pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of Object Trackers in Distorted Surveillance Videos
Authors: Roger Gomez Nieto, H. D. Benitez-Restrepo, Ivan Mauricio Cabezas
Date: 2018-04-04 00:00:00
Tasks: Object, Object Tracking, Open-Ended Question Answering

Object tracking in realistic scenarios is a difficult problem affected by
various image factors such as occlusion, clutter, confusion, object shape,
unstable speed, and zooming. While these conditions do affect tracking
performance, there is no clear distinction between the scene dependent
challenges like occlusion, clutter, etc., and the challenges imposed by
traditional notions of impairments from capture, compression, processing, and
transmission. This paper is concerned with the latter interpretation of quality
as it affects video tracking performance. In this work we aim to evaluate two
state-of-the-art trackers (STRUCK and TLD) systematically and experimentally in
surveillance videos affected by in-capture distortions such as under-exposure
and defocus. We evaluate these trackers with the area under curve (AUC) values
of success plots and precision curves. In spite of the fact that STRUCK and TLD
have ranked high in video tracking surveys. This study concludes that incapture
distortions severely affect the performance of these trackers. For this reason,
the design and construction of a robust tracker with respect to these
distortions remains an open question that can be answered by creating
algorithms that makes use of perceptual features to compensate the degradations
provided by these distortions.
Non-line-of-sight Imaging with Partial Occluders and Surface Normals
Authors: Felix Heide, Matthew O'Toole, Kai Zang, David Lindell, Steven Diamond, Gordon Wetzstein
Date: 2017-11-20 00:00:00
Tasks: Autonomous Vehicles

Imaging objects obscured by occluders is a significant challenge for many
applications. A camera that could "see around corners" could help improve
navigation and mapping capabilities of autonomous vehicles or make search and
rescue missions more effective. Time-resolved single-photon imaging systems
have recently been demonstrated to record optical information of a scene that
can lead to an estimation of the shape and reflectance of objects hidden from
the line of sight of a camera. However, existing non-line-of-sight (NLOS)
reconstruction algorithms have been constrained in the types of light transport
effects they model for the hidden scene parts. We introduce a factored NLOS
light transport representation that accounts for partial occlusions and surface
normals. Based on this model, we develop a factorization approach for inverse
time-resolved light transport and demonstrate high-fidelity NLOS
reconstructions for challenging scenes both in simulation and with an
experimental NLOS imaging system.
Image Generation from Scene Graphs
Authors: Justin Johnson, Agrim Gupta, Li Fei-Fei
Date: 2018-04-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Generation, Image Generation from Scene Graphs, Layout-to-Image Generation

To truly understand the visual world our models should be able not only to
recognize images but also generate them. To this end, there has been exciting
recent progress on generating images from natural language descriptions. These
methods give stunning results on limited domains such as descriptions of birds
or flowers, but struggle to faithfully reproduce complex sentences with many
objects and relationships. To overcome this limitation we propose a method for
generating images from scene graphs, enabling explicitly reasoning about
objects and their relationships. Our model uses graph convolution to process
input graphs, computes a scene layout by predicting bounding boxes and
segmentation masks for objects, and converts the layout to an image with a
cascaded refinement network. The network is trained adversarially against a
pair of discriminators to ensure realistic outputs. We validate our approach on
Visual Genome and COCO-Stuff, where qualitative results, ablations, and user
studies demonstrate our method's ability to generate complex images with
multiple objects.
Active covariance estimation by random sub-sampling of variables
Authors: Eduardo Pavez, Antonio Ortega
Date: 2018-04-04 00:00:00
Tasks: Active Learning

We study covariance matrix estimation for the case of partially observed
random vectors, where different samples contain different subsets of vector
coordinates. Each observation is the product of the variable of interest with a
$0-1$ Bernoulli random variable. We analyze an unbiased covariance estimator
under this model, and derive an error bound that reveals relations between the
sub-sampling probabilities and the entries of the covariance matrix. We apply
our analysis in an active learning framework, where the expected number of
observed variables is small compared to the dimension of the vector of
interest, and propose a design of optimal sub-sampling probabilities and an
active covariance matrix estimation algorithm.
Stability and Convergence Trade-off of Iterative Optimization Algorithms
Authors: Yuansi Chen, Chi Jin, Bin Yu
Date: 2018-04-04 00:00:00

