pwc-archive/papers-with-abstracts huggingface dataset:
Multi-task Learning of Hierarchical Vision-Language Representation
Authors: Duy-Kien Nguyen, Takayuki Okatani
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Multi-Task Learning, Question Answering, Retrieval, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

It is still challenging to build an AI system that can perform tasks that
involve vision and language at human level. So far, researchers have singled
out individual tasks separately, for each of which they have designed networks
and trained them on its dedicated datasets. Although this approach has seen a
certain degree of success, it comes with difficulties of understanding
relations among different tasks and transferring the knowledge learned for a
task to others. We propose a multi-task learning approach that enables to learn
vision-language representation that is shared by many tasks from their diverse
datasets. The representation is hierarchical, and prediction for each task is
computed from the representation at its corresponding level of the hierarchy.
We show through experiments that our method consistently outperforms previous
single-task-learning methods on image caption retrieval, visual question
answering, and visual grounding. We also analyze the learned hierarchical
representation by visualizing attention maps generated in our network.
Towards a More Practice-Aware Runtime Analysis of Evolutionary Algorithms
Authors: Eduardo Carvalho Pinto, Carola Doerr
Date: 2018-12-03 00:00:00
Tasks: Evolutionary Algorithms

Theory of evolutionary computation (EC) aims at providing mathematically
founded statements about the performance of evolutionary algorithms (EAs). The
predominant topic in this research domain is runtime analysis, which studies
the time it takes a given EA to solve a given optimization problem. Runtime
analysis has witnessed significant advances in the last couple of years,
allowing us to compute precise runtime estimates for several EAs and several
problems. Runtime analysis is, however (and unfortunately!), often judged by
practitioners to be of little relevance for real applications of EAs. Several
reasons for this claim exist. We address two of them in this present work:
  (1) EA implementations often differ from their vanilla pseudocode
description, which, in turn, typically form the basis for runtime analysis. To
close the resulting gap between empirically observed and theoretically derived
performance estimates, we therefore suggest to take this discrepancy into
account in the mathematical analysis and to adjust, for example, the cost
assigned to the evaluation of search points that equal one of their direct
parents (provided that this is easy to verify as is the case in almost all
standard EAs).
  (2) Most runtime analysis results make statements about the expected time to
reach an optimal solution (and possibly the distribution of this optimization
time) only, thus explicitly or implicitly neglecting the importance of
understanding how the function values evolve over time. We suggest to extend
runtime statements to runtime profiles, covering the expected time needed to
reach points of intermediate fitness values.
  As a direct consequence, we obtain a result showing that the greedy (2+1) GA
of Sudholt [GECCO 2012] outperforms any unary unbiased black-box algorithm on
OneMax.
Adversarial Domain Randomization
Authors: Rawal Khirodkar, Kris M. Kitani
Date: 2018-12-03 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, object-detection, Object Detection

Domain Randomization (DR) is known to require a significant amount of training data for good performance. We argue that this is due to DR's strategy of random data generation using a uniform distribution over simulation parameters, as a result, DR often generates samples which are uninformative for the learner. In this work, we theoretically analyze DR using ideas from multi-source domain adaptation. Based on our findings, we propose Adversarial Domain Randomization (ADR) as an efficient variant of DR which generates adversarial samples with respect to the learner during training. We implement ADR as a policy whose action space is the quantized simulation parameter space. At each iteration, the policy's action generates labeled data and the reward is set as negative of learner's loss on this data. As a result, we observe ADR frequently generates novel samples for the learner like truncated and occluded objects for object detection and confusing classes for image classification. We perform evaluations on datasets like CLEVR, Syn2Real, and VIRAT for various tasks where we demonstrate that ADR outperforms DR by generating fewer data samples.
Improving Clinical Predictions through Unsupervised Time Series Representation Learning
Authors: Xinrui Lyu, Matthias Hueser, Stephanie L. Hyland, George Zerveas, Gunnar Raetsch
Date: 2018-12-02 00:00:00
Tasks: Decision Making, Representation Learning, Time Series, Time Series Analysis

In this work, we investigate unsupervised representation learning on medical
time series, which bears the promise of leveraging copious amounts of existing
unlabeled data in order to eventually assist clinical decision making. By
evaluating on the prediction of clinically relevant outcomes, we show that in a
practical setting, unsupervised representation learning can offer clear
performance benefits over end-to-end supervised architectures. We experiment
with using sequence-to-sequence (Seq2Seq) models in two different ways, as an
autoencoder and as a forecaster, and show that the best performance is achieved
by a forecasting Seq2Seq model with an integrated attention mechanism, proposed
here for the first time in the setting of unsupervised learning for medical
time series.
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image
Authors: Jiaxiong Qiu, Zhaopeng Cui, yinda zhang, Xingdi Zhang, Shuaicheng Liu, Bing Zeng, Marc Pollefeys
Date: 2018-12-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Depth Completion, Depth Estimation, Depth Prediction

