pwc-archive/papers-with-abstracts huggingface dataset:
Approximate Policy Iteration for Budgeted Semantic Video Segmentation
Authors: Behrooz Mahasseni, Sinisa Todorovic, Alan Fern
Date: 2016-07-26 00:00:00
Tasks: Video Segmentation, Video Semantic Segmentation

This paper formulates and presents a solution to the new problem of budgeted
semantic video segmentation. Given a video, the goal is to accurately assign a
semantic class label to every pixel in the video within a specified time
budget. Typical approaches to such labeling problems, such as Conditional
Random Fields (CRFs), focus on maximizing accuracy but do not provide a
principled method for satisfying a time budget. For video data, the time
required by CRF and related methods is often dominated by the time to compute
low-level descriptors of supervoxels across the video. Our key contribution is
the new budgeted inference framework for CRF models that intelligently selects
the most useful subsets of descriptors to run on subsets of supervoxels within
the time budget. The objective is to maintain an accuracy as close as possible
to the CRF model with no time bound, while remaining within the time budget.
Our second contribution is the algorithm for learning a policy for the sparse
selection of supervoxels and their descriptors for budgeted CRF inference. This
learning algorithm is derived by casting our problem in the framework of Markov
Decision Processes, and then instantiating a state-of-the-art policy learning
algorithm known as Classification-Based Approximate Policy Iteration. Our
experiments on multiple video datasets show that our learning approach and
framework is able to significantly reduce computation time, and maintain
competitive accuracy under varying budgets.
Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks
Authors: Yanghao Li, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Chunfeng Yuan, Jiaying Liu
Date: 2016-04-19 00:00:00
Tasks: Action Detection, Action Recognition, Computational Efficiency, General Classification, Online Action Detection, regression, Temporal Action Localization, Temporal Localization

Human action recognition from well-segmented 3D skeleton data has been
intensively studied and has been attracting an increasing attention. Online
action detection goes one step further and is more challenging, which
identifies the action type and localizes the action positions on the fly from
the untrimmed stream data. In this paper, we study the problem of online action
detection from streaming skeleton data. We propose a multi-task end-to-end
Joint Classification-Regression Recurrent Neural Network to better explore the
action type and temporal localization information. By employing a joint
classification and regression optimization objective, this network is capable
of automatically localizing the start and end points of actions more
accurately. Specifically, by leveraging the merits of the deep Long Short-Term
Memory (LSTM) subnetwork, the proposed model automatically captures the complex
long-range temporal dynamics, which naturally avoids the typical sliding window
design and thus ensures high computational efficiency. Furthermore, the subtask
of regression optimization provides the ability to forecast the action prior to
its occurrence. To evaluate our proposed model, we build a large streaming
video dataset with annotations. Experimental results on our dataset and the
public G3D dataset both demonstrate very promising performance of our scheme.
Symbols of a cosmic order
Authors: F. Hadi Madjid, John M. Myers
Date: 2016-07-26 00:00:00

The world runs on communicated sequences of symbols, e.g. numerals. Examining
both engineered and natural communications networks reveals an unsuspected
order that depends on contact with an unpredictable entity. This order has
three roots. The first is a proof within quantum theory that no evidence can
ever determine its explanation, so that an agent choosing an explanation must
do so unpredictably. The second root is the showing that clocks that step
computers do not "tell time" but serve as self-adjusting symbol-handling agents
that regulate "logically synchronized" motion in response to unpredictable
disturbances. Such a clock-agent has a certain independence as well as the
capacity to communicate via unpredictable symbols with other clock-agents and
to adjust its own tick rate in response to that communication. The third root
is the noticing of unpredictable symbol exchange in natural systems, including
the transmission of symbols found in molecular biology. We introduce a
symbol-handling agent as a role played in some cases by a person, for example a
physicist who chooses an explanation of given experimental outcomes, and in
other cases by some other biological entity, and in still other cases by an
inanimate device, such as a computer-based detector used in physical
measurements. While we forbear to try to explain the propensity of agents at
all levels from cells to civilizations to form and operate networks of
logically synchronized symbol-handling agents, we point to this propensity as
an overlooked cosmic order, an order structured by the unpredictability ensuing
from the proof. Appreciating the cosmic order leads to a conception of agency
that replaces volition by unpredictability and re-conceives the notion of
objectivity in a way that makes a place for agency in the world as described by
physics. Some specific implications for physics are outlined.
Leveraging Unstructured Data to Detect Emerging Reliability Issues
Authors: Deovrat Kakde, Arin Chaudhuri
Date: 2016-07-26 00:00:00

