pwc-archive/papers-with-abstracts huggingface dataset:
Deep Neural Networks In Fully Connected CRF For Image Labeling With Social Network Metadata
Authors: Chengjiang Long, Roddy Collins, Eran Swears, Anthony Hoogs
Date: 2018-01-27 00:00:00

We propose a novel method for predicting image labels by fusing image content
descriptors with the social media context of each image. An image uploaded to a
social media site such as Flickr often has meaningful, associated information,
such as comments and other images the user has uploaded, that is complementary
to pixel content and helpful in predicting labels. Prediction challenges such
as ImageNet~\cite{imagenet_cvpr09} and MSCOCO~\cite{LinMBHPRDZ:ECCV14} use only
pixels, while other methods make predictions purely from social media context
\cite{McAuleyECCV12}. Our method is based on a novel fully connected
Conditional Random Field (CRF) framework, where each node is an image, and
consists of two deep Convolutional Neural Networks (CNN) and one Recurrent
Neural Network (RNN) that model both textual and visual node/image information.
The edge weights of the CRF graph represent textual similarity and link-based
metadata such as user sets and image groups. We model the CRF as an RNN for
both learning and inference, and incorporate the weighted ranking loss and
cross entropy loss into the CRF parameter optimization to handle the training
data imbalance issue. Our proposed approach is evaluated on the MIR-9K dataset
and experimentally outperforms current state-of-the-art approaches.
Understanding Deep Architectures by Visual Summaries
Authors: Marco Carletti, Marco Godi, Maedeh Aghaei, Francesco Giuliari, Marco Cristani
Date: 2018-01-27 00:00:00
Tasks: General Classification, image-classification, Image Classification

In deep learning, visualization techniques extract the salient patterns exploited by deep networks for image classification, focusing on single images; no effort has been spent in investigating whether these patterns are systematically related to precise semantic entities over multiple images belonging to a same class, thus failing to capture the very understanding of the image class the network has realized. This paper goes in this direction, presenting a visualization framework which produces a group of clusters or summaries, each one formed by crisp salient image regions focusing on a particular part that the network has exploited with high regularity to decide for a given class. The approach is based on a sparse optimization step providing sharp image saliency masks that are clustered together by means of a semantic flow similarity measure. The summaries communicate clearly what a network has exploited of a particular image class, and this is proved through automatic image tagging and with a user study. Beyond the deep network understanding, summaries are also useful for many quantitative reasons: their number is correlated with ability of a network to classify (more summaries, better performances), and they can be used to improve the classification accuracy of a network through summary-driven specializations.
Towards an Understanding of Neural Networks in Natural-Image Spaces
Authors: Yifei Fan, Anthony Yezzi
Date: 2018-01-27 00:00:00
Tasks: Philosophy

Two major uncertainties, dataset bias and adversarial examples, prevail in
state-of-the-art AI algorithms with deep neural networks. In this paper, we
present an intuitive explanation for these issues as well as an interpretation
of the performance of deep networks in a natural-image space. The explanation
consists of two parts: the philosophy of neural networks and a hypothetical
model of natural-image spaces. Following the explanation, we 1) demonstrate
that the values of training samples differ, 2) provide incremental boost to the
accuracy of a CIFAR-10 classifier by introducing an additional "random-noise"
category during training, 3) alleviate over-fitting thereby enhancing the
robustness against adversarial examples by detecting and excluding illusive
training samples that are consistently misclassified. Our overall contribution
is therefore twofold. First, while most existing algorithms treat data equally
and have a strong appetite for more data, we demonstrate in contrast that an
individual datum can sometimes have disproportionate and counterproductive
influence and that it is not always better to train neural networks with more
data. Next, we consider more thoughtful strategies by taking into account the
geometric and topological properties of natural-image spaces to which deep
networks are applied.
A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders
Authors: Ashish Mishra, M Shiva Krishna Reddy, Anurag Mittal, Hema A. Murthy
Date: 2017-09-03 00:00:00
Tasks: Attribute, General Classification, image-classification, Image Classification, Zero-Shot Learning

