pwc-archive/papers-with-abstracts huggingface dataset:
Robust Point Light Source Estimation Using Differentiable Rendering
Authors: Grégoire Nieto, Salma Jiddi, Philippe Robert
Date: 2018-12-12 00:00:00
Tasks: Mixed Reality, Object, Outdoor Light Source Estimation, Retrieval

Illumination estimation is often used in mixed reality to re-render a scene
from another point of view, to change the color/texture of an object, or to
insert a virtual object consistently lit into a real video or photograph.
Specifically, the estimation of a point light source is required for the
shadows cast by the inserted object to be consistent with the real scene. We
tackle the problem of illumination retrieval given an RGBD image of the scene
as an inverse problem: we aim to find the illumination that minimizes the
photometric error between the rendered image and the observation. In particular
we propose a novel differentiable renderer based on the Blinn-Phong model with
cast shadows. We compare our differentiable renderer to state-of-the-art
methods and demonstrate its robustness to an incorrect reflectance estimation.
Recurrent Neural Networks for Fuzz Testing Web Browsers
Authors: Martin Sablotny, Bjørn Sand Jensen, Chris W. Johnson
Date: 2018-12-12 00:00:00
Tasks: Model Selection, software testing

Generation-based fuzzing is a software testing approach which is able to
discover different types of bugs and vulnerabilities in software. It is,
however, known to be very time consuming to design and fine tune classical
fuzzers to achieve acceptable coverage, even for small-scale software systems.
To address this issue, we investigate a machine learning-based approach to fuzz
testing in which we outline a family of test-case generators based on Recurrent
Neural Networks (RNNs) and train those on readily available datasets with a
minimum of human fine tuning. The proposed generators do, in contrast to
previous work, not rely on heuristic sampling strategies but principled
sampling from the predictive distributions. We provide a detailed analysis to
demonstrate the characteristics and efficacy of the proposed generators in a
challenging web browser testing scenario. The empirical results show that the
RNN-based generators are able to provide better coverage than a mutation based
method and are able to discover paths not discovered by a classical fuzzer. Our
results supplement findings in other domains suggesting that generation based
fuzzing with RNNs is a viable route to better software quality conditioned on
the use of a suitable model selection/analysis procedure.
Towards Understanding Language through Perception in Situated Human-Robot Interaction: From Word Grounding to Grammar Induction
Authors: Amir Aly, Tadahiro Taniguchi
Date: 2018-12-12 00:00:00

Robots are widely collaborating with human users in diferent tasks that require high-level cognitive functions to make them able to discover the surrounding environment. A difcult challenge that we briefy highlight in this short paper is inferring the latent grammatical structure of language, which includes grounding parts of speech (e.g., verbs, nouns, adjectives, and prepositions) through visual perception, and induction of Combinatory Categorial Grammar (CCG) for phrases. This paves the way towards grounding phrases so as to make a robot able to understand human instructions appropriately during interaction.
Weakly Supervised Instance Segmentation Using Hybrid Network
Authors: Shisha Liao, Yongqing Sun, Chenqiang Gao, Pranav Shenoy K P, Song Mu, Jun Shimamura, Atsushi Sagata
Date: 2018-12-12 00:00:00
Tasks: Image Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation, valid, Weakly-supervised instance segmentation

Weakly-supervised instance segmentation, which could greatly save labor and
time cost of pixel mask annotation, has attracted increasing attention in
recent years. The commonly used pipeline firstly utilizes conventional image
segmentation methods to automatically generate initial masks and then use them
to train an off-the-shelf segmentation network in an iterative way. However,
the initial generated masks usually contains a notable proportion of invalid
masks which are mainly caused by small object instances. Directly using these
initial masks to train segmentation model is harmful for the performance. To
address this problem, we propose a hybrid network in this paper. In our
architecture, there is a principle segmentation network which is used to handle
the normal samples with valid generated masks. In addition, a complementary
branch is added to handle the small and dim objects without valid masks.
Experimental results indicate that our method can achieve significantly
performance improvement both on the small object instances and large ones, and
outperforms all state-of-the-art methods.
Spatial-Temporal Digital Image Correlation: A Unified Framework
Authors: Yuxi Chi, Bing Pan
Date: 2018-12-12 00:00:00
Tasks: Translation

A comprehensive and systematic framework for easily extending and
implementing the subset-based spatial-temporal digital image correlation (DIC)
algorithm is presented. The framework decouples the three main factors (i.e.
shape function, correlation criterion, and optimization algorithm) involved in
algorithm implementation of DIC and represents different algorithms in a
uniform form. One can freely choose and combine the three factors to meet his
own need, or freely add more parameters to extract analytic results. Subpixel
translation and a simulated image series with different velocity characters are
analyzed using different algorithms based on the proposed framework, confirming
the merit of noise suppression and velocity compatibility. An application of
mitigating air disturbance due to heat haze using spatial-temporal DIC is given
to demonstrate the applicability of the framework.
Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN
Authors: Shervin Minaee, Amirali Abdolrashidi
Date: 2018-12-12 00:00:00
Tasks: Diversity, Generative Adversarial Network

