pwc-archive/papers-with-abstracts huggingface dataset:
A modular architecture for transparent computation in Recurrent Neural Networks
Authors: Giovanni Sirio Carmantini, Peter beim Graben, Mathieu Desroches, Serafim Rodrigues
Date: 2016-09-07 00:00:00

Computation is classically studied in terms of automata, formal languages and
algorithms; yet, the relation between neural dynamics and symbolic
representations and operations is still unclear in traditional eliminative
connectionism. Therefore, we suggest a unique perspective on this central
issue, to which we would like to refer as to transparent connectionism, by
proposing accounts of how symbolic computation can be implemented in neural
substrates. In this study we first introduce a new model of dynamics on a
symbolic space, the versatile shift, showing that it supports the real-time
simulation of a range of automata. We then show that the Goedelization of
versatile shifts defines nonlinear dynamical automata, dynamical systems
evolving on a vectorial space. Finally, we present a mapping between nonlinear
dynamical automata and recurrent artificial neural networks. The mapping
defines an architecture characterized by its granular modularity, where data,
symbolic operations and their control are not only distinguishable in
activation space, but also spatially localizable in the network itself, while
maintaining a distributed encoding of symbolic representations. The resulting
networks simulate automata in real-time and are programmed directly, in absence
of network training. To discuss the unique characteristics of the architecture
and their consequences, we present two examples: i) the design of a Central
Pattern Generator from a finite-state locomotive controller, and ii) the
creation of a network simulating a system of interactive automata that supports
the parsing of garden-path sentences as investigated in psycholinguistics
experiments.
Towards Segmenting Consumer Stereo Videos: Benchmark, Baselines and Ensembles
Authors: Wei-Chen Chiu, Fabio Galasso, Mario Fritz
Date: 2016-09-03 00:00:00
Tasks: Segmentation, Video Segmentation, Video Semantic Segmentation

Are we ready to segment consumer stereo videos? The amount of this data type
is rapidly increasing and encompasses rich information of appearance, motion
and depth cues. However, the segmentation of such data is still largely
unexplored. First, we propose therefore a new benchmark: videos, annotations
and metrics to measure progress on this emerging challenge. Second, we evaluate
several state of the art segmentation methods and propose a novel ensemble
method based on recent spectral theory. This combines existing image and video
segmentation techniques in an efficient scheme. Finally, we propose and
integrate into this model a novel regressor, learnt to optimize the stereo
segmentation performance directly via a differentiable proxy. The regressor
makes our segmentation ensemble adaptive to each stereo video and outperforms
the segmentations of the ensemble as well as a most recent RGB-D segmentation
technique.
Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review
Authors: V. B. Surya Prasath
Date: 2016-09-07 00:00:00

Video capsule endoscopy (VCE) is used widely nowadays for visualizing the
gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as
an additional monitoring mechanism and can help in identifying polyps,
bleeding, etc. To analyze the large scale video data produced by VCE exams
automatic image processing, computer vision, and learning algorithms are
required. Recently, automatic polyp detection algorithms have been proposed
with various degrees of success. Though polyp detection in colonoscopy and
other traditional endoscopy procedure based images is becoming a mature field,
due to its unique imaging characteristics detecting polyps automatically in VCE
is a hard problem. We review different polyp detection approaches for VCE
imagery and provide systematic analysis with challenges faced by standard image
processing and computer vision methods.
Label distribution based facial attractiveness computation by deep residual learning
Authors: Shu Liu, Bo Li, Yangyu Fan, Zhe Guo, Ashok Samal
Date: 2016-09-02 00:00:00
Tasks: Deep Learning, Face Recognition

