pwc-archive/papers-with-abstracts huggingface dataset:
Visual Summary of Egocentric Photostreams by Representative Keyframes
Authors: Marc Bolaños, Ricard Mestre, Estefanía Talavera, Xavier Giró-i-Nieto, Petia Radeva
Date: 2015-05-05 00:00:00
Tasks: Clustering

Building a visual summary from an egocentric photostream captured by a
lifelogging wearable camera is of high interest for different applications
(e.g. memory reinforcement). In this paper, we propose a new summarization
method based on keyframes selection that uses visual features extracted by
means of a convolutional neural network. Our method applies an unsupervised
clustering for dividing the photostreams into events, and finally extracts the
most relevant keyframe for each event. We assess the results by applying a
blind-taste test on a group of 20 people who assessed the quality of the
summaries.
Classification of Occluded Objects using Fast Recurrent Processing
Authors: Ozgur Yilmaz
Date: 2015-05-06 00:00:00
Tasks: Classification, Computational Efficiency, General Classification

Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2$\times$ improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.
Template-based Monocular 3D Shape Recovery using Laplacian Meshes
Authors: Dat Tien Ngo, Jonas Ostlund, Pascal Fua
Date: 2015-03-16 00:00:00
Tasks: 3D Shape Reconstruction, Surface Reconstruction

We show that by extending the Laplacian formalism, which was first introduced
in the Graphics community to regularize 3D meshes, we can turn the monocular 3D
shape reconstruction of a deformable surface given correspondences with a
reference image into a much better-posed problem. This allows us to quickly and
reliably eliminate outliers by simply solving a linear least squares problem.
This yields an initial 3D shape estimate, which is not necessarily accurate,
but whose 2D projections are. The initial shape is then refined by a
constrained optimization problem to output the final surface reconstruction.
  Our approach allows us to reduce the dimensionality of the surface
reconstruction problem without sacrificing accuracy, thus allowing for
real-time implementations.
Cats & Co: Categorical Time Series Coclustering
Authors: Dominique Gay, Romain Guigourès, Marc Boullé, Fabrice Clérot
Date: 2015-05-06 00:00:00
Tasks: Clustering, Model Selection, Time Series, Time Series Analysis

We suggest a novel method of clustering and exploratory analysis of temporal
event sequences data (also known as categorical time series) based on
three-dimensional data grid models. A data set of temporal event sequences can
be represented as a data set of three-dimensional points, each point is defined
by three variables: a sequence identifier, a time value and an event value.
Instantiating data grid models to the 3D-points turns the problem into
3D-coclustering.
  The sequences are partitioned into clusters, the time variable is discretized
into intervals and the events are partitioned into clusters. The cross-product
of the univariate partitions forms a multivariate partition of the
representation space, i.e., a grid of cells and it also represents a
nonparametric estimator of the joint distribution of the sequences, time and
events dimensions. Thus, the sequences are grouped together because they have
similar joint distribution of time and events, i.e., similar distribution of
events along the time dimension. The best data grid is computed using a
parameter-free Bayesian model selection approach. We also suggest several
criteria for exploiting the resulting grid through agglomerative hierarchies,
for interpreting the clusters of sequences and characterizing their components
through insightful visualizations. Extensive experiments on both synthetic and
real-world data sets demonstrate that data grid models are efficient, effective
and discover meaningful underlying patterns of categorical time series data.
A Deeper Look at Dataset Bias
Authors: Tatiana Tommasi, Novi Patricia, Barbara Caputo, Tinne Tuytelaars
Date: 2015-05-06 00:00:00

