pwc-archive/papers-with-abstracts huggingface dataset:
Analysing domain shift factors between videos and images for object detection
Authors: Vicky Kalogeiton, Vittorio Ferrari, Cordelia Schmid
Date: 2015-01-06 00:00:00
Tasks: Diversity, Object, object-detection, Object Detection

Object detection is one of the most important challenges in computer vision.
Object detectors are usually trained on bounding-boxes from still images.
Recently, video has been used as an alternative source of data. Yet, for a
given test domain (image or video), the performance of the detector depends on
the domain it was trained on. In this paper, we examine the reasons behind this
performance gap. We define and evaluate different domain shift factors: spatial
location accuracy, appearance diversity, image quality and aspect distribution.
We examine the impact of these factors by comparing performance before and
after factoring them out. The results show that all four factors affect the
performance of the detectors and their combined effect explains nearly the
whole performance gap.
Unsupervised Semantic Parsing of Video Collections
Authors: Ozan Sener, Amir Zamir, Silvio Savarese, Ashutosh Saxena
Date: 2015-06-28 00:00:00
Proceeding: ICCV 2015 12
Tasks: Semantic Parsing, Unsupervised semantic parsing

Human communication typically has an underlying structure. This is reflected
in the fact that in many user generated videos, a starting point, ending, and
certain objective steps between these two can be identified. In this paper, we
propose a method for parsing a video into such semantic steps in an
unsupervised way. The proposed method is capable of providing a semantic
"storyline" of the video composed of its objective steps. We accomplish this
using both visual and language cues in a joint generative model. The proposed
method can also provide a textual description for each of the identified
semantic steps and video segments. We evaluate this method on a large number of
complex YouTube videos and show results of unprecedented quality for this
intricate and impactful problem.
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices
Authors: Ming Yin, Shengli Xie, Yi Guo, Junbin Gao, Yun Zhang
Date: 2016-01-27 00:00:00
Tasks: Classification, General Classification, Robust classification, Sparse Representation-based Classification

Due to its promising classification performance, sparse representation based
classification(SRC) algorithm has attracted great attention in the past few
years. However, the existing SRC type methods apply only to vector data in
Euclidean space. As such, there is still no satisfactory approach to conduct
classification task for symmetric positive definite (SPD) matrices which is
very useful in computer vision. To address this problem, in this paper, a
neighborhood preserved kernel SRC method is proposed on SPD manifolds.
Specifically, by embedding the SPD matrices into a Reproducing Kernel Hilbert
Space (RKHS), the proposed method can perform classification on SPD manifolds
through an appropriate Log-Euclidean kernel. Through exploiting the geodesic
distance between SPD matrices, our method can effectively characterize the
intrinsic local Riemannian geometry within data so as to well unravel the
underlying sub-manifold structure. Despite its simplicity, experimental results
on several famous database demonstrate that the proposed method achieves better
classification results than the state-of-the-art approaches.
Performance measures for classification systems with rejection
Authors: Filipe Condessa, Jelena Kovacevic, Jose Bioucas-Dias
Date: 2015-04-10 00:00:00
Tasks: Classification, Decision Making, General Classification

Classifiers with rejection are essential in real-world applications where
misclassifications and their effects are critical. However, if no problem
specific cost function is defined, there are no established measures to assess
the performance of such classifiers. We introduce a set of desired properties
for performance measures for classifiers with rejection, based on which we
propose a set of three performance measures for the evaluation of the
performance of classifiers with rejection that satisfy the desired properties.
The nonrejected accuracy measures the ability of the classifier to accurately
classify nonrejected samples; the classification quality measures the correct
decision making of the classifier with rejector; and the rejection quality
measures the ability to concentrate all misclassified samples onto the set of
rejected samples. From the measures, we derive the concept of relative
optimality that allows us to connect the measures to a family of cost functions
that take into account the trade-off between rejection and misclassification.
We illustrate the use of the proposed performance measures on classifiers with
rejection applied to synthetic and real-world data.
The Computational Power of Optimization in Online Learning
Authors: Elad Hazan, Tomer Koren
Date: 2015-04-08 00:00:00

