pwc-archive/papers-with-abstracts huggingface dataset:
Improving Pairwise Ranking for Multi-label Image Classification
Authors: Yuncheng Li, Yale Song, Jiebo Luo
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, General Classification, image-classification, Image Classification, Learning-To-Rank, Multi-Label Image Classification

Learning to rank has recently emerged as an attractive technique to train
deep convolutional neural networks for various computer vision tasks. Pairwise
ranking, in particular, has been successful in multi-label image
classification, achieving state-of-the-art results on various benchmarks.
However, most existing approaches use the hinge loss to train their models,
which is non-smooth and thus is difficult to optimize especially with deep
networks. Furthermore, they employ simple heuristics, such as top-k or
thresholding, to determine which labels to include in the output from a ranked
list of labels, which limits their use in the real-world setting. In this work,
we propose two techniques to improve pairwise ranking based multi-label image
classification: (1) we propose a novel loss function for pairwise ranking,
which is smooth everywhere and thus is easier to optimize; and (2) we
incorporate a label decision module into the model, estimating the optimal
confidence thresholds for each visual concept. We provide theoretical analyses
of our loss function in the Bayes consistency and risk minimization framework,
and show its benefit over existing pairwise ranking formulations. We
demonstrate the effectiveness of our approach on three large-scale datasets,
VOC2007, NUS-WIDE and MS-COCO, achieving the best reported results in the
literature.
Shape and Positional Geometry of Multi-Object Configurations
Authors: James Damon, Ellen Gasparovic
Date: 2017-06-01 00:00:00
Tasks: Object

In previous work, we introduced a method for modeling a configuration of
objects in 2D and 3D images using a mathematical "medial/skeletal linking
structure." In this paper, we show how these structures allow us to capture
positional properties of a multi-object configuration in addition to the shape
properties of the individual objects. In particular, we introduce numerical
invariants for positional properties which measure the closeness of neighboring
objects, including identifying the parts of the objects which are close, and
the "relative significance" of objects compared with the other objects in the
configuration. Using these numerical measures, we introduce a hierarchical
ordering and relations between the individual objects, and quantitative
criteria for identifying subconfigurations. In addition, the invariants provide
a "proximity matrix" which yields a unique set of weightings measuring overall
proximity of objects in the configuration. Furthermore, we show that these
invariants, which are volumetrically defined and involve external regions, may
be computed via integral formulas in terms of "skeletal linking integrals"
defined on the internal skeletal structures of the objects.
Faster Spatially Regularized Correlation Filters for Visual Tracking
Authors: Xiaoxiang Hu, Yujiu Yang
Date: 2017-06-01 00:00:00
Tasks: Visual Tracking

Discriminatively learned correlation filters (DCF) have been widely used in
online visual tracking filed due to its simplicity and efficiency. These
methods utilize a periodic assumption of the training samples to construct a
circulant data matrix, which implicitly increases the training samples and
reduces both storage and computational complexity.The periodic assumption also
introduces unwanted boundary effects. Recently, Spatially Regularized
Correlation Filters (SRDCF) solved this issue by introducing penalization on
correlation filter coefficients depending on their spatial location. However,
SRDCF's efficiency dramatically decreased due to the breaking of circulant
structure.
  We propose Faster Spatially Regularized Discriminative Correlation Filters
(FSRDCF) for tracking. The FSRDCF is constructed from Ridge Regression, the
circulant structure of training samples in the spatial domain is fully used,
more importantly, we further exploit the circulant structure of regularization
function in the Fourier domain, which allows our problem to be solved more
directly and efficiently. Experiments are conducted on three benchmark
datasets: OTB-2013, OTB-2015 and VOT2016. Our approach achieves equivalent
performance to the baseline tracker SRDCF on all three datasets. On OTB-2013
and OTB-2015 datasets, our approach obtains a more than twice faster running
speed and a more than third times shorter start-up time than the SRDCF. For
state-of-the-art comparison, our approach demonstrates superior performance
compared to other non-spatial-regularization trackers.
Diversified Top-k Partial MaxSAT Solving
Authors: Junping Zhou, Huanyao Sun, Feifei Ma, Jian Gao, Ke Xu, Minghao Yin
Date: 2017-05-31 00:00:00
Tasks: Community Detection