Generating iris images which look realistic is both an interesting and
challenging problem. Most of the classical statistical models are not powerful
enough to capture the complicated texture representation in iris images, and
therefore fail to generate iris images which look realistic. In this work, we
present a machine learning framework based on generative adversarial network
(GAN), which is able to generate iris images sampled from a prior distribution
(learned from a set of training images). We apply this framework to two popular
iris databases, and generate images which look very realistic, and similar to
the image distribution in those databases. Through experimental results, we
show that the generated iris images have a good diversity, and are able to
capture different part of the prior distribution.
Efficient Super Resolution For Large-Scale Images Using Attentional GAN
Authors: Harsh Nilesh Pathak, Xinxin Li, Shervin Minaee, Brooke Cowan
Date: 2018-12-12 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, SSIM, Super-Resolution

Single Image Super Resolution (SISR) is a well-researched problem with broad
commercial relevance. However, most of the SISR literature focuses on
small-size images under 500px, whereas business needs can mandate the
generation of very high resolution images. At Expedia Group, we were tasked
with generating images of at least 2000px for display on the website, four
times greater than the sizes typically reported in the literature. This
requirement poses a challenge that state-of-the-art models, validated on small
images, have not been proven to handle. In this paper, we investigate solutions
to the problem of generating high-quality images for large-scale super
resolution in a commercial setting. We find that training a generative
adversarial network (GAN) with attention from scratch using a large-scale
lodging image data set generates images with high PSNR and SSIM scores. We
describe a novel attentional SISR model for large-scale images, A-SRGAN, that
uses a Flexible Self Attention layer to enable processing of large-scale
images. We also describe a distributed algorithm which speeds up training by
around a factor of five.
LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices
Authors: Saeed Saadatnejad, Mohammadhosein Oveisi, Matin Hashemi
Date: 2018-12-12 00:00:00
Tasks: Classification, ECG Classification, General Classification

Objective: A novel ECG classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. Methods: The proposed solution employs a novel architecture consisting of wavelet transform and multiple LSTM recurrent neural networks. Results: Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. Conclusion: In contrast to many compute-intensive deep-learning based approaches, the proposed algorithm is lightweight, and therefore, brings continuous monitoring with accurate LSTM-based ECG classification to wearable devices. Significance: The proposed algorithm is both accurate and lightweight. The source code is available online [1].
Image Segmentation Based on Multiscale Fast Spectral Clustering
Authors: Chongyang Zhang, Guofeng Zhu, Minxin Chen, Hong Chen, Chenjian Wu
Date: 2018-12-12 00:00:00
Tasks: Clustering, Image Segmentation, Semantic Segmentation, Superpixels, Tree Decomposition

In recent years, spectral clustering has become one of the most popular
clustering algorithms for image segmentation. However, it has restricted
applicability to large-scale images due to its high computational complexity.
In this paper, we first propose a novel algorithm called Fast Spectral
Clustering based on quad-tree decomposition. The algorithm focuses on the
spectral clustering at superpixel level and its computational complexity is
O(nlogn) + O(m) + O(m^(3/2)); its memory cost is O(m), where n and m are the
numbers of pixels and the superpixels of a image. Then we propose Multiscale
Fast Spectral Clustering by improving Fast Spectral Clustering, which is based
on the hierarchical structure of the quad-tree. The computational complexity of
Multiscale Fast Spectral Clustering is O(nlogn) and its memory cost is O(m).
Extensive experiments on real large-scale images demonstrate that Multiscale
Fast Spectral Clustering outperforms Normalized cut in terms of lower
computational complexity and memory cost, with comparable clustering accuracy.
Linking Artificial Intelligence Principles
Authors: Yi Zeng, Enmeng Lu, Cunqing Huangfu
Date: 2018-12-12 00:00:00

Artificial Intelligence principles define social and ethical considerations
to develop future AI. They come from research institutes, government
organizations and industries. All versions of AI principles are with different
considerations covering different perspectives and making different emphasis.
None of them can be considered as complete and can cover the rest AI principle
proposals. Here we introduce LAIP, an effort and platform for linking and
analyzing different Artificial Intelligence Principles. We want to explicitly
establish the common topics and links among AI Principles proposed by different
organizations and investigate on their uniqueness. Based on these efforts, for
the long-term future of AI, instead of directly adopting any of the AI
principles, we argue for the necessity of incorporating various AI Principles
into a comprehensive framework and focusing on how they can interact and
complete each other.
Can I trust you more? Model-Agnostic Hierarchical Explanations
Authors: Michael Tsang, Youbang Sun, Dongxu Ren, Yan Liu
Date: 2018-12-12 00:00:00
Proceeding: ICLR 2019 5
Tasks: BIG-bench Machine Learning, Negation