Unstructured data refers to information that does not have a predefined data
model or is not organized in a pre-defined manner. Loosely speaking,
unstructured data refers to text data that is generated by humans. In
after-sales service businesses, there are two main sources of unstructured
data: customer complaints, which generally describe symptoms, and technician
comments, which outline diagnostics and treatment information. A legitimate
customer complaint can eventually be tracked to a failure or a claim. However,
there is a delay between the time of a customer complaint and the time of a
failure or a claim. A proactive strategy aimed at analyzing customer complaints
for symptoms can help service providers detect reliability problems in advance
and initiate corrective actions such as recalls. This paper introduces
essential text mining concepts in the context of reliability analysis and a
method to detect emerging reliability issues. The application of the method is
illustrated using a case study.
Joint Optical Flow and Temporally Consistent Semantic Segmentation
Authors: Junhwa Hur, Stefan Roth
Date: 2016-07-26 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation, Scene Understanding, Segmentation, Semantic Segmentation

The importance and demands of visual scene understanding have been steadily
increasing along with the active development of autonomous systems.
Consequently, there has been a large amount of research dedicated to semantic
segmentation and dense motion estimation. In this paper, we propose a method
for jointly estimating optical flow and temporally consistent semantic
segmentation, which closely connects these two problem domains and leverages
each other. Semantic segmentation provides information on plausible physical
motion to its associated pixels, and accurate pixel-level temporal
correspondences enhance the accuracy of semantic segmentation in the temporal
domain. We demonstrate the benefits of our approach on the KITTI benchmark,
where we observe performance gains for flow and segmentation. We achieve
state-of-the-art optical flow results, and outperform all published algorithms
by a large margin on challenging, but crucial dynamic objects.
The Price of Anarchy in Auctions
Authors: Tim Roughgarden, Vasilis Syrgkanis, Eva Tardos
Date: 2016-07-26 00:00:00

This survey outlines a general and modular theory for proving approximation
guarantees for equilibria of auctions in complex settings. This theory
complements traditional economic techniques, which generally focus on exact and
optimal solutions and are accordingly limited to relatively stylized settings.
  We highlight three user-friendly analytical tools: smoothness-type
inequalities, which immediately yield approximation guarantees for many auction
formats of interest in the special case of complete information and
deterministic strategies; extension theorems, which extend such guarantees to
randomized strategies, no-regret learning outcomes, and incomplete-information
settings; and composition theorems, which extend such guarantees from simpler
to more complex auctions. Combining these tools yields tight worst-case
approximation guarantees for the equilibria of many widely-used auction
formats.
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
Authors: Yifan Wang, Lijun Wang, Hongyu Wang, Peihua Li
Date: 2016-07-26 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

One impressive advantage of convolutional neural networks (CNNs) is their
ability to automatically learn feature representation from raw pixels,
eliminating the need for hand-designed procedures. However, recent methods for
single image super-resolution (SR) fail to maintain this advantage. They
utilize CNNs in two decoupled steps, i.e., first upsampling the low resolution
(LR) image to the high resolution (HR) size with hand-designed techniques
(e.g., bicubic interpolation), and then applying CNNs on the upsampled LR image
to reconstruct HR results. In this paper, we seek an alternative and propose a
new image SR method, which jointly learns the feature extraction, upsampling
and HR reconstruction modules, yielding a completely end-to-end trainable deep
CNN. As opposed to existing approaches, the proposed method conducts upsampling
in the latent feature space with filters that are optimized for the task of
image SR. In addition, the HR reconstruction is performed in a multi-scale
manner to simultaneously incorporate both short- and long-range contextual
information, ensuring more accurate restoration of HR images. To facilitate
network training, a new training approach is designed, which jointly trains the
proposed deep network with a relatively shallow network, leading to faster
convergence and more superior performance. The proposed method is extensively
evaluated on widely adopted data sets and improves the performance of
state-of-the-art methods with a considerable margin. Moreover, in-depth
ablation studies are conducted to verify the contribution of different network
designs to image SR, providing additional insights for future research.
Region-based semantic segmentation with end-to-end training
Authors: Holger Caesar, Jasper Uijlings, Vittorio Ferrari
Date: 2016-07-26 00:00:00
Tasks: Segmentation, Semantic Segmentation

We propose a novel method for semantic segmentation, the task of labeling
each pixel in an image with a semantic class. Our method combines the
advantages of the two main competing paradigms. Methods based on region
classification offer proper spatial support for appearance measurements, but
typically operate in two separate stages, none of which targets pixel labeling
performance at the end of the pipeline. More recent fully convolutional methods
are capable of end-to-end training for the final pixel labeling, but resort to
fixed patches as spatial support. We show how to modify modern region-based
approaches to enable end-to-end training for semantic segmentation. This is
achieved via a differentiable region-to-pixel layer and a differentiable
free-form Region-of-Interest pooling layer. Our method improves the
state-of-the-art in terms of class-average accuracy with 64.0% on SIFT Flow and
49.9% on PASCAL Context, and is particularly accurate at object boundaries.
Fundamental Matrices from Moving Objects Using Line Motion Barcodes
Authors: Yoni Kasten, Gil Ben-Artzi, Shmuel Peleg, Michael Werman
Date: 2016-07-26 00:00:00