We introduce a diversified top-k partial MaxSAT problem, a combination of
partial MaxSAT problem and enumeration problem. Given a partial MaxSAT formula
F and a positive integer k, the diversified top-k partial MaxSAT is to find k
maximal solutions for F such that the k maximal solutions satisfy the maximum
number of soft clauses of F. This problem can be widely used in many
applications including community detection, sensor place, motif discovery, and
combinatorial testing. We prove the problem is NP-hard and propose an approach
for solving the problem. The concrete idea of the approach is to design an
encoding EE which reduces diversified top-k partial MaxSAT problem into partial
MaxSAT problem, and then solve the resulting problem with state-of-art solvers.
In addition, we present an algorithm MEMKC exactly solving the diversified
top-k partial MaxSAT. Through several experiments we show that our approach can
be successfully applied to the interesting problem.
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
Authors: Kisuk Lee, Jonathan Zung, Peter Li, Viren Jain, H. Sebastian Seung
Date: 2017-05-31 00:00:00
Tasks: 3D Reconstruction, Electron Microscopy Image Segmentation

For the past decade, convolutional networks have been used for 3D
reconstruction of neurons from electron microscopic (EM) brain images. Recent
years have seen great improvements in accuracy, as evidenced by submissions to
the SNEMI3D benchmark challenge. Here we report the first submission to surpass
the estimate of human accuracy provided by the SNEMI3D leaderboard. A variant
of 3D U-Net is trained on a primary task of predicting affinities between
nearest neighbor voxels, and an auxiliary task of predicting long-range
affinities. The training data is augmented by simulated image defects. The
nearest neighbor affinities are used to create an oversegmentation, and then
supervoxels are greedily agglomerated based on mean affinity. The resulting
SNEMI3D score exceeds the estimate of human accuracy by a large margin. While
one should be cautious about extrapolating from the SNEMI3D benchmark to
real-world accuracy of large-scale neural circuit reconstruction, our result
inspires optimism that the goal of full automation may be realizable in the
future.
Bayesian fairness
Authors: Christos Dimitrakakis, Yang Liu, David Parkes, Goran Radanovic
Date: 2017-05-31 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness

We consider the problem of how decision making can be fair when the
underlying probabilistic model of the world is not known with certainty. We
argue that recent notions of fairness in machine learning need to explicitly
incorporate parameter uncertainty, hence we introduce the notion of {\em
Bayesian fairness} as a suitable candidate for fair decision rules. Using
balance, a definition of fairness introduced by Kleinberg et al (2016), we show
how a Bayesian perspective can lead to well-performing, fair decision rules
even under high uncertainty.
The in-town monitoring system for ambulance dispatch centre
Authors: Bartlomiej Placzek, Jolnta Golosz
Date: 2017-05-31 00:00:00
Tasks: Management

The paper presents the vehicles integrated monitoring system giving
priorities for emergency vehicles. The described system exploits the data
gathered by: geographical positioning systems and geographical information
systems. The digital maps and roadside cameras provide the dispatchers with
aims for in town ambulances traffic management. The method of vehicles
positioning in the city network and algorithms for ambulances recognition by
image processing techniques have been discussed in the paper. These priorities
are needed for an efficient life-saving actions that require the real-time
controlling strategies.
Multi-View Task-Driven Recognition in Visual Sensor Networks
Authors: Ali Taalimi, Alireza Rahimpour, Liu Liu, Hairong Qi
Date: 2017-05-30 00:00:00
Tasks: Image Retrieval, Multi-Task Learning, Object Recognition, Representation Learning, Retrieval, Sensor Fusion

