pwc-archive/papers-with-abstracts huggingface dataset:
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars
Authors: Xionghao Liu, Wei Yang, Liang Lin, Qing Wang, Zhaoquan Cai, Jian-Huang Lai
Date: 2015-02-03 00:00:00
Tasks: Scene Understanding, Semantic Segmentation, Superpixels

This article investigates a data-driven approach for semantically scene
understanding, without pixelwise annotation and classifier training. Our
framework parses a target image with two steps: (i) retrieving its exemplars
(i.e. references) from an image database, where all images are unsegmented but
annotated with tags; (ii) recovering its pixel labels by propagating semantics
from the references. We present a novel framework making the two steps mutually
conditional and bootstrapped under the probabilistic Expectation-Maximization
(EM) formulation. In the first step, the references are selected by jointly
matching their appearances with the target as well as the semantics (i.e. the
assigned labels of the target and the references). We process the second step
via a combinatorial graphical representation, in which the vertices are
superpixels extracted from the target and its selected references. Then we
derive the potentials of assigning labels to one vertex of the target, which
depend upon the graph edges that connect the vertex to its spatial neighbors of
the target and to its similar vertices of the references. Besides, the proposed
framework can be naturally applied to perform image annotation on new test
images. In the experiments, we validate our approach on two public databases,
and demonstrate superior performances over the state-of-the-art methods in both
semantic segmentation and image annotation tasks.
Numerical Solution of Fuzzy Stochastic Differential Equation
Authors: Sukanta Nayak, Snehashish Chakraverty
Date: 2015-02-03 00:00:00

In this paper an alternative approach to solve uncertain Stochastic
Differential Equation (SDE) is proposed. This uncertainty occurs due to the
involved parameters in system and these are considered as Triangular Fuzzy
Numbers (TFN). Here the proposed fuzzy arithmetic in [2] is used as a tool to
handle Fuzzy Stochastic Differential Equation (FSDE). In particular, a system
of Ito stochastic differential equations is analysed with fuzzy parameters.
Further exact and Euler Maruyama approximation methods with fuzzy values are
demonstrated and solved some standard SDE.
Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object Recognition and Detection
Authors: Xiaolong Wang, Liang Lin, Lichao Huang, Shuicheng Yan
Date: 2015-02-03 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object Recognition, valid

This paper proposes a reconfigurable model to recognize and detect multiclass
(or multiview) objects with large variation in appearance. Compared with well
acknowledged hierarchical models, we study two advanced capabilities in
hierarchy for object modeling: (i) "switch" variables(i.e. or-nodes) for
specifying alternative compositions, and (ii) making local classifiers (i.e.
leaf-nodes) shared among different classes. These capabilities enable us to
account well for structural variabilities while preserving the model compact.
Our model, in the form of an And-Or Graph, comprises four layers: a batch of
leaf-nodes with collaborative edges in bottom for localizing object parts; the
or-nodes over bottom to activate their children leaf-nodes; the and-nodes to
classify objects as a whole; one root-node on the top for switching multiclass
classification, which is also an or-node. For model training, we present an
EM-type algorithm, namely dynamical structural optimization (DSO), to
iteratively determine the structural configuration, (e.g., leaf-node generation
associated with their parent or-nodes and shared across other classes), along
with optimizing multi-layer parameters. The proposed method is valid on
challenging databases, e.g., PASCAL VOC 2007 and UIUC-People, and it achieves
state-of-the-arts performance.
Deep Joint Task Learning for Generic Object Extraction
Authors: Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, WangMeng Zuo
Date: 2015-02-03 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Object, Object Localization, Semantic Segmentation

This paper investigates how to extract objects-of-interest without relying on
hand-craft features and sliding windows approaches, that aims to jointly solve
two sub-tasks: (i) rapidly localizing salient objects from images, and (ii)
accurately segmenting the objects based on the localizations. We present a
general joint task learning framework, in which each task (either object
localization or object segmentation) is tackled via a multi-layer convolutional
neural network, and the two networks work collaboratively to boost performance.
In particular, we propose to incorporate latent variables bridging the two
networks in a joint optimization manner. The first network directly predicts
the positions and scales of salient objects from raw images, and the latent
variables adjust the object localizations to feed the second network that
produces pixelwise object masks. An EM-type method is presented for the
optimization, iterating with two steps: (i) by using the two networks, it
estimates the latent variables by employing an MCMC-based sampling method; (ii)
it optimizes the parameters of the two networks unitedly via back propagation,
with the fixed latent variables. Extensive experiments suggest that our
framework significantly outperforms other state-of-the-art approaches in both
accuracy and efficiency (e.g. 1000 times faster than competing approaches).
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection
Authors: Xiaolong Wang, Liang Lin
Date: 2015-02-03 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Graph Learning

