pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Speech Emotion Recognition and Ambiguity Resolution
Authors: Gaurav Sahu
Date: 2019-04-12 00:00:00
Tasks: BIG-bench Machine Learning, Emotion Recognition, Feature Engineering, Multi-class Classification, Multimodal Emotion Recognition, Speech Emotion Recognition

Identifying emotion from speech is a non-trivial task pertaining to the
ambiguous definition of emotion itself. In this work, we adopt a
feature-engineering based approach to tackle the task of speech emotion
recognition. Formalizing our problem as a multi-class classification problem,
we compare the performance of two categories of models. For both, we extract
eight hand-crafted features from the audio signal. In the first approach, the
extracted features are used to train six traditional machine learning
classifiers, whereas the second approach is based on deep learning wherein a
baseline feed-forward neural network and an LSTM-based classifier are trained
over the same features. In order to resolve ambiguity in communication, we also
include features from the text domain. We report accuracy, f-score, precision,
and recall for the different experiment settings we evaluated our models in.
Overall, we show that lighter machine learning based models trained over a few
hand-crafted features are able to achieve performance comparable to the current
deep learning based state-of-the-art method for emotion recognition.
Real-Time Dense Stereo Embedded in A UAV for Road Inspection
Authors: Rui Fan, Jianhao Jiao, Jie Pan, Huaiyang Huang, Shaojie Shen, Ming Liu
Date: 2019-04-12 00:00:00
Tasks: GPU, Stereo Matching, Stereo Matching Hand

The condition assessment of road surfaces is essential to ensure their
serviceability while still providing maximum road traffic safety. This paper
presents a robust stereo vision system embedded in an unmanned aerial vehicle
(UAV). The perspective view of the target image is first transformed into the
reference view, and this not only improves the disparity accuracy, but also
reduces the algorithm's computational complexity. The cost volumes generated
from stereo matching are then filtered using a bilateral filter. The latter has
been proved to be a feasible solution for the functional minimisation problem
in a fully connected Markov random field model. Finally, the disparity maps are
transformed by minimising an energy function with respect to the roll angle and
disparity projection model. This makes the damaged road areas more
distinguishable from the road surface. The proposed system is implemented on an
NVIDIA Jetson TX2 GPU with CUDA for real-time purposes. It is demonstrated
through experiments that the damaged road areas can be easily distinguished
from the transformed disparity maps.
A New Loss Function for CNN Classifier Based on Pre-defined Evenly-Distributed Class Centroids
Authors: Qiuyu Zhu, Pengju Zhang, Xin Ye
Date: 2019-04-12 00:00:00
Tasks: Classification, Face Recognition, General Classification, image-classification, Image Classification, object-detection, Object Detection

With the development of convolutional neural networks (CNNs) in recent years,
the network structure has become more and more complex and varied, and has
achieved very good results in pattern recognition, image classification, object
detection and tracking. For CNNs used for image classification, in addition to
the network structure, more and more research is now focusing on the
improvement of the loss function, so as to enlarge the inter-class feature
differences, and reduce the intra-class feature variations as soon as possible.
Besides the traditional Softmax, typical loss functions include L-Softmax,
AM-Softmax, ArcFace, and Center loss, etc. Based on the concept of predefined
evenly-distributed class centroids (PEDCC) in CSAE network, this paper proposes
a PEDCC-based loss function called PEDCC-Loss, which can make the inter-class
distance maximal and intra-class distance small enough in hidden feature space.
Multiple experiments on image classification and face recognition have proved
that our method achieve the best recognition accuracy, and network training is
stable and easy to converge. Code is available in
https://github.com/ZLeopard/PEDCC-Loss
Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance
Authors: Md Nasir, Sandeep Nallan Chakravarthula, Brian Baucom, David C. Atkins, Panayiotis Georgiou, Shrikanth Narayanan
Date: 2019-04-12 00:00:00

Linguistic coordination is a well-established phenomenon in spoken
conversations and often associated with positive social behaviors and outcomes.
While there have been many attempts to measure lexical coordination or
entrainment in literature, only a few have explored coordination in syntactic
or semantic space. In this work, we attempt to combine these different aspects
of coordination into a single measure by leveraging distances in a neural word
representation space. In particular, we adopt the recently proposed Word
Mover's Distance with word2vec embeddings and extend it to measure the
dissimilarity in language used in multiple consecutive speaker turns. To
validate our approach, we apply this measure for two case studies in the
clinical psychology domain. We find that our proposed measure is correlated
with the therapist's empathy towards their patient in Motivational Interviewing
and with affective behaviors in Couples Therapy. In both case studies, our
proposed metric exhibits higher correlation than previously proposed measures.
When applied to the couples with relationship improvement, we also notice a
significant decrease in the proposed measure over the course of therapy,
indicating higher linguistic coordination.
An Introduction to Person Re-identification with Generative Adversarial Networks
Authors: Hamed Alqahtani, Manolya Kavakli-Thorne, Charles Z. Liu
Date: 2019-04-12 00:00:00
Tasks: Person Re-Identification

