pwc-archive/papers-with-abstracts huggingface dataset:
Fast Parallel Randomized Algorithm for Nonnegative Matrix Factorization with KL Divergence for Large Sparse Datasets
Authors: Duy Khuong Nguyen, Tu Bao Ho
Date: 2016-04-14 00:00:00

Nonnegative Matrix Factorization (NMF) with Kullback-Leibler Divergence
(NMF-KL) is one of the most significant NMF problems and equivalent to
Probabilistic Latent Semantic Indexing (PLSI), which has been successfully
applied in many applications. For sparse count data, a Poisson distribution and
KL divergence provide sparse models and sparse representation, which describe
the random variation better than a normal distribution and Frobenius norm.
Specially, sparse models provide more concise understanding of the appearance
of attributes over latent components, while sparse representation provides
concise interpretability of the contribution of latent components over
instances. However, minimizing NMF with KL divergence is much more difficult
than minimizing NMF with Frobenius norm; and sparse models, sparse
representation and fast algorithms for large sparse datasets are still
challenges for NMF with KL divergence. In this paper, we propose a fast
parallel randomized coordinate descent algorithm having fast convergence for
large sparse datasets to archive sparse models and sparse representation. The
proposed algorithm's experimental results overperform the current studies' ones
in this problem.
Learning Deep Structure-Preserving Image-Text Embeddings
Authors: Liwei Wang, Yin Li, Svetlana Lazebnik
Date: 2015-11-19 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Retrieval, Image to text, Metric Learning, Phrase Grounding, Retrieval, Sentence

This paper proposes a method for learning joint embeddings of images and text
using a two-branch neural network with multiple layers of linear projections
followed by nonlinearities. The network is trained using a large margin
objective that combines cross-view ranking constraints with within-view
neighborhood structure preservation constraints inspired by metric learning
literature. Extensive experiments show that our approach gains significant
improvements in accuracy for image-to-text and text-to-image retrieval. Our
method achieves new state-of-the-art results on the Flickr30K and MSCOCO
image-sentence datasets and shows promise on the new task of phrase
localization on the Flickr30K Entities dataset.
Simple one-pass algorithm for penalized linear regression with cross-validation on MapReduce
Authors: Kun Yang
Date: 2013-06-28 00:00:00
Tasks: regression

In this paper, we propose a one-pass algorithm on MapReduce for penalized
linear regression
  \[f_\lambda(\alpha, \beta) = \|Y - \alpha\mathbf{1} - X\beta\|_2^2 +
p_{\lambda}(\beta)\] where $\alpha$ is the intercept which can be omitted
depending on application; $\beta$ is the coefficients and $p_{\lambda}$ is the
penalized function with penalizing parameter $\lambda$. $f_\lambda(\alpha,
\beta)$ includes interesting classes such as Lasso, Ridge regression and
Elastic-net. Compared to latest iterative distributed algorithms requiring
multiple MapReduce jobs, our algorithm achieves huge performance improvement;
moreover, our algorithm is exact compared to the approximate algorithms such as
parallel stochastic gradient decent. Moreover, what our algorithm distinguishes
with others is that it trains the model with cross validation to choose optimal
$\lambda$ instead of user specified one.
  Key words: penalized linear regression, lasso, elastic-net, ridge, MapReduce
Learnt quasi-transitive similarity for retrieval from large collections of faces
Authors: Ognjen Arandjelovic
Date: 2016-03-02 00:00:00
Proceeding: CVPR 2016 6
Tasks: Retrieval