Interactions such as double negation in sentences and scene interactions in
images are common forms of complex dependencies captured by state-of-the-art
machine learning models. We propose Mah\'e, a novel approach to provide
Model-agnostic hierarchical \'explanations of how powerful machine learning
models, such as deep neural networks, capture these interactions as either
dependent on or free of the context of data instances. Specifically, Mah\'e
provides context-dependent explanations by a novel local interpretation
algorithm that effectively captures any-order interactions, and obtains
context-free explanations through generalizing context-dependent interactions
to explain global behaviors. Experimental results show that Mah\'e obtains
improved local interaction interpretations over state-of-the-art methods and
successfully explains interactions that are context-free.
Strong-Weak Distribution Alignment for Adaptive Object Detection
Authors: Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko
Date: 2018-12-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, object-detection, Object Detection, Unsupervised Domain Adaptation

We propose an approach for unsupervised adaptation of object detectors from
label-rich to label-poor domains which can significantly reduce annotation
costs associated with detection. Recently, approaches that align distributions
of source and target images using an adversarial loss have been proven
effective for adapting object classifiers. However, for object detection, fully
matching the entire distributions of source and target images to each other at
the global image level may fail, as domains could have distinct scene layouts
and different combinations of objects. On the other hand, strong matching of
local features such as texture and color makes sense, as it does not change
category level semantics. This motivates us to propose a novel method for
detector adaptation based on strong local alignment and weak global alignment.
Our key contribution is the weak alignment model, which focuses the adversarial
alignment loss on images that are globally similar and puts less emphasis on
aligning images that are globally dissimilar. Additionally, we design the
strong domain alignment model to only look at local receptive fields of the
feature map. We empirically verify the effectiveness of our method on four
datasets comprising both large and small domain shifts. Our code is available
at \url{https://github.com/VisionLearningGroup/DA_Detection}
Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks
Authors: Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, Anton Van Den Hengel
Date: 2018-12-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Attention, Object, Referring Expression, Referring Expression Comprehension

The task in referring expression comprehension is to localise the object
instance in an image described by a referring expression phrased in natural
language. As a language-to-vision matching task, the key to this problem is to
learn a discriminative object feature that can adapt to the expression used. To
avoid ambiguity, the expression normally tends to describe not only the
properties of the referent itself, but also its relationships to its
neighbourhood. To capture and exploit this important information we propose a
graph-based, language-guided attention mechanism. Being composed of node
attention component and edge attention component, the proposed graph attention
mechanism explicitly represents inter-object relationships, and properties with
a flexibility and power impossible with competing approaches. Furthermore, the
proposed graph attention mechanism enables the comprehension decision to be
visualisable and explainable. Experiments on three referring expression
comprehension datasets show the advantage of the proposed approach.
Sentence-wise Smooth Regularization for Sequence to Sequence Learning
Authors: Chengyue Gong, Xu Tan, Di He, Tao Qin
Date: 2018-12-12 00:00:00
Tasks: Machine Translation, Multi-class Classification, Prediction, Sentence, Text Summarization, Translation

Maximum-likelihood estimation (MLE) is widely used in sequence to sequence
tasks for model training. It uniformly treats the generation/prediction of each
target token as multi-class classification, and yields non-smooth prediction
probabilities: in a target sequence, some tokens are predicted with small
probabilities while other tokens are with large probabilities. According to our
empirical study, we find that the non-smoothness of the probabilities results
in low quality of generated sequences. In this paper, we propose a
sentence-wise regularization method which aims to output smooth prediction
probabilities for all the tokens in the target sequence. Our proposed method
can automatically adjust the weights and gradients of each token in one
sentence to ensure the predictions in a sequence uniformly well. Experiments on
three neural machine translation tasks and one text summarization task show
that our method outperforms conventional MLE loss on all these tasks and
achieves promising BLEU scores on WMT14 English-German and WMT17
Chinese-English translation task.
Deep Air Quality Forecasting Using Hybrid Deep Learning Framework
Authors: Shengdong Du, Tianrui Li, Yan Yang, Shi-Jinn Horng
Date: 2018-12-12 00:00:00
Tasks: Deep Learning, Management, Time Series, Time Series Analysis

