pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning based fence segmentation and removal from an image using a video sequence
Authors: Sankaraganesh Jonna, Krishna K. Nakka, Rajiv R. Sahay
Date: 2016-09-25 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation, Segmentation

Conventional approaches to image de-fencing use multiple adjacent frames for
segmentation of fences in the reference image and are limited to restoring
images of static scenes only. In this paper, we propose a de-fencing algorithm
for images of dynamic scenes using an occlusion-aware optical flow method. We
divide the problem of image de-fencing into the tasks of automated fence
segmentation from a single image, motion estimation under known occlusions and
fusion of data from multiple frames of a captured video of the scene.
Specifically, we use a pre-trained convolutional neural network to segment
fence pixels from a single image. The knowledge of spatial locations of fences
is used to subsequently estimate optical flow in the occluded frames of the
video for the final data fusion step. We cast the fence removal problem in an
optimization framework by modeling the formation of the degraded observations.
The inverse problem is solved using fast iterative shrinkage thresholding
algorithm (FISTA). Experimental results show the effectiveness of proposed
algorithm.
Maximally Divergent Intervals for Anomaly Detection
Authors: Erik Rodner, Björn Barz, Yanira Guanche, Milan Flach, Miguel Mahecha, Paul Bodesheim, Markus Reichstein, Joachim Denzler
Date: 2016-10-21 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

We present new methods for batch anomaly detection in multivariate time
series. Our methods are based on maximizing the Kullback-Leibler divergence
between the data distribution within and outside an interval of the time
series. An empirical analysis shows the benefits of our algorithms compared to
methods that treat each time step independently from each other without
optimizing with respect to all possible intervals.
Stochastic Heavy Ball
Authors: Sébastien Gadat, Fabien Panloup, Sofiane Saadane
Date: 2016-09-14 00:00:00
Tasks: Second-order methods, Stochastic Optimization

This paper deals with a natural stochastic optimization procedure derived
from the so-called Heavy-ball method differential equation, which was
introduced by Polyak in the 1960s with his seminal contribution [Pol64]. The
Heavy-ball method is a second-order dynamics that was investigated to minimize
convex functions f . The family of second-order methods recently received a
large amount of attention, until the famous contribution of Nesterov [Nes83],
leading to the explosion of large-scale optimization problems. This work
provides an in-depth description of the stochastic heavy-ball method, which is
an adaptation of the deterministic one when only unbiased evalutions of the
gradient are available and used throughout the iterations of the algorithm. We
first describe some almost sure convergence results in the case of general
non-convex coercive functions f . We then examine the situation of convex and
strongly convex potentials and derive some non-asymptotic results about the
stochastic heavy-ball method. We end our study with limit theorems on several
rescaled algorithms.
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
Authors: Erik Rodner, Marcel Simon, Robert B. Fisher, Joachim Denzler
Date: 2016-10-21 00:00:00
Tasks: Sensitivity

In this paper, we study the sensitivity of CNN outputs with respect to image
transformations and noise in the area of fine-grained recognition. In
particular, we answer the following questions (1) how sensitive are CNNs with
respect to image transformations encountered during wild image capture?; (2)
how can we predict CNN sensitivity?; and (3) can we increase the robustness of
CNNs with respect to image degradations? To answer the first question, we
provide an extensive empirical sensitivity analysis of commonly used CNN
architectures (AlexNet, VGG19, GoogleNet) across various types of image
degradations. This allows for predicting CNN performance for new domains
comprised by images of lower quality or captured from a different viewpoint. We
also show how the sensitivity of CNN outputs can be predicted for single
images. Furthermore, we demonstrate that input layer dropout or pre-filtering
during test time only reduces CNN sensitivity for high levels of degradation.
  Experiments for fine-grained recognition tasks reveal that VGG19 is more
robust to severe image degradations than AlexNet and GoogleNet. However, small
intensity noise can lead to dramatic changes in CNN performance even for VGG19.
General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues
Authors: Helge Rhodin, Nadia Robertini, Dan Casas, Christian Richardt, Hans-Peter Seidel, Christian Theobalt
Date: 2016-07-28 00:00:00
Tasks: Markerless Motion Capture

