pwc-archive/papers-with-abstracts huggingface dataset:
Online Illumination Invariant Moving Object Detection by Generative Neural Network
Authors: Fateme Bahri, Moein Shakeri, Nilanjan Ray
Date: 2018-08-03 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection

Moving object detection (MOD) is a significant problem in computer vision
that has many real world applications. Different categories of methods have
been proposed to solve MOD. One of the challenges is to separate moving objects
from illumination changes and shadows that are present in most real world
videos. State-of-the-art methods that can handle illumination changes and
shadows work in a batch mode; thus, these methods are not suitable for long
video sequences or real-time applications. In this paper, we propose an
extension of a state-of-the-art batch MOD method (ILISD) to an
online/incremental MOD using unsupervised and generative neural networks, which
use illumination invariant image representations. For each image in a sequence,
we use a low-dimensional representation of a background image by a neural
network and then based on the illumination invariant representation, decompose
the foreground image into: illumination change and moving objects. Optimization
is performed by stochastic gradient descent in an end-to-end and unsupervised
fashion. Our algorithm can work in both batch and online modes. In the batch
mode, like other batch methods, optimizer uses all the images. In online mode,
images can be incrementally fed into the optimizer. Based on our experimental
evaluation on benchmark image sequences, both the online and the batch modes of
our algorithm achieve state-of-the-art accuracy on most data sets.
Cortical Microcircuits from a Generative Vision Model
Authors: Dileep George, Alexander Lavin, J. Swaroop Guntupalli, David Mely, Nick Hay, Miguel Lazaro-Gredilla
Date: 2018-08-03 00:00:00
Tasks: Bayesian Inference, model

Understanding the information processing roles of cortical circuits is an
outstanding problem in neuroscience and artificial intelligence. The
theoretical setting of Bayesian inference has been suggested as a framework for
understanding cortical computation. Based on a recently published generative
model for visual inference (George et al., 2017), we derive a family of
anatomically instantiated and functional cortical circuit models. In contrast
to simplistic models of Bayesian inference, the underlying generative model's
representational choices are validated with real-world tasks that required
efficient inference and strong generalization. The cortical circuit model is
derived by systematically comparing the computational requirements of this
model with known anatomical constraints. The derived model suggests precise
functional roles for the feedforward, feedback and lateral connections observed
in different laminae and columns, and assigns a computational role for the path
through the thalamus.
Robust Regression for Automatic Fusion Plasma Analysis based on Generative Modeling
Authors: Keisuke Fujii, Chihiro Suzuki, Masahiro Hasuo
Date: 2018-08-03 00:00:00
Tasks: regression

The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely. However, it has been difficult to construct algorithms that fit all the data without over- and under-fitting. In this paper, we show that this difficulty originates from the lack of knowledge of the probability distribution that the measurement data follow. We demonstrate the use of a machine learning technique to estimate the data distribution and to construct an optimal generative model. We show that the fitting algorithm based on the generative modeling outperforms classical heuristic methods in terms of the stability as well as the accuracy.
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
Authors: Haroon Idrees, Muhmmad Tayyab, Kishan Athrey, Dong Zhang, Somaya Al-Maadeed, Nasir Rajpoot, Mubarak Shah
Date: 2018-08-02 00:00:00
Proceeding: ECCV 2018 9
Tasks: Crowd Counting, Management, Visual Crowd Analysis