We are interested in identity-based retrieval of face sets from large
unlabelled collections acquired in uncontrolled environments. Given a baseline
algorithm for measuring the similarity of two face sets, the meta-algorithm
introduced in this paper seeks to leverage the structure of the data corpus to
make the best use of the available baseline. In particular, we show how partial
transitivity of inter-personal similarity can be exploited to improve the
retrieval of particularly challenging sets which poorly match the query under
the baseline measure. We: (i) describe the use of proxy sets as a means of
computing the similarity between two sets, (ii) introduce transitivity
meta-features based on the similarity of salient modes of appearance variation
between sets, (iii) show how quasi-transitivity can be learnt from such
features without any labelling or manual intervention, and (iv) demonstrate the
effectiveness of the proposed methodology through experiments on the
notoriously challenging YouTube database and two successful baselines from the
literature.
Factors in Finetuning Deep Model for object detection
Authors: Wanli Ouyang, Xiaogang Wang, Cong Zhang, Xiaokang Yang
Date: 2016-01-20 00:00:00
Tasks: model, Object, object-detection, Object Detection

Finetuning from a pretrained deep model is found to yield state-of-the-art
performance for many vision tasks. This paper investigates many factors that
influence the performance in finetuning for object detection. There is a
long-tailed distribution of sample numbers for classes in object detection. Our
analysis and empirical results show that classes with more samples have higher
impact on the feature learning. And it is better to make the sample number more
uniform across classes. Generic object detection can be considered as multiple
equally important tasks. Detection of each class is a task. These classes/tasks
have their individuality in discriminative visual appearance representation.
Taking this individuality into account, we cluster objects into visually
similar class groups and learn deep representations for these groups
separately. A hierarchical feature learning scheme is proposed. In this scheme,
the knowledge from the group with large number of classes is transferred for
learning features in its sub-groups. Finetuned on the GoogLeNet model,
experimental results show 4.7% absolute mAP improvement of our approach on the
ImageNet object detection dataset without increasing much computational cost at
the testing stage.
A Linearly-Convergent Stochastic L-BFGS Algorithm
Authors: Philipp Moritz, Robert Nishihara, Michael. I. Jordan
Date: 2015-08-09 00:00:00

We propose a new stochastic L-BFGS algorithm and prove a linear convergence
rate for strongly convex and smooth functions. Our algorithm draws heavily from
a recent stochastic variant of L-BFGS proposed in Byrd et al. (2014) as well as
a recent approach to variance reduction for stochastic gradient descent from
Johnson and Zhang (2013). We demonstrate experimentally that our algorithm
performs well on large-scale convex and non-convex optimization problems,
exhibiting linear convergence and rapidly solving the optimization problems to
high levels of precision. Furthermore, we show that our algorithm performs well
for a wide-range of step sizes, often differing by several orders of magnitude.
Conditional Random Fields as Recurrent Neural Networks
Authors: Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr
Date: 2015-02-11 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Segmentation, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

Pixel-level labelling tasks, such as semantic segmentation, play a central
role in image understanding. Recent approaches have attempted to harness the
capabilities of deep learning techniques for image recognition to tackle
pixel-level labelling tasks. One central issue in this methodology is the
limited capacity of deep learning techniques to delineate visual objects. To
solve this problem, we introduce a new form of convolutional neural network
that combines the strengths of Convolutional Neural Networks (CNNs) and
Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To
this end, we formulate mean-field approximate inference for the Conditional
Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks.
This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a
deep network that has desirable properties of both CNNs and CRFs. Importantly,
our system fully integrates CRF modelling with CNNs, making it possible to
train the whole deep network end-to-end with the usual back-propagation
algorithm, avoiding offline post-processing methods for object delineation. We
apply the proposed method to the problem of semantic image segmentation,
obtaining top results on the challenging Pascal VOC 2012 segmentation
benchmark.
Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer
Authors: Yusen Zhan, Haitham Bou Ammar, Matthew E. Taylor
Date: 2016-04-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Policy advice is a transfer learning method where a student agent is able to
learn faster via advice from a teacher. However, both this and other
reinforcement learning transfer methods have little theoretical analysis. This
paper formally defines a setting where multiple teacher agents can provide
advice to a student and introduces an algorithm to leverage both autonomous
exploration and teacher's advice. Our regret bounds justify the intuition that
good teachers help while bad teachers hurt. Using our formalization, we are
also able to quantify, for the first time, when negative transfer can occur
within such a reinforcement learning setting.
Visual Storytelling
Authors: Ting-Hao, Huang, Francis Ferraro, Nasrin Mostafazadeh, Ishan Misra, Aishwarya Agrawal, Jacob Devlin, Ross Girshick, Xiaodong He, Pushmeet Kohli, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, Lucy Vanderwende, Michel Galley, Margaret Mitchell
Date: 2016-04-13 00:00:00
Proceeding: NAACL 2016 6
Tasks: Descriptive, Visual Storytelling

