pwc-archive/papers-with-abstracts huggingface dataset:
Simultaneous Deep Transfer Across Domains and Tasks
Authors: Eric Tzeng, Judy Hoffman, Trevor Darrell, Kate Saenko
Date: 2015-10-08 00:00:00
Proceeding: ICCV 2015 12
Tasks: Domain Adaptation

Recent reports suggest that a generic supervised deep CNN model trained on a
large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning
deep models in a new domain can require a significant amount of labeled data,
which for many applications is simply not available. We propose a new CNN
architecture to exploit unlabeled and sparsely labeled target domain data. Our
approach simultaneously optimizes for domain invariance to facilitate domain
transfer and uses a soft label distribution matching loss to transfer
information between tasks. Our proposed adaptation method offers empirical
performance which exceeds previously published results on two standard
benchmark visual domain adaptation tasks, evaluated across supervised and
semi-supervised adaptation settings.
Webly Supervised Learning of Convolutional Networks
Authors: Xinlei Chen, Abhinav Gupta
Date: 2015-05-07 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Retrieval

We present an approach to utilize large amounts of web data for learning
CNNs. Specifically inspired by curriculum learning, we present a two-step
approach for CNN training. First, we use easy images to train an initial visual
representation. We then use this initial CNN and adapt it to harder, more
realistic images by leveraging the structure of data and categories. We
demonstrate that our two-stage CNN outperforms a fine-tuned CNN trained on
ImageNet on Pascal VOC 2012. We also demonstrate the strength of webly
supervised learning by localizing objects in web images and training a R-CNN
style detector. It achieves the best performance on VOC 2007 where no VOC
training data is used. Finally, we show our approach is quite robust to noise
and performs comparably even when we use image search results from March 2013
(pre-CNN image search era).
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer
Authors: Forrest N. Iandola, Anting Shen, Peter Gao, Kurt Keutzer
Date: 2015-10-07 00:00:00
Tasks: 2D Object Detection, Image Classification, Logo Recognition, Object Detection, Object Recognition

Recently, there has been a flurry of industrial activity around logo
recognition, such as Ditto's service for marketers to track their brands in
user-generated images, and LogoGrab's mobile app platform for logo recognition.
However, relatively little academic or open-source logo recognition progress
has been made in the last four years. Meanwhile, deep convolutional neural
networks (DCNNs) have revolutionized a broad range of object recognition
applications. In this work, we apply DCNNs to logo recognition. We propose
several DCNN architectures, with which we surpass published state-of-art
accuracy on a popular logo recognition dataset.
On the Projective Geometry of Kalman Filter
Authors: Francesca Paola Carli, Rodolphe Sepulchre
Date: 2015-03-31 00:00:00

Convergence of the Kalman filter is best analyzed by studying the contraction
of the Riccati map in the space of positive definite (covariance) matrices. In
this paper, we explore how this contraction property relates to a more
fundamental non-expansiveness property of filtering maps in the space of
probability distributions endowed with the Hilbert metric. This is viewed as a
preliminary step towards improving the convergence analysis of filtering
algorithms over general graphical models.
Leveraging Context to Support Automated Food Recognition in Restaurants
Authors: Vinay Bettadapura, Edison Thomaz, Aman Parnami, Gregory Abowd, Irfan Essa
Date: 2015-10-07 00:00:00
Tasks: Food Recognition

The pervasiveness of mobile cameras has resulted in a dramatic increase in
food photos, which are pictures reflecting what people eat. In this paper, we
study how taking pictures of what we eat in restaurants can be used for the
purpose of automating food journaling. We propose to leverage the context of
where the picture was taken, with additional information about the restaurant,
available online, coupled with state-of-the-art computer vision techniques to
recognize the food being consumed. To this end, we demonstrate image-based
recognition of foods eaten in restaurants by training a classifier with images
from restaurant's online menu databases. We evaluate the performance of our
system in unconstrained, real-world settings with food images taken in 10
restaurants across 5 different types of food (American, Indian, Italian,
Mexican and Thai).
Egocentric Field-of-View Localization Using First-Person Point-of-View Devices
Authors: Vinay Bettadapura, Irfan Essa, Caroline Pantofaru
Date: 2015-10-07 00:00:00