Person re-identification is a basic subject in the field of computer vision.
The traditional methods have several limitations in solving the problems of
person illumination like occlusion, pose variation and feature variation under
complex background. Fortunately, deep learning paradigm opens new ways of the
person re-identification research and becomes a hot spot in this field.
Generative Adversarial Nets (GANs) in the past few years attracted lots of
attention in solving these problems. This paper reviews the GAN based methods
for person re-identification focuses on the related papers about different GAN
based frameworks and discusses their advantages and disadvantages. Finally, it
proposes the direction of future research, especially the prospect of person
re-identification methods based on GANs.
The iWildCam 2018 Challenge Dataset
Authors: Sara Beery, Grant van Horn, Oisin Mac Aodha, Pietro Perona
Date: 2019-04-11 00:00:00

Camera traps are a valuable tool for studying biodiversity, but research
using this data is limited by the speed of human annotation. With the vast
amounts of data now available it is imperative that we develop automatic
solutions for annotating camera trap data in order to allow this research to
scale. A promising approach is based on deep networks trained on
human-annotated images. We provide a challenge dataset to explore whether such
solutions generalize to novel locations, since systems that are trained once
and may be deployed to operate automatically in new locations would be most
useful.
Reference Product Search
Authors: Chu Wang, Lei Tang, Shujun Bian, Da Zhang, Zuohua Zhang, Yongning Wu
Date: 2019-04-11 00:00:00
Tasks: Representation Learning, Retrieval

For a product of interest, we propose a search method to surface a set of
reference products. The reference products can be used as candidates to support
downstream modeling tasks and business applications. The search method consists
of product representation learning and fingerprint-type vector searching. The
product catalog information is transformed into a high-quality embedding of low
dimensions via a novel attention auto-encoder neural network, and the embedding
is further coupled with a binary encoding vector for fast retrieval. We conduct
extensive experiments to evaluate the proposed method, and compare it with peer
services to demonstrate its advantage in terms of search return rate and
precision.
Cramnet: Layer-wise Deep Neural Network Compression with Knowledge Transfer from a Teacher Network
Authors: Jon Hoffman
Date: 2019-04-11 00:00:00
Tasks: Neural Network Compression, Transfer Learning

Neural Networks accomplish amazing things, but they suffer from computational
and memory bottlenecks that restrict their usage. Nowhere can this be better
seen than in the mobile space, where specialized hardware is being created just
to satisfy the demand for neural networks. Previous studies have shown that
neural networks have vastly more connections than they actually need to do
their work. This thesis develops a method that can compress networks to less
than 10% of memory and less than 25% of computational power, without loss of
accuracy, and without creating sparse networks that require special code to
run.
Community detection in the sparse hypergraph stochastic block model
Authors: Soumik Pal, Yizhe Zhu
Date: 2019-04-11 00:00:00
Tasks: Community Detection, Stochastic Block Model

We consider the community detection problem in sparse random hypergraphs. Angelini et al. (2015) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block model. We solve the positive part of the conjecture for the case of two blocks: above the threshold, there is a spectral algorithm which asymptotically almost surely constructs a partition of the hypergraph correlated with the true partition. Our method is a generalization to random hypergraphs of the method developed by Massouli\'{e} (2014) for sparse random graphs.
The Sound of Motions
Authors: Hang Zhao, Chuang Gan, Wei-Chiu Ma, Antonio Torralba
Date: 2019-04-11 00:00:00
Proceeding: ICCV 2019 10

Sounds originate from object motions and vibrations of surrounding air.
Inspired by the fact that humans is capable of interpreting sound sources from
how objects move visually, we propose a novel system that explicitly captures
such motion cues for the task of sound localization and separation. Our system
is composed of an end-to-end learnable model called Deep Dense Trajectory
(DDT), and a curriculum learning scheme. It exploits the inherent coherence of
audio-visual signals from a large quantities of unlabeled videos. Quantitative
and qualitative evaluations show that comparing to previous models that rely on
visual appearance cues, our motion based system improves performance in
separating musical instrument sounds. Furthermore, it separates sound
components from duets of the same category of instruments, a challenging
problem that has not been addressed before.
TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning
Authors: Xin Wang, Fisher Yu, Ruth Wang, Trevor Darrell, Joseph E. Gonzalez
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Few-Shot Learning, Meta-Learning, Prediction, Zero-Shot Learning

