pwc-archive/papers-with-abstracts huggingface dataset:
Alternating Back-Propagation for Generator Network
Authors: Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu
Date: 2016-06-28 00:00:00

This paper proposes an alternating back-propagation algorithm for learning
the generator network model. The model is a non-linear generalization of factor
analysis. In this model, the mapping from the continuous latent factors to the
observed signal is parametrized by a convolutional neural network. The
alternating back-propagation algorithm iterates the following two steps: (1)
Inferential back-propagation, which infers the latent factors by Langevin
dynamics or gradient descent. (2) Learning back-propagation, which updates the
parameters given the inferred latent factors by gradient descent. The gradient
computations in both steps are powered by back-propagation, and they share most
of their code in common. We show that the alternating back-propagation
algorithm can learn realistic generator models of natural images, video
sequences, and sounds. Moreover, it can also be used to learn from incomplete
or indirect training data.
Binary Subspace Coding for Query-by-Image Video Retrieval
Authors: Ruicong Xu, Yang Yang, Yadan Luo, Fumin Shen, Zi Huang, Heng Tao Shen
Date: 2016-12-06 00:00:00
Tasks: Retrieval, Video Retrieval

The query-by-image video retrieval (QBIVR) task has been attracting
considerable research attention recently. However, most existing methods
represent a video by either aggregating or projecting all its frames into a
single datum point, which may easily cause severe information loss. In this
paper, we propose an efficient QBIVR framework to enable an effective and
efficient video search with image query. We first define a
similarity-preserving distance metric between an image and its orthogonal
projection in the subspace of the video, which can be equivalently transformed
to a Maximum Inner Product Search (MIPS) problem.
  Besides, to boost the efficiency of solving the MIPS problem, we propose two
asymmetric hashing schemes, which bridge the domain gap of images and videos.
The first approach, termed Inner-product Binary Coding (IBC), preserves the
inner relationships of images and videos in a common Hamming space. To further
improve the retrieval efficiency, we devise a Bilinear Binary Coding (BBC)
approach, which employs compact bilinear projections instead of a single large
projection matrix. Extensive experiments have been conducted on four real-world
video datasets to verify the effectiveness of our proposed approaches as
compared to the state-of-the-arts.
Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images
Authors: Xin Yang, Lequan Yu, Lingyun Wu, Yi Wang, Dong Ni, Jing Qin, Pheng-Ann Heng
Date: 2016-12-06 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Boundary incompleteness raises great challenges to automatic prostate
segmentation in ultrasound images. Shape prior can provide strong guidance in
estimating the missing boundary, but traditional shape models often suffer from
hand-crafted descriptors and local information loss in the fitting procedure.
In this paper, we attempt to address those issues with a novel framework. The
proposed framework can seamlessly integrate feature extraction and shape prior
exploring, and estimate the complete boundary with a sequential manner. Our
framework is composed of three key modules. Firstly, we serialize the static 2D
prostate ultrasound images into dynamic sequences and then predict prostate
shapes by sequentially exploring shape priors. Intuitively, we propose to learn
the shape prior with the biologically plausible Recurrent Neural Networks
(RNNs). This module is corroborated to be effective in dealing with the
boundary incompleteness. Secondly, to alleviate the bias caused by different
serialization manners, we propose a multi-view fusion strategy to merge shape
predictions obtained from different perspectives. Thirdly, we further implant
the RNN core into a multiscale Auto-Context scheme to successively refine the
details of the shape prediction map. With extensive validation on challenging
prostate ultrasound images, our framework bridges severe boundary
incompleteness and achieves the best performance in prostate boundary
delineation when compared with several advanced methods. Additionally, our
approach is general and can be extended to other medical image segmentation
tasks, where boundary incompleteness is one of the main challenges.
Automatic Event Detection for Signal-based Surveillance
Authors: Jingxin Xu, Clinton Fookes, Sridha Sridharan
Date: 2016-12-06 00:00:00
Tasks: Event Detection, Experimental Design

Signal-based Surveillance systems such as Closed Circuits Televisions (CCTV)
have been widely installed in public places. Those systems are normally used to
find the events with security interest, and play a significant role in public
safety. Though such systems are still heavily reliant on human labour to
monitor the captured information, there have been a number of automatic
techniques proposed to analysing the data. This article provides an overview of
automatic surveillance event detection techniques . Despite it's popularity in
research, it is still too challenging a problem to be realised in a real world
deployment. The challenges come from not only the detection techniques such as
signal processing and machine learning, but also the experimental design with
factors such as data collection, evaluation protocols, and ground-truth
annotation. Finally, this article propose that multi-disciplinary research is
the path towards a solution to this problem.
AI Researchers, Video Games Are Your Friends!
Authors: Julian Togelius
Date: 2016-12-06 00:00:00

