pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Generative Active Deep Learning
Authors: Toan Tran, Thanh-Toan Do, Ian Reid, Gustavo Carneiro
Date: 2019-04-26 00:00:00
Tasks: Active Learning, Data Augmentation, Deep Learning

Deep learning models have demonstrated outstanding performance in several
problems, but their training process tends to require immense amounts of
computational and human resources for training and labeling, constraining the
types of problems that can be tackled. Therefore, the design of effective
training methods that require small labeled training sets is an important
research direction that will allow a more effective use of resources.Among
current approaches designed to address this issue, two are particularly
interesting: data augmentation and active learning. Data augmentation achieves
this goal by artificially generating new training points, while active learning
relies on the selection of the "most informative" subset of unlabeled training
samples to be labelled by an oracle. Although successful in practice, data
augmentation can waste computational resources because it indiscriminately
generates samples that are not guaranteed to be informative, and active
learning selects a small subset of informative samples (from a large
un-annotated set) that may be insufficient for the training process. In this
paper, we propose a Bayesian generative active deep learning approach that
combines active learning with data augmentation -- we provide theoretical and
empirical evidence (MNIST, CIFAR-$\{10,100\}$, and SVHN) that our approach has
more efficient training and better classification results than data
augmentation and active learning.
Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods
Authors: Mohammed Senoussaoui, Patrick Cardinal, Najim Dehak, Alessandro Lameiras Koerich
Date: 2019-04-26 00:00:00

Automatic measuring of speaker sincerity degree is a novel research problem
in computational paralinguistics. This paper proposes covariance-based feature
vectors to model speech and ensembles of support vector regressors to estimate
the degree of sincerity of a speaker. The elements of each covariance vector
are pairwise statistics between the short-term feature components. These
features are used alone as well as in combination with the ComParE acoustic
feature set. The experimental results on the development set of the Sincerity
Speech Corpus using a cross-validation procedure have shown an 8.1% relative
improvement in the Spearman's correlation coefficient over the baseline system.
From Predictions to Prescriptions in Multistage Optimization Problems
Authors: Dimitris Bertsimas, Christopher McCord
Date: 2019-04-26 00:00:00
Tasks: regression

In this paper, we introduce a framework for solving finite-horizon multistage
optimization problems under uncertainty in the presence of auxiliary data. We
assume the joint distribution of the uncertain quantities is unknown, but noisy
observations, along with observations of auxiliary covariates, are available.
We utilize effective predictive methods from machine learning (ML), including
$k$-nearest neighbors regression ($k$NN), classification and regression trees
(CART), and random forests (RF), to develop specific methods that are
applicable to a wide variety of problems. We demonstrate that our solution
methods are asymptotically optimal under mild conditions. Additionally, we
establish finite sample guarantees for the optimality of our method with $k$NN
weight functions. Finally, we demonstrate the practicality of our approach with
computational examples. We see a significant decrease in cost by taking into
account the auxiliary data in the multistage setting.
Weighted second-order cone programming twin support vector machine for imbalanced data classification
Authors: Saeideh Roshanfekr, Shahriar Esmaeili, Hassan Ataeian, Ali Amiri
Date: 2019-04-26 00:00:00
Tasks: General Classification

We propose a method of using a Weighted second-order cone programming twin support vector machine (WSOCP-TWSVM) for imbalanced data classification. This method constructs a graph based under-sampling method which is utilized to remove outliers and reduce the dispensable majority samples. Then, appropriate weights are set in order to decrease the impact of samples of the majority class and increase the effect of the minority class in the optimization formula of the classifier. These weights are embedded in the optimization problem of the Second Order Cone Programming (SOCP) Twin Support Vector Machine formulations. This method is tested, and its performance is compared to previous methods on standard datasets. Results of experiments confirm the feasibility and efficiency of the proposed method.
Scene Graph Prediction with Limited Labels
Authors: Vincent S. Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher Re, Li Fei-Fei
Date: 2019-04-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Knowledge Base Completion, Prediction, Question Answering, Transfer Learning, Visual Question Answering, Visual Question Answering (VQA)

Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete relationships. All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each. Hiring human annotators is expensive, and using textual knowledge base completion methods are incompatible with visual data. In this paper, we introduce a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few labeled examples. We analyze visual relationships to suggest two types of image-agnostic features that are used to generate noisy heuristics, whose outputs are aggregated using a factor graph-based generative model. With as few as 10 labeled examples per relationship, the generative model creates enough training data to train any existing state-of-the-art scene graph model. We demonstrate that our method outperforms all baseline approaches on scene graph prediction by 5.16 recall@100 for PREDCLS. In our limited label setting, we define a complexity metric for relationships that serves as an indicator (R^2 = 0.778) for conditions under which our method succeeds over transfer learning, the de-facto approach for training with limited labels.
Meta-Sim: Learning to Generate Synthetic Datasets
Authors: Amlan Kar, Aayush Prakash, Ming-Yu Liu, Eric Cameracci, Justin Yuan, Matt Rusiniak, David Acuna, Antonio Torralba, Sanja Fidler
Date: 2019-04-25 00:00:00
Proceeding: ICCV 2019 10