Markerless motion capture algorithms require a 3D body with properly
personalized skeleton dimension and/or body shape and appearance to
successfully track a person. Unfortunately, many tracking methods consider
model personalization a different problem and use manual or semi-automatic
model initialization, which greatly reduces applicability. In this paper, we
propose a fully automatic algorithm that jointly creates a rigged actor model
commonly used for animation - skeleton, volumetric shape, appearance, and
optionally a body surface - and estimates the actor's motion from multi-view
video input only. The approach is rigorously designed to work on footage of
general outdoor scenes recorded with very few cameras and without background
subtraction. Our method uses a new image formation model with analytic
visibility and analytically differentiable alignment energy. For
reconstruction, 3D body shape is approximated as Gaussian density field. For
pose and shape estimation, we minimize a new edge-based alignment energy
inspired by volume raycasting in an absorbing medium. We further propose a new
statistical human body model that represents the body surface, volumetric
Gaussian density, as well as variability in skeleton shape. Given any
multi-view sequence, our method jointly optimizes the pose and shape parameters
of this model fully automatically in a spatiotemporal way.
Model-based Outdoor Performance Capture
Authors: Nadia Robertini, Dan Casas, Helge Rhodin, Hans-Peter Seidel, Christian Theobalt
Date: 2016-10-21 00:00:00
Tasks: Edge Detection, model

We propose a new model-based method to accurately reconstruct human
performances captured outdoors in a multi-camera setup. Starting from a
template of the actor model, we introduce a new unified implicit representation
for both, articulated skeleton tracking and nonrigid surface shape refinement.
Our method fits the template to unsegmented video frames in two stages - first,
the coarse skeletal pose is estimated, and subsequently non-rigid surface shape
and body pose are jointly refined. Particularly for surface shape refinement we
propose a new combination of 3D Gaussians designed to align the projected model
with likely silhouette contours without explicit segmentation or edge
detection. We obtain reconstructions of much higher quality in outdoor settings
than existing methods, and show that we are on par with state-of-the-art
methods on indoor scenes for which they were designed
Hybrid clustering-classification neural network in the medical diagnostics of reactive arthritis
Authors: Yevgeniy Bodyanskiy, Olena Vynokurova, Volodymyr Savvo, Tatiana Tverdokhlib, Pavlo Mulesa
Date: 2016-10-21 00:00:00
Tasks: Classification, Clustering, General Classification, Self-Learning

The hybrid clustering-classification neural network is proposed. This network
allows increasing a quality of information processing under the condition of
overlapping classes due to the rational choice of a learning rate parameter and
introducing a special procedure of fuzzy reasoning in the clustering process,
which occurs both with an external learning signal (supervised) and without the
one (unsupervised). As similarity measure neighborhood function or membership
one, cosine structures are used, which allow to provide a high flexibility due
to self-learning-learning process and to provide some new useful properties.
Many realized experiments have confirmed the efficiency of proposed hybrid
clustering-classification neural network; also, this network was used for
solving diagnostics task of reactive arthritis.
End-to-End Training Approaches for Discriminative Segmental Models
Authors: Hao Tang, Weiran Wang, Kevin Gimpel, Karen Livescu
Date: 2016-10-21 00:00:00
Tasks: speech-recognition, Speech Recognition