With multiple crowd gatherings of millions of people every year in events
ranging from pilgrimages to protests, concerts to marathons, and festivals to
funerals; visual crowd analysis is emerging as a new frontier in computer
vision. In particular, counting in highly dense crowds is a challenging problem
with far-reaching applicability in crowd safety and management, as well as
gauging political significance of protests and demonstrations. In this paper,
we propose a novel approach that simultaneously solves the problems of
counting, density map estimation and localization of people in a given dense
crowd image. Our formulation is based on an important observation that the
three problems are inherently related to each other making the loss function
for optimizing a deep CNN decomposable. Since localization requires
high-quality images and annotations, we introduce UCF-QNRF dataset that
overcomes the shortcomings of previous datasets, and contains 1.25 million
humans manually marked with dot annotations. Finally, we present evaluation
measures and comparison with recent deep CNN networks, including those
developed specifically for crowd counting. Our approach significantly
outperforms state-of-the-art on the new dataset, which is the most challenging
dataset with the largest number of crowd annotations in the most diverse set of
scenes.
Variational Information Bottleneck on Vector Quantized Autoencoders
Authors: Hanwei Wu, Markus Flierl
Date: 2018-08-02 00:00:00

In this paper, we provide an information-theoretic interpretation of the
Vector Quantized-Variational Autoencoder (VQ-VAE). We show that the loss
function of the original VQ-VAE can be derived from the variational
deterministic information bottleneck (VDIB) principle. On the other hand, the
VQ-VAE trained by the Expectation Maximization (EM) algorithm can be viewed as
an approximation to the variational information bottleneck(VIB) principle.
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
Authors: Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez
Date: 2018-08-02 00:00:00
Tasks: Hyperspectral Unmixing, Superpixels

Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes. Its occurrence may lead to the propagation of significant estimation errors in the unmixing process. To address this issue, extended linear mixing models have been proposed which lead to large scale nonsmooth ill-posed inverse problems. Furthermore, the regularization strategies used to obtain meaningful results have introduced interdependencies among abundance solutions that further increase the complexity of the resulting optimization problem. In this paper we present a novel data dependent multiscale model for hyperspectral unmixing accounting for spectral variability. The new method incorporates spatial contextual information to the abundances in extended linear mixing models by using a multiscale transform based on superpixels. The proposed method results in a fast algorithm that solves the abundance estimation problem only once in each scale during each iteration. Simulation results using synthetic and real images compare the performances, both in accuracy and execution time, of the proposed algorithm and other state-of-the-art solutions.
What Goes Where: Predicting Object Distributions from Above
Authors: Connor Greenwell, Scott Workman, Nathan Jacobs
Date: 2018-08-02 00:00:00
Tasks: Object

In this work, we propose a cross-view learning approach, in which images
captured from a ground-level view are used as weakly supervised annotations for
interpreting overhead imagery. The outcome is a convolutional neural network
for overhead imagery that is capable of predicting the type and count of
objects that are likely to be seen from a ground-level perspective. We
demonstrate our approach on a large dataset of geotagged ground-level and
overhead imagery and find that our network captures semantically meaningful
features, despite being trained without manual annotations.
Constructionist Steps Towards an Autonomously Empathetic System
Authors: Trevor Buteau, Damian Lyons
Date: 2018-08-02 00:00:00

Prior efforts to create an autonomous computer system capable of predicting
what a human being is thinking or feeling from facial expression data have been
largely based on outdated, inaccurate models of how emotions work that rely on
many scientifically questionable assumptions. In our research, we are creating
an empathetic system that incorporates the latest provable scientific
understanding of emotions: that they are constructs of the human mind, rather
than universal expressions of distinct internal states. Thus, our system uses a
user-dependent method of analysis and relies heavily on contextual information
to make predictions about what subjects are experiencing. Our system's accuracy
and therefore usefulness are built on provable ground truths that prohibit the
drawing of inaccurate conclusions that other systems could too easily make.
Likelihood-free inference with an improved cross-entropy estimator
Authors: Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
Date: 2018-08-02 00:00:00

We extend recent work (Brehmer, et. al., 2018) that use neural networks as
surrogate models for likelihood-free inference. As in the previous work, we
exploit the fact that the joint likelihood ratio and joint score, conditioned
on both observed and latent variables, can often be extracted from an implicit
generative model or simulator to augment the training data for these surrogate
models. We show how this augmented training data can be used to provide a new
cross-entropy estimator, which provides improved sample efficiency compared to
previous loss functions exploiting this augmented training data.
Impacts of Weather Conditions on District Heat System
Authors: Jiyang Xie, Zhanyu Ma, Jun Guo
Date: 2018-08-02 00:00:00

