pwc-archive/papers-with-abstracts huggingface dataset:
Mid-level Elements for Object Detection
Authors: Aayush Bansal, Abhinav Shrivastava, Carl Doersch, Abhinav Gupta
Date: 2015-04-27 00:00:00
Tasks: Object, object-detection, Object Detection

Building on the success of recent discriminative mid-level elements, we
propose a surprisingly simple approach for object detection which performs
comparable to the current state-of-the-art approaches on PASCAL VOC comp-3
detection challenge (no external data). Through extensive experiments and
ablation analysis, we show how our approach effectively improves upon the
HOG-based pipelines by adding an intermediate mid-level representation for the
task of object detection. This representation is easily interpretable and
allows us to visualize what our object detector "sees". We also discuss the
insights our approach shares with CNN-based methods, such as sharing
representation between categories helps.
EgoSampling: Fast-Forward and Stereo for Egocentric Videos
Authors: Yair Poleg, Tavi Halperin, Chetan Arora, Shmuel Peleg
Date: 2014-12-11 00:00:00
Proceeding: CVPR 2015 6

While egocentric cameras like GoPro are gaining popularity, the videos they
capture are long, boring, and difficult to watch from start to end. Fast
forwarding (i.e. frame sampling) is a natural choice for faster video browsing.
However, this accentuates the shake caused by natural head motion, making the
fast forwarded video useless.
  We propose EgoSampling, an adaptive frame sampling that gives more stable
fast forwarded videos. Adaptive frame sampling is formulated as energy
minimization, whose optimal solution can be found in polynomial time.
  In addition, egocentric video taken while walking suffers from the left-right
movement of the head as the body weight shifts from one leg to another. We turn
this drawback into a feature: Stereo video can be created by sampling the
frames from the left most and right most head positions of each step, forming
approximate stereo-pairs.
Learning Deep Structured Models
Authors: Liang-Chieh Chen, Alexander G. Schwing, Alan L. Yuille, Raquel Urtasun
Date: 2014-07-09 00:00:00
Tasks: GPU, Multi-class Classification

Many problems in real-world applications involve predicting several random
variables which are statistically related. Markov random fields (MRFs) are a
great mathematical tool to encode such relationships. The goal of this paper is
to combine MRFs with deep learning algorithms to estimate complex
representations while taking into account the dependencies between the output
random variables. Towards this goal, we propose a training algorithm that is
able to learn structured models jointly with deep features that form the MRF
potentials. Our approach is efficient as it blends learning and inference and
makes use of GPU acceleration. We demonstrate the effectiveness of our
algorithm in the tasks of predicting words from noisy images, as well as
multi-class classification of Flickr photographs. We show that joint learning
of the deep features and the MRF parameters results in significant performance
gains.
Optimal Convergence Rate in Feed Forward Neural Networks using HJB Equation
Authors: Vipul Arora, Laxmidhar Behera, Ajay Pratap Yadav
Date: 2015-04-27 00:00:00
Tasks: global-optimization

A control theoretic approach is presented in this paper for both batch and
instantaneous updates of weights in feed-forward neural networks. The popular
Hamilton-Jacobi-Bellman (HJB) equation has been used to generate an optimal
weight update law. The remarkable contribution in this paper is that closed
form solutions for both optimal cost and weight update can be achieved for any
feed-forward network using HJB equation in a simple yet elegant manner. The
proposed approach has been compared with some of the existing best performing
learning algorithms. It is found as expected that the proposed approach is
faster in convergence in terms of computational time. Some of the benchmark
test data such as 8-bit parity, breast cancer and credit approval, as well as
2D Gabor function have been used to validate our claims. The paper also
discusses issues related to global optimization. The limitations of popular
deterministic weight update laws are critiqued and the possibility of global
optimization using HJB formulation is discussed. It is hoped that the proposed
algorithm will bring in a lot of interest in researchers working in developing
fast learning algorithms and global optimization.
Dynamic Body VSLAM with Semantic Constraints
Authors: N. Dinesh Reddy, Prateek Singhal, Visesh Chari, K. Madhava Krishna
Date: 2015-04-27 00:00:00
Tasks: Autonomous Navigation, Motion Segmentation