Recent work on discriminative segmental models has shown that they can
achieve competitive speech recognition performance, using features based on
deep neural frame classifiers. However, segmental models can be more
challenging to train than standard frame-based approaches. While some segmental
models have been successfully trained end to end, there is a lack of
understanding of their training under different settings and with different
losses.
  We investigate a model class based on recent successful approaches,
consisting of a linear model that combines segmental features based on an LSTM
frame classifier. Similarly to hybrid HMM-neural network models, segmental
models of this class can be trained in two stages (frame classifier training
followed by linear segmental model weight training), end to end (joint training
of both frame classifier and linear weights), or with end-to-end fine-tuning
after two-stage training.
  We study segmental models trained end to end with hinge loss, log loss,
latent hinge loss, and marginal log loss. We consider several losses for the
case where training alignments are available as well as where they are not.
  We find that in general, marginal log loss provides the most consistent
strong performance without requiring ground-truth alignments. We also find that
training with dropout is very important in obtaining good performance with
end-to-end training. Finally, the best results are typically obtained by a
combination of two-stage training and fine-tuning.
Robust Bayesian Compressed sensing
Authors: Qian Wan, Huiping Duan, Jun Fang, Hongbin Li
Date: 2016-10-10 00:00:00
Tasks: compressed sensing

We consider the problem of robust compressed sensing whose objective is to
recover a high-dimensional sparse signal from compressed measurements corrupted
by outliers. A new sparse Bayesian learning method is developed for robust
compressed sensing. The basic idea of the proposed method is to identify and
remove the outliers from sparse signal recovery. To automatically identify the
outliers, we employ a set of binary indicator hyperparameters to indicate which
observations are outliers. These indicator hyperparameters are treated as
random variables and assigned a beta process prior such that their values are
confined to be binary. In addition, a Gaussian-inverse Gamma prior is imposed
on the sparse signal to promote sparsity. Based on this hierarchical prior
model, we develop a variational Bayesian method to estimate the indicator
hyperparameters as well as the sparse signal. Simulation results show that the
proposed method achieves a substantial performance improvement over existing
robust compressed sensing techniques.
Deep Aesthetic Quality Assessment with Semantic Information
Authors: Yueying Kao, Ran He, Kaiqi Huang
Date: 2016-04-18 00:00:00
Tasks: Aesthetics Quality Assessment

Human beings often assess the aesthetic quality of an image coupled with the
identification of the image's semantic content. This paper addresses the
correlation issue between automatic aesthetic quality assessment and semantic
recognition. We cast the assessment problem as the main task among a multi-task
deep model, and argue that semantic recognition task offers the key to address
this problem. Based on convolutional neural networks, we employ a single and
simple multi-task framework to efficiently utilize the supervision of aesthetic
and semantic labels. A correlation item between these two tasks is further
introduced to the framework by incorporating the inter-task relationship
learning. This item not only provides some useful insight about the correlation
but also improves assessment accuracy of the aesthetic task. Particularly, an
effective strategy is developed to keep a balance between the two tasks, which
facilitates to optimize the parameters of the framework. Extensive experiments
on the challenging AVA dataset and Photo.net dataset validate the importance of
semantic recognition in aesthetic quality assessment, and demonstrate that
multi-task deep models can discover an effective aesthetic representation to
achieve state-of-the-art results.
Multispectral image denoising with optimized vector non-local mean filter
Authors: Ahmed Ben Said, Rachid Hadjidj, Kamel Eddine Melkemi, Sebti Foufou
Date: 2016-10-21 00:00:00
Tasks: Denoising, Image Denoising

Nowadays, many applications rely on images of high quality to ensure good
performance in conducting their tasks. However, noise goes against this
objective as it is an unavoidable issue in most applications. Therefore, it is
essential to develop techniques to attenuate the impact of noise, while
maintaining the integrity of relevant information in images. We propose in this
work to extend the application of the Non-Local Means filter (NLM) to the
vector case and apply it for denoising multispectral images. The objective is
to benefit from the additional information brought by multispectral imaging
systems. The NLM filter exploits the redundancy of information in an image to
remove noise. A restored pixel is a weighted average of all pixels in the
image. In our contribution, we propose an optimization framework where we
dynamically fine tune the NLM filter parameters and attenuate its computational
complexity by considering only pixels which are most similar to each other in
computing a restored pixel. Filter parameters are optimized using Stein's
Unbiased Risk Estimator (SURE) rather than using ad hoc means. Experiments have
been conducted on multispectral images corrupted with additive white Gaussian
noise and PSNR and similarity comparison with other approaches are provided to
illustrate the efficiency of our approach in terms of both denoising
performance and computation complexity.
Translation Quality Estimation using Recurrent Neural Network
Authors: Raj Nath Patel, Sasikumar M
Date: 2016-10-16 00:00:00
Proceeding: WS 2016 8
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