In this paper, we propose a deep learning architecture that produces accurate
dense depth for the outdoor scene from a single color image and a sparse depth.
Inspired by the indoor depth completion, our network estimates surface normals
as the intermediate representation to produce dense depth, and can be trained
end-to-end. With a modified encoder-decoder structure, our network effectively
fuses the dense color image and the sparse LiDAR depth. To address outdoor
specific challenges, our network predicts a confidence mask to handle mixed
LiDAR signals near foreground boundaries due to occlusion, and combines
estimates from the color image and surface normals with learned attention maps
to improve the depth accuracy especially for distant areas. Extensive
experiments demonstrate that our model improves upon the state-of-the-art
performance on KITTI depth completion benchmark. Ablation study shows the
positive impact of each model components to the final performance, and
comprehensive analysis shows that our model generalizes well to the input with
higher sparsity or from indoor scenes.
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
Authors: Siyuan Qiao, Zhe Lin, Jianming Zhang, Alan Yuille
Date: 2018-12-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Network Pruning, Neural Architecture Search

In this paper, we study the problem of improving computational resource
utilization of neural networks. Deep neural networks are usually
over-parameterized for their tasks in order to achieve good performances, thus
are likely to have underutilized computational resources. This observation
motivates a lot of research topics, e.g. network pruning, architecture search,
etc. As models with higher computational costs (e.g. more parameters or more
computations) usually have better performances, we study the problem of
improving the resource utilization of neural networks so that their potentials
can be further realized. To this end, we propose a novel optimization method
named Neural Rejuvenation. As its name suggests, our method detects dead
neurons and computes resource utilization in real time, rejuvenates dead
neurons by resource reallocation and reinitialization, and trains them with new
training schemes. By simply replacing standard optimizers with Neural
Rejuvenation, we are able to improve the performances of neural networks by a
very large margin while using similar training efforts and maintaining their
original resource usages.
Unsupervised Domain Adaptation using Generative Models and Self-ensembling
Authors: Eman T. Hassan, Xin Chen, David Crandall
Date: 2018-12-02 00:00:00
Tasks: Data Augmentation, Domain Adaptation, Style Transfer, Unsupervised Domain Adaptation

Transferring knowledge across different datasets is an important approach to
successfully train deep models with a small-scale target dataset or when few
labeled instances are available. In this paper, we aim at developing a model
that can generalize across multiple domain shifts, so that this model can adapt
from a single source to multiple targets. This can be achieved by randomizing
the generation of the data of various styles to mitigate the domain mismatch.
First, we present a new adaptation to the CycleGAN model to produce stochastic
style transfer between two image batches of different domains. Second, we
enhance the classifier performance by using a self-ensembling technique with a
teacher and student model to train on both original and generated data.
Finally, we present experimental results on three datasets Office-31,
Office-Home, and Visual Domain adaptation. The results suggest that
selfensembling is better than simple data augmentation with the newly generated
data and a single model trained this way can have the best performance across
all different transfer tasks.
Ego-Downward and Ambient Video based Person Location Association
Authors: Liang Yang, Hao Jiang, Jizhong Xiao, Zhouyuan Huo
Date: 2018-12-02 00:00:00
Tasks: Diversity

Using an ego-centric camera to do localization and tracking is highly needed
for urban navigation and indoor assistive system when GPS is not available or
not accurate enough. The traditional hand-designed feature tracking and
estimation approach would fail without visible features. Recently, there are
several works exploring to use context features to do localization. However,
all of these suffer severe accuracy loss if given no visual context
information. To provide a possible solution to this problem, this paper
proposes a camera system with both ego-downward and third-static view to
perform localization and tracking in a learning approach. Besides, we also
proposed a novel action and motion verification model for cross-view
verification and localization. We performed comparative experiments based on
our collected dataset which considers the same dressing, gender, and background
diversity. Results indicate that the proposed model can achieve $18.32 \%$
improvement in accuracy performance. Eventually, we tested the model on
multi-people scenarios and obtained an average $67.767 \%$ accuracy.
Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems
Authors: Bashir Rastegarpanah, Krishna P. Gummadi, Mark Crovella
Date: 2018-12-02 00:00:00
Tasks: Fairness, Recommendation Systems