Two challenges lie in the facial attractiveness computation research: the
lack of true attractiveness labels (scores), and the lack of an accurate face
representation. In order to address the first challenge, this paper recasts
facial attractiveness computation as a label distribution learning (LDL)
problem rather than a traditional single-label supervised learning task. In
this way, the negative influence of the label incomplete problem can be
reduced. Inspired by the recent promising work in face recognition using deep
neural networks to learn effective features, the second challenge is expected
to be solved from a deep learning point of view. A very deep residual network
is utilized to enable automatic learning of hierarchical aesthetics
representation. Integrating these two ideas, an end-to-end deep learning
framework is established. Our approach achieves the best results on a standard
benchmark SCUT-FBP dataset compared with other state-of-the-art work.
Chaining Bounds for Empirical Risk Minimization
Authors: Gábor Balázs, András György, Csaba Szepesvári
Date: 2016-09-07 00:00:00
Tasks: regression

This paper extends the standard chaining technique to prove excess risk upper
bounds for empirical risk minimization with random design settings even if the
magnitude of the noise and the estimates is unbounded. The bound applies to
many loss functions besides the squared loss, and scales only with the
sub-Gaussian or subexponential parameters without further statistical
assumptions such as the bounded kurtosis condition over the hypothesis class. A
detailed analysis is provided for slope constrained and penalized linear least
squares regression with a sub-Gaussian setting, which often proves tight sample
complexity bounds up to logartihmic factors.
Automatic Visual Theme Discovery from Joint Image and Text Corpora
Authors: Ke Sun, Xianxu Hou, Qian Zhang, Guoping Qiu
Date: 2016-09-07 00:00:00
Tasks: Clustering, Descriptive, Image Retrieval, Semantic Similarity, Semantic Textual Similarity, TAG

A popular approach to semantic image understanding is to manually tag images
with keywords and then learn a mapping from vi- sual features to keywords.
Manually tagging images is a subjective pro- cess and the same or very similar
visual contents are often tagged with different keywords. Furthermore, not all
tags have the same descriptive power for visual contents and large vocabulary
available from natural language could result in a very diverse set of keywords.
In this paper, we propose an unsupervised visual theme discovery framework as a
better (more compact, efficient and effective) alternative to semantic
represen- tation of visual contents. We first show that tag based annotation
lacks consistency and compactness for describing visually similar contents. We
then learn the visual similarity between tags based on the visual features of
the images containing the tags. At the same time, we use a natural language
processing technique (word embedding) to measure the seman- tic similarity
between tags. Finally, we cluster tags into visual themes based on their visual
similarity and semantic similarity measures using a spectral clustering
algorithm. We conduct user studies to evaluate the effectiveness and
rationality of the visual themes discovered by our unsu- pervised algorithm and
obtains promising result. We then design three common computer vision tasks,
example based image search, keyword based image search and image labelling to
explore potential applica- tion of our visual themes discovery framework. In
experiments, visual themes significantly outperforms tags on semantic image
understand- ing and achieve state-of-art performance in all three tasks. This
again demonstrate the effectiveness and versatility of proposed framework.
Learning Boltzmann Machine with EM-like Method
Authors: Jinmeng Song, Chun Yuan
Date: 2016-09-07 00:00:00

We propose an expectation-maximization-like(EMlike) method to train Boltzmann
machine with unconstrained connectivity. It adopts Monte Carlo approximation in
the E-step, and replaces the intractable likelihood objective with efficiently
computed objectives or directly approximates the gradient of likelihood
objective in the M-step. The EM-like method is a modification of alternating
minimization. We prove that EM-like method will be the exactly same with
contrastive divergence in restricted Boltzmann machine if the M-step of this
method adopts special approximation. We also propose a new measure to assess
the performance of Boltzmann machine as generative models of data, and its
computational complexity is O(Rmn). Finally, we demonstrate the performance of
EM-like method using numerical experiments.
Guided Filter based Edge-preserving Image Non-blind Deconvolution
Authors: Hang Yang, Ming Zhu, Zhongbo Zhang, He-Yan Huang
Date: 2016-09-07 00:00:00
Tasks: Deblurring, Denoising, Image Deconvolution