Computing the epipolar geometry between cameras with very different
viewpoints is often very difficult. The appearance of objects can vary greatly,
and it is difficult to find corresponding feature points. Prior methods
searched for corresponding epipolar lines using points on the convex hull of
the silhouette of a single moving object. These methods fail when the scene
includes multiple moving objects. This paper extends previous work to scenes
having multiple moving objects by using the "Motion Barcodes", a temporal
signature of lines. Corresponding epipolar lines have similar motion barcodes,
and candidate pairs of corresponding epipoar lines are found by the similarity
of their motion barcodes. As in previous methods we assume that cameras are
relatively stationary and that moving objects have already been extracted using
background subtraction.
Machine Learned Resume-Job Matching Solution
Authors: Yiou Lin, Hang Lei, Prince Clement Addo, Xiaoyu Li
Date: 2016-07-26 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Job search through online matching engines nowadays are very prominent and
beneficial to both job seekers and employers. But the solutions of traditional
engines without understanding the semantic meanings of different resumes have
not kept pace with the incredible changes in machine learning techniques and
computing capability. These solutions are usually driven by manual rules and
predefined weights of keywords which lead to an inefficient and frustrating
search experience. To this end, we present a machine learned solution with rich
features and deep learning methods. Our solution includes three configurable
modules that can be plugged with little restrictions. Namely, unsupervised
feature extraction, base classifiers training and ensemble method learning. In
our solution, rather than using manual rules, machine learned methods to
automatically detect the semantic similarity of positions are proposed. Then
four competitive "shallow" estimators and "deep" estimators are selected.
Finally, ensemble methods to bag these estimators and aggregate their
individual predictions to form a final prediction are verified. Experimental
results of over 47 thousand resumes show that our solution can significantly
improve the predication precision current position, salary, educational
background and company scale.
Label Tree Embeddings for Acoustic Scene Classification
Authors: Huy Phan, Lars Hertel, Marco Maass, Philipp Koch, Alfred Mertins
Date: 2016-06-25 00:00:00
Tasks: Acoustic Scene Classification, Classification, Clustering, General Classification, Scene Classification

We present in this paper an efficient approach for acoustic scene
classification by exploring the structure of class labels. Given a set of class
labels, a category taxonomy is automatically learned by collectively optimizing
a clustering of the labels into multiple meta-classes in a tree structure. An
acoustic scene instance is then embedded into a low-dimensional feature
representation which consists of the likelihoods that it belongs to the
meta-classes. We demonstrate state-of-the-art results on two different datasets
for the acoustic scene classification task, including the DCASE 2013 and LITIS
Rouen datasets.
Emotion-Based Crowd Representation for Abnormality Detection
Authors: Hamidreza Rabiee, Javad Haddadnia, Hossein Mousavi, Moin Nabi, Vittorio Murino, Nicu Sebe
Date: 2016-07-26 00:00:00
Tasks: Action Recognition, Anomaly Detection, Attribute, Descriptive, Temporal Action Localization

In crowd behavior understanding, a model of crowd behavior need to be trained
using the information extracted from video sequences. Since there is no
ground-truth available in crowd datasets except the crowd behavior labels, most
of the methods proposed so far are just based on low-level visual features.
However, there is a huge semantic gap between low-level motion/appearance
features and high-level concept of crowd behaviors. In this paper we propose an
attribute-based strategy to alleviate this problem. While similar strategies
have been recently adopted for object and action recognition, as far as we
know, we are the first showing that the crowd emotions can be used as
attributes for crowd behavior understanding. The main idea is to train a set of
emotion-based classifiers, which can subsequently be used to represent the
crowd motion. For this purpose, we collect a big dataset of video clips and
provide them with both annotations of "crowd behaviors" and "crowd emotions".
We show the results of the proposed method on our dataset, which demonstrate
that the crowd emotions enable the construction of more descriptive models for
crowd behaviors. We aim at publishing the dataset with the article, to be used
as a benchmark for the communities.
Scale Invariant Interest Points with Shearlets
Authors: Miguel A. Duval-Poo, Nicoletta Noceti, Francesca Odone, Ernesto de Vito
Date: 2016-07-26 00:00:00

