pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Human-Centric Images: From Geometry to Fashion
Authors: Edgar Simo-Serra
Date: 2015-12-14 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation, Semantic Segmentation

Understanding humans from photographs has always been a fundamental goal of
computer vision. In this thesis we have developed a hierarchy of tools that
cover a wide range of topics with the objective of understanding humans from
monocular RGB image: from low level feature point descriptors to high level
fashion-aware conditional random fields models. In order to build these high
level models it is paramount to have a battery of robust and reliable low and
mid level cues. Along these lines, we have proposed two low-level keypoint
descriptors: one based on the theory of the heat diffusion on images, and the
other that uses a convolutional neural network to learn discriminative image
patch representations. We also introduce distinct low-level generative models
for representing human pose: in particular we present a discrete model based on
a directed acyclic graph and a continuous model that consists of poses
clustered on a Riemannian manifold. As mid level cues we propose two 3D human
pose estimation algorithms: one that estimates the 3D pose given a noisy 2D
estimation, and an approach that simultaneously estimates both the 2D and 3D
pose. Finally, we formulate higher level models built upon low and mid level
cues for understanding humans from single images. Concretely, we focus on two
different tasks in the context of fashion: semantic segmentation of clothing,
and predicting the fashionability from images with metadata to ultimately
provide fashion advice to the user. For all presented approaches we present
extensive results and comparisons against the state-of-the-art and show
significant improvements on the entire variety of tasks we tackle.
Cross-validation of matching correlation analysis by resampling matching weights
Authors: Hidetoshi Shimodaira
Date: 2015-03-29 00:00:00
Tasks: Dimensionality Reduction, Graph Embedding

The strength of association between a pair of data vectors is represented by
a nonnegative real number, called matching weight. For dimensionality
reduction, we consider a linear transformation of data vectors, and define a
matching error as the weighted sum of squared distances between transformed
vectors with respect to the matching weights. Given data vectors and matching
weights, the optimal linear transformation minimizing the matching error is
solved by the spectral graph embedding of Yan et al. (2007). This method is a
generalization of the canonical correlation analysis, and will be called as
matching correlation analysis (MCA). In this paper, we consider a novel
sampling scheme where the observed matching weights are randomly sampled from
underlying true matching weights with small probability, whereas the data
vectors are treated as constants. We then investigate a cross-validation by
resampling the matching weights. Our asymptotic theory shows that the
cross-validation, if rescaled properly, computes an unbiased estimate of the
matching error with respect to the true matching weights. Existing ideas of
cross-validation for resampling data vectors, instead of resampling matching
weights, are not applicable here. MCA can be used for data vectors from
multiple domains with different dimensions via an embarrassingly simple idea of
coding the data vectors. This method will be called as cross-domain matching
correlation analysis (CDMCA), and an interesting connection to the classical
associative memory model of neural networks is also discussed.
Spectral Smoothing via Random Matrix Perturbations
Authors: Jacob Abernethy, Chansoo Lee, Ambuj Tewari
Date: 2015-07-10 00:00:00

We consider stochastic smoothing of spectral functions of matrices using
perturbations commonly studied in random matrix theory. We show that a spectral
function remains spectral when smoothed using a unitarily invariant
perturbation distribution. We then derive state-of-the-art smoothing bounds for
the maximum eigenvalue function using the Gaussian Orthogonal Ensemble (GOE).
Smoothing the maximum eigenvalue function is important for applications in
semidefinite optimization and online learning. As a direct consequence of our
GOE smoothing results, we obtain an $O((N \log N)^{1/4} \sqrt{T})$ expected
regret bound for the online variance minimization problem using an algorithm
that performs only a single maximum eigenvector computation per time step. Here
$T$ is the number of rounds and $N$ is the matrix dimension. Our algorithm and
its analysis also extend to the more general online PCA problem where the
learner has to output a rank $k$ subspace. The algorithm just requires
computing $k$ maximum eigenvectors per step and enjoys an $O(k (N \log N)^{1/4}
\sqrt{T})$ expected regret bound.
Fighting Bandits with a New Kind of Smoothness
Authors: Jacob Abernethy, Chansoo Lee, Ambuj Tewari
Date: 2015-12-14 00:00:00
Proceeding: NeurIPS 2015 12