Zero shot learning in Image Classification refers to the setting where images
from some novel classes are absent in the training data but other information
such as natural language descriptions or attribute vectors of the classes are
available. This setting is important in the real world since one may not be
able to obtain images of all the possible classes at training. While previous
approaches have tried to model the relationship between the class attribute
space and the image space via some kind of a transfer function in order to
model the image space correspondingly to an unseen class, we take a different
approach and try to generate the samples from the given attributes, using a
conditional variational autoencoder, and use the generated samples for
classification of the unseen classes. By extensive testing on four benchmark
datasets, we show that our model outperforms the state of the art, particularly
in the more realistic generalized setting, where the training classes can also
appear at the test time along with the novel classes.
A Generative Approach to Zero-Shot and Few-Shot Action Recognition
Authors: Ashish Mishra, Vinay Kumar Verma, M Shiva Krishna Reddy, Arulkumar S, Piyush Rai, Anurag Mittal
Date: 2018-01-27 00:00:00
Tasks: Action Recognition, Attribute, Few-Shot action recognition, Few Shot Action Recognition, Generalized Zero-Shot Learning, Temporal Action Localization, Zero-Shot Action Recognition, Zero-Shot Learning

We present a generative framework for zero-shot action recognition where some
of the possible action classes do not occur in the training data. Our approach
is based on modeling each action class using a probability distribution whose
parameters are functions of the attribute vector representing that action
class. In particular, we assume that the distribution parameters for any action
class in the visual space can be expressed as a linear combination of a set of
basis vectors where the combination weights are given by the attributes of the
action class. These basis vectors can be learned solely using labeled data from
the known (i.e., previously seen) action classes, and can then be used to
predict the parameters of the probability distributions of unseen action
classes. We consider two settings: (1) Inductive setting, where we use only the
labeled examples of the seen action classes to predict the unseen action class
parameters; and (2) Transductive setting which further leverages unlabeled data
from the unseen action classes. Our framework also naturally extends to
few-shot action recognition where a few labeled examples from unseen classes
are available. Our experiments on benchmark datasets (UCF101, HMDB51 and
Olympic) show significant performance improvements as compared to various
baselines, in both standard zero-shot (disjoint seen and unseen classes) and
generalized zero-shot learning settings.
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
Authors: Gui-Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, Liangpei Zhang
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Earth Observation, Object, object-detection, Object Detection, Object Detection In Aerial Images

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect $2806$ aerial images from different sensors and platforms. Each image is of the size about 4000-by-4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using $15$ common object categories. The fully annotated DOTA images contains $188,282$ instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.
Interactive Deep Colorization With Simultaneous Global and Local Inputs
Authors: Yi Xiao, Peiyao Zhou, Yan Zheng
Date: 2018-01-27 00:00:00
Tasks: Colorization

Colorization methods using deep neural networks have become a recent trend.
However, most of them do not allow user inputs, or only allow limited user
inputs (only global inputs or only local inputs), to control the output
colorful images. The possible reason is that it's difficult to differentiate
the influence of different kind of user inputs in network training. To solve
this problem, we present a novel deep colorization method, which allows
simultaneous global and local inputs to better control the output colorized
images. The key step is to design an appropriate loss function that can
differentiate the influence of input data, global inputs and local inputs. With
this design, our method accepts no inputs, or global inputs, or local inputs,
or both global and local inputs, which is not supported in previous deep
colorization methods. In addition, we propose a global color theme
recommendation system to help users determine global inputs. Experimental
results shows that our methods can better control the colorized images and
generate state-of-art results.
Using Additional Indexes for Fast Full-Text Search of Phrases That Contain Frequently Used Words
Authors: A. B. Veretennikov
Date: 2018-01-27 00:00:00

Searches for phrases and word sets in large text arrays by means of
additional indexes are considered. Their use may reduce the query-processing
time by an order of magnitude in comparison with standard inverted files.
A Review of Multiple Try MCMC algorithms for Signal Processing
Authors: Luca Martino
Date: 2018-01-27 00:00:00
Tasks: Bayesian Inference

Many applications in signal processing require the estimation of some
parameters of interest given a set of observed data. More specifically,
Bayesian inference needs the computation of {\it a-posteriori} estimators which
are often expressed as complicated multi-dimensional integrals. Unfortunately,
analytical expressions for these estimators cannot be found in most real-world
applications, and Monte Carlo methods are the only feasible approach. A very
powerful class of Monte Carlo techniques is formed by the Markov Chain Monte
Carlo (MCMC) algorithms. They generate a Markov chain such that its stationary
distribution coincides with the target posterior density. In this work, we
perform a thorough review of MCMC methods using multiple candidates in order to
select the next state of the chain, at each iteration. With respect to the
classical Metropolis-Hastings method, the use of multiple try techniques foster
the exploration of the sample space. We present different Multiple Try
Metropolis schemes, Ensemble MCMC methods, Particle Metropolis-Hastings
algorithms and the Delayed Rejection Metropolis technique. We highlight
limitations, benefits, connections and differences among the different methods,
and compare them by numerical simulations.
Deep-Learning the Landscape
Authors: Yang-Hui He
Date: 2017-06-08 00:00:00
Tasks: Deep Learning