The overall performance or expected excess risk of an iterative machine
learning algorithm can be decomposed into training error and generalization
error. While the former is controlled by its convergence analysis, the latter
can be tightly handled by algorithmic stability. The machine learning community
has a rich history investigating convergence and stability separately. However,
the question about the trade-off between these two quantities remains open. In
this paper, we show that for any iterative algorithm at any iteration, the
overall performance is lower bounded by the minimax statistical error over an
appropriately chosen loss function class. This implies an important trade-off
between convergence and stability of the algorithm -- a faster converging
algorithm has to be less stable, and vice versa. As a direct consequence of
this fundamental tradeoff, new convergence lower bounds can be derived for
classes of algorithms constrained with different stability bounds. In
particular, when the loss function is convex (or strongly convex) and smooth,
we discuss the stability upper bounds of gradient descent (GD) and stochastic
gradient descent and their variants with decreasing step sizes. For Nesterov's
accelerated gradient descent (NAG) and heavy ball method (HB), we provide
stability upper bounds for the quadratic loss function. Applying existing
stability upper bounds for the gradient methods in our trade-off framework, we
obtain lower bounds matching the well-established convergence upper bounds up
to constants for these algorithms and conjecture similar lower bounds for NAG
and HB. Finally, we numerically demonstrate the tightness of our stability
bounds in terms of exponents in the rate and also illustrate via a simulated
logistic regression problem that our stability bounds reflect the
generalization errors better than the simple uniform convergence bounds for GD
and NAG.
Specifying and Computing Causes for Query Answers in Databases via Database Repairs and Repair Programs
Authors: Leopoldo Bertossi
Date: 2017-12-04 00:00:00
Tasks: Attribute

A correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints has already been established. In this work, answer-set programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes. Here, causes are also introduced at the attribute level by appealing to a both null-based and attribute-based repair semantics. The corresponding repair programs are presented, and they are used as a basis for computation and reasoning about attribute-level causes. They are extended to deal with the case of causality under integrity constraints.
Learnable Exposure Fusion for Dynamic Scenes
Authors: Fahd Bouzaraa, Ibrahim Halfaoui, Onay Urfalioglu
Date: 2018-04-04 00:00:00
Tasks: Image Registration

In this paper, we focus on Exposure Fusion (EF) [ExposFusi2] for dynamic
scenes. The task is to fuse multiple images obtained by exposure bracketing to
create an image which comprises a high level of details. Typically, such images
are not possible to obtain directly from a camera due to hardware limitations,
e.g., a limited dynamic range of the sensor. A major problem of such tasks is
that the images may not be spatially aligned due to scene motion or camera
motion. It is known that the required alignment by image registration problems
is ill-posed. In this case, the images to be aligned vary in their intensity
range, which makes the problem even more difficult.
  To address the mentioned problems, we propose an end-to-end
\emph{Convolutional Neural Network} (CNN) based approach to learn to estimate
exposure fusion from $2$ and $3$ Low Dynamic Range (LDR) images depicting
different scene contents. To the best of our knowledge, no efficient and robust
CNN-based end-to-end approach can be found in the literature for this kind of
problem. The idea is to create a dataset with perfectly aligned LDR images to
obtain ground-truth exposure fusion images. At the same time, we obtain
additional LDR images with some motion, having the same exposure fusion
ground-truth as the perfectly aligned LDR images. This way, we can train an
end-to-end CNN having misaligned LDR input images, but with a proper ground
truth exposure fusion image. We propose a specific CNN-architecture to solve
this problem. In various experiments, we show that the proposed approach yields
excellent results.
Nonlinear Information Bottleneck
Authors: Artemy Kolchinsky, Brendan D. Tracey, David H. Wolpert
Date: 2017-05-06 00:00:00

Information bottleneck (IB) is a technique for extracting information in one random variable $X$ that is relevant for predicting another random variable $Y$. IB works by encoding $X$ in a compressed "bottleneck" random variable $M$ from which $Y$ can be accurately decoded. However, finding the optimal bottleneck variable involves a difficult optimization problem, which until recently has been considered for only two limited cases: discrete $X$ and $Y$ with small state spaces, and continuous $X$ and $Y$ with a Gaussian joint distribution (in which case optimal encoding and decoding maps are linear). We propose a method for performing IB on arbitrarily-distributed discrete and/or continuous $X$ and $Y$, while allowing for nonlinear encoding and decoding maps. Our approach relies on a novel non-parametric upper bound for mutual information. We describe how to implement our method using neural networks. We then show that it achieves better performance than the recently-proposed "variational IB" method on several real-world datasets.
StainGAN: Stain Style Transfer for Digital Histological Images
Authors: M Tarek Shaban, Christoph Baur, Nassir Navab, Shadi Albarqouni
Date: 2018-04-04 00:00:00
Tasks: General Classification, SSIM, Style Transfer