Image based reconstruction of urban environments is a challenging problem
that deals with optimization of large number of variables, and has several
sources of errors like the presence of dynamic objects. Since most large scale
approaches make the assumption of observing static scenes, dynamic objects are
relegated to the noise modeling section of such systems. This is an approach of
convenience since the RANSAC based framework used to compute most multiview
geometric quantities for static scenes naturally confine dynamic objects to the
class of outlier measurements. However, reconstructing dynamic objects along
with the static environment helps us get a complete picture of an urban
environment. Such understanding can then be used for important robotic tasks
like path planning for autonomous navigation, obstacle tracking and avoidance,
and other areas. In this paper, we propose a system for robust SLAM that works
in both static and dynamic environments. To overcome the challenge of dynamic
objects in the scene, we propose a new model to incorporate semantic
constraints into the reconstruction algorithm. While some of these constraints
are based on multi-layered dense CRFs trained over appearance as well as motion
cues, other proposed constraints can be expressed as additional terms in the
bundle adjustment optimization process that does iterative refinement of 3D
structure and camera / object motion trajectories. We show results on the
challenging KITTI urban dataset for accuracy of motion segmentation and
reconstruction of the trajectory and shape of moving objects relative to ground
truth. We are able to show average relative error reduction by a significant
amount for moving object trajectory reconstruction relative to state-of-the-art
methods like VISO 2, as well as standard bundle adjustment algorithms.
Image Segmentation and Restoration Using Parametric Contours With Free Endpoints
Authors: Heike Benninghoff, Harald Garcke
Date: 2015-04-27 00:00:00
Tasks: Denoising, Image Segmentation, Segmentation, Semantic Segmentation

In this paper, we introduce a novel approach for active contours with free
endpoints. A scheme is presented for image segmentation and restoration based
on a discrete version of the Mumford-Shah functional where the contours can be
both closed and open curves. Additional to a flow of the curves in normal
direction, evolution laws for the tangential flow of the endpoints are derived.
Using a parametric approach to describe the evolving contours together with an
edge-preserving denoising, we obtain a fast method for image segmentation and
restoration. The analytical and numerical schemes are presented followed by
numerical experiments with artificial test images and with a real medical
image.
Sign Stable Random Projections for Large-Scale Learning
Authors: Ping Li
Date: 2015-04-27 00:00:00
Tasks: ARC, Clustering, General Classification

We study the use of "sign $\alpha$-stable random projections" (where
$0<\alpha\leq 2$) for building basic data processing tools in the context of
large-scale machine learning applications (e.g., classification, regression,
clustering, and near-neighbor search). After the processing by sign stable
random projections, the inner products of the processed data approximate
various types of nonlinear kernels depending on the value of $\alpha$. Thus,
this approach provides an effective strategy for approximating nonlinear
learning algorithms essentially at the cost of linear learning. When $\alpha
=2$, it is known that the corresponding nonlinear kernel is the arc-cosine
kernel. When $\alpha=1$, the procedure approximates the arc-cos-$\chi^2$ kernel
(under certain condition). When $\alpha\rightarrow0+$, it corresponds to the
resemblance kernel.
  From practitioners' perspective, the method of sign $\alpha$-stable random
projections is ready to be tested for large-scale learning applications, where
$\alpha$ can be simply viewed as a tuning parameter. What is missing in the
literature is an extensive empirical study to show the effectiveness of sign
stable random projections, especially for $\alpha\neq 2$ or 1. The paper
supplies such a study on a wide variety of classification datasets. In
particular, we compare shoulder-by-shoulder sign stable random projections with
the recently proposed "0-bit consistent weighted sampling (CWS)" (Li 2015).
A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution
Authors: Ji Wu, Miao Li, Chin-Hui Lee
Date: 2015-04-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Management, Natural Language Understanding, speech-recognition, Speech Recognition