If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints
Authors: Homa Foroughi, Nilanjan Ray, Hong Zhang
Date: 2016-03-24 00:00:00
Tasks: Dictionary Learning, Dimensionality Reduction, General Classification, image-classification, Image Classification

In this paper, we aim at learning simultaneously a discriminative dictionary
and a robust projection matrix from noisy data. The joint learning, makes the
learned projection and dictionary a better fit for each other, so a more
accurate classification can be obtained. However, current prevailing joint
dimensionality reduction and dictionary learning methods, would fail when the
training samples are noisy or heavily corrupted. To address this issue, we
propose a joint projection and dictionary learning using low-rank
regularization and graph constraints (JPDL-LR). Specifically, the
discrimination of the dictionary is achieved by imposing Fisher criterion on
the coding coefficients. In addition, our method explicitly encodes the local
structure of data by incorporating a graph regularization term, that further
improves the discriminative ability of the projection matrix. Inspired by
recent advances of low-rank representation for removing outliers and noise, we
enforce a low-rank constraint on sub-dictionaries of all classes to make them
more compact and robust to noise. Experimental results on several benchmark
datasets verify the effectiveness and robustness of our method for both
dimensionality reduction and image classification, especially when the data
contains considerable noise or variations.
Object Classification with Joint Projection and Low-rank Dictionary Learning
Authors: Homa Foroughi, Nilanjan Ray, Hong Zhang
Date: 2016-12-05 00:00:00
Tasks: Classification, Dictionary Learning, General Classification

For an object classification system, the most critical obstacles towards
real-world applications are often caused by large intra-class variability,
arising from different lightings, occlusion and corruption, in limited sample
sets. Most methods in the literature would fail when the training samples are
heavily occluded, corrupted or have significant illumination or viewpoint
variations. Besides, most of the existing methods and especially deep
learning-based methods, need large training sets to achieve a satisfactory
recognition performance. Although using the pre-trained network on a generic
large-scale dataset and fine-tune it to the small-sized target dataset is a
widely used technique, this would not help when the content of base and target
datasets are very different. To address these issues, we propose a joint
projection and low-rank dictionary learning method using dual graph constraints
(JP-LRDL). The proposed joint learning method would enable us to learn the
features on top of which dictionaries can be better learned, from the data with
large intra-class variability. Specifically, a structured class-specific
dictionary is learned and the discrimination is further improved by imposing a
graph constraint on the coding coefficients, that maximizes the intra-class
compactness and inter-class separability. We also enforce low-rank and
structural incoherence constraints on sub-dictionaries to make them more
compact and robust to variations and outliers and reduce the redundancy among
them, respectively. To preserve the intrinsic structure of data and penalize
unfavourable relationship among training samples simultaneously, we introduce a
projection graph into the framework, which significantly enhances the
discriminative ability of the projection matrix and makes the method robust to
small-sized and high-dimensional datasets.
Photographic home styles in Congress: a computer vision approach
Authors: L. Jason Anastasopoulos, Dhruvil Badani, Crystal Lee, Shiry Ginosar, Jake Williams
Date: 2016-11-29 00:00:00
Tasks: Image Manipulation

While members of Congress now routinely communicate with constituents using
images on a variety of internet platforms, little is known about how images are
used as a means of strategic political communication. This is due primarily to
computational limitations which have prevented large-scale, systematic analyses
of image features. New developments in computer vision, however, are bringing
the systematic study of images within reach. Here, we develop a framework for
understanding visual political communication by extending Fenno's analysis of
home style (Fenno 1978) to images and introduce "photographic" home styles.
Using approximately 192,000 photographs collected from MCs Facebook profiles,
we build machine learning software with convolutional neural networks and
conduct an image manipulation experiment to explore how the race of people that
MCs pose with shape photographic home styles. We find evidence that electoral
pressures shape photographic home styles and demonstrate that Democratic and
Republican members of Congress use images in very different ways.
Improving the Performance of Neural Networks in Regression Tasks Using Drawering
Authors: Konrad Zolna
Date: 2016-12-05 00:00:00
Tasks: regression

The method presented extends a given regression neural network to make its
performance improve. The modification affects the learning procedure only,
hence the extension may be easily omitted during evaluation without any change
in prediction. It means that the modified model may be evaluated as quickly as
the original one but tends to perform better.
  This improvement is possible because the modification gives better expressive
power, provides better behaved gradients and works as a regularization. The
knowledge gained by the temporarily extended neural network is contained in the
parameters shared with the original neural network.
  The only cost is an increase in learning time.
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
Authors: Suyog Gupta, Wei zhang, Fei Wang
Date: 2015-09-14 00:00:00
Tasks: Distributed Computing, image-classification, Image Classification