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality related time series data by hybrid deep learning architecture. Due to the nonlinear and dynamic characteristics of multivariate air quality time series data, the base modules of our model include one-dimensional Convolutional Neural Networks (1D-CNNs) and Bi-directional Long Short-term Memory networks (Bi-LSTM). The former is to extract the local trend features and spatial correlation features, and the latter is to learn spatial-temporal dependencies. Then we design a jointly hybrid deep learning framework based on one-dimensional CNNs and Bi-LSTM for shared representation features learning of multivariate air quality related time series data. We conduct extensive experimental evaluations using two real-world datasets, and the results show that our model is capable of dealing with PM2.5 air pollution forecasting with satisfied accuracy.
Bridging the Generalization Gap: Training Robust Models on Confounded Biological Data
Authors: Tzu-Yu Liu, Ajay Kannan, Adam Drake, Marvin Bertin, Nathan Wan
Date: 2018-12-12 00:00:00
Tasks: Prediction

Statistical learning on biological data can be challenging due to confounding
variables in sample collection and processing. Confounders can cause models to
generalize poorly and result in inaccurate prediction performance metrics if
models are not validated thoroughly. In this paper, we propose methods to
control for confounding factors and further improve prediction performance. We
introduce OrthoNormal basis construction In cOnfounding factor Normalization
(ONION) to remove confounding covariates and use the Domain-Adversarial Neural
Network (DANN) to penalize models for encoding confounder information. We apply
the proposed methods to simulated and empirical patient data and show
significant improvements in generalization.
Extreme View Synthesis
Authors: Inchang Choi, Orazio Gallo, Alejandro Troccoli, Min H. Kim, Jan Kautz
Date: 2018-12-12 00:00:00
Proceeding: ICCV 2019 10

We present Extreme View Synthesis, a solution for novel view extrapolation that works even when the number of input images is small--as few as two. In this context, occlusions and depth uncertainty are two of the most pressing issues, and worsen as the degree of extrapolation increases. We follow the traditional paradigm of performing depth-based warping and refinement, with a few key improvements. First, we estimate a depth probability volume, rather than just a single depth value for each pixel of the novel view. This allows us to leverage depth uncertainty in challenging regions, such as depth discontinuities. After using it to get an initial estimate of the novel view, we explicitly combine learned image priors and the depth uncertainty to synthesize a refined image with less artifacts. Our method is the first to show visually pleasing results for baseline magnifications of up to 30X.
Designing Artificial Cognitive Architectures: Brain Inspired or Biologically Inspired?
Authors: Emanuel Diamant
Date: 2018-12-12 00:00:00

Artificial Neural Networks (ANNs) were devised as a tool for Artificial
Intelligence design implementations. However, it was soon became obvious that
they are unable to fulfill their duties. The fully autonomous way of ANNs
working, precluded from any human intervention or supervision, deprived of any
theoretical underpinning, leads to a strange state of affairs, when ANN
designers cannot explain why and how they achieve their amazing and remarkable
results. Therefore, contemporary Artificial Intelligence R&D looks more like a
Modern Alchemy enterprise rather than a respected scientific or technological
undertaking. On the other hand, modern biological science posits that
intelligence can be distinguished not only in human brains. Intelligence today
is considered as a fundamental property of each and every living being.
Therefore, lower simplified forms of natural intelligence are more suitable for
investigation and further replication in artificial cognitive architectures.
Gradient Descent Happens in a Tiny Subspace
Authors: Guy Gur-Ari, Daniel A. Roberts, Ethan Dyer
Date: 2018-12-12 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification

We show that in a variety of large-scale deep learning scenarios the gradient
dynamically converges to a very small subspace after a short period of
training. The subspace is spanned by a few top eigenvectors of the Hessian
(equal to the number of classes in the dataset), and is mostly preserved over
long periods of training. A simple argument then suggests that gradient descent
may happen mostly in this subspace. We give an example of this effect in a
solvable model of classification, and we comment on possible implications for
optimization and learning.
Considering Race a Problem of Transfer Learning
Authors: Akbir Khan, Marwa Mahmoud
Date: 2018-12-12 00:00:00
Tasks: Classification, General Classification, Image Generation, Transfer Learning

As biometric applications are fielded to serve large population groups,
issues of performance differences between individual sub-groups are becoming
increasingly important. In this paper we examine cases where we believe race is
one such factor. We look in particular at two forms of problem; facial
classification and image synthesis. We take the novel approach of considering
race as a boundary for transfer learning in both the task (facial
classification) and the domain (synthesis over distinct datasets). We
demonstrate a series of techniques to improve transfer learning of facial
classification; outperforming similar models trained in the target's own
domain. We conduct a study to evaluate the performance drop of Generative
Adversarial Networks trained to conduct image synthesis, in this process, we
produce a new annotation for the Celeb-A dataset by race. These networks are
trained solely on one race and tested on another - demonstrating the subsets of
the CelebA to be distinct domains for this task.