The presence of a bias in each image data collection has recently attracted a
lot of attention in the computer vision community showing the limits in
generalization of any learning method trained on a specific dataset. At the
same time, with the rapid development of deep learning architectures, the
activation values of Convolutional Neural Networks (CNN) are emerging as
reliable and robust image descriptors. In this paper we propose to verify the
potential of the DeCAF features when facing the dataset bias problem. We
conduct a series of analyses looking at how existing datasets differ among each
other and verifying the performance of existing debiasing methods under
different representations. We learn important lessons on which part of the
dataset bias problem can be considered solved and which open questions still
need to be tackled.
On Pairwise Costs for Network Flow Multi-Object Tracking
Authors: Visesh Chari, Simon Lacoste-Julien, Ivan Laptev, Josef Sivic
Date: 2014-08-14 00:00:00
Proceeding: CVPR 2015 6
Tasks: Multi-Object Tracking, Object, Object Tracking

Multi-object tracking has been recently approached with the min-cost network
flow optimization techniques. Such methods simultaneously resolve multiple
object tracks in a video and enable modeling of dependencies among tracks.
Min-cost network flow methods also fit well within the "tracking-by-detection"
paradigm where object trajectories are obtained by connecting per-frame outputs
of an object detector. Object detectors, however, often fail due to occlusions
and clutter in the video. To cope with such situations, we propose to add
pairwise costs to the min-cost network flow framework. While integer solutions
to such a problem become NP-hard, we design a convex relaxation solution with
an efficient rounding heuristic which empirically gives certificates of small
suboptimality. We evaluate two particular types of pairwise costs and
demonstrate improvements over recent tracking methods in real-world video
sequences.
Learning Style Similarity for Searching Infographics
Authors: Babak Saleh, Mira Dontcheva, Aaron Hertzmann, Zhicheng Liu
Date: 2015-05-05 00:00:00
Tasks: Image Retrieval, Retrieval

Infographics are complex graphic designs integrating text, images, charts and
sketches. Despite the increasing popularity of infographics and the rapid
growth of online design portfolios, little research investigates how we can
take advantage of these design resources. In this paper we present a method for
measuring the style similarity between infographics. Based on human perception
data collected from crowdsourced experiments, we use computer vision and
machine learning algorithms to learn a style similarity metric for infographic
designs. We evaluate different visual features and learning algorithms and find
that a combination of color histograms and Histograms-of-Gradients (HoG)
features is most effective in characterizing the style of infographics. We
demonstrate our similarity metric on a preliminary image retrieval test.
Trees Assembling Mann Whitney Approach for Detecting Genome-wide Joint Association among Low Marginal Effect loci
Authors: Changshuai Wei, Daniel J. Schaid, Qing Lu
Date: 2015-05-05 00:00:00
Tasks: Dimensionality Reduction

Common complex diseases are likely influenced by the interplay of hundreds,
or even thousands, of genetic variants. Converging evidence shows that genetic
variants with low marginal effects (LME) play an important role in disease
development. Despite their potential significance, discovering LME genetic
variants and assessing their joint association on high dimensional data (e.g.,
genome wide association studies) remain a great challenge. To facilitate joint
association analysis among a large ensemble of LME genetic variants, we
proposed a computationally efficient and powerful approach, which we call Trees
Assembling Mann whitney (TAMW). Through simulation studies and an empirical
data application, we found that TAMW outperformed multifactor dimensionality
reduction (MDR) and the likelihood ratio based Mann whitney approach (LRMW)
when the underlying complex disease involves multiple LME loci and their
interactions. For instance, in a simulation with 20 interacting LME loci, TAMW
attained a higher power (power=0.931) than both MDR (power=0.599) and LRMW
(power=0.704). In an empirical study of 29 known Crohn's disease (CD) loci,
TAMW also identified a stronger joint association with CD than those detected
by MDR and LRMW. Finally, we applied TAMW to Wellcome Trust CD GWAS to conduct
a genome wide analysis. The analysis of 459K single nucleotide polymorphisms
was completed in 40 hours using parallel computing, and revealed a joint
association predisposing to CD (p-value=2.763e-19). Further analysis of the
newly discovered association suggested that 13 genes, such as ATG16L1 and
LACC1, may play an important role in CD pathophysiological and etiological
processes.
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
Authors: David Tolpin, Jan Willem van de Meent, Brooks Paige, Frank Wood
Date: 2015-01-22 00:00:00