This paper studies a novel discriminative part-based model to represent and
recognize object shapes with an "And-Or graph". We define this model consisting
of three layers: the leaf-nodes with collaborative edges for localizing local
parts, the or-nodes specifying the switch of leaf-nodes, and the root-node
encoding the global verification. A discriminative learning algorithm, extended
from the CCCP [23], is proposed to train the model in a dynamical manner: the
model structure (e.g., the configuration of the leaf-nodes associated with the
or-nodes) is automatically determined with optimizing the multi-layer
parameters during the iteration. The advantages of our method are two-fold. (i)
The And-Or graph model enables us to handle well large intra-class variance and
background clutters for object shape detection from images. (ii) The proposed
learning algorithm is able to obtain the And-Or graph representation without
requiring elaborate supervision and initialization. We validate the proposed
method on several challenging databases (e.g., INRIA-Horse, ETHZ-Shape, and
UIUC-People), and it outperforms the state-of-the-arts approaches.
Clothing Co-Parsing by Joint Image Segmentation and Labeling
Authors: Wei Yang, Ping Luo, Liang Lin
Date: 2015-02-03 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Segmentation, Semantic Segmentation

This paper aims at developing an integrated system of clothing co-parsing, in
order to jointly parse a set of clothing images (unsegmented but annotated with
tags) into semantic configurations. We propose a data-driven framework
consisting of two phases of inference. The first phase, referred as "image
co-segmentation", iterates to extract consistent regions on images and jointly
refines the regions over all images by employing the exemplar-SVM (E-SVM)
technique [23]. In the second phase (i.e. "region co-labeling"), we construct a
multi-image graphical model by taking the segmented regions as vertices, and
incorporate several contexts of clothing configuration (e.g., item location and
mutual interactions). The joint label assignment can be solved using the
efficient Graph Cuts algorithm. In addition to evaluate our framework on the
Fashionista dataset [30], we construct a dataset called CCP consisting of 2098
high-resolution street fashion photos to demonstrate the performance of our
system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89%
recognition rate on the Fashionista and the CCP datasets, respectively, which
are superior compared with state-of-the-art methods.
Cheaper and Better: Selecting Good Workers for Crowdsourcing
Authors: Hongwei Li, Qiang Liu
Date: 2015-02-03 00:00:00
Tasks: Combinatorial Optimization

Crowdsourcing provides a popular paradigm for data collection at scale. We
study the problem of selecting subsets of workers from a given worker pool to
maximize the accuracy under a budget constraint. One natural question is
whether we should hire as many workers as the budget allows, or restrict on a
small number of top-quality workers. By theoretically analyzing the error rate
of a typical setting in crowdsourcing, we frame the worker selection problem
into a combinatorial optimization problem and propose an algorithm to solve it
efficiently. Empirical results on both simulated and real-world datasets show
that our algorithm is able to select a small number of high-quality workers,
and performs as good as, sometimes even better than, the much larger crowds as
the budget allows.
Learning Contour-Fragment-based Shape Model with And-Or Tree Representation
Authors: Liang Lin, Xiaolong Wang, Wei Yang, Jian-Huang Lai
Date: 2015-02-03 00:00:00
Tasks: Clustering, Edge Detection, Model Optimization

This paper proposes a simple yet effective method to learn the hierarchical
object shape model consisting of local contour fragments, which represents a
category of shapes in the form of an And-Or tree. This model extends the
traditional hierarchical tree structures by introducing the "switch" variables
(i.e. the or-nodes) that explicitly specify production rules to capture shape
variations. We thus define the model with three layers: the leaf-nodes for
detecting local contour fragments, the or-nodes specifying selection of
leaf-nodes, and the root-node encoding the holistic distortion. In the training
stage, for optimization of the And-Or tree learning, we extend the
concave-convex procedure (CCCP) by embedding the structural clustering during
the iterative learning steps. The inference of shape detection is consistent
with the model optimization, which integrates the local testings via the
leaf-nodes and or-nodes with the global verification via the root-node. The
advantages of our approach are validated on the challenging shape databases
(i.e., ETHZ and INRIA Horse) and summarized as follows. (1) The proposed method
is able to accurately localize shape contours against unreliable edge detection
and edge tracing. (2) The And-Or tree model enables us to well capture the
intraclass variance.
Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation
Authors: Hongyuan Zhu, Fanman Meng, Jianfei Cai, Shijian Lu
Date: 2015-02-03 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation refers to the process to divide an image into
nonoverlapping meaningful regions according to human perception, which has
become a classic topic since the early ages of computer vision. A lot of
research has been conducted and has resulted in many applications. However,
while many segmentation algorithms exist, yet there are only a few sparse and
outdated summarizations available, an overview of the recent achievements and
issues is lacking. We aim to provide a comprehensive review of the recent
progress in this field. Covering 180 publications, we give an overview of broad
areas of segmentation topics including not only the classic bottom-up
approaches, but also the recent development in superpixel, interactive methods,
object proposals, semantic image parsing and image cosegmentation. In addition,
we also review the existing influential datasets and evaluation metrics.
Finally, we suggest some design flavors and research directions for future
research in image segmentation.
Deep Boosting: Layered Feature Mining for General Image Classification
Authors: Zhanglin Peng, Liang Lin, Ruimao Zhang, Jing Xu
Date: 2015-02-03 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Constructing effective representations is a critical but challenging problem
in multimedia understanding. The traditional handcraft features often rely on
domain knowledge, limiting the performances of exiting methods. This paper
discusses a novel computational architecture for general image feature mining,
which assembles the primitive filters (i.e. Gabor wavelets) into compositional
features in a layer-wise manner. In each layer, we produce a number of base
classifiers (i.e. regression stumps) associated with the generated features,
and discover informative compositions by using the boosting algorithm. The
output compositional features of each layer are treated as the base components
to build up the next layer. Our framework is able to generate expressive image
representations while inducing very discriminate functions for image
classification. The experiments are conducted on several public datasets, and
we demonstrate superior performances over state-of-the-art approaches.
Recovery of Piecewise Smooth Images from Few Fourier Samples
Authors: Greg Ongie, Mathews Jacob
Date: 2015-02-03 00:00:00
Tasks: Matrix Completion, Relation