This paper describes our submission to the shared task on word/phrase level
Quality Estimation (QE) in the First Conference on Statistical Machine
Translation (WMT16). The objective of the shared task was to predict if the
given word/phrase is a correct/incorrect (OK/BAD) translation in the given
sentence. In this paper, we propose a novel approach for word level Quality
Estimation using Recurrent Neural Network Language Model (RNN-LM) architecture.
RNN-LMs have been found very effective in different Natural Language Processing
(NLP) applications. RNN-LM is mainly used for vector space language modeling
for different NLP problems. For this task, we modify the architecture of
RNN-LM. The modified system predicts a label (OK/BAD) in the slot rather than
predicting the word. The input to the system is a word sequence, similar to the
standard RNN-LM. The approach is language independent and requires only the
translated text for QE. To estimate the phrase level quality, we use the output
of the word level QE system.
Multi-view metric learning for multi-instance image classification
Authors: Dewei Li, Yingjie Tian
Date: 2016-10-21 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Retrieval, Metric Learning, MULTI-VIEW LEARNING, object-detection, Object Detection, Retrieval

It is critical and meaningful to make image classification since it can help
human in image retrieval and recognition, object detection, etc. In this paper,
three-sides efforts are made to accomplish the task. First, visual features
with bag-of-words representation, not single vector, are extracted to
characterize the image. To improve the performance, the idea of multi-view
learning is implemented and three kinds of features are provided, each one
corresponds to a single view. The information from three views is complementary
to each other, which can be unified together. Then a new distance function is
designed for bags by computing the weighted sum of the distances between
instances. The technique of metric learning is explored to construct a
data-dependent distance metric to measure the relationships between instances,
meanwhile between bags and images, more accurately. Last, a novel approach,
called MVML, is proposed, which optimizes the joint probability that every
image is similar with its nearest image. MVML learns multiple distance metrics,
each one models a single view, to unifies the information from multiple views.
The method can be solved by alternate optimization iteratively. Gradient ascent
and positive semi-definite projection are utilized in the iterations. Distance
comparisons verified that the new bag distance function is prior to previous
functions. In model evaluation, numerical experiments show that MVML with
multiple views performs better than single view condition, which demonstrates
that our model can assemble the complementary information efficiently and
measure the distance between images more precisely. Experiments on influence of
parameters and instance number validate the consistency of the method.
Scalable Pooled Time Series of Big Video Data from the Deep Web
Authors: Chris Mattmann, Madhav Sharan
Date: 2016-10-21 00:00:00
Tasks: Time Series, Time Series Analysis

We contribute a scalable implementation of Ryoo et al's Pooled Time Series
algorithm from CVPR 2015. The updated algorithm has been evaluated on a large
and diverse dataset of approximately 6800 videos collected from a crawl of the
deep web related to human trafficking on DARPA's MEMEX effort. We describe the
properties of Pooled Time Series and the motivation for using it to relate
videos collected from the deep web. We highlight issues that we found while
running Pooled Time Series on larger datasets and discuss solutions for those
issues. Our solution centers are re-imagining Pooled Time Series as a
Hadoop-based algorithm in which we compute portions of the eventual solution in
parallel on large commodity clusters. We demonstrate that our new Hadoop-based
algorithm works well on the 6800 video dataset and shares all of the properties
described in the CVPR 2015 paper. We suggest avenues of future work in the
project.
Detecting Rainfall Onset Using Sky Images
Authors: Soumyabrata Dev, Shilpa Manandhar, Yee Hui Lee, Stefan Winkler
Date: 2016-10-21 00:00:00