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for
probabilistic models expressed as programs, Adaptive Lightweight
Metropolis-Hastings (AdLMH). The algorithm extends Lightweight
Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random
variables for modification to improve convergence of the program output. We
show that AdLMH converges to the correct equilibrium distribution and compare
convergence of AdLMH to that of LMH on several test problems to highlight
different aspects of the adaptation scheme. We observe consistent improvement
in convergence on the test problems.
Actions and Attributes from Wholes and Parts
Authors: Georgia Gkioxari, Ross Girshick, Jitendra Malik
Date: 2014-12-08 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Classification, General Classification, Human Detection

We investigate the importance of parts for the tasks of action and attribute
classification. We develop a part-based approach by leveraging convolutional
network features inspired by recent advances in computer vision. Our part
detectors are a deep version of poselets and capture parts of the human body
under a distinct set of poses. For the tasks of action and attribute
classification, we train holistic convolutional neural networks and show that
adding parts leads to top-performing results for both tasks. In addition, we
demonstrate the effectiveness of our approach when we replace an oracle person
detector, as is the default in the current evaluation protocol for both tasks,
with a state-of-the-art person detection system.
An Extreme Learning Machine Approach to Predicting Near Chaotic HCCI Combustion Phasing in Real-Time
Authors: Adam Vaughan, Stanislav V. Bohac
Date: 2013-10-14 00:00:00
Tasks: Model Predictive Control

Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine
combustion timing predictions must contend with non-linear chemistry,
non-linear physics, period doubling bifurcation(s), turbulent mixing, model
parameters that can drift day-to-day, and air-fuel mixture state information
that cannot typically be resolved on a cycle-to-cycle basis, especially during
transients. In previous work, an abstract cycle-to-cycle mapping function
coupled with $\epsilon$-Support Vector Regression was shown to predict
experimentally observed cycle-to-cycle combustion timing over a wide range of
engine conditions, despite some of the aforementioned difficulties. The main
limitation of the previous approach was that a partially acausual randomly
sampled training dataset was used to train proof of concept offline
predictions. The objective of this paper is to address this limitation by
proposing a new online adaptive Extreme Learning Machine (ELM) extension named
Weighted Ring-ELM. This extension enables fully causal combustion timing
predictions at randomly chosen engine set points, and is shown to achieve
results that are as good as or better than the previous offline method. The
broader objective of this approach is to enable a new class of real-time model
predictive control strategies for high variability HCCI and, ultimately, to
bring HCCI's low engine-out NOx and reduced CO2 emissions to production
engines.
Deep Learning for Object Saliency Detection and Image Segmentation
Authors: Hengyue Pan, Bo wang, Hui Jiang
Date: 2015-05-05 00:00:00
Tasks: Deep Learning, GPU, Image Segmentation, Object, Saliency Detection, Semantic Segmentation

In this paper, we propose several novel deep learning methods for object
saliency detection based on the powerful convolutional neural networks. In our
approach, we use a gradient descent method to iteratively modify an input image
based on the pixel-wise gradients to reduce a cost function measuring the
class-specific objectness of the image. The pixel-wise gradients can be
efficiently computed using the back-propagation algorithm. The discrepancy
between the modified image and the original one may be used as a saliency map
for the image. Moreover, we have further proposed several new training methods
to learn saliency-specific convolutional nets for object saliency detection, in
order to leverage the available pixel-wise segmentation information. Our
methods are extremely computationally efficient (processing 20-40 images per
second in one GPU). In this work, we use the computed saliency maps for image
segmentation. Experimental results on two benchmark tasks, namely Microsoft
COCO and Pascal VOC 2012, have shown that our proposed methods can generate
high-quality salience maps, clearly outperforming many existing methods. In
particular, our approaches excel in handling many difficult images, which
contain complex background, highly-variable salient objects, multiple objects,
and/or very small salient objects.
Achieving a Hyperlocal Housing Price Index: Overcoming Data Sparsity by Bayesian Dynamical Modeling of Multiple Data Streams
Authors: You Ren, Emily B. Fox, Andrew Bruce
Date: 2015-05-05 00:00:00
Tasks: Clustering