We propose a paradigm to deep-learn the ever-expanding databases which have
emerged in mathematical physics and particle phenomenology, as diverse as the
statistics of string vacua or combinatorial and algebraic geometry. As concrete
examples, we establish multi-layer neural networks as both classifiers and
predictors and train them with a host of available data ranging from Calabi-Yau
manifolds and vector bundles, to quiver representations for gauge theories. We
find that even a relatively simple neural network can learn many significant
quantities to astounding accuracy in a matter of minutes and can also predict
hithertofore unencountered results. This paradigm should prove a valuable tool
in various investigations in landscapes in physics as well as pure mathematics.
A Multi-Biometrics for Twins Identification Based Speech and Ear
Authors: Cihan Akin, Umit Kacar, Murvet Kirci
Date: 2018-01-27 00:00:00
Tasks: speech-recognition, Speech Recognition

The development of technology biometrics becomes crucial more. To define
human characteristic biometric systems are used but because of inability of
traditional biometric systems to recognize twins, multimodal biometric systems
are developed. In this study a multimodal biometric recognition system is
proposed to recognize twins from each other and from the other people by using
image and speech data. The speech or image data can be enough to recognize
people from each other but twins cannot be distinguished with one of these
data. Therefore a robust recognition system with the combine of speech and ear
images is needed. As database, the photos and speech data of 39 twins are used.
For speech recognition MFCC and DTW algorithms are used. Also, Gabor filter and
DCVA algorithms are used for ear identification. Multi-biometrics success rate
is increased by making matching score level fusion. Especially, rank-5 is
reached 100%. We think that speech and ear can be complementary. Therefore, it
is result that multi-biometrics based speech and ear is effective for human
identifications.
Solving for multi-class using orthogonal coding matrices
Authors: Peter Mills
Date: 2018-01-27 00:00:00
Tasks: General Classification, Multi-class Classification

A common method of generalizing binary to multi-class classification is the error correcting code (ECC). ECCs may be optimized in a number of ways, for instance by making them orthogonal. Here we test two types of orthogonal ECCs on seven different datasets using three types of binary classifier and compare them with three other multi-class methods: 1 vs. 1, one-versus-the-rest and random ECCs. The first type of orthogonal ECC, in which the codes contain no zeros, admits a fast and simple method of solving for the probabilities. Orthogonal ECCs are always more accurate than random ECCs as predicted by recent literature. Improvments in uncertainty coefficient (U.C.) range between 0.4--17.5% (0.004--0.139, absolute), while improvements in Brier score between 0.7--10.7%. Unfortunately, orthogonal ECCs are rarely more accurate than 1 vs. 1. Disparities are worst when the methods are paired with logistic regression, with orthogonal ECCs never beating 1 vs. 1. When the methods are paired with SVM, the losses are less significant, peaking at 1.5%, relative, 0.011 absolute in uncertainty coefficient and 6.5% in Brier scores. Orthogonal ECCs are always the fastest of the five multi-class methods when paired with linear classifiers. When paired with a piecewise linear classifier, whose classification speed does not depend on the number of training samples, classifications using orthogonal ECCs were always more accurate than the other methods and also faster than 1 vs. 1. Losses against 1 vs. 1 here were higher, peaking at 1.9% (0.017, absolute), in U.C. and 39% in Brier score. Gains in speed ranged between 1.1% and over 100%. Whether the speed increase is worth the penalty in accuracy will depend on the application.
Ear Recognition With Score-Level Fusion Based On CMC In Long-Wave Infrared Spectrum
Authors: Umit Kacar, Murvet Kirci
Date: 2018-01-27 00:00:00
Tasks: subspace methods

Only a few studies have been reported regarding human ear recognition in long
wave infrared band. Thus, we have created ear database based on long wave
infrared band. We have called that the database is long wave infrared band
MIDAS consisting of 2430 records of 81 subjects. Thermal band provides seamless
operation both night and day, robust against spoofing with understanding live
ear and invariant to illumination conditions for human ear recognition. We have
proposed to use different algorithms to reveal the distinctive features. Then,
we have reduced the number of dimensions using subspace methods. Finally, the
dimension of data is reduced in accordance with the classifier methods. After
this, the decision is determined by the best sores or combining some of the
best scores with matching fusion. The results have showed that the fusion
technique was successful. We have reached 97.71% for rank-1 with 567 test
probes. Furthermore, we have defined the perfect rank which is rank number when
recognition rate reaches 100% in cumulative matching curve. This evaluation is
important for especially forensics, for example corpse identification, criminal
investigation etc.
Combining Convolution and Recursive Neural Networks for Sentiment Analysis
Authors: Vinh D. Van, Thien Thai, Minh-Quoc Nghiem
Date: 2018-01-27 00:00:00
Tasks: Sentence, Sentiment Analysis, Transfer Learning