We present a technique that uses images, videos and sensor data taken from
first-person point-of-view devices to perform egocentric field-of-view (FOV)
localization. We define egocentric FOV localization as capturing the visual
information from a person's field-of-view in a given environment and
transferring this information onto a reference corpus of images and videos of
the same space, hence determining what a person is attending to. Our method
matches images and video taken from the first-person perspective with the
reference corpus and refines the results using the first-person's head
orientation information obtained using the device sensors. We demonstrate
single and multi-user egocentric FOV localization in different indoor and
outdoor environments with applications in augmented reality, event
understanding and studying social interactions.
Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition
Authors: Vinay Bettadapura, Grant Schindler, Thomaz Plotz, Irfan Essa
Date: 2015-10-07 00:00:00
Proceeding: CVPR 2013 6
Tasks: Activity Recognition

We present data-driven techniques to augment Bag of Words (BoW) models, which
allow for more robust modeling and recognition of complex long-term activities,
especially when the structure and topology of the activities are not known a
priori. Our approach specifically addresses the limitations of standard BoW
approaches, which fail to represent the underlying temporal and causal
information that is inherent in activity streams. In addition, we also propose
the use of randomly sampled regular expressions to discover and encode patterns
in activities. We demonstrate the effectiveness of our approach in experimental
evaluations where we successfully recognize activities and detect anomalies in
four complex datasets.
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
Authors: Brendan Jou, Tao Chen, Nikolaos Pappas, Miriam Redi, Mercan Topkara, Shih-Fu Chang
Date: 2015-08-16 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Every culture and language is unique. Our work expressly focuses on the
uniqueness of culture and language in relation to human affect, specifically
sentiment and emotion semantics, and how they manifest in social multimedia. We
develop sets of sentiment- and emotion-polarized visual concepts by adapting
semantic structures called adjective-noun pairs, originally introduced by Borth
et al. (2013), but in a multilingual context. We propose a new
language-dependent method for automatic discovery of these adjective-noun
constructs. We show how this pipeline can be applied on a social multimedia
platform for the creation of a large-scale multilingual visual sentiment
concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our
unified ontology is organized hierarchically by multilingual clusters of
visually detectable nouns and subclusters of emotionally biased versions of
these nouns. In addition, we present an image-based prediction task to show how
generalizable language-specific models are in a multilingual context. A new,
publicly available dataset of >15.6K sentiment-biased visual concepts across 12
languages with language-specific detector banks, >7.36M images and their
metadata is also released.
Diverse Large-Scale ITS Dataset Created from Continuous Learning for Real-Time Vehicle Detection
Authors: Justin A. Eichel, Akshaya Mishra, Nicholas Miller, Nicholas Jankovic, Mohan A. Thomas, Tyler Abbott, Douglas Swanson, Joel Keller
Date: 2015-10-07 00:00:00
Tasks: Cloud Computing, Diversity, Event Detection, vehicle detection

In traffic engineering, vehicle detectors are trained on limited datasets
resulting in poor accuracy when deployed in real world applications. Annotating
large-scale high quality datasets is challenging. Typically, these datasets
have limited diversity; they do not reflect the real-world operating
environment. There is a need for a large-scale, cloud based positive and
negative mining (PNM) process and a large-scale learning and evaluation system
for the application of traffic event detection. The proposed positive and
negative mining process addresses the quality of crowd sourced ground truth
data through machine learning review and human feedback mechanisms. The
proposed learning and evaluation system uses a distributed cloud computing
framework to handle data-scaling issues associated with large numbers of
samples and a high-dimensional feature space. The system is trained using
AdaBoost on $1,000,000$ Haar-like features extracted from $70,000$ annotated
video frames. The trained real-time vehicle detector achieves an accuracy of at
least $95\%$ for $1/2$ and about $78\%$ for $19/20$ of the time when tested on
approximately $7,500,000$ video frames. At the end of 2015, the dataset is
expect to have over one billion annotated video frames.
Stochastic Optimization for Deep CCA via Nonlinear Orthogonal Iterations
Authors: Weiran Wang, Raman Arora, Karen Livescu, Nathan Srebro
Date: 2015-10-07 00:00:00
Tasks: Representation Learning, Stochastic Optimization

