pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting deep residual networks for human action recognition from skeletal data
Authors: Huy-Hieu Pham, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio A. Velastin
Date: 2018-03-21 00:00:00
Tasks: Action Recognition, Temporal Action Localization

The computer vision community is currently focusing on solving action
recognition problems in real videos, which contain thousands of samples with
many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs)
have played a significant role in advancing the state-of-the-art in various
vision-based action recognition systems. Recently, the introduction of residual
connections in conjunction with a more traditional CNN model in a single
architecture called Residual Network (ResNet) has shown impressive performance
and great potential for image recognition tasks. In this paper, we investigate
and apply deep ResNets for human action recognition using skeletal data
provided by depth sensors. Firstly, the 3D coordinates of the human body joints
carried in skeleton sequences are transformed into image-based representations
and stored as RGB images. These color images are able to capture the
spatial-temporal evolutions of 3D motions from skeleton sequences and can be
efficiently learned by D-CNNs. We then propose a novel deep learning
architecture based on ResNets to learn features from obtained color-based
representations and classify them into action classes. The proposed method is
evaluated on three challenging benchmark datasets including MSR Action 3D,
KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method
achieves state-of-the-art performance for all these benchmarks whilst requiring
less computation resource. In particular, the proposed method surpasses
previous approaches by a significant margin of 3.4% on MSR Action 3D dataset,
0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset.
Global Bandits
Authors: Onur Atan, Cem Tekin, Mihaela van der Schaar
Date: 2015-03-29 00:00:00
Tasks: Decision Making, Informativeness, Multi-Armed Bandits, Sequential Decision Making

Multi-armed bandits (MAB) model sequential decision making problems, in which
a learner sequentially chooses arms with unknown reward distributions in order
to maximize its cumulative reward. Most of the prior work on MAB assumes that
the reward distributions of each arm are independent. But in a wide variety of
decision problems -- from drug dosage to dynamic pricing -- the expected
rewards of different arms are correlated, so that selecting one arm provides
information about the expected rewards of other arms as well. We propose and
analyze a class of models of such decision problems, which we call {\em global
bandits}. In the case in which rewards of all arms are deterministic functions
of a single unknown parameter, we construct a greedy policy that achieves {\em
bounded regret}, with a bound that depends on the single true parameter of the
problem. Hence, this policy selects suboptimal arms only finitely many times
with probability one. For this case we also obtain a bound on regret that is
{\em independent of the true parameter}; this bound is sub-linear, with an
exponent that depends on the informativeness of the arms. We also propose a
variant of the greedy policy that achieves $\tilde{\mathcal{O}}(\sqrt{T})$
worst-case and $\mathcal{O}(1)$ parameter dependent regret. Finally, we perform
experiments on dynamic pricing and show that the proposed algorithms achieve
significant gains with respect to the well-known benchmarks.
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
Authors: Huy-Hieu Pham, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio A. Velastin
Date: 2018-03-21 00:00:00
Tasks: Action Recognition, Deep Learning, Retrieval, Temporal Action Localization, Video Retrieval

Automatic human action recognition is indispensable for almost artificial
intelligent systems such as video surveillance, human-computer interfaces,
video retrieval, etc. Despite a lot of progress, recognizing actions in an
unknown video is still a challenging task in computer vision. Recently, deep
learning algorithms have proved its great potential in many vision-related
recognition tasks. In this paper, we propose the use of Deep Residual Neural
Networks (ResNets) to learn and recognize human action from skeleton data
provided by Kinect sensor. Firstly, the body joint coordinates are transformed
into 3D-arrays and saved in RGB images space. Five different deep learning
models based on ResNet have been designed to extract image features and
classify them into classes. Experiments are conducted on two public video
datasets for human action recognition containing various challenges. The
results show that our method achieves the state-of-the-art performance
comparing with existing approaches.
$ρ$-hot Lexicon Embedding-based Two-level LSTM for Sentiment Analysis
Authors: Ou Wu, Tao Yang, Mengyang Li, Ming Li
Date: 2018-03-21 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification, Vocal Bursts Valence Prediction

Sentiment analysis is a key component in various text mining applications.
Numerous sentiment classification techniques, including conventional and deep
learning-based methods, have been proposed in the literature. In most existing
methods, a high-quality training set is assumed to be given. Nevertheless,
constructing a high-quality training set that consists of highly accurate
labels is challenging in real applications. This difficulty stems from the fact
that text samples usually contain complex sentiment representations, and their
annotation is subjective. We address this challenge in this study by leveraging
a new labeling strategy and utilizing a two-level long short-term memory
network to construct a sentiment classifier. Lexical cues are useful for
sentiment analysis, and they have been utilized in conventional studies. For
example, polar and privative words play important roles in sentiment analysis.
A new encoding strategy, that is, $\rho$-hot encoding, is proposed to alleviate
the drawbacks of one-hot encoding and thus effectively incorporate useful
lexical cues. We compile three Chinese data sets on the basis of our label
strategy and proposed methodology. Experiments on the three data sets
demonstrate that the proposed method outperforms state-of-the-art algorithms.
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
Authors: Christopher J. Cueva, Xue-Xin Wei
Date: 2018-03-21 00:00:00
Proceeding: ICLR 2018 1