We consider the fundamental problem of prediction with expert advice where
the experts are "optimizable": there is a black-box optimization oracle that
can be used to compute, in constant time, the leading expert in retrospect at
any point in time. In this setting, we give a novel online algorithm that
attains vanishing regret with respect to $N$ experts in total
$\widetilde{O}(\sqrt{N})$ computation time. We also give a lower bound showing
that this running time cannot be improved (up to log factors) in the oracle
model, thereby exhibiting a quadratic speedup as compared to the standard,
oracle-free setting where the required time for vanishing regret is
$\widetilde{\Theta}(N)$. These results demonstrate an exponential gap between
the power of optimization in online learning and its power in statistical
learning: in the latter, an optimization oracle---i.e., an efficient empirical
risk minimizer---allows to learn a finite hypothesis class of size $N$ in time
$O(\log{N})$. We also study the implications of our results to learning in
repeated zero-sum games, in a setting where the players have access to oracles
that compute, in constant time, their best-response to any mixed strategy of
their opponent. We show that the runtime required for approximating the minimax
value of the game in this setting is $\widetilde{\Theta}(\sqrt{N})$, yielding
again a quadratic improvement upon the oracle-free setting, where
$\widetilde{\Theta}(N)$ is known to be tight.
Comprehensive Feature-based Robust Video Fingerprinting Using Tensor Model
Authors: Xiushan Nie, Yilong Yin, Jiande Sun
Date: 2016-01-27 00:00:00
Tasks: Retrieval, Tensor Decomposition

Content-based near-duplicate video detection (NDVD) is essential for
effective search and retrieval, and robust video fingerprinting is a good
solution for NDVD. Most existing video fingerprinting methods use a single
feature or concatenating different features to generate video fingerprints, and
show a good performance under single-mode modifications such as noise addition
and blurring. However, when they suffer combined modifications, the performance
is degraded to a certain extent because such features cannot characterize the
video content completely. By contrast, the assistance and consensus among
different features can improve the performance of video fingerprinting.
Therefore, in the present study, we mine the assistance and consensus among
different features based on tensor model, and present a new comprehensive
feature to fully use them in the proposed video fingerprinting framework. We
also analyze what the comprehensive feature really is for representing the
original video. In this framework, the video is initially set as a high-order
tensor that consists of different features, and the video tensor is decomposed
via the Tucker model with a solution that determines the number of components.
Subsequently, the comprehensive feature is generated by the low-order tensor
obtained from tensor decomposition. Finally, the video fingerprint is computed
using this feature. A matching strategy used for narrowing the search is also
proposed based on the core tensor. The robust video fingerprinting framework is
resistant not only to single-mode modifications, but also to the combination of
them.
Deep Learning Driven Visual Path Prediction from a Single Image
Authors: Siyu Huang, Xi Li, Zhongfei Zhang, Zhouzhou He, Fei Wu, Wei Liu, Jinhui Tang, Yueting Zhuang
Date: 2016-01-27 00:00:00
Tasks: Deep Learning, Prediction

Capabilities of inference and prediction are significant components of visual
systems. In this paper, we address an important and challenging task of them:
visual path prediction. Its goal is to infer the future path for a visual
object in a static scene. This task is complicated as it needs high-level
semantic understandings of both the scenes and motion patterns underlying video
sequences. In practice, cluttered situations have also raised higher demands on
the effectiveness and robustness of the considered models. Motivated by these
observations, we propose a deep learning framework which simultaneously
performs deep feature learning for visual representation in conjunction with
spatio-temporal context modeling. After that, we propose a unified path
planning scheme to make accurate future path prediction based on the analytic
results of the context models. The highly effective visual representation and
deep context models ensure that our framework makes a deep semantic
understanding of the scene and motion pattern, consequently improving the
performance of the visual path prediction task. In order to comprehensively
evaluate the model's performance on the visual path prediction task, we
construct two large benchmark datasets from the adaptation of video tracking
datasets. The qualitative and quantitative experimental results show that our
approach outperforms the existing approaches and owns a better generalization
capability.
Fast Integral Image Estimation at 1% measurement rate
Authors: Kuldeep Kulkarni, Pavan Turaga
Date: 2016-01-27 00:00:00
Tasks: Object Tracking, Visual Object Tracking