We introduce the first dataset for sequential vision-to-language, and explore
how this data may be used for the task of visual storytelling. The first
release of this dataset, SIND v.1, includes 81,743 unique photos in 20,211
sequences, aligned to both descriptive (caption) and story language. We
establish several strong baselines for the storytelling task, and motivate an
automatic metric to benchmark progress. Modelling concrete description as well
as figurative and social language, as provided in this dataset and the
storytelling task, has the potential to move artificial intelligence from basic
understandings of typical visual scenes towards more and more human-like
understanding of grounded event structure and subjective expression.
Accelerated graph-based nonlinear denoising filters
Authors: Andrew Knyazev, Alexander Malyshev
Date: 2015-12-01 00:00:00
Tasks: Denoising, Image Denoising

Denoising filters, such as bilateral, guided, and total variation filters,
applied to images on general graphs may require repeated application if noise
is not small enough. We formulate two acceleration techniques of the resulted
iterations: conjugate gradient method and Nesterov's acceleration. We
numerically show efficiency of the accelerated nonlinear filters for image
denoising and demonstrate 2-12 times speed-up, i.e., the acceleration
techniques reduce the number of iterations required to reach a given peak
signal-to-noise ratio (PSNR) by the above indicated factor of 2-12.
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
Authors: Jialei Wang, Peder A. Olsen, Andrew R. Conn, Aurelie C. Lozano
Date: 2016-04-13 00:00:00
Proceeding: CVPR 2016 6
Tasks: Matrix Completion

We consider the problem of removing and replacing clouds in satellite image
sequences, which has a wide range of applications in remote sensing. Our
approach first detects and removes the cloud-contaminated part of the image
sequences. It then recovers the missing scenes from the clean parts using the
proposed "TECROMAC" (TEmporally Contiguous RObust MAtrix Completion) objective.
The objective function balances temporal smoothness with a low rank solution
while staying close to the original observations. The matrix whose the rows are
pixels and columnsare days corresponding to the image, has low-rank because the
pixels reflect land-types such as vegetation, roads and lakes and there are
relatively few variations as a result. We provide efficient optimization
algorithms for TECROMAC, so we can exploit images containing millions of
pixels. Empirical results on real satellite image sequences, as well as
simulated data, demonstrate that our approach is able to recover underlying
images from heavily cloud-contaminated observations.
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
Authors: Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard
Date: 2016-04-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Inverse Reinforcement Learning (IRL) describes the problem of learning an
unknown reward function of a Markov Decision Process (MDP) from observed
behavior of an agent. Since the agent's behavior originates in its policy and
MDP policies depend on both the stochastic system dynamics as well as the
reward function, the solution of the inverse problem is significantly
influenced by both. Current IRL approaches assume that if the transition model
is unknown, additional samples from the system's dynamics are accessible, or
the observed behavior provides enough samples of the system's dynamics to solve
the inverse problem accurately. These assumptions are often not satisfied. To
overcome this, we present a gradient-based IRL approach that simultaneously
estimates the system's dynamics. By solving the combined optimization problem,
our approach takes into account the bias of the demonstrations, which stems
from the generating policy. The evaluation on a synthetic MDP and a transfer
learning task shows improvements regarding the sample efficiency as well as the
accuracy of the estimated reward functions and transition models.
Staple: Complementary Learners for Real-Time Tracking
Authors: Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, Philip Torr
Date: 2015-12-04 00:00:00
Proceeding: CVPR 2016 6
Tasks: regression, Video Object Tracking, Visual Object Tracking