In this work, we propose a new approach for efficient edge-preserving image
deconvolution. Our algorithm is based on a novel type of explicit image filter
- guided filter. The guided filter can be used as an edge-preserving smoothing
operator like the popular bilateral filter, but has better behaviors near
edges. We propose an efficient iterative algorithm with the decouple of
deblurring and denoising steps in the restoration process. In deblurring step,
we proposed two cost function which could be computed with fast Fourier
transform efficiently. The solution of the first one is used as the guidance
image, and another solution will be filtered in next step. In the denoising
step, the guided filter is used with the two obtained images for efficient
edge-preserving filtering. Furthermore, we derive a simple and effective method
to automatically adjust the regularization parameter at each iteration. We
compare our deconvolution algorithm with many competitive deconvolution
techniques in terms of ISNR and visual quality.
Feature Based Task Recommendation in Crowdsourcing with Implicit Observations
Authors: Habibur Rahman, Lucas Joppa, Senjuti Basu Roy
Date: 2016-02-10 00:00:00

Existing research in crowdsourcing has investigated how to recommend tasks to
workers based on which task the workers have already completed, referred to as
{\em implicit feedback}. We, on the other hand, investigate the task
recommendation problem, where we leverage both implicit feedback and explicit
features of the task. We assume that we are given a set of workers, a set of
tasks, interactions (such as the number of times a worker has completed a
particular task), and the presence of explicit features of each task (such as,
task location). We intend to recommend tasks to the workers by exploiting the
implicit interactions, and the presence or absence of explicit features in the
tasks. We formalize the problem as an optimization problem, propose two
alternative problem formulations and respective solutions that exploit implicit
feedback, explicit features, as well as similarity between the tasks. We
compare the efficacy of our proposed solutions against multiple
state-of-the-art techniques using two large scale real world datasets.
Semantic Video Trailers
Authors: Harrie Oosterhuis, Sujith Ravi, Michael Bendersky
Date: 2016-09-07 00:00:00
Tasks: Video Summarization

Query-based video summarization is the task of creating a brief visual
trailer, which captures the parts of the video (or a collection of videos) that
are most relevant to the user-issued query. In this paper, we propose an
unsupervised label propagation approach for this task. Our approach effectively
captures the multimodal semantics of queries and videos using state-of-the-art
deep neural networks and creates a summary that is both semantically coherent
and visually attractive. We describe the theoretical framework of our
graph-based approach and empirically evaluate its effectiveness in creating
relevant and attractive trailers. Finally, we showcase example video trailers
generated by our system.
A Supervised Authorship Attribution Framework for Bengali Language
Authors: Shanta Phani, Shibamouli Lahiri, Arindam Biswas
Date: 2016-07-13 00:00:00
Tasks: Authorship Attribution

Authorship Attribution is a long-standing problem in Natural Language
Processing. Several statistical and computational methods have been used to
find a solution to this problem. In this paper, we have proposed methods to
deal with the authorship attribution problem in Bengali.
Tracking System to Automate Data Collection of Microscopic Pedestrian Traffic Flow
Authors: Kardi Teknomo, Yasushi Takeyama, Hajime Inamura
Date: 2016-09-07 00:00:00
Tasks: Object Tracking

To deal with many pedestrian data, automatic data collection is needed. This
paper describes how to automate the microscopic pedestrian flow data collection
from video files. The study is restricted only to pedestrians without
considering vehicular - pedestrian interaction. Pedestrian tracking system
consists of three sub-systems, which calculates the image processing, object
tracking and traffic flow variables. The system receives input of stacks of
images and parameters. The first sub-system performs Image Processing analysis
while the second sub-system carries out the tracking of pedestrians by matching
the features and tracing the pedestrian numbers frame by frame. The last
sub-system deals with a NTXY database to calculate the pedestrian traffic-flow
characteristic such as flow rate, speed and area module. Comparison with manual
data collection method confirmed that the procedures described have significant
potential to automate the data collection of both microscopic and macroscopic
pedestrian flow variables.
Tracking Algorithm for Microscopic Flow Data Collection
Authors: Kardi Teknomo, Yasushi Takeyama, Hajime Inamura
Date: 2016-09-07 00:00:00