We propose a framework called ReFInE to directly obtain integral image
estimates from a very small number of spatially multiplexed measurements of the
scene without iterative reconstruction of any auxiliary image, and demonstrate
their practical utility in visual object tracking. Specifically, we design
measurement matrices which are tailored to facilitate extremely fast estimation
of the integral image, by using a single-shot linear operation on the measured
vector. Leveraging a prior model for the images, we formulate a nuclear norm
minimization problem with second order conic constraints to jointly obtain the
measurement matrix and the linear operator. Through qualitative and
quantitative experiments, we show that high quality integral image estimates
can be obtained using our framework at very low measurement rates. Further, on
a standard dataset of 50 videos, we present object tracking results which are
comparable to the state-of-the-art methods, even at an extremely low
measurement rate of 1%.
How do you revise your belief set with %$;@*?
Authors: Ryuta Arisaka
Date: 2015-04-21 00:00:00

In the classic AGM belief revision theory, beliefs are static and do not
change their own shape. For instance, if p is accepted by a rational agent, it
will remain p to the agent. But such rarely happens to us. Often, when we
accept some information p, what is actually accepted is not the whole p, but
only a portion of it; not necessarily because we select the portion but because
p must be perceived. Only the perceived p is accepted; and the perception is
subject to what we already believe (know). What may, however, happen to the
rest of p that initially escaped our attention? In this work we argue that the
invisible part is also accepted to the agent, if only unconsciously. Hence some
parts of p are accepted as visible, while some other parts as latent, beliefs.
The division is not static. As the set of beliefs changes, what were hidden may
become visible. We present a perception-based belief theory that incorporates
latent beliefs.
Font Identification in Historical Documents Using Active Learning
Authors: Anshul Gupta, Ricardo Gutierrez-Osuna, Matthew Christy, Richard Furuta, Laura Mandell
Date: 2016-01-27 00:00:00
Tasks: Active Learning, Diversity, Optical Character Recognition, Optical Character Recognition (OCR)

Identifying the type of font (e.g., Roman, Blackletter) used in historical
documents can help optical character recognition (OCR) systems produce more
accurate text transcriptions. Towards this end, we present an active-learning
strategy that can significantly reduce the number of labeled samples needed to
train a font classifier. Our approach extracts image-based features that
exploit geometric differences between fonts at the word level, and combines
them into a bag-of-word representation for each page in a document. We evaluate
six sampling strategies based on uncertainty, dissimilarity and diversity
criteria, and test them on a database containing over 3,000 historical
documents with Blackletter, Roman and Mixed fonts. Our results show that a
combination of uncertainty and diversity achieves the highest predictive
accuracy (89% of test cases correctly classified) while requiring only a small
fraction of the data (17%) to be labeled. We discuss the implications of this
result for mass digitization projects of historical documents.
GraphConnect: A Regularization Framework for Neural Networks
Authors: Jiaji Huang, Qiang Qiu, Robert Calderbank, Guillermo Sapiro
Date: 2015-12-21 00:00:00

Deep neural networks have proved very successful in domains where large
training sets are available, but when the number of training samples is small,
their performance suffers from overfitting. Prior methods of reducing
overfitting such as weight decay, Dropout and DropConnect are data-independent.
This paper proposes a new method, GraphConnect, that is data-dependent, and is
motivated by the observation that data of interest lie close to a manifold. The
new method encourages the relationships between the learned decisions to
resemble a graph representing the manifold structure. Essentially GraphConnect
is designed to learn attributes that are present in data samples in contrast to
weight decay, Dropout and DropConnect which are simply designed to make it more
difficult to fit to random error or noise. Empirical Rademacher complexity is
used to connect the generalization error of the neural network to spectral
properties of the graph learned from the input data. This framework is used to
show that GraphConnect is superior to weight decay. Experimental results on
several benchmark datasets validate the theoretical analysis, and show that
when the number of training samples is small, GraphConnect is able to
significantly improve performance over weight decay.
Latent Belief Theory and Belief Dependencies: A Solution to the Recovery Problem in the Belief Set Theories
Authors: Ryuta Arisaka
Date: 2015-07-06 00:00:00