Correlation Filter-based trackers have recently achieved excellent
performance, showing great robustness to challenging situations exhibiting
motion blur and illumination changes. However, since the model that they learn
depends strongly on the spatial layout of the tracked object, they are
notoriously sensitive to deformation. Models based on colour statistics have
complementary traits: they cope well with variation in shape, but suffer when
illumination is not consistent throughout a sequence. Moreover, colour
distributions alone can be insufficiently discriminative. In this paper, we
show that a simple tracker combining complementary cues in a ridge regression
framework can operate faster than 80 FPS and outperform not only all entries in
the popular VOT14 competition, but also recent and far more sophisticated
trackers according to multiple benchmarks.
The Effect of Distortions on the Prediction of Visual Attention
Authors: Milind S. Gide, Samuel F. Dodge, Lina J. Karam
Date: 2016-04-13 00:00:00
Tasks: Saliency Prediction

Existing saliency models have been designed and evaluated for predicting the
saliency in distortion-free images. However, in practice, the image quality is
affected by a host of factors at several stages of the image processing
pipeline such as acquisition, compression and transmission. Several studies
have explored the effect of distortion on human visual attention; however, none
of them have considered the performance of visual saliency models in the
presence of distortion. Furthermore, given that one potential application of
visual saliency prediction is to aid pooling of objective visual quality
metrics, it is important to compare the performance of existing saliency models
on distorted images. In this paper, we evaluate several state-of-the-art visual
attention models over different databases consisting of distorted images with
various types of distortions such as blur, noise and compression with varying
levels of distortion severity. This paper also introduces new improved
performance evaluation metrics that are shown to overcome shortcomings in
existing performance metrics. We find that the performance of most models
improves with moderate and high levels of distortions as compared to the near
distortion-free case. In addition, model performance is also found to decrease
with an increase in image complexity.
Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly
Authors: Hao Jiang, Kristen Grauman
Date: 2016-04-13 00:00:00
Proceeding: CVPR 2017 7
Tasks: Human Detection, Semantic Segmentation

Today's person detection methods work best when people are in common upright
poses and appear reasonably well spaced out in the image. However, in many real
images, that's not what people do. People often appear quite close to each
other, e.g., with limbs linked or heads touching, and their poses are often not
pedestrian-like. We propose an approach to detangle people in multi-person
images. We formulate the task as a region assembly problem. Starting from a
large set of overlapping regions from body part semantic segmentation and
generic object proposals, our optimization approach reassembles those pieces
together into multiple person instances. It enforces that the composed body
part regions of each person instance obey constraints on relative sizes, mutual
spatial relationships, foreground coverage, and exclusive label assignments
when overlapping. Since optimal region assembly is a challenging combinatorial
problem, we present a Lagrangian relaxation method to accelerate the lower
bound estimation, thereby enabling a fast branch and bound solution for the
global optimum. As output, our method produces a pixel-level map indicating
both 1) the body part labels (arm, leg, torso, and head), and 2) which parts
belong to which individual person. Our results on three challenging datasets
show our method is robust to clutter, occlusion, and complex poses. It
outperforms a variety of competing methods, including existing detector CRF
methods and region CNN approaches. In addition, we demonstrate its impact on a
proxemics recognition task, which demands a precise representation of "whose
body part is where" in crowded images.
Reversible Image Merging for Low-level Machine Vision
Authors: Mikhail Kharinov
Date: 2016-04-13 00:00:00
Tasks: Clustering, Image Segmentation, Object, Semantic Segmentation

In this paper a hierarchical model for pixel clustering and image
segmentation is developed. In the model an image is hierarchically structured.
The original image is treated as a set of nested images, which are capable to
reversibly merge with each other. An object is defined as a structural element
of an image, so that, an image is regarded as a maximal object. The simulating
of none-hierarchical optimal pixel clustering by hierarchical clustering is
studied. To generate a hierarchy of optimized piecewise constant image
approximations, estimated by the standard deviation of approximation from the
image, the conversion of any hierarchy of approximations into the hierarchy
described in relation to the number of intensity levels by convex sequence of
total squared errors is proposed.
Animation and Chirplet-Based Development of a PIR Sensor Array for Intruder Classification in an Outdoor Environment
Authors: Raviteja Upadrashta, Tarun Choubisa, A. Praneeth, Tony G., Aswath V. S., P. Vijay Kumar, Sripad Kowshik, Hari Prasad Gokul R, T. V. Prabhakar
Date: 2016-04-13 00:00:00
Tasks: Classification, General Classification