Training models to high-end performance requires availability of large
labeled datasets, which are expensive to get. The goal of our work is to
automatically synthesize labeled datasets that are relevant for a downstream
task. We propose Meta-Sim, which learns a generative model of synthetic scenes,
and obtain images as well as its corresponding ground-truth via a graphics
engine. We parametrize our dataset generator with a neural network, which
learns to modify attributes of scene graphs obtained from probabilistic scene
grammars, so as to minimize the distribution gap between its rendered outputs
and target data. If the real dataset comes with a small labeled validation set,
we additionally aim to optimize a meta-objective, i.e. downstream task
performance. Experiments show that the proposed method can greatly improve
content generation quality over a human-engineered probabilistic scene grammar,
both qualitatively and quantitatively as measured by performance on a
downstream task.
Improved visible to IR image transformation using synthetic data augmentation with cycle-consistent adversarial networks
Authors: Kyongsik Yun, Kevin Yu, Joseph Osborne, Sarah Eldin, Luan Nguyen, Alexander Huyen, Thomas Lu
Date: 2019-04-25 00:00:00
Tasks: Data Augmentation, Object Recognition, Unity

Infrared (IR) images are essential to improve the visibility of dark or
camouflaged objects. Object recognition and segmentation based on a neural
network using IR images provide more accuracy and insight than color visible
images. But the bottleneck is the amount of relevant IR images for training. It
is difficult to collect real-world IR images for special purposes, including
space exploration, military and fire-fighting applications. To solve this
problem, we created color visible and IR images using a Unity-based 3D game
editor. These synthetically generated color visible and IR images were used to
train cycle consistent adversarial networks (CycleGAN) to convert visible
images to IR images. CycleGAN has the advantage that it does not require
precisely matching visible and IR pairs for transformation training. In this
study, we discovered that additional synthetic data can help improve CycleGAN
performance. Neural network training using real data (N = 20) performed more
accurate transformations than training using real (N = 10) and synthetic (N =
10) data combinations. The result indicates that the synthetic data cannot
exceed the quality of the real data. Neural network training using real (N =
10) and synthetic (N = 100) data combinations showed almost the same
performance as training using real data (N = 20). At least 10 times more
synthetic data than real data is required to achieve the same performance. In
summary, CycleGAN is used with synthetic data to improve the IR image
conversion performance of visible images.
Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation
Authors: Kyongsik Yun, Luan Nguyen, Tuan Nguyen, Doyoung Kim, Sarah Eldin, Alexander Huyen, Thomas Lu, Edward Chow
Date: 2019-04-25 00:00:00
Tasks: Data Augmentation, Image Segmentation, object-detection, Object Detection, Semantic Segmentation, Synthetic Data Generation

It is important to find the target as soon as possible for search and rescue
operations. Surveillance camera systems and unmanned aerial vehicles (UAVs) are
used to support search and rescue. Automatic object detection is important
because a person cannot monitor multiple surveillance screens simultaneously
for 24 hours. Also, the object is often too small to be recognized by the human
eye on the surveillance screen. This study used UAVs around the Port of Houston
and fixed surveillance cameras to build an automatic target detection system
that supports the US Coast Guard (USCG) to help find targets (e.g., person
overboard). We combined image segmentation, enhancement, and convolution neural
networks to reduce detection time to detect small targets. We compared the
performance between the auto-detection system and the human eye. Our system
detected the target within 8 seconds, but the human eye detected the target
within 25 seconds. Our systems also used synthetic data generation and data
augmentation techniques to improve target detection accuracy. This solution may
help the search and rescue operations of the first responders in a timely
manner.
High-Resolution Network for Photorealistic Style Transfer
Authors: Ming Li, Chunyang Ye, Wei Li
Date: 2019-04-25 00:00:00
Tasks: Image Generation, Style Transfer, Vocal Bursts Intensity Prediction