Learning good feature embeddings for images often requires substantial
training data. As a consequence, in settings where training data is limited
(e.g., few-shot and zero-shot learning), we are typically forced to use a
generic feature embedding across various tasks. Ideally, we want to construct
feature embeddings that are tuned for the given task. In this work, we propose
Task-Aware Feature Embedding Networks (TAFE-Nets) to learn how to adapt the
image representation to a new task in a meta learning fashion. Our network is
composed of a meta learner and a prediction network. Based on a task input, the
meta learner generates parameters for the feature layers in the prediction
network so that the feature embedding can be accurately adjusted for that task.
We show that TAFE-Net is highly effective in generalizing to new tasks or
concepts and evaluate the TAFE-Net on a range of benchmarks in zero-shot and
few-shot learning. Our model matches or exceeds the state-of-the-art on all
tasks. In particular, our approach improves the prediction accuracy of unseen
attribute-object pairs by 4 to 15 points on the challenging visual
attribute-object composition task.
Robust Coreset Construction for Distributed Machine Learning
Authors: Hanlin Lu, Ming-Ju Li, Ting He, Shiqiang Wang, Vijaykrishnan Narayanan, Kevin S. Chan
Date: 2019-04-11 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data. Although viewed as a proxy of the original dataset, each coreset is only designed to approximate the cost function of a specific machine learning problem, and thus different coresets are often required to solve different machine learning problems, increasing the communication overhead. We resolve this dilemma by developing robust coreset construction algorithms that can support a variety of machine learning problems. Motivated by empirical evidence that suitably-weighted k-clustering centers provide a robust coreset, we harden the observation by establishing theoretical conditions under which the coreset provides a guaranteed approximation for a broad range of machine learning problems, and developing both centralized and distributed algorithms to generate coresets satisfying the conditions. The robustness of the proposed algorithms is verified through extensive experiments on diverse datasets with respect to both supervised and unsupervised learning problems.
Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection
Authors: Sunyi Zheng, Jiapan Guo, Xiaonan Cui, Raymond N. J. Veldhuis, Matthijs Oudkerk, Peter M. A. van Ooijen
Date: 2019-04-11 00:00:00
Tasks: Computed Tomography (CT), Lung Nodule Detection, Sensitivity

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.
Strong Baselines for Complex Word Identification across Multiple Languages
Authors: Pierre Finnimore, Elisabeth Fritzsch, Daniel King, Alison Sneyd, Aneeq Ur Rehman, Fernando Alva-Manchego, Andreas Vlachos
Date: 2019-04-11 00:00:00
Proceeding: NAACL 2019 6
Tasks: Complex Word Identification, Multi-Task Learning, Sentence

Complex Word Identification (CWI) is the task of identifying which words or
phrases in a sentence are difficult to understand by a target audience. The
latest CWI Shared Task released data for two settings: monolingual (i.e. train
and test in the same language) and cross-lingual (i.e. test in a language not
seen during training). The best monolingual models relied on language-dependent
features, which do not generalise in the cross-lingual setting, while the best
cross-lingual model used neural networks with multi-task learning. In this
paper, we present monolingual and cross-lingual CWI models that perform as well
as (or better than) most models submitted to the latest CWI Shared Task. We
show that carefully selected features and simple learning models can achieve
state-of-the-art performance, and result in strong baselines for future
development in this area. Finally, we discuss how inconsistencies in the
annotation of the data can explain some of the results obtained.
Variational AutoEncoder For Regression: Application to Brain Aging Analysis
Authors: Qingyu Zhao, Ehsan Adeli, Nicolas Honnorat, Tuo Leng, Kilian M. Pohl
Date: 2019-04-11 00:00:00
Tasks: Disentanglement, regression, Variational Inference

While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified probabilistic model for learning the latent space of imaging data and performing supervised regression. Based on recent advances in learning disentangled representations, the novel generative process explicitly models the conditional distribution of latent representations with respect to the regression target variable. Performing a variational inference procedure on this model leads to joint regularization between the VAE and a neural-network regressor. In predicting the age of 245 subjects from their structural Magnetic Resonance (MR) images, our model is more accurate than state-of-the-art methods when applied to either region-of-interest (ROI) measurements or raw 3D volume images. More importantly, unlike simple feed-forward neural-networks, disentanglement of age in latent representations allows for intuitive interpretation of the structural developmental patterns of the human brain.
Absolute Human Pose Estimation with Depth Prediction Network
Authors: Márton Véges, András Lőrincz
Date: 2019-04-11 00:00:00
Tasks: 3D Human Pose Estimation, Depth Estimation, Depth Prediction, Pose Estimation, Prediction, Translation