Decades of research on the neural code underlying spatial navigation have
revealed a diverse set of neural response properties. The Entorhinal Cortex
(EC) of the mammalian brain contains a rich set of spatial correlates,
including grid cells which encode space using tessellating patterns. However,
the mechanisms and functional significance of these spatial representations
remain largely mysterious. As a new way to understand these neural
representations, we trained recurrent neural networks (RNNs) to perform
navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find
that grid-like spatial response patterns emerge in trained networks, along with
units that exhibit other spatial correlates, including border cells and
band-like cells. All these different functional types of neurons have been
observed experimentally. The order of the emergence of grid-like and border
cells is also consistent with observations from developmental studies.
Together, our results suggest that grid cells, border cells and others as
observed in EC may be a natural solution for representing space efficiently
given the predominant recurrent connections in the neural circuits.
Sample Complexity of Sparse System Identification Problem
Authors: Salar Fattahi, Somayeh Sojoudi
Date: 2018-03-21 00:00:00

In this paper, we study the system identification problem for sparse linear
time-invariant systems. We propose a sparsity promoting block-regularized
estimator to identify the dynamics of the system with only a limited number of
input-state data samples. We characterize the properties of this estimator
under high-dimensional scaling, where the growth rate of the system dimension
is comparable to or even faster than that of the number of available sample
trajectories. In particular, using contemporary results on high-dimensional
statistics, we show that the proposed estimator results in a small element-wise
error, provided that the number of sample trajectories is above a threshold.
This threshold depends polynomially on the size of each block and the number of
nonzero elements at different rows of input and state matrices, but only
logarithmically on the system dimension. A by-product of this result is that
the number of sample trajectories required for sparse system identification is
significantly smaller than the dimension of the system. Furthermore, we show
that, unlike the recently celebrated least-squares estimators for system
identification problems, the method developed in this work is capable of
\textit{exact recovery} of the underlying sparsity structure of the system with
the aforementioned number of data samples. Extensive case studies on
synthetically generated systems, physical mass-spring networks, and multi-agent
systems are offered to demonstrate the effectiveness of the proposed method.
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
Authors: Sachin Mehta, Mohammad Rastegari, Anat Caspi, Linda Shapiro, Hannaneh Hajishirzi
Date: 2018-03-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: GPU, Panoptic Segmentation, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

We introduce a fast and efficient convolutional neural network, ESPNet, for
semantic segmentation of high resolution images under resource constraints.
ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP),
which is efficient in terms of computation, memory, and power. ESPNet is 22
times faster (on a standard GPU) and 180 times smaller than the
state-of-the-art semantic segmentation network PSPNet, while its category-wise
accuracy is only 8% less. We evaluated ESPNet on a variety of semantic
segmentation datasets including Cityscapes, PASCAL VOC, and a breast biopsy
whole slide image dataset. Under the same constraints on memory and
computation, ESPNet outperforms all the current efficient CNN networks such as
MobileNet, ShuffleNet, and ENet on both standard metrics and our newly
introduced performance metrics that measure efficiency on edge devices. Our
network can process high resolution images at a rate of 112 and 9 frames per
second on a standard GPU and edge device, respectively.
Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation
Authors: Edmond Awad, Sydney Levine, Max Kleiman-Weiner, Sohan Dsouza, Joshua B. Tenenbaum, Azim Shariff, Jean-François Bonnefon, Iyad Rahwan
Date: 2018-03-19 00:00:00

When a semi-autonomous car crashes and harms someone, how are blame and
causal responsibility distributed across the human and machine drivers? In this
article, we consider cases in which a pedestrian was hit and killed by a car
being operated under shared control of a primary and a secondary driver. We
find that when only one driver makes an error, that driver receives the blame
and is considered causally responsible for the harm, regardless of whether that
driver is a machine or a human. However, when both drivers make errors in cases
of shared control between a human and a machine, the blame and responsibility
attributed to the machine is reduced. This finding portends a public
under-reaction to the malfunctioning AI components of semi-autonomous cars and
therefore has a direct policy implication: a bottom-up regulatory scheme (which
operates through tort law that is adjudicated through the jury system) could
fail to properly regulate the safety of shared-control vehicles; instead, a
top-down scheme (enacted through federal laws) may be called for.
Assessing Shape Bias Property of Convolutional Neural Networks
Authors: Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, Radha Poovendran
Date: 2018-03-21 00:00:00
Tasks: One-Shot Learning