Photorealistic style transfer aims to transfer the style of one image to
another, but preserves the original structure and detail outline of the content
image, which makes the content image still look like a real shot after the
style transfer. Although some realistic image styling methods have been
proposed, these methods are vulnerable to lose the details of the content image
and produce some irregular distortion structures. In this paper, we use a
high-resolution network as the image generation network. Compared to other
methods, which reduce the resolution and then restore the high resolution, our
generation network maintains high resolution throughout the process. By
connecting high-resolution subnets to low-resolution subnets in parallel and
repeatedly multi-scale fusion, high-resolution subnets can continuously receive
information from low-resolution subnets. This allows our network to discard
less information contained in the image, so the generated images may have a
more elaborate structure and less distortion, which is crucial to the visual
quality. We conducted extensive experiments and compared the results with
existing methods. The experimental results show that our model is effective and
produces better results than existing methods for photorealistic image
stylization. Our source code with PyTorch framework will be publicly available
at https://github.com/limingcv/Photorealistic-Style-Transfer
Look Who's Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog
Authors: Charles Welch, Verónica Pérez-Rosas, Jonathan K. Kummerfeld, Rada Mihalcea
Date: 2019-04-25 00:00:00
Tasks: Attribute

We examine a large dialog corpus obtained from the conversation history of a
single individual with 104 conversation partners. The corpus consists of half a
million instant messages, across several messaging platforms. We focus our
analyses on seven speaker attributes, each of which partitions the set of
speakers, namely: gender; relative age; family member; romantic partner;
classmate; co-worker; and native to the same country. In addition to the
content of the messages, we examine conversational aspects such as the time
messages are sent, messaging frequency, psycholinguistic word categories,
linguistic mirroring, and graph-based features reflecting how people in the
corpus mention each other. We present two sets of experiments predicting each
attribute using (1) short context windows; and (2) a larger set of messages. We
find that using all features leads to gains of 9-14% over using message text
only.
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Authors: Yao Ma, Alex Olshevsky, Venkatesh Saligrama, Csaba Szepesvari
Date: 2019-04-25 00:00:00
Proceeding: ICML 2018 7
Tasks: Matrix Completion

We consider worker skill estimation for the single-coin Dawid-Skene crowdsourcing model. In practice, skill-estimation is challenging because worker assignments are sparse and irregular due to the arbitrary and uncontrolled availability of workers. We formulate skill estimation as a rank-one correlation-matrix completion problem, where the observed components correspond to observed label correlations between workers. We show that the correlation matrix can be successfully recovered and skills are identifiable if and only if the sampling matrix (observed components) does not have a bipartite connected component. We then propose a projected gradient descent scheme and show that skill estimates converge to the desired global optima for such sampling matrices. Our proof is original and the results are surprising in light of the fact that even the weighted rank-one matrix factorization problem is NP-hard in general. Next, we derive sample complexity bounds in terms of spectral properties of the signless Laplacian of the sampling matrix. Our proposed scheme achieves state-of-art performance on a number of real-world datasets.
Optical Flow Techniques for Facial Expression Analysis -- a Practical Evaluation Study
Authors: Benjamin Allaert, Isaac Ronald Ward, Ioan Marius Bilasco, Chaabane Djeraba, Mohammed Bennamoun
Date: 2019-04-25 00:00:00
Tasks: Data Augmentation, Facial Expression Recognition, Facial Expression Recognition (FER), Optical Flow Estimation

Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition. In this work, a variety of optical flow approaches are evaluated across multiple facial expression datasets, so as to provide a consistent performance evaluation. The aim of this work is not to propose a new expression recognition technique, but to understand better the adequacy of existing state-of-the art optical flow for encoding facial motion in the context of facial expression recognition. Our evaluations highlight the fact that motion approximation methods used to overcome motion discontinuities have a significant impact when optical flows are used to characterize facial expressions.
Multiple Linear Regression Haze-removal Model Based on Dark Channel Prior
Authors: Binghan Li, Wenrui Zhang, Mi Lu
Date: 2019-04-25 00:00:00
Tasks: regression, SSIM

Dark Channel Prior (DCP) is a widely recognized traditional dehazing
algorithm. However, it may fail in bright region and the brightness of the
restored image is darker than hazy image. In this paper, we propose an
effective method to optimize DCP. We build a multiple linear regression
haze-removal model based on DCP atmospheric scattering model and train this
model with RESIDE dataset, which aims to reduce the unexpected errors caused by
the rough estimations of transmission map t(x) and atmospheric light A. The
RESIDE dataset provides enough synthetic hazy images and their corresponding
groundtruth images to train and test. We compare the performances of different
dehazing algorithms in terms of two important full-reference metrics, the
peak-signal-to-noise ratio (PSNR) as well as the structural similarity index
measure (SSIM). The experiment results show that our model gets highest SSIM
value and its PSNR value is also higher than most of state-of-the-art dehazing
algorithms. Our results also overcome the weakness of DCP on real-world hazy
images
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks
Authors: Bahram Mohammadi, Mohammad Sabokrou
Date: 2019-04-25 00:00:00
Tasks: Diversity, Intrusion Detection, One-Class Classification