Various methods to automate traffic data collection have recently been
developed by many researchers. A macroscopic data collection through image
processing has been proposed. For microscopic traffic flow data, such as
individual speed and time or distance headway, tracking of individual movement
is needed. The tracking algorithms for pedestrian or vehicle have been
developed to trace the movement of one or two pedestrians based on sign
pattern, and feature detection. No research has been done to track many
pedestrians or vehicles at once. This paper describes a new and fast algorithm
to track the movement of many individual vehicles or pedestrians
A General Method for Robust Bayesian Modeling
Authors: Chong Wang, David M. Blei
Date: 2015-10-17 00:00:00
Tasks: regression, Topic Models

Robust Bayesian models are appealing alternatives to standard models,
providing protection from data that contains outliers or other departures from
the model assumptions. Historically, robust models were mostly developed on a
case-by-case basis; examples include robust linear regression, robust mixture
models, and bursty topic models. In this paper we develop a general approach to
robust Bayesian modeling. We show how to turn an existing Bayesian model into a
robust model, and then develop a generic strategy for computing with it. We use
our method to study robust variants of several models, including linear
regression, Poisson regression, logistic regression, and probabilistic topic
models. We discuss the connections between our methods and existing approaches,
especially empirical Bayes and James-Stein estimation.
Defeating Image Obfuscation with Deep Learning
Authors: Richard McPherson, Reza Shokri, Vitaly Shmatikov
Date: 2016-09-01 00:00:00
Tasks: Deep Learning, Privacy Preserving

We demonstrate that modern image recognition methods based on artificial
neural networks can recover hidden information from images protected by various
forms of obfuscation. The obfuscation techniques considered in this paper are
mosaicing (also known as pixelation), blurring (as used by YouTube), and P3, a
recently proposed system for privacy-preserving photo sharing that encrypts the
significant JPEG coefficients to make images unrecognizable by humans. We
empirically show how to train artificial neural networks to successfully
identify faces and recognize objects and handwritten digits even if the images
are protected using any of the above obfuscation techniques.
Human pose estimation via Convolutional Part Heatmap Regression
Authors: Adrian Bulat, Georgios Tzimiropoulos
Date: 2016-09-06 00:00:00
Tasks: Pose Estimation, regression

This paper is on human pose estimation using Convolutional Neural Networks.
Our main contribution is a CNN cascaded architecture specifically designed for
learning part relationships and spatial context, and robustly inferring pose
even for the case of severe part occlusions. To this end, we propose a
detection-followed-by-regression CNN cascade. The first part of our cascade
outputs part detection heatmaps and the second part performs regression on
these heatmaps. The benefits of the proposed architecture are multi-fold: It
guides the network where to focus in the image and effectively encodes part
constraints and context. More importantly, it can effectively cope with
occlusions because part detection heatmaps for occluded parts provide low
confidence scores which subsequently guide the regression part of our network
to rely on contextual information in order to predict the location of these
parts. Additionally, we show that the proposed cascade is flexible enough to
readily allow the integration of various CNN architectures for both detection
and regression, including recent ones based on residual learning. Finally, we
illustrate that our cascade achieves top performance on the MPII and LSP data
sets. Code can be downloaded from http://www.cs.nott.ac.uk/~psxab5/
End-to-End Learning for Image Burst Deblurring
Authors: Patrick Wieschollek, Bernhard Schölkopf, Hendrik P. A. Lensch, Michael Hirsch
Date: 2016-07-15 00:00:00
Tasks: Deblurring, Image Deblurring