In this paper, we present a probabilistic framework for goal-driven spoken
dialog systems. A new dynamic stochastic state (DS-state) is then defined to
characterize the goal set of a dialog state at different stages of the dialog
process. Furthermore, an entropy minimization dialog management(EMDM) strategy
is also proposed to combine with the DS-states to facilitate a robust and
efficient solution in reaching a user's goals. A Song-On-Demand task, with a
total of 38117 songs and 12 attributes corresponding to each song, is used to
test the performance of the proposed approach. In an ideal simulation, assuming
no errors, the EMDM strategy is the most efficient goal-seeking method among
all tested approaches, returning the correct song within 3.3 dialog turns on
average. Furthermore, in a practical scenario, with top five candidates to
handle the unavoidable automatic speech recognition (ASR) and natural language
understanding (NLU) errors, the results show that only 61.7\% of the dialog
goals can be successfully obtained in 6.23 dialog turns on average when random
questions are asked by the system, whereas if the proposed DS-states are
updated with the top 5 candidates from the SLU output using the proposed EMDM
strategy executed at every DS-state, then a 86.7\% dialog success rate can be
accomplished effectively within 5.17 dialog turns on average. We also
demonstrate that entropy-based DM strategies are more efficient than
non-entropy based DM. Moreover, using the goal set distributions in EMDM, the
results are better than those without them, such as in sate-of-the-art database
summary DM.
Further Connections Between Contract-Scheduling and Ray-Searching Problems
Authors: Spyros Angelopoulos
Date: 2015-04-27 00:00:00
Tasks: Scheduling

This paper addresses two classes of different, yet interrelated optimization
problems. The first class of problems involves a robot that must locate a
hidden target in an environment that consists of a set of concurrent rays. The
second class pertains to the design of interruptible algorithms by means of a
schedule of contract algorithms. We study several variants of these families of
problems, such as searching and scheduling with probabilistic considerations,
redundancy and fault-tolerance issues, randomized strategies, and trade-offs
between performance and preemptions. For many of these problems we present the
first known results that apply to multi-ray and multi-problem domains. Our
objective is to demonstrate that several well-motivated settings can be
addressed using the same underlying approach.
Self-Adaptive Hierarchical Sentence Model
Authors: Han Zhao, Zhengdong Lu, Pascal Poupart
Date: 2015-04-20 00:00:00
Tasks: General Classification, model, Sentence, Subjectivity Analysis

The ability to accurately model a sentence at varying stages (e.g.,
word-phrase-sentence) plays a central role in natural language processing. As
an effort towards this goal we propose a self-adaptive hierarchical sentence
model (AdaSent). AdaSent effectively forms a hierarchy of representations from
words to phrases and then to sentences through recursive gated local
composition of adjacent segments. We design a competitive mechanism (through
gating networks) to allow the representations of the same sentence to be
engaged in a particular learning task (e.g., classification), therefore
effectively mitigating the gradient vanishing problem persistent in other
recursive models. Both qualitative and quantitative analysis shows that AdaSent
can automatically form and select the representations suitable for the task at
hand during training, yielding superior classification performance over
competitor models on 5 benchmark data sets.
Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
Authors: Xiaochuan Fan, Kang Zheng, Yuewei Lin, Song Wang
Date: 2015-04-27 00:00:00
Proceeding: CVPR 2015 6
Tasks: Pose Estimation

We propose a new learning-based method for estimating 2D human pose from a
single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
Recently, many methods have been developed to estimate human pose by using pose
priors that are estimated from physiologically inspired graphical models or
learned from a holistic perspective. In this paper, we propose to integrate
both the local (body) part appearance and the holistic view of each local part
for more accurate human pose estimation. Specifically, the proposed DS-CNN
takes a set of image patches (category-independent object proposals for
training and multi-scale sliding windows for testing) as the input and then
learns the appearance of each local part by considering their holistic views in
the full body. Using DS-CNN, we achieve both joint detection, which determines
whether an image patch contains a body joint, and joint localization, which
finds the exact location of the joint in the image patch. Finally, we develop
an algorithm to combine these joint detection/localization results from all the
image patches for estimating the human pose. The experimental results show the
effectiveness of the proposed method by comparing to the state-of-the-art
human-pose estimation methods based on pose priors that are estimated from
physiologically inspired graphical models or learned from a holistic
perspective.
Shape Representation and Classification through Pattern Spectrum and Local Binary Pattern - A Decision Level Fusion Approach
Authors: B. H. Shekar, Bharathi Pilar
Date: 2015-04-27 00:00:00
Tasks: General Classification, Retrieval