This paper addresses the problem of sentence-level sentiment analysis. In
recent years, Convolution and Recursive Neural Networks have been proven to be
effective network architecture for sentence-level sentiment analysis.
Nevertheless, each of them has their own potential drawbacks. For alleviating
their weaknesses, we combined Convolution and Recursive Neural Networks into a
new network architecture. In addition, we employed transfer learning from a
large document-level labeled sentiment dataset to improve the word embedding in
our models. The resulting models outperform all recent Convolution and
Recursive Neural Networks. Beyond that, our models achieve comparable
performance with state-of-the-art systems on Stanford Sentiment Treebank.
Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense Stationary Ergodic Processes
Authors: Qidi Peng, Nan Rao, Ran Zhao
Date: 2018-01-27 00:00:00
Tasks: Clustering

We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a formal criterion on the efficiency of dissimilarity measures, and discuss of some approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.
Randomized LU decomposition: An Algorithm for Dictionaries Construction
Authors: Aviv Rotbart, Gil Shabat, Yaniv Shmueli, Amir Averbuch
Date: 2015-02-17 00:00:00
Tasks: Computer Security, File Type Identification, General Classification

In recent years, distinctive-dictionary construction has gained importance
due to his usefulness in data processing. Usually, one or more dictionaries are
constructed from a training data and then they are used to classify signals
that did not participate in the training process. A new dictionary construction
algorithm is introduced. It is based on a low-rank matrix factorization being
achieved by the application of the randomized LU decomposition to a training
data. This method is fast, scalable, parallelizable, consumes low memory,
outperforms SVD in these categories and works also extremely well on large
sparse matrices. In contrast to existing methods, the randomized LU
decomposition constructs an under-complete dictionary, which simplifies both
the construction and the classification processes of newly arrived signals. The
dictionary construction is generic and general that fits different
applications. We demonstrate the capabilities of this algorithm for file type
identification, which is a fundamental task in digital security arena,
performed nowadays for example by sandboxing mechanism, deep packet inspection,
firewalls and anti-virus systems. We propose a content-based method that
detects file types that neither depend on file extension nor on metadata. Such
approach is harder to deceive and we show that only a few file fragments from a
whole file are needed for a successful classification. Based on the constructed
dictionaries, we show that the proposed method can effectively identify
execution code fragments in PDF files.
  $\textbf{Keywords.}$ Dictionary construction, classification, LU
decomposition, randomized LU decomposition, content-based file detection,
computer security.
Preferential Attachment Graphs with Planted Communities
Authors: Bruce Hajek, Suryanarayana Sankagiri
Date: 2018-01-21 00:00:00

A variation of the preferential attachment random graph model of Barab\'asi
and Albert is defined that incorporates planted communities. The graph is built
progressively, with new vertices attaching to the existing ones one-by-one. At
every step, the incoming vertex is randomly assigned a label, which represents
a community it belongs to. This vertex then chooses certain vertices as its
neighbors, with the choice of each vertex being proportional to the degree of
the vertex multiplied by an affinity depending on the labels of the new vertex
and a potential neighbor. It is shown that the fraction of half-edges attached
to vertices with a given label converges almost surely for some classes of
affinity matrices. In addition, the empirical degree distribution for the set
of vertices with a given label converges to a heavy tailed distribution, such
that the tail decay parameter can be different for different communities. Our
proof method may be of independent interest, both for the classical Barab\'asi
-Albert model and for other possible extensions.
Image2GIF: Generating Cinemagraphs using Recurrent Deep Q-Networks
Authors: Yipin Zhou, Yale Song, Tamara L. Berg
Date: 2018-01-27 00:00:00

Given a still photograph, one can imagine how dynamic objects might move
against a static background. This idea has been actualized in the form of
cinemagraphs, where the motion of particular objects within a still image is
repeated, giving the viewer a sense of animation. In this paper, we learn
computational models that can generate cinemagraph sequences automatically
given a single image. To generate cinemagraphs, we explore combining generative
models with a recurrent neural network and deep Q-networks to enhance the power
of sequence generation. To enable and evaluate these models we make use of two
datasets, one synthetically generated and the other containing real video
generated cinemagraphs. Both qualitative and quantitative evaluations
demonstrate the effectiveness of our models on the synthetic and real datasets.