It is known that humans display "shape bias" when classifying new items,
i.e., they prefer to categorize objects based on their shape rather than color.
Convolutional Neural Networks (CNNs) are also designed to take into account the
spatial structure of image data. In fact, experiments on image datasets,
consisting of triples of a probe image, a shape-match and a color-match, have
shown that one-shot learning models display shape bias as well.
  In this paper, we examine the shape bias property of CNNs. In order to
conduct large scale experiments, we propose using the model accuracy on images
with reversed brightness as a metric to evaluate the shape bias property. Such
images, called negative images, contain objects that have the same shape as
original images, but with different colors. Through extensive systematic
experiments, we investigate the role of different factors, such as training
data, model architecture, initialization and regularization techniques, on the
shape bias property of CNNs. We show that it is possible to design different
CNNs that achieve similar accuracy on original images, but perform
significantly different on negative images, suggesting that CNNs do not
intrinsically display shape bias. We then show that CNNs are able to learn and
generalize the structures, when the model is properly initialized or data is
properly augmented, and if batch normalization is used.
Speech Emotion Recognition Considering Local Dynamic Features
Authors: Haotian Guan, Zhilei Liu, Longbiao Wang, Jianwu Dang, Ruiguo Yu
Date: 2018-03-21 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Recently, increasing attention has been directed to the study of the speech
emotion recognition, in which global acoustic features of an utterance are
mostly used to eliminate the content differences. However, the expression of
speech emotion is a dynamic process, which is reflected through dynamic
durations, energies, and some other prosodic information when one speaks. In
this paper, a novel local dynamic pitch probability distribution feature, which
is obtained by drawing the histogram, is proposed to improve the accuracy of
speech emotion recognition. Compared with most of the previous works using
global features, the proposed method takes advantage of the local dynamic
information conveyed by the emotional speech. Several experiments on Berlin
Database of Emotional Speech are conducted to verify the effectiveness of the
proposed method. The experimental results demonstrate that the local dynamic
information obtained with the proposed method is more effective for speech
emotion recognition than the traditional global features.
PyramidBox: A Context-assisted Single Shot Face Detector
Authors: Xu Tang, Daniel K. Du, Zeqiang He, Jingtuo Liu
Date: 2018-03-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Detection

Face detection has been well studied for many years and one of remaining
challenges is to detect small, blurred and partially occluded faces in
uncontrolled environment. This paper proposes a novel context-assisted single
shot face detector, named \emph{PyramidBox} to handle the hard face detection
problem. Observing the importance of the context, we improve the utilization of
contextual information in the following three aspects. First, we design a novel
context anchor to supervise high-level contextual feature learning by a
semi-supervised method, which we call it PyramidAnchors. Second, we propose the
Low-level Feature Pyramid Network to combine adequate high-level context
semantic feature and Low-level facial feature together, which also allows the
PyramidBox to predict faces of all scales in a single shot. Third, we introduce
a context-sensitive structure to increase the capacity of prediction network to
improve the final accuracy of output. In addition, we use the method of
Data-anchor-sampling to augment the training samples across different scales,
which increases the diversity of training data for smaller faces. By exploiting
the value of context, PyramidBox achieves superior performance among the
state-of-the-art over the two common face detection benchmarks, FDDB and WIDER
FACE. Our code is available in PaddlePaddle:
\href{https://github.com/PaddlePaddle/models/tree/develop/fluid/face_detection}{\url{https://github.com/PaddlePaddle/models/tree/develop/fluid/face_detection}}.
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation
Authors: Xin Wang, Wenhan Xiong, Hongmin Wang, William Yang Wang
Date: 2018-03-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Deep Reinforcement Learning, model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation, Vision and Language Navigation, Vision-Language Navigation

Existing research studies on vision and language grounding for robot
navigation focus on improving model-free deep reinforcement learning (DRL)
models in synthetic environments. However, model-free DRL models do not
consider the dynamics in the real-world environments, and they often fail to
generalize to new scenes. In this paper, we take a radical approach to bridge
the gap between synthetic studies and real-world practices---We propose a
novel, planned-ahead hybrid reinforcement learning model that combines
model-free and model-based reinforcement learning to solve a real-world
vision-language navigation task. Our look-ahead module tightly integrates a
look-ahead policy model with an environment model that predicts the next state
and the reward. Experimental results suggest that our proposed method
significantly outperforms the baselines and achieves the best on the real-world
Room-to-Room dataset. Moreover, our scalable method is more generalizable when
transferring to unseen environments.
Unsupervised Representation Learning by Predicting Image Rotations
Authors: Spyros Gidaris, Praveer Singh, Nikos Komodakis
Date: 2018-03-21 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, Representation Learning, Self-Supervised Image Classification