We introduce a Prony-like method to recover a continuous domain 2-D piecewise
smooth image from few of its Fourier samples. Assuming the discontinuity set of
the image is localized to the zero level-set of a trigonometric polynomial, we
show the Fourier transform coefficients of partial derivatives of the signal
satisfy an annihilation relation. We present necessary and sufficient
conditions for unique recovery of piecewise constant images using the above
annihilation relation. We pose the recovery of the Fourier coefficients of the
signal from the measurements as a convex matrix completion algorithm, which
relies on the lifting of the Fourier data to a structured low-rank matrix; this
approach jointly estimates the signal and the annihilating filter. Finally, we
demonstrate our algorithm on the recovery of MRI phantoms from few
low-resolution Fourier samples.
Perturbed Message Passing for Constraint Satisfaction Problems
Authors: Siamak Ravanbakhsh, Russell Greiner
Date: 2014-01-26 00:00:00
Tasks: Survey

We introduce an efficient message passing scheme for solving Constraint
Satisfaction Problems (CSPs), which uses stochastic perturbation of Belief
Propagation (BP) and Survey Propagation (SP) messages to bypass decimation and
directly produce a single satisfying assignment. Our first CSP solver, called
Perturbed Blief Propagation, smoothly interpolates two well-known inference
procedures; it starts as BP and ends as a Gibbs sampler, which produces a
single sample from the set of solutions. Moreover we apply a similar
perturbation scheme to SP to produce another CSP solver, Perturbed Survey
Propagation. Experimental results on random and real-world CSPs show that
Perturbed BP is often more successful and at the same time tens to hundreds of
times more efficient than standard BP guided decimation. Perturbed BP also
compares favorably with state-of-the-art SP-guided decimation, which has a
computational complexity that generally scales exponentially worse than our
method (wrt the cardinality of variable domains and constraints). Furthermore,
our experiments with random satisfiability and coloring problems demonstrate
that Perturbed SP can outperform SP-guided decimation, making it the best
incomplete random CSP-solver in difficult regimes.
Learning the Matching Function
Authors: Ľubor Ladický, Christian Häne, Marc Pollefeys
Date: 2015-02-02 00:00:00
Tasks: Change Detection, Optical Flow Estimation, Stereo Matching, Stereo Matching Hand

The matching function for the problem of stereo reconstruction or optical
flow has been traditionally designed as a function of the distance between the
features describing matched pixels. This approach works under assumption, that
the appearance of pixels in two stereo cameras or in two consecutive video
frames does not change dramatically. However, this might not be the case, if we
try to match pixels over a large interval of time.
  In this paper we propose a method, which learns the matching function, that
automatically finds the space of allowed changes in visual appearance, such as
due to the motion blur, chromatic distortions, different colour calibration or
seasonal changes. Furthermore, it automatically learns the importance of
matching scores of contextual features at different relative locations and
scales. Proposed classifier gives reliable estimations of pixel disparities
already without any form of regularization.
  We evaluated our method on two standard problems - stereo matching on KITTI
outdoor dataset, optical flow on Sintel data set, and on newly introduced
TimeLapse change detection dataset. Our algorithm obtained very promising
results comparable to the state-of-the-art.
Minimax rates in permutation estimation for feature matching
Authors: Olivier Collier, Arnak S. Dalalyan
Date: 2013-10-17 00:00:00