We present a neural network model approach for multi-frame blind
deconvolution. The discriminative approach adopts and combines two recent
techniques for image deblurring into a single neural network architecture. Our
proposed hybrid-architecture combines the explicit prediction of a
deconvolution filter and non-trivial averaging of Fourier coefficients in the
frequency domain. In order to make full use of the information contained in all
images in one burst, the proposed network embeds smaller networks, which
explicitly allow the model to transfer information between images in early
layers. Our system is trained end-to-end using standard backpropagation on a
set of artificially generated training examples, enabling competitive
performance in multi-frame blind deconvolution, both with respect to quality
and runtime.
Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015
Authors: Luca Ghiani, David A. Yambay, Valerio Mura, Gian Luca Marcialis, Fabio Roli, Stephanie A. Schuckers
Date: 2016-09-06 00:00:00
Tasks: Artifact Detection

A spoof attack, a subset of presentation attacks, is the use of an artificial
replica of a biometric in an attempt to circumvent a biometric sensor. Liveness
detection, or presentation attack detection, distinguishes between live and
fake biometric traits and is based on the principle that additional information
can be garnered above and beyond the data procured by a standard authentication
system to determine if a biometric measure is authentic. The goals for the
Liveness Detection (LivDet) competitions are to compare software-based
fingerprint liveness detection and artifact detection algorithms (Part 1), as
well as fingerprint systems which incorporate liveness detection or artifact
detection capabilities (Part 2), using a standardized testing protocol and
large quantities of spoof and live tests. The competitions are open to all
academic and industrial institutions which have a solution for either
softwarebased or system-based fingerprint liveness detection. The LivDet
competitions have been hosted in 2009, 2011, 2013 and 2015 and have shown
themselves to provide a crucial look at the current state of the art in
liveness detection schemes. There has been a noticeable increase in the number
of participants in LivDet competitions as well as a noticeable decrease in
error rates across competitions. Participants have grown from four to the most
recent thirteen submissions for Fingerprint Part 1. Fingerprints Part 2 has
held steady at two submissions each competition in 2011 and 2013 and only one
for the 2015 edition. The continuous increase of competitors demonstrates a
growing interest in the topic.
Vanishing point attracts gaze in free-viewing and visual search tasks
Authors: Ali Borji, Mengyang Feng
Date: 2015-12-06 00:00:00

To investigate whether the vanishing point (VP) plays a significant role in
gaze guidance, we ran two experiments. In the first one, we recorded fixations
of 10 observers (4 female; mean age 22; SD=0.84) freely viewing 532 images, out
of which 319 had VP (shuffled presentation; each image for 4 secs). We found
that the average number of fixations at a local region (80x80 pixels) centered
at the VP is significantly higher than the average fixations at random
locations (t-test; n=319; p=1.8e-35). To address the confounding factor of
saliency, we learned a combined model of bottom-up saliency and VP. AUC score
of our model (0.85; SD=0.01) is significantly higher than the original saliency
model (e.g., 0.8 using AIM model by Bruce & Tsotsos (2009), t-test; p=
3.14e-16) and the VP-only model (0.64, t-test; p= 4.02e-22). In the second
experiment, we asked 14 subjects (4 female, mean age 23.07, SD=1.26) to search
for a target character (T or L) placed randomly on a 3x3 imaginary grid
overlaid on top of an image. Subjects reported their answers by pressing one of
two keys. Stimuli consisted of 270 color images (180 with a single VP, 90
without). The target happened with equal probability inside each cell (15 times
L, 15 times T). We found that subjects were significantly faster (and more
accurate) when target happened inside the cell containing the VP compared to
cells without VP (median across 14 subjects 1.34 sec vs. 1.96; Wilcoxon
rank-sum test; p = 0.0014). Response time at VP cells were also significantly
lower than response time on images without VP (median 2.37; p= 4.77e-05). These
findings support the hypothesis that vanishing point, similar to face and text
(Cerf et al., 2009) as well as gaze direction (Borji et al., 2014) attracts
attention in free-viewing and visual search.