Ground-based sky cameras (popularly known as Whole Sky Imagers) are
increasingly used now-a-days for continuous monitoring of the atmosphere. These
imagers have higher temporal and spatial resolutions compared to conventional
satellite images. In this paper, we use ground-based sky cameras to detect the
onset of rainfall. These images contain additional information about cloud
coverage and movement and are therefore useful for accurate rainfall nowcast.
We validate our results using rain gauge measurement recordings and achieve an
accuracy of 89% for correct detection of rainfall onset.
Short-term prediction of localized cloud motion using ground-based sky imagers
Authors: Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, Stefan Winkler
Date: 2016-10-21 00:00:00
Tasks: motion prediction, Optical Flow Estimation, Prediction

Fine-scale short-term cloud motion prediction is needed for several
applications, including solar energy generation and satellite communications.
In tropical regions such as Singapore, clouds are mostly formed by convection;
they are very localized, and evolve quickly. We capture hemispherical images of
the sky at regular intervals of time using ground-based cameras. They provide a
high resolution and localized cloud images. We use two successive frames to
compute optical flow and predict the future location of clouds. We achieve good
prediction accuracy for a lead time of up to 5 minutes.
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Authors: Changyou Chen, Nan Ding, Lawrence Carin
Date: 2016-10-21 00:00:00
Proceeding: NeurIPS 2015 12

Recent advances in Bayesian learning with large-scale data have witnessed
emergence of stochastic gradient MCMC algorithms (SG-MCMC), such as stochastic
gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian MCMC
(SGHMC), and the stochastic gradient thermostat. While finite-time convergence
properties of the SGLD with a 1st-order Euler integrator have recently been
studied, corresponding theory for general SG-MCMCs has not been explored. In
this paper we consider general SG-MCMCs with high-order integrators, and
develop theory to analyze finite-time convergence properties and their
asymptotic invariant measures. Our theoretical results show faster convergence
rates and more accurate invariant measures for SG-MCMCs with higher-order
integrators. For example, with the proposed efficient 2nd-order symmetric
splitting integrator, the {\em mean square error} (MSE) of the posterior
average for the SGHMC achieves an optimal convergence rate of $L^{-4/5}$ at $L$
iterations, compared to $L^{-2/3}$ for the SGHMC and SGLD with 1st-order Euler
integrators. Furthermore, convergence results of decreasing-step-size SG-MCMCs
are also developed, with the same convergence rates as their fixed-step-size
counterparts for a specific decreasing sequence. Experiments on both synthetic
and real datasets verify our theory, and show advantages of the proposed method
in two large-scale real applications.
Stochastic Gradient MCMC with Stale Gradients
Authors: Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin
Date: 2016-10-21 00:00:00
Proceeding: NeurIPS 2016 12

Stochastic gradient MCMC (SG-MCMC) has played an important role in
large-scale Bayesian learning, with well-developed theoretical convergence
properties. In such applications of SG-MCMC, it is becoming increasingly
popular to employ distributed systems, where stochastic gradients are computed
based on some outdated parameters, yielding what are termed stale gradients.
While stale gradients could be directly used in SG-MCMC, their impact on
convergence properties has not been well studied. In this paper we develop
theory to show that while the bias and MSE of an SG-MCMC algorithm depend on
the staleness of stochastic gradients, its estimation variance (relative to the
expected estimate, based on a prescribed number of samples) is independent of
it. In a simple Bayesian distributed system with SG-MCMC, where stale gradients
are computed asynchronously by a set of workers, our theory indicates a linear
speedup on the decrease of estimation variance w.r.t. the number of workers.
Experiments on synthetic data and deep neural networks validate our theory,
demonstrating the effectiveness and scalability of SG-MCMC with stale
gradients.