Shearlets are a relatively new directional multi-scale framework for signal
analysis, which have been shown effective to enhance signal discontinuities
such as edges and corners at multiple scales. In this work we address the
problem of detecting and describing blob-like features in the shearlets
framework. We derive a measure which is very effective for blob detection and
closely related to the Laplacian of Gaussian. We demonstrate the measure
satisfies the perfect scale invariance property in the continuous case. In the
discrete setting, we derive algorithms for blob detection and keypoint
description. Finally, we provide qualitative justifications of our findings as
well as a quantitative evaluation on benchmark data. We also report an
experimental evidence that our method is very suitable to deal with compressed
and noisy images, thanks to the sparsity property of shearlets.
Grounded Lexicon Acquisition - Case Studies in Spatial Language
Authors: Michael Spranger
Date: 2016-07-26 00:00:00

This paper discusses grounded acquisition experiments of increasing
complexity. Humanoid robots acquire English spatial lexicons from robot tutors.
We identify how various spatial language systems, such as projective, absolute
and proximal can be learned. The proposed learning mechanisms do not rely on
direct meaning transfer or direct access to world models of interlocutors.
Finally, we show how multiple systems can be acquired at the same time.
A New PAC-Bayesian Perspective on Domain Adaptation
Authors: Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
Date: 2015-06-15 00:00:00
Tasks: Domain Adaptation

We study the issue of PAC-Bayesian domain adaptation: We want to learn, from
a source domain, a majority vote model dedicated to a target one. Our
theoretical contribution brings a new perspective by deriving an upper-bound on
the target risk where the distributions' divergence---expressed as a
ratio---controls the trade-off between a source error measure and the target
voters' disagreement. Our bound suggests that one has to focus on regions where
the source data is informative.From this result, we derive a PAC-Bayesian
generalization bound, and specialize it to linear classifiers. Then, we infer a
learning algorithmand perform experiments on real data.
Semantic Clustering for Robust Fine-Grained Scene Recognition
Authors: Marian George, Mandar Dixit, Gábor Zogg, Nuno Vasconcelos
Date: 2016-07-26 00:00:00
Tasks: Clustering, Domain Generalization, Informativeness, Scene Recognition

In domain generalization, the knowledge learnt from one or multiple source
domains is transferred to an unseen target domain. In this work, we propose a
novel domain generalization approach for fine-grained scene recognition. We
first propose a semantic scene descriptor that jointly captures the subtle
differences between fine-grained scenes, while being robust to varying object
configurations across domains. We model the occurrence patterns of objects in
scenes, capturing the informativeness and discriminability of each object for
each scene. We then transform such occurrences into scene probabilities for
each scene image. Second, we argue that scene images belong to hidden semantic
topics that can be discovered by clustering our semantic descriptors. To
evaluate the proposed method, we propose a new fine-grained scene dataset in
cross-domain settings. Extensive experiments on the proposed dataset and three
benchmark scene datasets show the effectiveness of the proposed approach for
fine-grained scene transfer, where we outperform state-of-the-art scene
recognition and domain generalization methods.
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing
Authors: Vivek S. Borkar, Vikranth R. Dwaracherla, Neeraja Sahasrabudhe
Date: 2015-11-27 00:00:00
Tasks: Compressive Sensing

This paper aims at achieving a "good" estimator for the gradient of a
function on a high-dimensional space. Often such functions are not sensitive in
all coordinates and the gradient of the function is almost sparse. We propose a
method for gradient estimation that combines ideas from Spall's Simultaneous
Perturbation Stochastic Approximation with compressive sensing. The aim is to
obtain "good" estimator without too many function evaluations. Application to
estimating gradient outer product matrix as well as standard optimization
problems are illustrated via simulations.
Learning Null Space Projections in Operational Space Formulation
Authors: Hsiu-Chin Lin, Matthew Howard
Date: 2016-07-26 00:00:00

In recent years, a number of tools have become available that recover the
underlying control policy from constrained movements. However, few have
explicitly considered learning the constraints of the motion and ways to cope
with unknown environment. In this paper, we consider learning the null space
projection matrix of a kinematically constrained system in the absence of any
prior knowledge either on the underlying policy, the geometry, or
dimensionality of the constraints. Our evaluations have demonstrated the
effectiveness of the proposed approach on problems of differing dimensionality,
and with different degrees of non-linearity.
Infrared Colorization Using Deep Convolutional Neural Networks
Authors: Matthias Limmer, Hendrik P. A. Lensch
Date: 2016-04-08 00:00:00
Tasks: Colorization

This paper proposes a method for transferring the RGB color spectrum to
near-infrared (NIR) images using deep multi-scale convolutional neural
networks. A direct and integrated transfer between NIR and RGB pixels is
trained. The trained model does not require any user guidance or a reference
image database in the recall phase to produce images with a natural appearance.
To preserve the rich details of the NIR image, its high frequency features are
transferred to the estimated RGB image. The presented approach is trained and
evaluated on a real-world dataset containing a large amount of road scene
images in summer. The dataset was captured by a multi-CCD NIR/RGB camera, which
ensures a perfect pixel to pixel registration.