Deep CCA is a recently proposed deep neural network extension to the
traditional canonical correlation analysis (CCA), and has been successful for
multi-view representation learning in several domains. However, stochastic
optimization of the deep CCA objective is not straightforward, because it does
not decouple over training examples. Previous optimizers for deep CCA are
either batch-based algorithms or stochastic optimization using large
minibatches, which can have high memory consumption. In this paper, we tackle
the problem of stochastic optimization for deep CCA with small minibatches,
based on an iterative solution to the CCA objective, and show that we can
achieve as good performance as previous optimizers and thus alleviate the
memory requirement.
Assisting Composition of Email Responses: a Topic Prediction Approach
Authors: Spandana Gella, Marc Dymetman, Jean Michel Renders, Sriram Venkatapathy
Date: 2015-10-07 00:00:00
Tasks: Sentence

We propose an approach for helping agents compose email replies to customer
requests. To enable that, we use LDA to extract latent topics from a collection
of email exchanges. We then use these latent topics to label our data,
obtaining a so-called "silver standard" topic labelling. We exploit this
labelled set to train a classifier to: (i) predict the topic distribution of
the entire agent's email response, based on features of the customer's email;
and (ii) predict the topic distribution of the next sentence in the agent's
reply, based on the customer's email features and on features of the agent's
current sentence. The experimental results on a large email collection from a
contact center in the tele- com domain show that the proposed ap- proach is
effective in predicting the best topic of the agent's next sentence. In 80% of
the cases, the correct topic is present among the top five recommended topics
(out of fifty possible ones). This shows the potential of this method to be
applied in an interactive setting, where the agent is presented a small list of
likely topics to choose from for the next sentence.
Budget Constraints in Prediction Markets
Authors: Nikhil Devanur, Miroslav Dudík, Zhiyi Huang, David M. Pennock
Date: 2015-10-07 00:00:00
Tasks: Prediction, scoring rule

We give a detailed characterization of optimal trades under budget
constraints in a prediction market with a cost-function-based automated market
maker. We study how the budget constraints of individual traders affect their
ability to impact the market price. As a concrete application of our
characterization, we give sufficient conditions for a property we call budget
additivity: two traders with budgets B and B' and the same beliefs would have a
combined impact equal to a single trader with budget B+B'. That way, even if a
single trader cannot move the market much, a crowd of like-minded traders can
have the same desired effect. When the set of payoff vectors associated with
outcomes, with coordinates corresponding to securities, is affinely
independent, we obtain that a generalization of the heavily-used logarithmic
market scoring rule is budget additive, but the quadratic market scoring rule
is not. Our results may be used both descriptively, to understand if a
particular market maker is affected by budget constraints or not, and
prescriptively, as a recipe to construct markets.
A Common-Factor Approach for Multivariate Data Cleaning with an Application to Mars Phoenix Mission Data
Authors: Dongping Fang, Elizabeth Oberlin, Wei Ding, Samuel P. Kounaves
Date: 2015-10-05 00:00:00