The problem of matching two sets of features appears in various tasks of
computer vision and can be often formalized as a problem of permutation
estimation. We address this problem from a statistical point of view and
provide a theoretical analysis of the accuracy of several natural estimators.
To this end, the minimax rate of separation is investigated and its expression
is obtained as a function of the sample size, noise level and dimension. We
consider the cases of homoscedastic and heteroscedastic noise and establish, in
each case, tight upper bounds on the separation distance of several estimators.
These upper bounds are shown to be unimprovable both in the homoscedastic and
heteroscedastic settings. Interestingly, these bounds demonstrate that a phase
transition occurs when the dimension $d$ of the features is of the order of the
logarithm of the number of features $n$. For $d=O(\log n)$, the rate is
dimension free and equals $\sigma (\log n)^{1/2}$, where $\sigma$ is the noise
level. In contrast, when $d$ is larger than $c\log n$ for some constant $c>0$,
the minimax rate increases with $d$ and is of the order $\sigma(d\log
n)^{1/4}$. We also discuss the computational aspects of the estimators and
provide empirical evidence of their consistency on synthetic data. Finally, we
show that our results extend to more general matching criteria.
Generalized Dantzig Selector: Application to the k-support norm
Authors: Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee
Date: 2014-06-20 00:00:00
Proceeding: NeurIPS 2014 12

We propose a Generalized Dantzig Selector (GDS) for linear models, in which
any norm encoding the parameter structure can be leveraged for estimation. We
investigate both computational and statistical aspects of the GDS. Based on
conjugate proximal operator, a flexible inexact ADMM framework is designed for
solving GDS, and non-asymptotic high-probability bounds are established on the
estimation error, which rely on Gaussian width of unit norm ball and suitable
set encompassing estimation error. Further, we consider a non-trivial example
of the GDS using $k$-support norm. We derive an efficient method to compute the
proximal operator for $k$-support norm since existing methods are inapplicable
in this setting. For statistical analysis, we provide upper bounds for the
Gaussian widths needed in the GDS analysis, yielding the first statistical
recovery guarantee for estimation with the $k$-support norm. The experimental
results confirm our theoretical analysis.
Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm
Authors: Mohammadzaman Zamani, Hamid Beigy, Amirreza Shaban
Date: 2014-03-03 00:00:00
Tasks: Ensemble Learning

With the increasing volume of data in the world, the best approach for
learning from this data is to exploit an online learning algorithm. Online
ensemble methods are online algorithms which take advantage of an ensemble of
classifiers to predict labels of data. Prediction with expert advice is a
well-studied problem in the online ensemble learning literature. The Weighted
Majority algorithm and the randomized weighted majority (RWM) are the most
well-known solutions to this problem, aiming to converge to the best expert.
Since among some expert, the best one does not necessarily have the minimum
error in all regions of data space, defining specific regions and converging to
the best expert in each of these regions will lead to a better result. In this
paper, we aim to resolve this defect of RWM algorithms by proposing a novel
online ensemble algorithm to the problem of prediction with expert advice. We
propose a cascading version of RWM to achieve not only better experimental
results but also a better error bound for sufficiently large datasets.
Compressed Support Vector Machines
Authors: Zhixiang Xu, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger
Date: 2015-01-26 00:00:00

Support vector machines (SVM) can classify data sets along highly non-linear
decision boundaries because of the kernel-trick. This expressiveness comes at a
price: During test-time, the SVM classifier needs to compute the kernel
inner-product between a test sample and all support vectors. With large
training data sets, the time required for this computation can be substantial.
In this paper, we introduce a post-processing algorithm, which compresses the
learned SVM model by reducing and optimizing support vectors. We evaluate our
algorithm on several medium-scaled real-world data sets, demonstrating that it
maintains high test accuracy while reducing the test-time evaluation cost by
several orders of magnitude---in some cases from hours to seconds. It is fair
to say that most of the work in this paper was previously been invented by
Burges and Sch\"olkopf almost 20 years ago. For most of the time during which
we conducted this research, we were unaware of this prior work. However, in the
past two decades, computing power has increased drastically, and we can
therefore provide empirical insights that were not possible in their original
paper.
Scaling Recurrent Neural Network Language Models
Authors: Will Williams, Niranjani Prasad, David Mrva, Tom Ash, Tony Robinson
Date: 2015-02-02 00:00:00
Tasks: Language Modelling, Machine Translation, Translation

This paper investigates the scaling properties of Recurrent Neural Network
Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and
address the questions of how RNNLMs scale with respect to model size,
training-set size, computational costs and memory. Our analysis shows that
despite being more costly to train, RNNLMs obtain much lower perplexities on
standard benchmarks than n-gram models. We train the largest known RNNs and
present relative word error rates gains of 18% on an ASR task. We also present
the new lowest perplexities on the recently released billion word language
modelling benchmark, 1 BLEU point gain on machine translation and a 17%
relative hit rate gain in word prediction.