Understanding how housing values evolve over time is important to policy
makers, consumers and real estate professionals. Existing methods for
constructing housing indices are computed at a coarse spatial granularity, such
as metropolitan regions, which can mask or distort price dynamics apparent in
local markets, such as neighborhoods and census tracts. A challenge in moving
to estimates at, for example, the census tract level is the sparsity of
spatiotemporally localized house sales observations. Our work aims at
addressing this challenge by leveraging observations from multiple census
tracts discovered to have correlated valuation dynamics. Our proposed Bayesian
nonparametric approach builds on the framework of latent factor models to
enable a flexible, data-driven method for inferring the clustering of
correlated census tracts. We explore methods for scalability and
parallelizability of computations, yielding a housing valuation index at the
level of census tract rather than zip code, and on a monthly basis rather than
quarterly. Our analysis is provided on a large Seattle metropolitan housing
dataset.
Generalized Low Rank Models
Authors: Madeleine Udell, Corinne Horn, Reza Zadeh, Stephen Boyd
Date: 2014-10-01 00:00:00
Tasks: Clustering, Denoising, Matrix Completion

Principal components analysis (PCA) is a well-known technique for
approximating a tabular data set by a low rank matrix. Here, we extend the idea
of PCA to handle arbitrary data sets consisting of numerical, Boolean,
categorical, ordinal, and other data types. This framework encompasses many
well known techniques in data analysis, such as nonnegative matrix
factorization, matrix completion, sparse and robust PCA, $k$-means, $k$-SVD,
and maximum margin matrix factorization. The method handles heterogeneous data
sets, and leads to coherent schemes for compressing, denoising, and imputing
missing entries across all data types simultaneously. It also admits a number
of interesting interpretations of the low rank factors, which allow clustering
of examples or of features. We propose several parallel algorithms for fitting
generalized low rank models, and describe implementations and numerical
results.
Learnable Pooling Regions for Image Classification
Authors: Mateusz Malinowski, Mario Fritz
Date: 2013-01-15 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Translation

Biologically inspired, from the early HMAX model to Spatial Pyramid Matching,
pooling has played an important role in visual recognition pipelines. Spatial
pooling, by grouping of local codes, equips these methods with a certain degree
of robustness to translation and deformation yet preserving important spatial
information. Despite the predominance of this approach in current recognition
systems, we have seen little progress to fully adapt the pooling strategy to
the task at hand. This paper proposes a model for learning task dependent
pooling scheme -- including previously proposed hand-crafted pooling schemes as
a particular instantiation. In our work, we investigate the role of different
regularization terms showing that the smooth regularization term is crucial to
achieve strong performance using the presented architecture. Finally, we
propose an efficient and parallel method to train the model. Our experiments
show improved performance over hand-crafted pooling schemes on the CIFAR-10 and
CIFAR-100 datasets -- in particular improving the state-of-the-art to 56.29% on
the latter.
Towards a Visual Turing Challenge
Authors: Mateusz Malinowski, Mario Fritz
Date: 2014-10-29 00:00:00
Tasks: Question Answering