The increasing role of recommender systems in many aspects of society makes
it essential to consider how such systems may impact social good. Various
modifications to recommendation algorithms have been proposed to improve their
performance for specific socially relevant measures. However, previous
proposals are often not easily adapted to different measures, and they
generally require the ability to modify either existing system inputs, the
system's algorithm, or the system's outputs. As an alternative, in this paper
we introduce the idea of improving the social desirability of recommender
system outputs by adding more data to the input, an approach we view as
providing `antidote' data to the system. We formalize the antidote data
problem, and develop optimization-based solutions. We take as our model system
the matrix factorization approach to recommendation, and we propose a set of
measures to capture the polarization or fairness of recommendations. We then
show how to generate antidote data for each measure, pointing out a number of
computational efficiencies, and discuss the impact on overall system accuracy.
Our experiments show that a modest budget for antidote data can lead to
significant improvements in the polarization or fairness of recommendations.
Multiple Instance Learning for ECG Risk Stratification
Authors: Divya Shanmugam, Davis Blalock, John Guttag
Date: 2018-12-02 00:00:00
Tasks: Ecg Risk Stratification, Multiple Instance Learning

Patients who suffer an acute coronary syndrome are at elevated risk for adverse cardiovascular events such as myocardial infarction and cardiovascular death. Accurate assessment of this risk is crucial to their course of care. We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndrome based on a patient's raw electrocardiogram (ECG) signal. Learning from this signal is challenging for two reasons: 1) positive examples signifying a downstream cardiovascular event are scarce, causing drastic class imbalance, and 2) each patient's ECG signal consists of thousands of heartbeats, accompanied by a single label for the downstream outcome. Machine learning has been previously applied to this task, but most approaches rely on hand-crafted features and domain knowledge. We propose a method that learns a representation from the raw ECG signal by using a multiple instance learning framework. We present a learned risk score for cardiovascular death that outperforms existing risk metrics in predicting cardiovascular death within 30, 60, 90, and 365 days on a dataset of 5000 patients.
"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors
Authors: Yossi Gandelsman, Assaf Shocher, Michal Irani
Date: 2018-12-02 00:00:00
Proceeding: Computer Vision Foundation 2018 12
Tasks: Image Dehazing, Image Segmentation, Semantic Segmentation, Transparency Separation, Unsupervised Image Decomposition

Many seemingly unrelated computer vision tasks can be viewed as a special
case of image decomposition into separate layers. For example, image
segmentation (separation into foreground and background layers); transparent
layer separation (into reflection and transmission layers); Image dehazing
(separation into a clear image and a haze map), and more. In this paper we
propose a unified framework for unsupervised layer decomposition of a single
image, based on coupled "Deep-image-Prior" (DIP) networks. It was shown
[Ulyanov et al] that the structure of a single DIP generator network is
sufficient to capture the low-level statistics of a single image. We show that
coupling multiple such DIPs provides a powerful tool for decomposing images
into their basic components, for a wide variety of applications. This
capability stems from the fact that the internal statistics of a mixture of
layers is more complex than the statistics of each of its individual
components. We show the power of this approach for Image-Dehazing, Fg/Bg
Segmentation, Watermark-Removal, Transparency Separation in images and video,
and more. These capabilities are achieved in a totally unsupervised way, with
no training examples other than the input image/video itself.
Personalizing Intervention Probabilities By Pooling
Authors: Sabina Tomkins, Predrag Klasnja, Susan Murphy
Date: 2018-12-02 00:00:00

In many mobile health interventions, treatments should only be delivered in a
particular context, for example when a user is currently stressed, walking or
sedentary. Even in an optimal context, concerns about user burden can restrict
which treatments are sent. To diffuse the treatment delivery over times when a
user is in a desired context, it is critical to predict the future number of
times the context will occur. The focus of this paper is on whether
personalization can improve predictions in these settings. Though the variance
between individuals' behavioral patterns suggest that personalization should be
useful, the amount of individual-level data limits its capabilities. Thus, we
investigate several methods which pool data across users to overcome these
deficiencies and find that pooling lowers the overall error rate relative to
both personalized and batch approaches.
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
Authors: Zhao Song, Ronald E. Parr, Lawrence Carin
Date: 2018-12-02 00:00:00
Tasks: Atari Games, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

The impact of softmax on the value function itself in reinforcement learning (RL) is often viewed as problematic because it leads to sub-optimal value (or Q) functions and interferes with the contraction properties of the Bellman operator. Surprisingly, despite these concerns, and independent of its effect on exploration, the softmax Bellman operator when combined with Deep Q-learning, leads to Q-functions with superior policies in practice, even outperforming its double Q-learning counterpart. To better understand how and why this occurs, we revisit theoretical properties of the softmax Bellman operator, and prove that $(i)$ it converges to the standard Bellman operator exponentially fast in the inverse temperature parameter, and $(ii)$ the distance of its Q function from the optimal one can be bounded. These alone do not explain its superior performance, so we also show that the softmax operator can reduce the overestimation error, which may give some insight into why a sub-optimal operator leads to better performance in the presence of value function approximation. A comparison among different Bellman operators is then presented, showing the trade-offs when selecting them.
Disentangling Propagation and Generation for Video Prediction
Authors: Hang Gao, Huazhe Xu, Qi-Zhi Cai, Ruth Wang, Fisher Yu, Trevor Darrell
Date: 2018-12-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: Predict Future Video Frames, Prediction, Video Prediction