Using artificial neural network for the prediction of heat demand has attracted more and more attention. Weather conditions, such as ambient temperature, wind speed and direct solar irradiance, have been identified as key input parameters. In order to further improve the model accuracy, it is of great importance to understand the influence of different parameters. Based on an Elman neural network (ENN), this paper investigates the impact of direct solar irradiance and wind speed on predicting the heat demand of a district heating network. Results show that including wind speed can generally result in a lower overall mean absolute percentage error (MAPE) (6.43%) than including direct solar irradiance (6.47%); while including direct solar irradiance can achieve a lower maximum absolute deviation (71.8%) than including wind speed (81.53%). In addition, even though including both wind speed and direct solar irradiance shows the best overall performance (MAPE=6.35%).
Histogram Transform-based Speaker Identification
Authors: Zhanyu Ma, Hong Yu
Date: 2018-08-02 00:00:00
Tasks: Speaker Identification

A novel text-independent speaker identification (SI) method is proposed. This method uses the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic information among adjacent frames as feature sets to capture speaker's characteristics. In order to utilize dynamic information, we design super-MFCCs features by cascading three neighboring MFCCs frames together. The probability density function (PDF) of these super-MFCCs features is estimated by the recently proposed histogram transform~(HT) method, which generates more training data by random transforms to realize the histogram PDF estimation and recedes the commonly occurred discontinuity problem in multivariate histograms computing. Compared to the conventional PDF estimation methods, such as Gaussian mixture models, the HT model shows promising improvement in the SI performance.
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection
Authors: Vaibhav Kumar, Mrinal Dhar, Dhruv Khattar, Yash Kumar Lal, Abhimanshu Mishra, Manish Shrivastava, Vasudeva Varma
Date: 2018-08-02 00:00:00
Tasks: Articles, Clickbait Detection, Document Embedding, Feature Engineering

In order to expand their reach and increase website ad revenue, media outlets
have started using clickbait techniques to lure readers to click on articles on
their digital platform. Having successfully enticed the user to open the
article, the article fails to satiate his curiosity serving only to boost
click-through rates. Initial methods for this task were dependent on feature
engineering, which varies with each dataset. Industry systems have relied on an
exhaustive set of rules to get the job done. Neural networks have barely been
explored to perform this task. We propose a novel approach considering
different textual embeddings of a news headline and the related article. We
generate sub-word level embeddings of the title using Convolutional Neural
Networks and use them to train a bidirectional LSTM architecture. An attention
layer allows for calculation of significance of each term towards the nature of
the post. We also generate Doc2Vec embeddings of the title and article text and
model how they interact, following which it is concatenated with the output of
the previous component. Finally, this representation is passed through a neural
network to obtain a score for the headline. We test our model over 2538 posts
(having trained it on 17000 records) and achieve an accuracy of 83.49%
outscoring previous state-of-the-art approaches.
Generation Meets Recommendation: Proposing Novel Items for Groups of Users
Authors: Vinh Vo Thanh, Harold Soh
Date: 2018-08-02 00:00:00
Tasks: TAG

