pwc-archive/papers-with-abstracts huggingface dataset:
Human Skin Detection Using RGB, HSV and YCbCr Color Models
Authors: S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat, J. Jatakia
Date: 2017-08-09 00:00:00

Human Skin detection deals with the recognition of skin-colored pixels and
regions in a given image. Skin color is often used in human skin detection
because it is invariant to orientation and size and is fast to process. A new
human skin detection algorithm is proposed in this paper. The three main
parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue,
Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The
objective of proposed algorithm is to improve the recognition of skin pixels in
given images. The algorithm not only considers individual ranges of the three
color parameters but also takes into ac- count combinational ranges which
provide greater accuracy in recognizing the skin area in a given image.
Quasar: Datasets for Question Answering by Search and Reading
Authors: Bhuwan Dhingra, Kathryn Mazaitis, William W. Cohen
Date: 2017-07-12 00:00:00
Tasks: Question Answering, Retrieval

We present two new large-scale datasets aimed at evaluating systems designed
to comprehend a natural language query and extract its answer from a large
corpus of text. The Quasar-S dataset consists of 37000 cloze-style
(fill-in-the-gap) queries constructed from definitions of software entity tags
on the popular website Stack Overflow. The posts and comments on the website
serve as the background corpus for answering the cloze questions. The Quasar-T
dataset consists of 43000 open-domain trivia questions and their answers
obtained from various internet sources. ClueWeb09 serves as the background
corpus for extracting these answers. We pose these datasets as a challenge for
two related subtasks of factoid Question Answering: (1) searching for relevant
pieces of text that include the correct answer to a query, and (2) reading the
retrieved text to answer the query. We also describe a retrieval system for
extracting relevant sentences and documents from the corpus given a query, and
include these in the release for researchers wishing to only focus on (2). We
evaluate several baselines on both datasets, ranging from simple heuristics to
powerful neural models, and show that these lag behind human performance by
16.4% and 32.1% for Quasar-S and -T respectively. The datasets are available at
https://github.com/bdhingra/quasar .
Reference-Aware Language Models
Authors: Zichao Yang, Phil Blunsom, Chris Dyer, Wang Ling
Date: 2016-11-05 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dialogue Generation, Recipe Generation

We propose a general class of language models that treat reference as an
explicit stochastic latent variable. This architecture allows models to create
mentions of entities and their attributes by accessing external databases
(required by, e.g., dialogue generation and recipe generation) and internal
state (required by, e.g. language models which are aware of coreference). This
facilitates the incorporation of information that can be accessed in
predictable locations in databases or discourse context, even when the targets
of the reference may be rare words. Experiments on three tasks shows our model
variants based on deterministic attention.
Phrase Localization and Visual Relationship Detection with Comprehensive Image-Language Cues
Authors: Bryan A. Plummer, Arun Mallya, Christopher M. Cervantes, Julia Hockenmaier, Svetlana Lazebnik
Date: 2016-11-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Position, Relationship Detection, Visual Relationship Detection

This paper presents a framework for localization or grounding of phrases in
images using a large collection of linguistic and visual cues. We model the
appearance, size, and position of entity bounding boxes, adjectives that
contain attribute information, and spatial relationships between pairs of
entities connected by verbs or prepositions. Special attention is given to
relationships between people and clothing or body part mentions, as they are
useful for distinguishing individuals. We automatically learn weights for
combining these cues and at test time, perform joint inference over all phrases
in a caption. The resulting system produces state of the art performance on
phrase localization on the Flickr30k Entities dataset and visual relationship
detection on the Stanford VRD dataset.
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
Authors: Minsuk Kahng, Pierre Y. Andrews, Aditya Kalro, Duen Horng Chau
Date: 2017-04-06 00:00:00
Tasks: Deep Learning

While deep learning models have achieved state-of-the-art accuracies for many
prediction tasks, understanding these models remains a challenge. Despite the
recent interest in developing visual tools to help users interpret deep
learning models, the complexity and wide variety of models deployed in
industry, and the large-scale datasets that they used, pose unique design
challenges that are inadequately addressed by existing work. Through
participatory design sessions with over 15 researchers and engineers at
Facebook, we have developed, deployed, and iteratively improved ActiVis, an
interactive visualization system for interpreting large-scale deep learning
models and results. By tightly integrating multiple coordinated views, such as
a computation graph overview of the model architecture, and a neuron activation
view for pattern discovery and comparison, users can explore complex deep
neural network models at both the instance- and subset-level. ActiVis has been
deployed on Facebook's machine learning platform. We present case studies with
Facebook researchers and engineers, and usage scenarios of how ActiVis may work
with different models.
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
Authors: Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng, Weidong Cai, Heng Huang
Date: 2017-04-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: Clustering, Deep Clustering, Image Clustering