As language and visual understanding by machines progresses rapidly, we are observing an increasing interest in holistic architectures that tightly interlink both modalities in a joint learning and inference process. This trend has allowed the community to progress towards more challenging and open tasks and refueled the hope at achieving the old AI dream of building machines that could pass a turing test in open domains. In order to steadily make progress towards this goal, we realize that quantifying performance becomes increasingly difficult. Therefore we ask how we can precisely define such challenges and how we can evaluate different algorithms on this open tasks? In this paper, we summarize and discuss such challenges as well as try to give answers where appropriate options are available in the literature. We exemplify some of the solutions on a recently presented dataset of question-answering task based on real-world indoor images that establishes a visual turing challenge. Finally, we argue despite the success of unique ground-truth annotation, we likely have to step away from carefully curated dataset and rather rely on 'social consensus' as the main driving force to create suitable benchmarks. Providing coverage in this inherently ambiguous output space is an emerging challenge that we face in order to make quantifiable progress in this area.
A Pooling Approach to Modelling Spatial Relations for Image Retrieval and Annotation
Authors: Mateusz Malinowski, Mario Fritz
Date: 2014-11-19 00:00:00
Tasks: Image Retrieval, Retrieval

Over the last two decades we have witnessed strong progress on modeling
visual object classes, scenes and attributes that have significantly
contributed to automated image understanding. On the other hand, surprisingly
little progress has been made on incorporating a spatial representation and
reasoning in the inference process. In this work, we propose a pooling
interpretation of spatial relations and show how it improves image retrieval
and annotations tasks involving spatial language. Due to the complexity of the
spatial language, we argue for a learning-based approach that acquires a
representation of spatial relations by learning parameters of the pooling
operator. We show improvements on previous work on two datasets and two
different tasks as well as provide additional insights on a new dataset with an
explicit focus on spatial relations.
Variation of word frequencies in Russian literary texts
Authors: Vladislav Kargin
Date: 2015-03-01 00:00:00

We study the variation of word frequencies in Russian literary texts. Our
findings indicate that the standard deviation of a word's frequency across
texts depends on its average frequency according to a power law with exponent
$0.62,$ showing that the rarer words have a relatively larger degree of
frequency volatility (i.e., "burstiness").
  Several latent factors models have been estimated to investigate the
structure of the word frequency distribution. The dependence of a word's
frequency volatility on its average frequency can be explained by the asymmetry
in the distribution of latent factors.
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
Authors: Mateusz Malinowski, Mario Fritz
Date: 2014-10-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Question Answering

We propose a method for automatically answering questions about images by
bringing together recent advances from natural language processing and computer
vision. We combine discrete reasoning with uncertain predictions by a
multi-world approach that represents uncertainty about the perceived world in a
bayesian framework. Our approach can handle human questions of high complexity
about realistic scenes and replies with range of answer like counts, object
classes, instances and lists of them. The system is directly trained from
question-answer pairs. We establish a first benchmark for this task that can be
seen as a modern attempt at a visual turing test.
In Defense of the Direct Perception of Affordances
Authors: David F. Fouhey, Xiaolong Wang, Abhinav Gupta
Date: 2015-05-05 00:00:00

The field of functional recognition or affordance estimation from images has
seen a revival in recent years. As originally proposed by Gibson, the
affordances of a scene were directly perceived from the ambient light: in other
words, functional properties like sittable were estimated directly from
incoming pixels. Recent work, however, has taken a mediated approach in which
affordances are derived by first estimating semantics or geometry and then
reasoning about the affordances. In a tribute to Gibson, this paper explores
his theory of affordances as originally proposed. We propose two approaches for
direct perception of affordances and show that they obtain good results and can
out-perform mediated approaches. We hope this paper can rekindle discussion
around direct perception and its implications in the long term.
Social choice rules driven by propositional logic
Authors: Rosa Camps, Xavier Mora, Laia Saumell
Date: 2011-07-28 00:00:00
Tasks: Attribute

Several rules for social choice are examined from a unifying point of view
that looks at them as procedures for revising a system of degrees of belief in
accordance with certain specified logical constraints. Belief is here a social
attribute, its degrees being measured by the fraction of people who share a
given opinion. Different known rules and some new ones are obtained depending
on which particular constraints are assumed. These constraints allow to model
different notions of choiceness. In particular, we give a new method to deal
with approval-disapproval-preferential voting.