This paper presents a simple yet efficient method for an anomaly-based
Intrusion Detection System (IDS). In reality, IDSs can be defined as a
one-class classification system, where the normal traffic is the target class.
The high diversity of network attacks in addition to the need for
generalization, motivate us to propose a semi-supervised method. Inspired by
the successes of Generative Adversarial Networks (GANs) for training deep
models in semi-unsupervised setting, we have proposed an end-to-end deep
architecture for IDS. The proposed architecture is composed of two deep
networks, each of which trained by competing with each other to understand the
underlying concept of the normal traffic class. The key idea of this paper is
to compensate the lack of anomalous traffic by approximately obtain them from
normal flows. In this case, our method is not biased towards the available
intrusions in the training set leading to more accurate detection. The proposed
method has been evaluated on NSL-KDD dataset. The results confirm that our
method outperforms the other state-of-the-art approaches.
TVQA+: Spatio-Temporal Grounding for Video Question Answering
Authors: Jie Lei, Licheng Yu, Tamara L. Berg, Mohit Bansal
Date: 2019-04-25 00:00:00
Proceeding: ACL 2020 6
Tasks: Question Answering, Video Question Answering

We present the task of Spatio-Temporal Video Question Answering, which requires intelligent systems to simultaneously retrieve relevant moments and detect referenced visual concepts (people and objects) to answer natural language questions about videos. We first augment the TVQA dataset with 310.8K bounding boxes, linking depicted objects to visual concepts in questions and answers. We name this augmented version as TVQA+. We then propose Spatio-Temporal Answerer with Grounded Evidence (STAGE), a unified framework that grounds evidence in both spatial and temporal domains to answer questions about videos. Comprehensive experiments and analyses demonstrate the effectiveness of our framework and how the rich annotations in our TVQA+ dataset can contribute to the question answering task. Moreover, by performing this joint task, our model is able to produce insightful and interpretable spatio-temporal attention visualizations. Dataset and code are publicly available at: http: //tvqa.cs.unc.edu, https://github.com/jayleicn/TVQAplus
Unsupervised Deep Learning by Neighbourhood Discovery
Authors: Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu
Date: 2019-04-25 00:00:00
Tasks: Deep Learning, image-classification, Image Classification

Deep convolutional neural networks (CNNs) have demonstrated remarkable success in computer vision by supervisedly learning strong visual feature representations. However, training CNNs relies heavily on the availability of exhaustive training data annotations, limiting significantly their deployment and scalability in many application scenarios. In this work, we introduce a generic unsupervised deep learning approach to training deep models without the need for any manual label supervision. Specifically, we progressively discover sample anchored/centred neighbourhoods to reason and learn the underlying class decision boundaries iteratively and accumulatively. Every single neighbourhood is specially formulated so that all the member samples can share the same unseen class labels at high probability for facilitating the extraction of class discriminative feature representations during training. Experiments on image classification show the performance advantages of the proposed method over the state-of-the-art unsupervised learning models on six benchmarks including both coarse-grained and fine-grained object image categorisation.
Pedestrian Collision Avoidance System for Scenarios with Occlusions
Authors: Markus Schratter, Maxime Bouton, Mykel J. Kochenderfer, Daniel Watzenig
Date: 2019-04-25 00:00:00
Tasks: Autonomous Driving, Collision Avoidance

Safe autonomous driving in urban areas requires robust algorithms to avoid
collisions with other traffic participants with limited perception ability.
Current deployed approaches relying on Autonomous Emergency Braking (AEB)
systems are often overly conservative. In this work, we formulate the problem
as a partially observable Markov decision process (POMDP), to derive a policy
robust to uncertainty in the pedestrian location. We investigate how to
integrate such a policy with an AEB system that operates only when a collision
is unavoidable. In addition, we propose a rigorous evaluation methodology on a
set of well defined scenarios. We show that combining the two approaches
provides a robust autonomous braking system that reduces unnecessary braking
caused by using the AEB system on its own.
Neural Text Generation from Rich Semantic Representations
Authors: Valerie Hajdik, Jan Buys, Michael W. Goodman, Emily M. Bender
Date: 2019-04-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Abstract Meaning Representation, Text Generation

We propose neural models to generate high-quality text from structured
representations based on Minimal Recursion Semantics (MRS). MRS is a rich
semantic representation that encodes more precise semantic detail than other
representations such as Abstract Meaning Representation (AMR). We show that a
sequence-to-sequence model that maps a linearization of Dependency MRS, a
graph-based representation of MRS, to English text can achieve a BLEU score of
66.11 when trained on gold data. The performance can be improved further using
a high-precision, broad coverage grammar-based parser to generate a large
silver training corpus, achieving a final BLEU score of 77.17 on the full test
set, and 83.37 on the subset of test data most closely matching the silver data
domain. Our results suggest that MRS-based representations are a good choice
for applications that need both structured semantics and the ability to produce
natural language text as output.