The AGM recovery postulate says: assume a set of propositions X; assume that
it is consistent and that it is closed under logical consequences; remove a
belief P from the set minimally, but make sure that the resultant set is again
some set of propositions X' which is closed under the logical consequences; now
add P again and close the set under the logical consequences; and we should get
a set of propositions that contains all the propositions that were in X. This
postulate has since met objections; many have observed that it could bear
counter-intuitive results. Nevertheless, the attempts that have been made so
far to amend it either recovered the postulate in full, had to relinquish the
assumption of the logical closure altogether, or else had to introduce fresh
controversies of their own. We provide a solution to the recovery paradox in
this work. Our theoretical basis is the recently proposed belief theory with
latent beliefs (simply the latent belief theory for short). Firstly, through
examples, we will illustrate that the vanilla latent belief theory can be made
more expressive. We will identify that a latent belief, when it becomes
visible, may remain visible only while the beliefs that triggered it into the
agent's consciousness are in the agent's belief set. In order that such
situations can be also handled, we will enrich the latent belief theory with
belief dependencies among attributive beliefs, recording the information as to
which belief is supported of its existence by which beliefs. We will show that
the enriched latent belief theory does not possess the recovery property. The
closure by logical consequences is maintained in the theory, however. Hence it
serves as a solution to the open problem in the belief set theories.
Predicting Drug Interactions and Mutagenicity with Ensemble Classifiers on Subgraphs of Molecules
Authors: Andrew Schaumberg, Angela Yu, Tatsuhiro Koshi, Xiaochan Zong, Santoshkalyan Rayadhurgam
Date: 2016-01-27 00:00:00
Tasks: BIG-bench Machine Learning

In this study, we intend to solve a mutual information problem in interacting
molecules of any type, such as proteins, nucleic acids, and small molecules.
Using machine learning techniques, we accurately predict pairwise interactions,
which can be of medical and biological importance. Graphs are are useful in
this problem for their generality to all types of molecules, due to the
inherent association of atoms through atomic bonds. Subgraphs can represent
different molecular domains. These domains can be biologically significant as
most molecules only have portions that are of functional significance and can
interact with other domains. Thus, we use subgraphs as features in different
machine learning algorithms to predict if two drugs interact and predict
potential single molecule effects.
A network that learns Strassen multiplication
Authors: Veit Elser
Date: 2016-01-26 00:00:00

We study neural networks whose only non-linear components are multipliers, to
test a new training rule in a context where the precise representation of data
is paramount. These networks are challenged to discover the rules of matrix
multiplication, given many examples. By limiting the number of multipliers, the
network is forced to discover the Strassen multiplication rules. This is the
mathematical equivalent of finding low rank decompositions of the $n\times n$
matrix multiplication tensor, $M_n$. We train these networks with the
conservative learning rule, which makes minimal changes to the weights so as to
give the correct output for each input at the time the input-output pair is
received. Conservative learning needs a few thousand examples to find the rank
7 decomposition of $M_2$, and $10^5$ for the rank 23 decomposition of $M_3$
(the lowest known). High precision is critical, especially for $M_3$, to
discriminate between true decompositions and "border approximations".
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)
Authors: Yura N. Perov
Date: 2016-01-26 00:00:00
Tasks: Probabilistic Programming