This paper presents the development of a passive infra-red sensor tower
platform along with a classification algorithm to distinguish between human
intrusion, animal intrusion and clutter arising from wind-blown vegetative
movement in an outdoor environment. The research was aimed at exploring the
potential use of wireless sensor networks as an early-warning system to help
mitigate human-wildlife conflicts occurring at the edge of a forest. There are
three important features to the development. Firstly, the sensor platform
employs multiple sensors arranged in the form of a two-dimensional array to
give it a key spatial-resolution capability that aids in classification.
Secondly, given the challenges of collecting data involving animal intrusion,
an Animation-based Simulation tool for Passive Infra-Red sEnsor (ASPIRE) was
developed that simulates signals corresponding to human and animal intrusion
and some limited models of vegetative clutter. This speeded up the process of
algorithm development by allowing us to test different hypotheses in a
time-efficient manner. Finally, a chirplet-based model for intruder signal was
developed that significantly helped boost classification accuracy despite
drawing data from a smaller number of sensors. An SVM-based classifier was used
which made use of chirplet, energy and signal cross-correlation-based features.
The average accuracy obtained for intruder detection and classification on
real-world and simulated data sets was in excess of 97%.
Loss Functions for Top-k Error: Analysis and Insights
Authors: Maksim Lapin, Matthias Hein, Bernt Schiele
Date: 2015-12-01 00:00:00
Proceeding: CVPR 2016 6

In order to push the performance on realistic computer vision tasks, the
number of classes in modern benchmark datasets has significantly increased in
recent years. This increase in the number of classes comes along with increased
ambiguity between the class labels, raising the question if top-1 error is the
right performance measure. In this paper, we provide an extensive comparison
and evaluation of established multiclass methods comparing their top-k
performance both from a practical as well as from a theoretical perspective.
Moreover, we introduce novel top-k loss functions as modifications of the
softmax and the multiclass SVM losses and provide efficient optimization
schemes for them. In the experiments, we compare on various datasets all of the
proposed and established methods for top-k error optimization. An interesting
insight of this paper is that the softmax loss yields competitive top-k
performance for all k simultaneously. For a specific top-k error, our new top-k
losses lead typically to further improvements while being faster to train than
the softmax.
HordeQBF: A Modular and Massively Parallel QBF Solver
Authors: Tomas Balyo, Florian Lonsing
Date: 2016-04-13 00:00:00

The recently developed massively parallel satisfiability (SAT) solver
HordeSAT was designed in a modular way to allow the integration of any
sequential CDCL-based SAT solver in its core. We integrated the QCDCL-based
quantified Boolean formula (QBF) solver DepQBF in HordeSAT to obtain a
massively parallel QBF solver---HordeQBF. In this paper we describe the details
of this integration and report on results of the experimental evaluation of
HordeQBF's performance. HordeQBF achieves superlinear average and median
speedup on the hard application instances of the 2014 QBF Gallery.
A Differentiable Transition Between Additive and Multiplicative Neurons
Authors: Wiebke Köpp, Patrick van der Smagt, Sebastian Urban
Date: 2016-04-13 00:00:00

Existing approaches to combine both additive and multiplicative neural units
either use a fixed assignment of operations or require discrete optimization to
determine what function a neuron should perform. However, this leads to an
extensive increase in the computational complexity of the training procedure.
  We present a novel, parameterizable transfer function based on the
mathematical concept of non-integer functional iteration that allows the
operation each neuron performs to be smoothly and, most importantly,
differentiablely adjusted between addition and multiplication. This allows the
decision between addition and multiplication to be integrated into the standard
backpropagation training procedure.