Over the last years, deep convolutional neural networks (ConvNets) have
transformed the field of computer vision thanks to their unparalleled capacity
to learn high level semantic image features. However, in order to successfully
learn those features, they usually require massive amounts of manually labeled
data, which is both expensive and impractical to scale. Therefore, unsupervised
semantic feature learning, i.e., learning without requiring manual annotation
effort, is of crucial importance in order to successfully harvest the vast
amount of visual data that are available today. In our work we propose to learn
image features by training ConvNets to recognize the 2d rotation that is
applied to the image that it gets as input. We demonstrate both qualitatively
and quantitatively that this apparently simple task actually provides a very
powerful supervisory signal for semantic feature learning. We exhaustively
evaluate our method in various unsupervised feature learning benchmarks and we
exhibit in all of them state-of-the-art performance. Specifically, our results
on those benchmarks demonstrate dramatic improvements w.r.t. prior
state-of-the-art approaches in unsupervised representation learning and thus
significantly close the gap with supervised feature learning. For instance, in
PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model
achieves the state-of-the-art (among unsupervised methods) mAP of 54.4% that is
only 2.4 points lower from the supervised case. We get similarly striking
results when we transfer our unsupervised learned features on various other
tasks, such as ImageNet classification, PASCAL classification, PASCAL
segmentation, and CIFAR-10 classification. The code and models of our paper
will be published on: https://github.com/gidariss/FeatureLearningRotNet .
Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval
Authors: Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma
Date: 2018-03-21 00:00:00
Tasks: Retrieval

This paper considers the problem of solving systems of quadratic equations,
namely, recovering an object of interest
$\mathbf{x}^{\natural}\in\mathbb{R}^{n}$ from $m$ quadratic equations/samples
$y_{i}=(\mathbf{a}_{i}^{\top}\mathbf{x}^{\natural})^{2}$, $1\leq i\leq m$. This
problem, also dubbed as phase retrieval, spans multiple domains including
physical sciences and machine learning.
  We investigate the efficiency of gradient descent (or Wirtinger flow)
designed for the nonconvex least squares problem. We prove that under Gaussian
designs, gradient descent --- when randomly initialized --- yields an
$\epsilon$-accurate solution in $O\big(\log n+\log(1/\epsilon)\big)$ iterations
given nearly minimal samples, thus achieving near-optimal computational and
sample complexities at once. This provides the first global convergence
guarantee concerning vanilla gradient descent for phase retrieval, without the
need of (i) carefully-designed initialization, (ii) sample splitting, or (iii)
sophisticated saddle-point escaping schemes. All of these are achieved by
exploiting the statistical models in analyzing optimization algorithms, via a
leave-one-out approach that enables the decoupling of certain statistical
dependency between the gradient descent iterates and the data.
Attention on Attention: Architectures for Visual Question Answering (VQA)
Authors: Jasdeep Singh, Vincent Ying, Alex Nutkiewicz
Date: 2018-03-21 00:00:00
Tasks: GPU, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) is an increasingly popular topic in deep
learning research, requiring coordination of natural language processing and
computer vision modules into a single architecture. We build upon the model
which placed first in the VQA Challenge by developing thirteen new attention
mechanisms and introducing a simplified classifier. We performed 300 GPU hours
of extensive hyperparameter and architecture searches and were able to achieve
an evaluation score of 64.78%, outperforming the existing state-of-the-art
single model's validation score of 63.15%.
InfyNLP at SMM4H Task 2: Stacked Ensemble of Shallow Convolutional Neural Networks for Identifying Personal Medication Intake from Twitter
Authors: Jasper Friedrichs, Debanjan Mahata, Shubham Gupta
Date: 2018-03-21 00:00:00
Tasks: General Classification, Pharmacovigilance, Task 2

This paper describes Infosys's participation in the "2nd Social Media Mining
for Health Applications Shared Task at AMIA, 2017, Task 2". Mining social media
messages for health and drug related information has received significant
interest in pharmacovigilance research. This task targets at developing
automated classification models for identifying tweets containing descriptions
of personal intake of medicines. Towards this objective we train a stacked
ensemble of shallow convolutional neural network (CNN) models on an annotated
dataset provided by the organizers. We use random search for tuning the
hyper-parameters of the CNN and submit an ensemble of best models for the
prediction task. Our system secured first place among 9 teams, with a
micro-averaged F-score of 0.693.