In this paper, we present a decision level fused local Morphological Pattern
Spectrum(PS) and Local Binary Pattern (LBP) approach for an efficient shape
representation and classification. This method makes use of Earth Movers
Distance(EMD) as the measure in feature matching and shape retrieval process.
The proposed approach has three major phases : Feature Extraction, Construction
of hybrid spectrum knowledge base and Classification. In the first phase,
feature extraction of the shape is done using pattern spectrum and local binary
pattern method. In the second phase, the histograms of both pattern spectrum
and local binary pattern are fused and stored in the knowledge base. In the
third phase, the comparison and matching of the features, which are represented
in the form of histograms, is done using Earth Movers Distance(EMD) as metric.
The top-n shapes are retrieved for each query shape. The accuracy is tested by
means of standard Bulls eye score method. The experiments are conducted on
publicly available shape datasets like Kimia-99, Kimia-216 and MPEG-7. The
comparative study is also provided with the well known approaches to exhibit
the retrieval accuracy of the proposed approach.
Exploring semantically-related concepts from Wikipedia: the case of SeRE
Authors: Daniel Hienert, Dennis Wegener, Siegfried Schomisch
Date: 2015-04-27 00:00:00
Tasks: Articles, General Classification

In this paper we present our web application SeRE designed to explore
semantically related concepts. Wikipedia and DBpedia are rich data sources to
extract related entities for a given topic, like in- and out-links, broader and
narrower terms, categorisation information etc. We use the Wikipedia full text
body to compute the semantic relatedness for extracted terms, which results in
a list of entities that are most relevant for a topic. For any given query, the
user interface of SeRE visualizes these related concepts, ordered by semantic
relatedness; with snippets from Wikipedia articles that explain the connection
between those two entities. In a user study we examine how SeRE can be used to
find important entities and their relationships for a given topic and to answer
the question of how the classification system can be used for filtering.
Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy
Authors: Paolo Napoletano, Giuseppe Boccignone, Francesco Tisato
Date: 2014-10-21 00:00:00
Tasks: Video Summarization

In this paper we shall consider the problem of deploying attention to subsets
of the video streams for collating the most relevant data and information of
interest related to a given task. We formalize this monitoring problem as a
foraging problem. We propose a probabilistic framework to model observer's
attentive behavior as the behavior of a forager. The forager, moment to moment,
focuses its attention on the most informative stream/camera, detects
interesting objects or activities, or switches to a more profitable stream. The
approach proposed here is suitable to be exploited for multi-stream video
summarization. Meanwhile, it can serve as a preliminary step for more
sophisticated video surveillance, e.g. activity and behavior analysis.
Experimental results achieved on the UCR Videoweb Activities Dataset, a
publicly available dataset, are presented to illustrate the utility of the
proposed technique.
On a Possible Similarity between Gene and Semantic Networks
Authors: Nicolas Turenne
Date: 2015-04-27 00:00:00
Tasks: Clustering, Object

In several domains such as linguistics, molecular biology or social sciences,
holistic effects are hardly well-defined by modeling with single units, but
more and more studies tend to understand macro structures with the help of
meaningful and useful associations in fields such as social networks, systems
biology or semantic web. A stochastic multi-agent system offers both accurate
theoretical framework and operational computing implementations to model
large-scale associations, their dynamics and patterns extraction. We show that
clustering around a target object in a set of associations of object prove some
similarity in specific data and two case studies about gene-gene and term-term
relationships leading to an idea of a common organizing principle of cognition
with random and deterministic effects.
SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization
Authors: Filipe Condessa, Jose Bioucas-Dias, Jelena Kovacevic
Date: 2015-04-27 00:00:00
Tasks: Hyperspectral Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