Consider a movie studio aiming to produce a set of new movies for summer
release: What types of movies it should produce? Who would the movies appeal
to? How many movies should it make? Similar issues are encountered by a variety
of organizations, e.g., mobile-phone manufacturers and online magazines, who
have to create new (non-existent) items to satisfy groups of users with
different preferences. In this paper, we present a joint problem formalization
of these interrelated issues, and propose generative methods that address these
questions simultaneously. Specifically, we leverage the latent space obtained
by training a deep generative model---the Variational Autoencoder (VAE)---via a
loss function that incorporates both rating performance and item reconstruction
terms. We then apply a greedy search algorithm that utilizes this learned
latent space to jointly obtain K plausible new items, and user groups that
would find the items appealing. An evaluation of our methods on a synthetic
dataset indicates that our approach is able to generate novel items similar to
highly-desirable unobserved items. As case studies on real-world data, we
applied our method on the MART abstract art and Movielens Tag Genome dataset,
which resulted in promising results: small and diverse sets of novel items.
Two-Layer Lossless HDR Coding using Histogram Packing Technique with Backward Compatibility to JPEG
Authors: Osamu Watanabe, Hiroyuki Kobayashi, Hitoshi Kiya
Date: 2018-08-02 00:00:00
Tasks: Image Compression

An efficient two-layer coding method using the histogram packing technique
with the backward compatibility to the legacy JPEG is proposed in this paper.
The JPEG XT, which is the international standard to compress HDR images, adopts
two-layer coding scheme for backward compatibility to the legacy JPEG. However,
this two-layer coding structure does not give better lossless performance than
the other existing methods for HDR image compression with single-layer
structure. Moreover, the lossless compression of the JPEG XT has a problem on
determination of the coding parameters; The lossless performance is affected by
the input images and/or the parameter values. That is, finding appropriate
combination of the values is necessary to achieve good lossless performance. It
is firstly pointed out that the histogram packing technique considering the
histogram sparseness of HDR images is able to improve the performance of
lossless compression. Then, a novel two-layer coding with the histogram packing
technique and an additional lossless encoder is proposed. The experimental
results demonstrate that not only the proposed method has a better lossless
compression performance than that of the JPEG XT, but also there is no need to
determine image-dependent parameter values for good compression performance
without losing the backward compatibility to the well known legacy JPEG
standard.
A Deep Relevance Matching Model for Ad-hoc Retrieval
Authors: Jiafeng Guo, Yixing Fan, Qingyao Ai, W. Bruce Croft
Date: 2017-11-23 00:00:00
Tasks: Ad-Hoc Information Retrieval, Paraphrase Identification, Question Answering, Retrieval, speech-recognition, Speech Recognition

In recent years, deep neural networks have led to exciting breakthroughs in
speech recognition, computer vision, and natural language processing (NLP)
tasks. However, there have been few positive results of deep models on ad-hoc
retrieval tasks. This is partially due to the fact that many important
characteristics of the ad-hoc retrieval task have not been well addressed in
deep models yet. Typically, the ad-hoc retrieval task is formalized as a
matching problem between two pieces of text in existing work using deep models,
and treated equivalent to many NLP tasks such as paraphrase identification,
question answering and automatic conversation. However, we argue that the
ad-hoc retrieval task is mainly about relevance matching while most NLP
matching tasks concern semantic matching, and there are some fundamental
differences between these two matching tasks. Successful relevance matching
requires proper handling of the exact matching signals, query term importance,
and diverse matching requirements. In this paper, we propose a novel deep
relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model
employs a joint deep architecture at the query term level for relevance
matching. By using matching histogram mapping, a feed forward matching network,
and a term gating network, we can effectively deal with the three relevance
matching factors mentioned above. Experimental results on two representative
benchmark collections show that our model can significantly outperform some
well-known retrieval models as well as state-of-the-art deep matching models.
Simultaneous Edge Alignment and Learning
Authors: Zhiding Yu, Weiyang Liu, Yang Zou, Chen Feng, Srikumar Ramalingam, B. V. K. Vijaya Kumar, Jan Kautz
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Edge Detection, Representation Learning