Digitized Histological diagnosis is in increasing demand. However, color
variations due to various factors are imposing obstacles to the diagnosis
process. The problem of stain color variations is a well-defined problem with
many proposed solutions. Most of these solutions are highly dependent on a
reference template slide. We propose a deep-learning solution inspired by
CycleGANs that is trained end-to-end, eliminating the need for an expert to
pick a representative reference slide. Our approach showed superior results
quantitatively and qualitatively against the state of the art methods (10%
improvement visually using SSIM). We further validated our method on a clinical
use-case, namely Breast Cancer tumor classification, showing 12% increase in
AUC. The code will be made publicly available.
The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching
Authors: Qian Yu, Xiaobin Chang, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales
Date: 2017-11-22 00:00:00
Tasks: Image Retrieval, Person Re-Identification, Retrieval, Sketch-Based Image Retrieval

Many vision problems require matching images of object instances across
different domains. These include fine-grained sketch-based image retrieval
(FG-SBIR) and Person Re-identification (person ReID). Existing approaches
attempt to learn a joint embedding space where images from different domains
can be directly compared. In most cases, this space is defined by the output of
the final layer of a deep neural network (DNN), which primarily contains
features of a high semantic level. In this paper, we argue that both high and
mid-level features are relevant for cross-domain instance matching (CDIM).
Importantly, mid-level features already exist in earlier layers of the DNN.
They just need to be extracted, represented, and fused properly with the final
layer. Based on this simple but powerful idea, we propose a unified framework
for CDIM. Instantiating our framework for FG-SBIR and ReID, we show that our
simple models can easily beat the state-of-the-art models, which are often
equipped with much more elaborate architectures.
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
Authors: Filippo Maria Bianchi, Lorenzo Livi, Alberto Ferrante, Jelena Milosevic, Miroslaw Malek
Date: 2018-01-21 00:00:00
Tasks: Classification, Electrocardiography (ECG), General Classification, Time Series, Time Series Analysis

We tackle the problem of classifying Electrocardiography (ECG) signals with
the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial
fibrillation is the most common type of arrhythmia, but in many cases PAF
episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is
important to design procedures for detecting and, more importantly, predicting
PAF episodes. We propose a method for predicting PAF events whose first step
consists of a feature extraction procedure that represents each ECG as a
multi-variate time series. Successively, we design a classification framework
based on kernel similarities for multi-variate time series, capable of handling
missing data. We consider different approaches to perform classification in the
original space of the multi-variate time series and in an embedding space,
defined by the kernel similarity measure. We achieve a classification accuracy
comparable with state of the art methods, with the additional advantage of
detecting the PAF onset up to 15 minutes in advance.
Robust and Resource Efficient Identification of Shallow Neural Networks by Fewest Samples
Authors: Massimo Fornasier, Jan Vybíral, Ingrid Daubechies
Date: 2018-04-04 00:00:00

We address the structure identification and the uniform approximation of sums of ridge functions $f(x)=\sum_{i=1}^m g_i(a_i\cdot x)$ on ${\mathbb R}^d$, representing a general form of a shallow feed-forward neural network, from a small number of query samples. Higher order differentiation, as used in our constructive approximations, of sums of ridge functions or of their compositions, as in deeper neural network, yields a natural connection between neural network weight identification and tensor product decomposition identification. In the case of the shallowest feed-forward neural network, second order differentiation and tensors of order two (i.e., matrices) suffice as we prove in this paper. We use two sampling schemes to perform approximate differentiation - active sampling, where the sampling points are universal, actively, and randomly designed, and passive sampling, where sampling points were preselected at random from a distribution with known density. Based on multiple gathered approximated first and second order differentials, our general approximation strategy is developed as a sequence of algorithms to perform individual sub-tasks. We first perform an active subspace search by approximating the span of the weight vectors $a_1,\dots,a_m$. Then we use a straightforward substitution, which reduces the dimensionality of the problem from $d$ to $m$. The core of the construction is then the stable and efficient approximation of weights expressed in terms of rank-$1$ matrices $a_i \otimes a_i$, realized by formulating their individual identification as a suitable nonlinear program. We prove the successful identification by this program of weight vectors being close to orthonormal and we also show how we can costructively reduce to this case by a whitening procedure, without loss of any generality.
On First-Order Meta-Learning Algorithms
Authors: Alex Nichol, Joshua Achiam, John Schulman
Date: 2018-03-08 00:00:00
Tasks: Few-Shot Image Classification, Few-Shot Learning, Meta-Learning

This paper considers meta-learning problems, where there is a distribution of
tasks, and we would like to obtain an agent that performs well (i.e., learns
quickly) when presented with a previously unseen task sampled from this
distribution. We analyze a family of algorithms for learning a parameter
initialization that can be fine-tuned quickly on a new task, using only
first-order derivatives for the meta-learning updates. This family includes and
generalizes first-order MAML, an approximation to MAML obtained by ignoring
second-order derivatives. It also includes Reptile, a new algorithm that we
introduce here, which works by repeatedly sampling a task, training on it, and
moving the initialization towards the trained weights on that task. We expand
on the results from Finn et al. showing that first-order meta-learning
algorithms perform well on some well-established benchmarks for few-shot
classification, and we provide theoretical analysis aimed at understanding why
these algorithms work.
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation
Authors: Ramji Venkataramanan, Oliver Johnson
Date: 2017-06-14 00:00:00
Tasks: Active Learning, compressed sensing, Density Estimation, Two-sample testing