Nowadays, distributed smart cameras are deployed for a wide set of tasks in
several application scenarios, ranging from object recognition, image
retrieval, and forensic applications. Due to limited bandwidth in distributed
systems, efficient coding of local visual features has in fact been an active
topic of research. In this paper, we propose a novel approach to obtain a
compact representation of high-dimensional visual data using sensor fusion
techniques. We convert the problem of visual analysis in resource-limited
scenarios to a multi-view representation learning, and we show that the key to
finding properly compressed representation is to exploit the position of
cameras with respect to each other as a norm-based regularization in the
particular signal representation of sparse coding. Learning the representation
of each camera is viewed as an individual task and a multi-task learning with
joint sparsity for all nodes is employed. The proposed representation learning
scheme is referred to as the multi-view task-driven learning for visual sensor
network (MT-VSN). We demonstrate that MT-VSN outperforms state-of-the-art in
various surveillance recognition tasks.
Bayesian $l_0$-regularized Least Squares
Authors: Nicholas G. Polson, Lei Sun
Date: 2017-05-31 00:00:00
Tasks: Computational Efficiency, Variable Selection

Bayesian $l_0$-regularized least squares is a variable selection technique
for high dimensional predictors. The challenge is optimizing a non-convex
objective function via search over model space consisting of all possible
predictor combinations. Spike-and-slab (a.k.a. Bernoulli-Gaussian) priors are
the gold standard for Bayesian variable selection, with a caveat of
computational speed and scalability. Single Best Replacement (SBR) provides a
fast scalable alternative. We provide a link between Bayesian regularization
and proximal updating, which provides an equivalence between finding a
posterior mode and a posterior mean with a different regularization prior. This
allows us to use SBR to find the spike-and-slab estimator. To illustrate our
methodology, we provide simulation evidence and a real data example on the
statistical properties and computational efficiency of SBR versus direct
posterior sampling using spike-and-slab priors. Finally, we conclude with
directions for future research.
Blood capillaries and vessels segmentation in optical coherence tomography angiogram using fuzzy C-means and Curvelet transform
Authors: Fariborz Taherkhani
Date: 2017-05-31 00:00:00

This paper has been removed from arXiv as the submitter did not have
ownership of the data presented in this work.
Megapixel Size Image Creation using Generative Adversarial Networks
Authors: Marco Marchesi
Date: 2017-05-31 00:00:00
Tasks: Image Generation

Since its appearance, Generative Adversarial Networks (GANs) have received a
lot of interest in the AI community. In image generation several projects
showed how GANs are able to generate photorealistic images but the results so
far did not look adequate for the quality standard of visual media production
industry. We present an optimized image generation process based on a Deep
Convolutional Generative Adversarial Networks (DCGANs), in order to create
photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore,
the system was fed with a limited dataset of images, less than two thousand
images. All these results give more clue about future exploitation of GANs in
Computer Graphics and Visual Effects.
Putting a Face to the Voice: Fusing Audio and Visual Signals Across a Video to Determine Speakers
Authors: Ken Hoover, Sourish Chaudhuri, Caroline Pantofaru, Malcolm Slaney, Ian Sturdy
Date: 2017-05-31 00:00:00

In this paper, we present a system that associates faces with voices in a
video by fusing information from the audio and visual signals. The thesis
underlying our work is that an extremely simple approach to generating (weak)
speech clusters can be combined with visual signals to effectively associate
faces and voices by aggregating statistics across a video. This approach does
not need any training data specific to this task and leverages the natural
coherence of information in the audio and visual streams. It is particularly
applicable to tracking speakers in videos on the web where a priori information
about the environment (e.g., number of speakers, spatial signals for
beamforming) is not available. We performed experiments on a real-world dataset
using this analysis framework to determine the speaker in a video. Given a
ground truth labeling determined by human rater consensus, our approach had
~71% accuracy.
Low-Rank Matrix Approximation in the Infinity Norm
Authors: Nicolas Gillis, Yaroslav Shitov
Date: 2017-05-31 00:00:00