This paper presents Rudra, a parameter server based distributed computing
framework tuned for training large-scale deep neural networks. Using variants
of the asynchronous stochastic gradient descent algorithm we study the impact
of synchronization protocol, stale gradient updates, minibatch size, learning
rates, and number of learners on runtime performance and model accuracy. We
introduce a new learning rate modulation strategy to counter the effect of
stale gradients and propose a new synchronization protocol that can effectively
bound the staleness in gradients, improve runtime performance and achieve good
model accuracy. Our empirical investigation reveals a principled approach for
distributed training of neural networks: the mini-batch size per learner should
be reduced as more learners are added to the system to preserve the model
accuracy. We validate this approach using commonly-used image classification
benchmarks: CIFAR10 and ImageNet.
Efficient Feature Group Sequencing for Anytime Linear Prediction
Authors: Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert
Date: 2014-09-19 00:00:00
Tasks: Prediction

We consider \textit{anytime} linear prediction in the common machine learning
setting, where features are in groups that have costs. We achieve anytime (or
interruptible) predictions by sequencing the computation of feature groups and
reporting results using the computed features at interruption. We extend
Orthogonal Matching Pursuit (OMP) and Forward Regression (FR) to learn the
sequencing greedily under this group setting with costs. We theoretically
guarantee that our algorithms achieve near-optimal linear predictions at each
budget when a feature group is chosen. With a novel analysis of OMP, we improve
its theoretical bound to the same strength as that of FR. In addition, we
develop a novel algorithm that consumes cost $4B$ to approximate the optimal
performance of \textit{any} cost $B$, and prove that with cost less than $4B$,
such an approximation is impossible. To our knowledge, these are the first
anytime bounds at \textit{all} budgets. We test our algorithms on two
real-world data-sets and evaluate them in terms of anytime linear prediction
performance against cost-weighted Group Lasso and alternative greedy
algorithms.
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval
Authors: Youssef Mroueh, Etienne Marcheret, Vaibhava Goel
Date: 2015-11-19 00:00:00
Tasks: Image-text Retrieval, Model Selection, Retrieval, Sentence, Sentence Embedding, Sentence-Embedding, Text Retrieval

Joint modeling of language and vision has been drawing increasing interest. A
multimodal data representation allowing for bidirectional retrieval of images
by sentences and vice versa is a key aspect. In this paper we present three
contributions in canonical correlation analysis (CCA) based multimodal
retrieval. Firstly, we show that an asymmetric weighting of the canonical
weights, while achieving a cross view mapping from the search to the query
space, improves the retrieval performance. Secondly, we devise a
computationally efficient model selection, crucial to generalization and
stability, in the framework of the Bj\"ork Golub algorithm for regularized CCA
via spectral filtering. Finally, we introduce a Hierarchical Kernel Sentence
Embedding (HKSE) that approximates Kernel CCA for a special similarity kernel
between distribution of words embedded in a vector space. State of the art
results are obtained on MSCOCO and Flickr benchmarks when these three
techniques are used in conjunction.
Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study
Authors: Jie Yang, Elsa D. Angelini, Benjamin M. Smith, John H. M. Austin, Eric A. Hoffman, David A. Bluemke, R. Graham Barr, Andrew F. Laine
Date: 2016-12-05 00:00:00
Tasks: Computed Tomography (CT)

Pulmonary emphysema is traditionally subcategorized into three subtypes,
which have distinct radiological appearances on computed tomography (CT) and
can help with the diagnosis of chronic obstructive pulmonary disease (COPD).
Automated texture-based quantification of emphysema subtypes has been
successfully implemented via supervised learning of these three emphysema
subtypes. In this work, we demonstrate that unsupervised learning on a large
heterogeneous database of CT scans can generate texture prototypes that are
visually homogeneous and distinct, reproducible across subjects, and capable of
predicting accurately the three standard radiological subtypes. These texture
prototypes enable automated labeling of lung volumes, and open the way to new
interpretations of lung CT scans with finer subtyping of emphysema.
Dynamic change-point detection using similarity networks
Authors: Shanshan Cao, Yao Xie
Date: 2016-12-05 00:00:00
Tasks: Change Detection, Change Point Detection, Community Detection

From a sequence of similarity networks, with edges representing certain
similarity measures between nodes, we are interested in detecting a
change-point which changes the statistical property of the networks. After the
change, a subset of anomalous nodes which compares dissimilarly with the normal
nodes. We study a simple sequential change detection procedure based on
node-wise average similarity measures, and study its theoretical property.
Simulation and real-data examples demonstrate such a simply stopping procedure
has reasonably good performance. We further discuss the faulty sensor isolation
(estimating anomalous nodes) using community detection.
Evaluating the Performance of ANN Prediction System at Shanghai Stock Market in the Period 21-Sep-2016 to 11-Oct-2016
Authors: Barack Wamkaya Wanjawa
Date: 2016-12-05 00:00:00
Tasks: Prediction, Stock Prediction, Time Series Analysis