Edge detection is among the most fundamental vision problems for its role in
perceptual grouping and its wide applications. Recent advances in
representation learning have led to considerable improvements in this area.
Many state of the art edge detection models are learned with fully
convolutional networks (FCNs). However, FCN-based edge learning tends to be
vulnerable to misaligned labels due to the delicate structure of edges. While
such problem was considered in evaluation benchmarks, similar issue has not
been explicitly addressed in general edge learning. In this paper, we show that
label misalignment can cause considerably degraded edge learning quality, and
address this issue by proposing a simultaneous edge alignment and learning
framework. To this end, we formulate a probabilistic model where edge alignment
is treated as latent variable optimization, and is learned end-to-end during
network training. Experiments show several applications of this work, including
improved edge detection with state of the art performance, and automatic
refinement of noisy annotations.
Hashing with Binary Matrix Pursuit
Authors: Fatih Cakir, Kun He, Stan Sclaroff
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Retrieval, Retrieval

We propose theoretical and empirical improvements for two-stage hashing
methods. We first provide a theoretical analysis on the quality of the binary
codes and show that, under mild assumptions, a residual learning scheme can
construct binary codes that fit any neighborhood structure with arbitrary
accuracy. Secondly, we show that with high-capacity hash functions such as
CNNs, binary code inference can be greatly simplified for many standard
neighborhood definitions, yielding smaller optimization problems and more
robust codes. Incorporating our findings, we propose a novel two-stage hashing
method that significantly outperforms previous hashing studies on widely used
image retrieval benchmarks.
Adversarial Vision Challenge
Authors: Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge
Date: 2018-08-06 00:00:00

The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate
measurable progress towards robust machine vision models and more generally
applicable adversarial attacks. This document is an updated version of our
competition proposal that was accepted in the competition track of 32nd
Conference on Neural Information Processing Systems (NIPS 2018).
A Survey on Surrogate Approaches to Non-negative Matrix Factorization
Authors: Pascal Fernsel, Peter Maass
Date: 2018-08-06 00:00:00
Tasks: Survey

Motivated by applications in hyperspectral imaging we investigate methods for
approximating a high-dimensional non-negative matrix $\mathbf{\mathit{Y}}$ by a
product of two lower-dimensional, non-negative matrices $\mathbf{\mathit{K}}$
and $\mathbf{\mathit{X}}.$ This so-called non-negative matrix factorization is
based on defining suitable Tikhonov functionals, which combine a discrepancy
measure for $\mathbf{\mathit{Y}}\approx\mathbf{\mathit{KX}}$ with penalty terms
for enforcing additional properties of $\mathbf{\mathit{K}}$ and
$\mathbf{\mathit{X}}$. The minimization is based on alternating minimization
with respect to $\mathbf{\mathit{K}}$ or $\mathbf{\mathit{X}}$, where in each
iteration step one replaces the original Tikhonov functional by a locally
defined surrogate functional. The choice of surrogate functionals is crucial:
It should allow a comparatively simple minimization and simultaneously its
first order optimality condition should lead to multiplicative update rules,
which automatically preserve non-negativity of the iterates. We review the most
standard construction principles for surrogate functionals for Frobenius-norm
and Kullback-Leibler discrepancy measures. We extend the known surrogate
constructions by a general framework, which allows to add a large variety of
penalty terms. The paper finishes by deriving the corresponding alternating
minimization schemes explicitely and by applying these methods to MALDI imaging
data.
A Survey on Deep Transfer Learning
Authors: Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, Chunfang Liu
Date: 2018-08-06 00:00:00
Tasks: General Classification, Survey, Transfer Learning

As a new classification platform, deep learning has recently received
increasing attention from researchers and has been successfully applied to many
domains. In some domains, like bioinformatics and robotics, it is very
difficult to construct a large-scale well-annotated dataset due to the expense
of data acquisition and costly annotation, which limits its development.
Transfer learning relaxes the hypothesis that the training data must be
independent and identically distributed (i.i.d.) with the test data, which
motivates us to use transfer learning to solve the problem of insufficient
training data. This survey focuses on reviewing the current researches of
transfer learning by using deep neural network and its applications. We defined
deep transfer learning, category and review the recent research works based on
the techniques used in deep transfer learning.