Data quality is fundamentally important to ensure the reliability of data for
stakeholders to make decisions. In real world applications, such as scientific
exploration of extreme environments, it is unrealistic to require raw data
collected to be perfect. As data miners, when it is infeasible to physically
know the why and the how in order to clean up the data, we propose to seek the
intrinsic structure of the signal to identify the common factors of
multivariate data. Using our new data driven learning method, the common-factor
data cleaning approach, we address an interdisciplinary challenge on
multivariate data cleaning when complex external impacts appear to interfere
with multiple data measurements. Existing data analyses typically process one
signal measurement at a time without considering the associations among all
signals. We analyze all signal measurements simultaneously to find the hidden
common factors that drive all measurements to vary together, but not as a
result of the true data measurements. We use common factors to reduce the
variations in the data without changing the base mean level of the data to
avoid altering the physical meaning.
Event-based Camera Pose Tracking using a Generative Event Model
Authors: Guillermo Gallego, Christian Forster, Elias Mueggler, Davide Scaramuzza
Date: 2015-10-07 00:00:00
Tasks: Camera Localization, Event-based vision, Pose Tracking

Event-based vision sensors mimic the operation of biological retina and they
represent a major paradigm shift from traditional cameras. Instead of providing
frames of intensity measurements synchronously, at artificially chosen rates,
event-based cameras provide information on brightness changes asynchronously,
when they occur. Such non-redundant pieces of information are called "events".
These sensors overcome some of the limitations of traditional cameras (response
time, bandwidth and dynamic range) but require new methods to deal with the
data they output. We tackle the problem of event-based camera localization in a
known environment, without additional sensing, using a probabilistic generative
event model in a Bayesian filtering framework. Our main contribution is the
design of the likelihood function used in the filter to process the observed
events. Based on the physical characteristics of the sensor and on empirical
evidence of the Gaussian-like distribution of spiked events with respect to the
brightness change, we propose to use the contrast residual as a measure of how
well the estimated pose of the event-based camera and the environment explain
the observed events. The filter allows for localization in the general case of
six degrees-of-freedom motions.
Towards a general framework for an observation and knowledge based model of occupant behaviour in office buildings
Authors: Khadija Tijani, Dung Ngo, Stephane Ploix, Benjamin Haas, Julie Dugdale
Date: 2015-10-07 00:00:00

This paper proposes a new general approach based on Bayesian networks to
model the human behaviour. This approach represents human behaviour
withprobabilistic cause-effect relations based not only on previous works, but
also with conditional probabilities coming either from expert knowledge or
deduced from observations. The approach has been used in the co-simulation of
building physics and human behaviour in order to assess the CO 2 concentration
in an office.
Quantitative evaluation of the performance of discrete-time reservoir computers in the forecasting, filtering, and reconstruction of stochastic stationary signals
Authors: Lyudmila Grigoryeva, Julie Henriques, Juan-Pablo Ortega
Date: 2015-08-01 00:00:00
Tasks: Time Series, Time Series Analysis, valid

This paper extends the notion of information processing capacity for
non-independent input signals in the context of reservoir computing (RC). The
presence of input autocorrelation makes worthwhile the treatment of forecasting
and filtering problems for which we explicitly compute this generalized
capacity as a function of the reservoir parameter values using a streamlined
model. The reservoir model leading to these developments is used to show that,
whenever that approximation is valid, this computational paradigm satisfies the
so called separation and fading memory properties that are usually associated
with good information processing performances. We show that several standard
memory, forecasting, and filtering problems that appear in the parametric
stochastic time series context can be readily formulated and tackled via RC
which, as we show, significantly outperforms standard techniques in some
instances.
Hierarchical Representation of Prosody for Statistical Speech Synthesis
Authors: Antti Suni, Daniel Aalto, Martti Vainio
Date: 2015-10-07 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Prominences and boundaries are the essential constituents of prosodic
structure in speech. They provide for means to chunk the speech stream into
linguistically relevant units by providing them with relative saliences and
demarcating them within coherent utterance structures. Prominences and
boundaries have both been widely used in both basic research on prosody as well
as in text-to-speech synthesis. However, there are no representation schemes
that would provide for both estimating and modelling them in a unified fashion.
Here we present an unsupervised unified account for estimating and representing
prosodic prominences and boundaries using a scale-space analysis based on
continuous wavelet transform. The methods are evaluated and compared to earlier
work using the Boston University Radio News corpus. The results show that the
proposed method is comparable with the best published supervised annotation
methods.
"Memory foam" approach to unsupervised learning
Authors: Natalia B. Janson, Christopher J. Marsden
Date: 2011-07-04 00:00:00