In statistical inference problems, we wish to obtain lower bounds on the
minimax risk, that is to bound the performance of any possible estimator. A
standard technique to obtain risk lower bounds involves the use of Fano's
inequality. In an information-theoretic setting, it is known that Fano's
inequality typically does not give a sharp converse result (error lower bound)
for channel coding problems. Moreover, recent work has shown that an argument
based on binary hypothesis testing gives tighter results. We adapt this
technique to the statistical setting, and argue that Fano's inequality can
always be replaced by this approach to obtain tighter lower bounds that can be
easily computed and are asymptotically sharp. We illustrate our technique in
three applications: density estimation, active learning of a binary classifier,
and compressed sensing, obtaining tighter risk lower bounds in each case.
Semi-Supervised Deep Metrics for Image Registration
Authors: Alireza Sedghi, Jie Luo, Alireza Mehrtash, Steve Pieper, Clare M. Tempany, Tina Kapur, Parvin Mousavi, William M. Wells III
Date: 2018-04-04 00:00:00
Tasks: Data Augmentation, Image Registration

Deep metrics have been shown effective as similarity measures in multi-modal
image registration; however, the metrics are currently constructed from aligned
image pairs in the training data. In this paper, we propose a strategy for
learning such metrics from roughly aligned training data. Symmetrizing the data
corrects bias in the metric that results from misalignment in the data (at the
expense of increased variance), while random perturbations to the data, i.e.
dithering, ensures that the metric has a single mode, and is amenable to
registration by optimization. Evaluation is performed on the task of
registration on separate unseen test image pairs. The results demonstrate the
feasibility of learning a useful deep metric from substantially misaligned
training data, in some cases the results are significantly better than from
Mutual Information. Data augmentation via dithering is, therefore, an effective
strategy for discharging the need for well-aligned training data; this brings
deep metric registration from the realm of supervised to semi-supervised
machine learning.
Learning a Discriminative Prior for Blind Image Deblurring
Authors: Lerenhan Li, Jinshan Pan, Wei-Sheng Lai, Changxin Gao, Nong Sang, Ming-Hsuan Yang
Date: 2018-03-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

We present an effective blind image deblurring method based on a data-driven
discriminative prior.Our work is motivated by the fact that a good image prior
should favor clear images over blurred images.In this work, we formulate the
image prior as a binary classifier which can be achieved by a deep
convolutional neural network (CNN).The learned prior is able to distinguish
whether an input image is clear or not.Embedded into the maximum a posterior
(MAP) framework, it helps blind deblurring in various scenarios, including
natural, face, text, and low-illumination images.However, it is difficult to
optimize the deblurring method with the learned image prior as it involves a
non-linear CNN.Therefore, we develop an efficient numerical approach based on
the half-quadratic splitting method and gradient decent algorithm to solve the
proposed model.Furthermore, the proposed model can be easily extended to
non-uniform deblurring.Both qualitative and quantitative experimental results
show that our method performs favorably against state-of-the-art algorithms as
well as domain-specific image deblurring approaches.
Multimodal Visual Concept Learning with Weakly Supervised Techniques
Authors: Giorgos Bouritsas, Petros Koutras, Athanasia Zlatintsi, Petros Maragos
Date: 2017-12-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Descriptive, Multiple Instance Learning, Semantic Similarity, Semantic Textual Similarity, Temporal Action Localization

Despite the availability of a huge amount of video data accompanied by
descriptive texts, it is not always easy to exploit the information contained
in natural language in order to automatically recognize video concepts. Towards
this goal, in this paper we use textual cues as means of supervision,
introducing two weakly supervised techniques that extend the Multiple Instance
Learning (MIL) framework: the Fuzzy Sets Multiple Instance Learning (FSMIL) and
the Probabilistic Labels Multiple Instance Learning (PLMIL). The former encodes
the spatio-temporal imprecision of the linguistic descriptions with Fuzzy Sets,
while the latter models different interpretations of each description's
semantics with Probabilistic Labels, both formulated through a convex
optimization algorithm. In addition, we provide a novel technique to extract
weak labels in the presence of complex semantics, that consists of semantic
similarity computations. We evaluate our methods on two distinct problems,
namely face and action recognition, in the challenging and realistic setting of
movies accompanied by their screenplays, contained in the COGNIMUSE database.
We show that, on both tasks, our method considerably outperforms a
state-of-the-art weakly supervised approach, as well as other baselines.