovigilance adopts in order to identify ADRs. The encoding of free-text
ADR descriptions according to MedDRA standard terminology is central for report
analysis. It is a complex work, which has to be manually implemented by the
pharmacovigilance experts. The manual encoding is expensive (in terms of time).
Moreover, a problem about the accuracy of the encoding may occur, since the
number of reports is growing up day by day. In this paper, we propose
MagiCoder, an efficient Natural Language Processing algorithm able to
automatically derive MedDRA terminologies from free-text ADR descriptions.
MagiCoder is part of VigiWork, a web application for online ADR reporting and
analysis. From a practical view-point, MagiCoder radically reduces the revision
time of ADR reports: the pharmacologist has simply to revise and validate the
automatic solution versus the hard task of choosing solutions in the 70k terms
of MedDRA. This improvement of the expert work efficiency has a meaningful
impact on the quality of data analysis. Moreover, our procedure is general
purpose. We developed MagiCoder for the Italian pharmacovigilance language, but
preliminarily analyses show that it is robust to language and dictionary
changes.
Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines
Authors: Matteo Zanotto, Riccardo Volpi, Alessandro Maccione, Luca Berdondini, Diego Sona, Vittorio Murino
Date: 2017-01-11 00:00:00
Tasks: Blocking

The retina is a complex nervous system which encodes visual stimuli before
higher order processing occurs in the visual cortex. In this study we evaluated
whether information about the stimuli received by the retina can be retrieved
from the firing rate distribution of Retinal Ganglion Cells (RGCs), exploiting
High-Density 64x64 MEA technology. To this end, we modeled the RGC population
activity using mean-covariance Restricted Boltzmann Machines, latent variable
models capable of learning the joint distribution of a set of continuous
observed random variables and a set of binary unobserved random units. The idea
was to figure out if binary latent states encode the regularities associated to
different visual stimuli, as modes in the joint distribution. We measured the
goodness of mcRBM encoding by calculating the Mutual Information between the
latent states and the stimuli shown to the retina. Results show that binary
states can encode the regularities associated to different stimuli, using both
gratings and natural scenes as stimuli. We also discovered that hidden
variables encode interesting properties of retinal activity, interpreted as
population receptive fields. We further investigated the ability of the model
to learn different modes in population activity by comparing results associated
to a retina in normal conditions and after pharmacologically blocking GABA
receptors (GABAC at first, and then also GABAA and GABAB). As expected, Mutual
Information tends to decrease if we pharmacologically block receptors. We
finally stress that the computational method described in this work could
potentially be applied to any kind of neural data obtained through MEA
technology, though different techniques should be applied to interpret the
results.
Faster K-Means Cluster Estimation
Authors: Siddhesh Khandelwal, Amit Awekar
Date: 2017-01-17 00:00:00
Tasks: Clustering

There has been considerable work on improving popular clustering algorithm
`K-means' in terms of mean squared error (MSE) and speed, both. However, most
of the k-means variants tend to compute distance of each data point to each
cluster centroid for every iteration. We propose a fast heuristic to overcome
this bottleneck with only marginal increase in MSE. We observe that across all
iterations of K-means, a data point changes its membership only among a small
subset of clusters. Our heuristic predicts such clusters for each data point by
looking at nearby clusters after the first iteration of k-means. We augment
well known variants of k-means with our heuristic to demonstrate effectiveness
of our heuristic. For various synthetic and real-world datasets, our heuristic
achieves speed-up of up-to 3 times when compared to efficient variants of
k-means.