We present a supervised hyperspectral image segmentation algorithm based on a
convex formulation of a marginal maximum a posteriori segmentation with hidden
fields and structure tensor regularization: Segmentation via the Constraint
Split Augmented Lagrangian Shrinkage by Structure Tensor Regularization
(SegSALSA-STR). This formulation avoids the generally discrete nature of
segmentation problems and the inherent NP-hardness of the integer optimization
associated.
  We extend the Segmentation via the Constraint Split Augmented Lagrangian
Shrinkage (SegSALSA) algorithm by generalizing the vectorial total variation
prior using a structure tensor prior constructed from a patch-based Jacobian.
The resulting algorithm is convex, time-efficient and highly parallelizable.
This shows the potential of combining hidden fields with convex optimization
through the inclusion of different regularizers. The SegSALSA-STR algorithm is
validated in the segmentation of real hyperspectral images.
On-Board Vision Processing For Small UAVs: Time to Rethink Strategy
Authors: Shoaib Ehsan, Klaus D. McDonald-Maier
Date: 2015-04-27 00:00:00

The ultimate research goal for unmanned aerial vehicles (UAVs) is to
facilitate autonomy of operation. Research in the last decade has highlighted
the potential of vision sensing in this regard. Although vital for
accomplishment of missions assigned to any type of unmanned aerial vehicles,
vision sensing is more critical for small aerial vehicles due to lack of high
precision inertial sensors. In addition, uncertainty of GPS signal in indoor
and urban environments calls for more reliance on vision sensing for such small
vehicles. With off-line processing does not offer an attractive option in terms
of autonomy, these vehicles have been challenging platforms to implement vision
processing onboard due to their strict payload capacity and power budget. The
strict constraints drive the need for new vision processing architectures for
small unmanned aerial vehicles. Recent research has shown encouraging results
with FPGA based hardware architectures. This paper reviews the bottle necks
involved in implementing vision processing on-board, advocates the potential of
hardware based solutions to tackle strict constraints of small unmanned aerial
vehicles and finally analyzes feasibility of ASICs, Structured ASICs and FPGAs
for use on future systems.
Theory of Semi-Instantiation in Abstract Argumentation
Authors: D. M. Gabbay
Date: 2015-04-27 00:00:00
Tasks: Abstract Argumentation

We study instantiated abstract argumentation frames of the form $(S,R,I)$,
where $(S,R)$ is an abstract argumentation frame and where the arguments $x$ of
$S$ are instantiated by $I(x)$ as well formed formulas of a well known logic,
for example as Boolean formulas or as predicate logic formulas or as modal
logic formulas. We use the method of conceptual analysis to derive the
properties of our proposed system. We seek to define the notion of complete
extensions for such systems and provide algorithms for finding such extensions.
We further develop a theory of instantiation in the abstract, using the
framework of Boolean attack formations and of conjunctive and disjunctive
attacks. We discuss applications and compare critically with the existing
related literature.
An Active Learning Based Approach For Effective Video Annotation And Retrieval
Authors: Moitreya Chatterjee, Anton Leuski
Date: 2015-04-27 00:00:00
Tasks: Active Learning, Clustering, Diversity, Retrieval

Conventional multimedia annotation/retrieval systems such as Normalized
Continuous Relevance Model (NormCRM) [16] require a fully labeled training data
for a good performance. Active Learning, by determining an order for labeling
the training data, allows for a good performance even before the training data
is fully annotated. In this work we propose an active learning algorithm, which
combines a novel measure of sample uncertainty with a novel clustering-based
approach for determining sample density and diversity and integrate it with
NormCRM. The clusters are also iteratively refined to ensure both feature and
label-level agreement among samples. We show that our approach outperforms
multiple baselines both on a recent, open character animation dataset and on
the popular TRECVID corpus at both the tasks of annotation and text-based
retrieval of videos.