Image clustering is one of the most important computer vision applications,
which has been extensively studied in literature. However, current clustering
methods mostly suffer from lack of efficiency and scalability when dealing with
large-scale and high-dimensional data. In this paper, we propose a new
clustering model, called DEeP Embedded RegularIzed ClusTering (DEPICT), which
efficiently maps data into a discriminative embedding subspace and precisely
predicts cluster assignments. DEPICT generally consists of a multinomial
logistic regression function stacked on top of a multi-layer convolutional
autoencoder. We define a clustering objective function using relative entropy
(KL divergence) minimization, regularized by a prior for the frequency of
cluster assignments. An alternating strategy is then derived to optimize the
objective by updating parameters and estimating cluster assignments.
Furthermore, we employ the reconstruction loss functions in our autoencoder, as
a data-dependent regularization term, to prevent the deep embedding function
from overfitting. In order to benefit from end-to-end optimization and
eliminate the necessity for layer-wise pretraining, we introduce a joint
learning framework to minimize the unified clustering and reconstruction loss
functions together and train all network layers simultaneously. Experimental
results indicate the superiority and faster running time of DEPICT in
real-world clustering tasks, where no labeled data is available for
hyper-parameter tuning.
Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks
Authors: Ozsel Kilinc, Ismail Uysal
Date: 2017-02-28 00:00:00
Tasks: Clustering, General Classification, image-classification, Image Classification, Protein Function Prediction, Text Categorization

In this paper, we discuss a different type of semi-supervised setting: a
coarse level of labeling is available for all observations but the model has to
learn a fine level of latent annotation for each one of them. Problems in this
setting are likely to be encountered in many domains such as text
categorization, protein function prediction, image classification as well as in
exploratory scientific studies such as medical and genomics research. We
consider this setting as simultaneously performed supervised classification
(per the available coarse labels) and unsupervised clustering (within each one
of the coarse labels) and propose a novel output layer modification called
auto-clustering output layer (ACOL) that allows concurrent classification and
clustering based on Graph-based Activity Regularization (GAR) technique. As the
proposed output layer modification duplicates the softmax nodes at the output
layer for each class, GAR allows for competitive learning between these
duplicates on a traditional error-correction learning framework to ultimately
enable a neural network to learn the latent annotations in this partially
supervised setup. We demonstrate how the coarse label supervision impacts
performance and helps propagate useful clustering information between
sub-classes. Comparative tests on three of the most popular image datasets
MNIST, SVHN and CIFAR-100 rigorously demonstrate the effectiveness and
competitiveness of the proposed approach.
Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces
Authors: He Zhang, Vishal M. Patel, Benjamin S. Riggan, Shuowen Hu
Date: 2017-08-08 00:00:00
Tasks: Face Generation, Face Recognition, Face Reconstruction, Generative Adversarial Network, Image Reconstruction

The large domain discrepancy between faces captured in polarimetric (or
conventional) thermal and visible domain makes cross-domain face recognition
quite a challenging problem for both human-examiners and computer vision
algorithms. Previous approaches utilize a two-step procedure (visible feature
estimation and visible image reconstruction) to synthesize the visible image
given the corresponding polarimetric thermal image. However, these are regarded
as two disjoint steps and hence may hinder the performance of visible face
reconstruction. We argue that joint optimization would be a better way to
reconstruct more photo-realistic images for both computer vision algorithms and
human-examiners to examine. To this end, this paper proposes a Generative
Adversarial Network-based Visible Face Synthesis (GAN-VFS) method to synthesize
more photo-realistic visible face images from their corresponding polarimetric
images. To ensure that the encoded visible-features contain more semantically
meaningful information in reconstructing the visible face image, a guidance
sub-network is involved into the training procedure. To achieve photo realistic
property while preserving discriminative characteristics for the reconstructed
outputs, an identity loss combined with the perceptual loss are optimized in
the framework. Multiple experiments evaluated on different experimental
protocols demonstrate that the proposed method achieves state-of-the-art
performance.
Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects
Authors: Farhad Pourkamali-Anaraki, Stephen Becker
Date: 2017-08-08 00:00:00

The Nystrom method is a popular technique that uses a small number of landmark points to compute a fixed-rank approximation of large kernel matrices that arise in machine learning problems. In practice, to ensure high quality approximations, the number of landmark points is chosen to be greater than the target rank. However, for simplicity the standard Nystrom method uses a sub-optimal procedure for rank reduction. In this paper, we examine the drawbacks of the standard Nystrom method in terms of poor performance and lack of theoretical guarantees. To address these issues, we present an efficient modification for generating improved fixed-rank Nystrom approximations. Theoretical analysis and numerical experiments are provided to demonstrate the advantages of the modified method over the standard Nystrom method. Overall, the aim of this paper is to convince researchers to use the modified method, as it has nearly identical computational complexity, is easy to code, has greatly improved accuracy in many cases, and is optimal in a sense that we make precise.
A discriminative view of MRF pre-processing algorithms
Authors: Chen Wang, Charles Herrmann, Ramin Zabih
Date: 2017-08-08 00:00:00
Proceeding: ICCV 2017 10