The common approach to 3D human pose estimation is predicting the body joint
coordinates relative to the hip. This works well for a single person but is
insufficient in the case of multiple interacting people. Methods predicting
absolute coordinates first estimate a root-relative pose then calculate the
translation via a secondary optimization task. We propose a neural network that
predicts joints in a camera centered coordinate system instead of a
root-relative one. Unlike previous methods, our network works in a single step
without any post-processing. Our network beats previous methods on the
MuPoTS-3D dataset and achieves state-of-the-art results.
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch
Authors: Huy Phan, Oliver Y. Chén, Philipp Koch, Alfred Mertins, Maarten De Vos
Date: 2019-04-11 00:00:00
Tasks: Sleep Staging, Transfer Learning

Many sleep studies suffer from the problem of insufficient data to fully utilize deep neural networks as different labs use different recordings set ups, leading to the need of training automated algorithms on rather small databases, whereas large annotated databases are around but cannot be directly included into these studies for data compensation due to channel mismatch. This work presents a deep transfer learning approach to overcome the channel mismatch problem and transfer knowledge from a large dataset to a small cohort to study automatic sleep staging with single-channel input. We employ the state-of-the-art SeqSleepNet and train the network in the source domain, i.e. the large dataset. Afterwards, the pretrained network is finetuned in the target domain, i.e. the small cohort, to complete knowledge transfer. We study two transfer learning scenarios with slight and heavy channel mismatch between the source and target domains. We also investigate whether, and if so, how finetuning entirely or partially the pretrained network would affect the performance of sleep staging on the target domain. Using the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and the Sleep-EDF Expanded database consisting of 20 subjects as the target domain in this study, our experimental results show significant performance improvement on sleep staging achieved with the proposed deep transfer learning approach. Furthermore, these results also reveal the essential of finetuning the feature-learning parts of the pretrained network to be able to bypass the channel mismatch problem.
Learning Digital Camera Pipeline for Extreme Low-Light Imaging
Authors: Syed Waqas Zamir, Aditya Arora, Salman Khan, Fahad Shahbaz Khan, Ling Shao
Date: 2019-04-11 00:00:00

In low-light conditions, a conventional camera imaging pipeline produces
sub-optimal images that are usually dark and noisy due to a low photon count
and low signal-to-noise ratio (SNR). We present a data-driven approach that
learns the desired properties of well-exposed images and reflects them in
images that are captured in extremely low ambient light environments, thereby
significantly improving the visual quality of these low-light images. We
propose a new loss function that exploits the characteristics of both
pixel-wise and perceptual metrics, enabling our deep neural network to learn
the camera processing pipeline to transform the short-exposure, low-light RAW
sensor data to well-exposed sRGB images. The results show that our method
outperforms the state-of-the-art according to psychophysical tests as well as
pixel-wise standard metrics and recent learning-based perceptual image quality
measures.
Ranking-Based Autoencoder for Extreme Multi-label Classification
Authors: Bingyu Wang, Li Chen, Wei Sun, Kechen Qin, Kefeng Li, Hui Zhou
Date: 2019-04-11 00:00:00
Proceeding: NAACL 2019 6
Tasks: Classification, Extreme Multi-Label Classification, Feature Importance, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Extreme Multi-label classification (XML) is an important yet challenging
machine learning task, that assigns to each instance its most relevant
candidate labels from an extremely large label collection, where the numbers of
labels, features and instances could be thousands or millions. XML is more and
more on demand in the Internet industries, accompanied with the increasing
business scale / scope and data accumulation. The extremely large label
collections yield challenges such as computational complexity, inter-label
dependency and noisy labeling. Many methods have been proposed to tackle these
challenges, based on different mathematical formulations. In this paper, we
propose a deep learning XML method, with a word-vector-based self-attention,
followed by a ranking-based AutoEncoder architecture. The proposed method has
three major advantages: 1) the autoencoder simultaneously considers the
inter-label dependencies and the feature-label dependencies, by projecting
labels and features onto a common embedding space; 2) the ranking loss not only
improves the training efficiency and accuracy but also can be extended to
handle noisy labeled data; 3) the efficient attention mechanism improves
feature representation by highlighting feature importance. Experimental results
on benchmark datasets show the proposed method is competitive to
state-of-the-art methods.