We propose an alternative approach to construct an artificial learning
system, which naturally learns in an unsupervised manner. Its mathematical
prototype is a dynamical system, which automatically shapes its vector field in
response to the input signal. The vector field converges to a gradient of a
multi-dimensional probability density distribution of the input process, taken
with negative sign. The most probable patterns are represented by the stable
fixed points, whose basins of attraction are formed automatically. The
performance of this system is illustrated with musical signals.
Helping Domain Experts Build Speech Translation Systems
Authors: Manny Rayner, Alejandro Armando, Pierrette Bouillon, Sarah Ebling, Johanna Gerlach, Sonia Halimi, Irene Strasly, Nikos Tsourakis
Date: 2015-10-07 00:00:00
Tasks: Sign Language Translation, Translation

We present a new platform, "Regulus Lite", which supports rapid development
and web deployment of several types of phrasal speech translation systems using
a minimal formalism. A distinguishing feature is that most development work can
be performed directly by domain experts. We motivate the need for platforms of
this type and discuss three specific cases: medical speech translation,
speech-to-sign-language translation and voice questionnaires. We briefly
describe initial experiences in developing practical systems.
Using Ontology-Based Context in the Portuguese-English Translation of Homographs in Textual Dialogues
Authors: Diego Moussallem, Ricardo Choren
Date: 2015-10-07 00:00:00
Tasks: Translation

This paper introduces a novel approach to tackle the existing gap on message
translations in dialogue systems. Currently, submitted messages to the dialogue
systems are considered as isolated sentences. Thus, missing context information
impede the disambiguation of homographs words in ambiguous sentences. Our
approach solves this disambiguation problem by using concepts over existing
ontologies.
Learning Spatiotemporal Features with 3D Convolutional Networks
Authors: Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri
Date: 2014-12-02 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Recognition, Action Recognition In Videos, Dynamic Facial Expression Recognition

We propose a simple, yet effective approach for spatiotemporal feature
learning using deep 3-dimensional convolutional networks (3D ConvNets) trained
on a large scale supervised video dataset. Our findings are three-fold: 1) 3D
ConvNets are more suitable for spatiotemporal feature learning compared to 2D
ConvNets; 2) A homogeneous architecture with small 3x3x3 convolution kernels in
all layers is among the best performing architectures for 3D ConvNets; and 3)
Our learned features, namely C3D (Convolutional 3D), with a simple linear
classifier outperform state-of-the-art methods on 4 different benchmarks and
are comparable with current best methods on the other 2 benchmarks. In
addition, the features are compact: achieving 52.8% accuracy on UCF101 dataset
with only 10 dimensions and also very efficient to compute due to the fast
inference of ConvNets. Finally, they are conceptually very simple and easy to
train and use.
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Authors: Ruining He, Julian McAuley
Date: 2015-10-06 00:00:00
Tasks: Multi-modal Recommendation, Recommendation Systems

Modern recommender systems model people and items by discovering or `teasing
apart' the underlying dimensions that encode the properties of items and users'
preferences toward them. Critically, such dimensions are uncovered based on
user feedback, often in implicit form (such as purchase histories, browsing
logs, etc.); in addition, some recommender systems make use of side
information, such as product attributes, temporal information, or review text.
However one important feature that is typically ignored by existing
personalized recommendation and ranking methods is the visual appearance of the
items being considered. In this paper we propose a scalable factorization model
to incorporate visual signals into predictors of people's opinions, which we
apply to a selection of large, real-world datasets. We make use of visual
features extracted from product images using (pre-trained) deep networks, on
top of which we learn an additional layer that uncovers the visual dimensions
that best explain the variation in people's feedback. This not only leads to
significantly more accurate personalized ranking methods, but also helps to
alleviate cold start issues, and qualitatively to analyze the visual dimensions
that influence people's opinions.