While Markov Random Fields (MRFs) are widely used in computer vision, they
present a quite challenging inference problem. MRF inference can be accelerated
by pre-processing techniques like Dead End Elimination (DEE) or QPBO-based
approaches which compute the optimal labeling of a subset of variables. These
techniques are guaranteed to never wrongly label a variable but they often
leave a large number of variables unlabeled. We address this shortcoming by
interpreting pre-processing as a classification problem, which allows us to
trade off false positives (i.e., giving a variable an incorrect label) versus
false negatives (i.e., failing to label a variable). We describe an efficient
discriminative rule that finds optimal solutions for a subset of variables. Our
technique provides both per-instance and worst-case guarantees concerning the
quality of the solution. Empirical studies were conducted over several
benchmark datasets. We obtain a speedup factor of 2 to 12 over expansion moves
without preprocessing, and on difficult non-submodular energy functions produce
slightly lower energy.
Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017)
Authors: Been Kim, Dmitry M. Malioutov, Kush R. Varshney, Adrian Weller
Date: 2017-08-08 00:00:00
Tasks: BIG-bench Machine Learning

This is the Proceedings of the 2017 ICML Workshop on Human Interpretability
in Machine Learning (WHI 2017), which was held in Sydney, Australia, August 10,
2017. Invited speakers were Tony Jebara, Pang Wei Koh, and David Sontag.
The Multivariate Generalised von Mises distribution: Inference and applications
Authors: Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner
Date: 2016-02-16 00:00:00
Tasks: Gaussian Processes

Circular variables arise in a multitude of data-modelling contexts ranging
from robotics to the social sciences, but they have been largely overlooked by
the machine learning community. This paper partially redresses this imbalance
by extending some standard probabilistic modelling tools to the circular
domain. First we introduce a new multivariate distribution over circular
variables, called the multivariate Generalised von Mises (mGvM) distribution.
This distribution can be constructed by restricting and renormalising a general
multivariate Gaussian distribution to the unit hyper-torus. Previously proposed
multivariate circular distributions are shown to be special cases of this
construction. Second, we introduce a new probabilistic model for circular
regression, that is inspired by Gaussian Processes, and a method for
probabilistic principal component analysis with circular hidden variables.
These models can leverage standard modelling tools (e.g. covariance functions
and methods for automatic relevance determination). Third, we show that the
posterior distribution in these models is a mGvM distribution which enables
development of an efficient variational free-energy scheme for performing
approximate inference and approximate maximum-likelihood learning.
A Novel Method for the Extrinsic Calibration of a 2D Laser Rangefinder and a Camera
Authors: Wenbo Dong, Volkan Isler
Date: 2016-03-14 00:00:00

We present a novel method for extrinsically calibrating a camera and a 2D
Laser Rangefinder (LRF) whose beams are invisible from the camera image. We
show that point-to-plane constraints from a single observation of a V-shaped
calibration pattern composed of two non-coplanar triangles suffice to uniquely
constrain the relative pose between two sensors. Next, we present an approach
to obtain analytical solutions using point-to-plane constraints from single or
multiple observations. Along the way, we also show that previous solutions, in
contrast to our method, have inherent ambiguities and therefore must rely on a
good initial estimate. Real and synthetic experiments validate our method and
show that it achieves better accuracy than previous methods.
Gradient-enhanced kriging for high-dimensional problems
Authors: Mohamed Amine Bouhlel, Joaquim R. R. A. Martins
Date: 2017-08-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Surrogate models provide a low computational cost alternative to evaluating
expensive functions. The construction of accurate surrogate models with large
numbers of independent variables is currently prohibitive because it requires a
large number of function evaluations. Gradient-enhanced kriging has the
potential to reduce the number of function evaluations for the desired accuracy
when efficient gradient computation, such as an adjoint method, is available.
However, current gradient-enhanced kriging methods do not scale well with the
number of sampling points due to the rapid growth in the size of the
correlation matrix where new information is added for each sampling point in
each direction of the design space. They do not scale well with the number of
independent variables either due to the increase in the number of
hyperparameters that needs to be estimated. To address this issue, we develop a
new gradient-enhanced surrogate model approach that drastically reduced the
number of hyperparameters through the use of the partial-least squares method
that maintains accuracy. In addition, this method is able to control the size
of the correlation matrix by adding only relevant points defined through the
information provided by the partial-least squares method. To validate our
method, we compare the global accuracy of the proposed method with conventional
kriging surrogate models on two analytic functions with up to 100 dimensions,
as well as engineering problems of varied complexity with up to 15 dimensions.
We show that the proposed method requires fewer sampling points than
conventional methods to obtain the desired accuracy, or provides more accuracy
for a fixed budget of sampling points. In some cases, we get over 3 times more
accurate models than a bench of surrogate models from the literature, and also
over 3200 times faster than standard gradient-enhanced kriging models.
Learning Visual Importance for Graphic Designs and Data Visualizations
Authors: Zoya Bylinskii, Nam Wook Kim, Peter O'Donovan, Sami Alsheikh, Spandan Madan, Hanspeter Pfister, Fredo Durand, Bryan Russell, Aaron Hertzmann
Date: 2017-08-08 00:00:00
Tasks: Retrieval