The low-rank matrix approximation problem with respect to the entry-wise
$\ell_{\infty}$-norm is the following: given a matrix $M$ and a factorization
rank $r$, find a matrix $X$ whose rank is at most $r$ and that minimizes
$\max_{i,j} |M_{ij} - X_{ij}|$. In this paper, we prove that the decision
variant of this problem for $r=1$ is NP-complete using a reduction from the
problem `not all equal 3SAT'. We also analyze several cases when the problem
can be solved in polynomial time, and propose a simple practical heuristic
algorithm which we apply on the problem of the recovery of a quantized low-rank
matrix.
Descriptions of Objectives and Processes of Mechanical Learning
Authors: Chuyu Xiong
Date: 2017-05-31 00:00:00
Tasks: Form

In [1], we introduced mechanical learning and proposed 2 approaches to
mechanical learning. Here, we follow one such approach to well describe the
objects and the processes of learning. We discuss 2 kinds of patterns:
objective and subjective pattern. Subjective pattern is crucial for learning
machine. We prove that for any objective pattern we can find a proper
subjective pattern based upon least base patterns to express the objective
pattern well. X-form is algebraic expression for subjective pattern. Collection
of X-forms form internal representation space, which is center of learning
machine. We discuss learning by teaching and without teaching. We define data
sufficiency by X-form. We then discussed some learning strategies. We show, in
each strategy, with sufficient data, and with certain capabilities, learning
machine indeed can learn any pattern (universal learning machine). In appendix,
with knowledge of learning machine, we try to view deep learning from a
different angle, i.e. its internal representation space and its learning
dynamics.
The Singularity May Be Near
Authors: Roman V. Yampolskiy
Date: 2017-05-31 00:00:00

Toby Walsh in 'The Singularity May Never Be Near' gives six arguments to
support his point of view that technological singularity may happen but that it
is unlikely. In this paper, we provide analysis of each one of his arguments
and arrive at similar conclusions, but with more weight given to the 'likely to
happen' probability.
Social Media-based Substance Use Prediction
Authors: Tao Ding, Warren K. Bickel, SHimei Pan
Date: 2017-05-16 00:00:00
Tasks: Prediction

In this paper, we demonstrate how the state-of-the-art machine learning and
text mining techniques can be used to build effective social media-based
substance use detection systems. Since a substance use ground truth is
difficult to obtain on a large scale, to maximize system performance, we
explore different feature learning methods to take advantage of a large amount
of unsupervised social media data. We also demonstrate the benefit of using
multi-view unsupervised feature learning to combine heterogeneous user
information such as Facebook `"likes" and "status updates" to enhance system
performance. Based on our evaluation, our best models achieved 86% AUC for
predicting tobacco use, 81% for alcohol use and 84% for drug use, all of which
significantly outperformed existing methods. Our investigation has also
uncovered interesting relations between a user's social media behavior (e.g.,
word usage) and substance use.
The Sample Complexity of Online One-Class Collaborative Filtering
Authors: Reinhard Heckel, Kannan Ramchandran
Date: 2017-05-31 00:00:00
Proceeding: ICML 2017 8
Tasks: Collaborative Filtering, Recommendation Systems

We consider the online one-class collaborative filtering (CF) problem that
consists of recommending items to users over time in an online fashion based on
positive ratings only. This problem arises when users respond only occasionally
to a recommendation with a positive rating, and never with a negative one. We
study the impact of the probability of a user responding to a recommendation,
p_f, on the sample complexity, i.e., the number of ratings required to make
`good' recommendations, and ask whether receiving positive and negative
ratings, instead of positive ratings only, improves the sample complexity. Both
questions arise in the design of recommender systems. We introduce a simple
probabilistic user model, and analyze the performance of an online user-based
CF algorithm. We prove that after an initial cold start phase, where
recommendations are invested in exploring the user's preferences, this
algorithm makes---up to a fraction of the recommendations required for updating
the user's preferences---perfect recommendations. The number of ratings
required for the cold start phase is nearly proportional to 1/p_f, and that for
updating the user's preferences is essentially independent of p_f. As a
consequence we find that, receiving positive and negative ratings instead of
only positive ones improves the number of ratings required for initial
exploration by a factor of 1/p_f, which can be significant.
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Authors: Morteza Mardani, Enhao Gong, Joseph Y. Cheng, Shreyas Vasanawala, Greg Zaharchuk, Marcus Alley, Neil Thakur, Song Han, William Dally, John M. Pauly, Lei Xing
Date: 2017-05-31 00:00:00
Tasks: compressed sensing, Diagnostic, MRI Reconstruction

Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear
inverse task demanding time and resource intensive computations that can
substantially trade off {\it accuracy} for {\it speed} in real-time imaging. In
addition, state-of-the-art compressed sensing (CS) analytics are not cognizant
of the image {\it diagnostic quality}. To cope with these challenges we put
forth a novel CS framework that permeates benefits from generative adversarial
networks (GAN) to train a (low-dimensional) manifold of diagnostic-quality MR
images from historical patients. Leveraging a mixture of least-squares (LS)
GANs and pixel-wise $\ell_1$ cost, a deep residual network with skip
connections is trained as the generator that learns to remove the {\it
aliasing} artifacts by projecting onto the manifold. LSGAN learns the texture
details, while $\ell_1$ controls the high-frequency noise. A multilayer
convolutional neural network is then jointly trained based on diagnostic
quality images to discriminate the projection quality. The test phase performs
feed-forward propagation over the generator network that demands a very low
computational overhead. Extensive evaluations are performed on a large
contrast-enhanced MR dataset of pediatric patients. In particular, images rated
based on expert radiologists corroborate that GANCS retrieves high contrast
images with detailed texture relative to conventional CS, and pixel-wise
schemes. In addition, it offers reconstruction under a few milliseconds, two
orders of magnitude faster than state-of-the-art CS-MRI schemes.
BEGAN: Boundary Equilibrium Generative Adversarial Networks
Authors: David Berthelot, Thomas Schumm, Luke Metz
Date: 2017-03-31 00:00:00
Tasks: Diversity, Image Generation

We propose a new equilibrium enforcing method paired with a loss derived from
the Wasserstein distance for training auto-encoder based Generative Adversarial
Networks. This method balances the generator and discriminator during training.
Additionally, it provides a new approximate convergence measure, fast and
stable training and high visual quality. We also derive a way of controlling
the trade-off between image diversity and visual quality. We focus on the image
generation task, setting a new milestone in visual quality, even at higher
resolutions. This is achieved while using a relatively simple model
architecture and a standard training procedure.
Reflection Invariant and Symmetry Detection
Authors: Erbo Li, Hua Li
Date: 2017-05-30 00:00:00
Tasks: Object Recognition, Retrieval, Symmetry Detection

Symmetry detection and discrimination are of fundamental meaning in science,
technology, and engineering. This paper introduces reflection invariants and
defines the directional moment to detect symmetry for shape analysis and object
recognition. And it demonstrates that detection of reflection symmetry can be
done in a simple way by solving a trigonometric system derived from the
directional moment, and discrimination of reflection symmetry can be achieved
by application of the reflection invariants in 2D and 3D. Rotation symmetry can
also be determined based on that.The experiments in 2D and 3D, including the
regular triangle, the square, and the five Platonic objects, show that all the
reflection lines or planes can be deterministically found using directional
moments up to order six. This result can be used to simplify the efforts of
symmetry detection in research areas, such as protein structure, model
retrieval, inverse engineering, and machine vision etc.
Word forms - not just their lengths- are optimized for efficient communication
Authors: Stephan C. Meylan, Thomas L. Griffiths
Date: 2017-03-06 00:00:00

The inverse relationship between the length of a word and the frequency of
its use, first identified by G.K. Zipf in 1935, is a classic empirical law that
holds across a wide range of human languages. We demonstrate that length is one
aspect of a much more general property of words: how distinctive they are with
respect to other words in a language. Distinctiveness plays a critical role in
recognizing words in fluent speech, in that it reflects the strength of
potential competitors when selecting the best candidate for an ambiguous
signal. Phonological information content, a measure of a word's string
probability under a statistical model of a language's sound or character
sequences, concisely captures distinctiveness. Examining large-scale corpora
from 13 languages, we find that distinctiveness significantly outperforms word
length as a predictor of frequency. This finding provides evidence that
listeners' processing constraints shape fine-grained aspects of word forms
across languages.