This Bachelor's thesis, written in Russian, is devoted to a relatively new
direction in the field of machine learning and artificial intelligence, namely
probabilistic programming. The thesis gives a brief overview to the already
existing probabilistic programming languages: Church, Venture, and Anglican. It
also describes the results of the first experiments on the automatic induction
of probabilistic programs. The thesis was submitted, in June 2014, in partial
fulfilment of the requirements for the degree of Bachelor of Science in
Mathematics in the Department of Mathematics and Computer Science, Siberian
Federal University, Krasnoyarsk, Russia. The work, which is described in this
thesis, has been performing in 2012-2014 in the Massachusetts Institute of
Technology and in the University of Oxford by the colleagues of the author and
by himself.
Recurrent Neural Network Postfilters for Statistical Parametric Speech Synthesis
Authors: Prasanna Kumar Muthukumar, Alan W. black
Date: 2016-01-26 00:00:00
Tasks: General Classification, regression, Speech Synthesis

In the last two years, there have been numerous papers that have looked into
using Deep Neural Networks to replace the acoustic model in traditional
statistical parametric speech synthesis. However, far less attention has been
paid to approaches like DNN-based postfiltering where DNNs work in conjunction
with traditional acoustic models. In this paper, we investigate the use of
Recurrent Neural Networks as a potential postfilter for synthesis. We explore
the possibility of replacing existing postfilters, as well as highlight the
ease with which arbitrary new features can be added as input to the postfilter.
We also tried a novel approach of jointly training the Classification And
Regression Tree and the postfilter, rather than the traditional approach of
training them independently.
Sentence Directed Video Object Codetection
Authors: Haonan Yu, Jeffrey Mark Siskind
Date: 2015-06-05 00:00:00
Tasks: Activity Recognition, Object, Sentence

We tackle the problem of video object codetection by leveraging the weak
semantic constraint implied by sentences that describe the video content.
Unlike most existing work that focuses on codetecting large objects which are
usually salient both in size and appearance, we can codetect objects that are
small or medium sized. Our method assumes no human pose or depth information
such as is required by the most recent state-of-the-art method. We employ weak
semantic constraint on the codetection process by pairing the video with
sentences. Although the semantic information is usually simple and weak, it can
greatly boost the performance of our codetection framework by reducing the
search space of the hypothesized object detections. Our experiment demonstrates
an average IoU score of 0.423 on a new challenging dataset which contains 15
object classes and 150 videos with 12,509 frames in total, and an average IoU
score of 0.373 on a subset of an existing dataset, originally intended for
activity recognition, which contains 5 object classes and 75 videos with 8,854
frames in total.
Stacked Attention Networks for Image Question Answering
Authors: Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola
Date: 2015-11-07 00:00:00
Proceeding: CVPR 2016 6
Tasks: Visual Question Answering (VQA)

This paper presents stacked attention networks (SANs) that learn to answer
natural language questions from images. SANs use semantic representation of a
question as query to search for the regions in an image that are related to the
answer. We argue that image question answering (QA) often requires multiple
steps of reasoning. Thus, we develop a multiple-layer SAN in which we query an
image multiple times to infer the answer progressively. Experiments conducted
on four image QA data sets demonstrate that the proposed SANs significantly
outperform previous state-of-the-art approaches. The visualization of the
attention layers illustrates the progress that the SAN locates the relevant
visual clues that lead to the answer of the question layer-by-layer.
Globally Optimal Cell Tracking using Integer Programming
Authors: Engin Türetken, Xinchao Wang, Carlos Becker, Carsten Haubold, Pascal Fua
Date: 2015-01-22 00:00:00
Tasks: Cell Tracking

We propose a novel approach to automatically tracking cell populations in
time-lapse images. To account for cell occlusions and overlaps, we introduce a
robust method that generates an over-complete set of competing detection
hypotheses. We then perform detection and tracking simultaneously on these
hypotheses by solving to optimality an integer program with only one type of
flow variables. This eliminates the need for heuristics to handle missed
detections due to occlusions and complex morphology. We demonstrate the
effectiveness of our approach on a range of challenging sequences consisting of
clumped cells and show that it outperforms state-of-the-art techniques.