Knowing where people look and click on visual designs can provide clues about
how the designs are perceived, and where the most important or relevant content
lies. The most important content of a visual design can be used for effective
summarization or to facilitate retrieval from a database. We present automated
models that predict the relative importance of different elements in data
visualizations and graphic designs. Our models are neural networks trained on
human clicks and importance annotations on hundreds of designs. We collected a
new dataset of crowdsourced importance, and analyzed the predictions of our
models with respect to ground truth importance and human eye movements. We
demonstrate how such predictions of importance can be used for automatic design
retargeting and thumbnailing. User studies with hundreds of MTurk participants
validate that, with limited post-processing, our importance-driven applications
are on par with, or outperform, current state-of-the-art methods, including
natural image saliency. We also provide a demonstration of how our importance
predictions can be built into interactive design tools to offer immediate
feedback during the design process.
NoScope: Optimizing Neural Network Queries over Video at Scale
Authors: Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia
Date: 2017-03-07 00:00:00
Tasks: Binary Classification, GPU

Recent advances in computer vision-in the form of deep neural networks-have
made it possible to query increasing volumes of video data with high accuracy.
However, neural network inference is computationally expensive at scale:
applying a state-of-the-art object detector in real time (i.e., 30+ frames per
second) to a single video requires a $4000 GPU. In response, we present
NoScope, a system for querying videos that can reduce the cost of neural
network video analysis by up to three orders of magnitude via
inference-optimized model search. Given a target video, object to detect, and
reference neural network, NoScope automatically searches for and trains a
sequence, or cascade, of models that preserves the accuracy of the reference
network but is specialized to the target video and are therefore far less
computationally expensive. NoScope cascades two types of models: specialized
models that forego the full generality of the reference model but faithfully
mimic its behavior for the target video and object; and difference detectors
that highlight temporal differences across frames. We show that the optimal
cascade architecture differs across videos and objects, so NoScope uses an
efficient cost-based optimizer to search across models and cascades. With this
approach, NoScope achieves two to three order of magnitude speed-ups
(265-15,500x real-time) on binary classification tasks over fixed-angle webcam
and surveillance video while maintaining accuracy within 1-5% of
state-of-the-art neural networks.
Variational Bayesian inference for linear and logistic regression
Authors: Jan Drugowitsch
Date: 2013-10-21 00:00:00
Tasks: Bayesian Inference, regression

The article describe the model, derivation, and implementation of variational Bayesian inference for linear and logistic regression, both with and without automatic relevance determination. It has the dual function of acting as a tutorial for the derivation of variational Bayesian inference for simple models, as well as documenting, and providing brief examples for the MATLAB/Octave functions that implement this inference. These functions are freely available online.
Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation
Authors: Guanghan Ning, Zhi Zhang, Zhihai He
Date: 2017-05-05 00:00:00
Tasks: Pose Estimation

Human pose estimation using deep neural networks aims to map input images
with large variations into multiple body keypoints which must satisfy a set of
geometric constraints and inter-dependency imposed by the human body model.
This is a very challenging nonlinear manifold learning process in a very high
dimensional feature space. We believe that the deep neural network, which is
inherently an algebraic computation system, is not the most effecient way to
capture highly sophisticated human knowledge, for example those highly coupled
geometric characteristics and interdependence between keypoints in human poses.
In this work, we propose to explore how external knowledge can be effectively
represented and injected into the deep neural networks to guide its training
process using learned projections that impose proper prior. Specifically, we
use the stacked hourglass design and inception-resnet module to construct a
fractal network to regress human pose images into heatmaps with no explicit
graphical modeling. We encode external knowledge with visual features which are
able to characterize the constraints of human body models and evaluate the
fitness of intermediate network output. We then inject these external features
into the neural network using a projection matrix learned using an auxiliary
cost function. The effectiveness of the proposed inception-resnet module and
the benefit in guided learning with knowledge projection is evaluated on two
widely used benchmarks. Our approach achieves state-of-the-art performance on
both datasets.