This research evaluates the performance of an Artificial Neural Network based
prediction system that was employed on the Shanghai Stock Exchange for the
period 21-Sep-2016 to 11-Oct-2016. It is a follow-up to a previous paper in
which the prices were predicted and published before September 21. Stock market
price prediction remains an important quest for investors and researchers. This
research used an Artificial Intelligence system, being an Artificial Neural
Network that is feedforward multi-layer perceptron with error backpropagation
for prediction, unlike other methods such as technical, fundamental or time
series analysis. While these alternative methods tend to guide on trends and
not the exact likely prices, neural networks on the other hand have the ability
to predict the real value prices, as was done on this research. Nonetheless,
determination of suitable network parameters remains a challenge in neural
network design, with this research settling on a configuration of 5:21:21:1
with 80% training data or 4-year of training data as a good enough model for
stock prediction, as already determined in a previous research by the author.
The comparative results indicate that neural network can predict typical stock
market prices with mean absolute percentage errors that are as low as 1.95%
over the ten prediction instances that was studied in this research.
Scribbler: Controlling Deep Image Synthesis with Sketch and Color
Authors: Patsorn Sangkloy, Jingwan Lu, Chen Fang, Fisher Yu, James Hays
Date: 2016-12-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: Colorization, Image Generation

Recently, there have been several promising methods to generate realistic
imagery from deep convolutional networks. These methods sidestep the
traditional computer graphics rendering pipeline and instead generate imagery
at the pixel level by learning from large collections of photos (e.g. faces or
bedrooms). However, these methods are of limited utility because it is
difficult for a user to control what the network produces. In this paper, we
propose a deep adversarial image synthesis architecture that is conditioned on
sketched boundaries and sparse color strokes to generate realistic cars,
bedrooms, or faces. We demonstrate a sketch based image synthesis system which
allows users to 'scribble' over the sketch to indicate preferred color for
objects. Our network can then generate convincing images that satisfy both the
color and the sketch constraints of user. The network is feed-forward which
allows users to see the effect of their edits in real time. We compare to
recent work on sketch to image synthesis and show that our approach can
generate more realistic, more diverse, and more controllable outputs. The
architecture is also effective at user-guided colorization of grayscale images.
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Authors: Ondrej Miksik, Juan-Manuel Pérez-Rúa, Philip H. S. Torr, Patrick Pérez
Date: 2016-12-05 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object

Rotoscoping, the detailed delineation of scene elements through a video shot,
is a painstaking task of tremendous importance in professional post-production
pipelines. While pixel-wise segmentation techniques can help for this task,
professional rotoscoping tools rely on parametric curves that offer the artists
a much better interactive control on the definition, editing and manipulation
of the segments of interest. Sticking to this prevalent rotoscoping paradigm,
we propose a novel framework to capture and track the visual aspect of an
arbitrary object in a scene, given a first closed outline of this object. This
model combines a collection of local foreground/background appearance models
spread along the outline, a global appearance model of the enclosed object and
a set of distinctive foreground landmarks. The structure of this rich
appearance model allows simple initialization, efficient iterative optimization
with exact minimization at each step, and on-line adaptation in videos. We
demonstrate qualitatively and quantitatively the merit of this framework
through comparisons with tools based on either dynamic segmentation with a
closed curve or pixel-wise binary labelling.
MCMC Louvain for Online Community Detection
Authors: Yves Darmaillac, Sébastien Loustau
Date: 2016-12-05 00:00:00
Tasks: Clustering, Community Detection, Online Community Detection

We introduce a novel algorithm of community detection that maintains
dynamically a community structure of a large network that evolves with time.
The algorithm maximizes the modularity index thanks to the construction of a
randomized hierarchical clustering based on a Monte Carlo Markov Chain (MCMC)
method. Interestingly, it could be seen as a dynamization of Louvain algorithm
(see Blondel et Al, 2008) where the aggregation step is replaced by the
hierarchical instrumental probability.
Ambient Sound Provides Supervision for Visual Learning
Authors: Andrew Owens, Jiajun Wu, Josh H. McDermott, William T. Freeman, Antonio Torralba
Date: 2016-08-25 00:00:00
Tasks: Object Recognition

The sound of crashing waves, the roar of fast-moving cars -- sound conveys
important information about the objects in our surroundings. In this work, we
show that ambient sounds can be used as a supervisory signal for learning
visual models. To demonstrate this, we train a convolutional neural network to
predict a statistical summary of the sound associated with a video frame. We
show that, through this process, the network learns a representation that
conveys information about objects and scenes. We evaluate this representation
on several recognition tasks, finding that its performance is comparable to
that of other state-of-the-art unsupervised learning methods. Finally, we show
through visualizations that the network learns units that are selective to
objects that are often associated with characteristic sounds.