A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated. Prior approaches to video prediction typically learn either to warp or to hallucinate future pixels, but not both. In this paper, we describe a computational model for high-fidelity video prediction which disentangles motion-specific propagation from motion-agnostic generation. We introduce a confidence-aware warping operator which gates the output of pixel predictions from a flow predictor for non-occluded regions and from a context encoder for occluded regions. Moreover, in contrast to prior works where confidence is jointly learned with flow and appearance using a single network, we compute confidence after a warping step, and employ a separate network to inpaint exposed regions. Empirical results on both synthetic and real datasets show that our disentangling approach provides better occlusion maps and produces both sharper and more realistic predictions compared to strong baselines.
Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer's disease
Authors: Stefan Konigorski, Shahryar Khorasani, Christoph Lippert
Date: 2018-12-02 00:00:00

For precision medicine and personalized treatment, we need to identify
predictive markers of disease. We focus on Alzheimer's disease (AD), where
magnetic resonance imaging scans provide information about the disease status.
By combining imaging with genome sequencing, we aim at identifying rare genetic
markers associated with quantitative traits predicted from convolutional neural
networks (CNNs), which traditionally have been derived manually by experts.
Kernel-based tests are a powerful tool for associating sets of genetic
variants, but how to optimally model rare genetic variants is still an open
research question. We propose a generalized set of kernels that incorporate
prior information from various annotations and multi-omics data. In the
analysis of data from the Alzheimer's Disease Neuroimaging Initiative (ADNI),
we evaluate whether (i) CNNs yield precise and reliable brain traits, and (ii)
the novel kernel-based tests can help to identify loci associated with AD. The
results indicate that CNNs provide a fast, scalable and precise tool to derive
quantitative AD traits and that new kernels integrating domain knowledge can
yield higher power in association tests of very rare variants.
Deep Cosine Metric Learning for Person Re-Identification
Authors: Nicolai Wojke, Alex Bewley
Date: 2018-12-02 00:00:00
Tasks: General Classification, Metric Learning, Person Re-Identification, Triplet

Metric learning aims to construct an embedding where two extracted features
corresponding to the same identity are likely to be closer than features from
different identities. This paper presents a method for learning such a feature
space where the cosine similarity is effectively optimized through a simple
re-parametrization of the conventional softmax classification regime. At test
time, the final classification layer can be stripped from the network to
facilitate nearest neighbor queries on unseen individuals using the cosine
similarity metric. This approach presents a simple alternative to direct metric
learning objectives such as siamese networks that have required sophisticated
pair or triplet sampling strategies in the past. The method is evaluated on two
large-scale pedestrian re-identification datasets where competitive results are
achieved overall. In particular, we achieve better generalization on the test
set compared to a network trained with triplet loss.
Dual Objective Approach Using A Convolutional Neural Network for Magnetic Resonance Elastography
Authors: Ligin Solamen, Yipeng Shi, Justice Amoh
Date: 2018-12-02 00:00:00

Traditionally, nonlinear inversion, direct inversion, or wave estimation
methods have been used for reconstructing images from MRE displacement data. In
this work, we propose a convolutional neural network architecture that can map
MRE displacement data directly into elastograms, circumventing the costly and
computationally intensive classical approaches. In addition to the mean squared
error reconstruction objective, we also introduce a secondary loss inspired by
the MRE mechanical models for training the neural network. Our network is
demonstrated to be effective for generating MRE images that compare well with
equivalents from the nonlinear inversion method.
Pedestrian Detection with Autoregressive Network Phases
Authors: Garrick Brazil, Xiaoming Liu
Date: 2018-12-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Pedestrian Detection, Region Proposal

We present an autoregressive pedestrian detection framework with cascaded
phases designed to progressively improve precision. The proposed framework
utilizes a novel lightweight stackable decoder-encoder module which uses
convolutional re-sampling layers to improve features while maintaining
efficient memory and runtime cost. Unlike previous cascaded detection systems,
our proposed framework is designed within a region proposal network and thus
retains greater context of nearby detections compared to independently
processed RoI systems. We explicitly encourage increasing levels of precision
by assigning strict labeling policies to each consecutive phase such that early
phases develop features primarily focused on achieving high recall and later on
accurate precision. In consequence, the final feature maps form more peaky
radial gradients emulating from the centroids of unique pedestrians. Using our
proposed autoregressive framework leads to new state-of-the-art performance on
the reasonable and occlusion settings of the Caltech pedestrian dataset, and
achieves competitive state-of-the-art performance on the KITTI dataset.