We define a novel family of algorithms for the adversarial multi-armed bandit
problem, and provide a simple analysis technique based on convex smoothing. We
prove two main results. First, we show that regularization via the
\emph{Tsallis entropy}, which includes EXP3 as a special case, achieves the
$\Theta(\sqrt{TN})$ minimax regret. Second, we show that a wide class of
perturbation methods achieve a near-optimal regret as low as $O(\sqrt{TN \log
N})$ if the perturbation distribution has a bounded hazard rate. For example,
the Gumbel, Weibull, Frechet, Pareto, and Gamma distributions all satisfy this
key property.
Learning Deep Features for Discriminative Localization
Authors: Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba
Date: 2015-12-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object Localization, Weakly-Supervised Object Localization

In this work, we revisit the global average pooling layer proposed in [13],
and shed light on how it explicitly enables the convolutional neural network to
have remarkable localization ability despite being trained on image-level
labels. While this technique was previously proposed as a means for
regularizing training, we find that it actually builds a generic localizable
deep representation that can be applied to a variety of tasks. Despite the
apparent simplicity of global average pooling, we are able to achieve 37.1%
top-5 error for object localization on ILSVRC 2014, which is remarkably close
to the 34.2% top-5 error achieved by a fully supervised CNN approach. We
demonstrate that our network is able to localize the discriminative image
regions on a variety of tasks despite not being trained for them
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Authors: Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick
Date: 2015-12-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, Small Object Detection

It is well known that contextual and multi-scale representations are
important for accurate visual recognition. In this paper we present the
Inside-Outside Net (ION), an object detector that exploits information both
inside and outside the region of interest. Contextual information outside the
region of interest is integrated using spatial recurrent neural networks.
Inside, we use skip pooling to extract information at multiple scales and
levels of abstraction. Through extensive experiments we evaluate the design
space and provide readers with an overview of what tricks of the trade are
important. ION improves state-of-the-art on PASCAL VOC 2012 object detection
from 73.9% to 76.4% mAP. On the new and more challenging MS COCO dataset, we
improve state-of-art-the from 19.7% to 33.1% mAP. In the 2015 MS COCO Detection
Challenge, our ION model won the Best Student Entry and finished 3rd place
overall. As intuition suggests, our detection results provide strong evidence
that context and multi-scale representations improve small object detection.
SentiCap: Generating Image Descriptions with Sentiments
Authors: Alexander Mathews, Lexing Xie, Xuming He
Date: 2015-10-06 00:00:00
Tasks: Decision Making, Descriptive, Image Captioning, Language Modeling, Language Modelling

The recent progress on image recognition and language modeling is making
automatic description of image content a reality. However, stylized,
non-factual aspects of the written description are missing from the current
systems. One such style is descriptions with emotions, which is commonplace in
everyday communication, and influences decision-making and interpersonal
relationships. We design a system to describe an image with emotions, and
present a model that automatically generates captions with positive or negative
sentiments. We propose a novel switching recurrent neural network with
word-level regularization, which is able to produce emotional image captions
using only 2000+ training sentences containing sentiments. We evaluate the
captions with different automatic and crowd-sourcing metrics. Our model
compares favourably in common quality metrics for image captioning. In 84.6% of
cases the generated positive captions were judged as being at least as
descriptive as the factual captions. Of these positive captions 88% were
confirmed by the crowd-sourced workers as having the appropriate sentiment.
Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect
Authors: Qifei Wang, Gregorij Kurillo, Ferda Ofli, Ruzena Bajcsy
Date: 2015-12-13 00:00:00
Tasks: Pose Tracking

Microsoft Kinect camera and its skeletal tracking capabilities have been
embraced by many researchers and commercial developers in various applications
of real-time human movement analysis. In this paper, we evaluate the accuracy
of the human kinematic motion data in the first and second generation of the
Kinect system, and compare the results with an optical motion capture system.
We collected motion data in 12 exercises for 10 different subjects and from
three different viewpoints. We report on the accuracy of the joint localization
and bone length estimation of Kinect skeletons in comparison to the motion
capture. We also analyze the distribution of the joint localization offsets by
fitting a mixture of Gaussian and uniform distribution models to determine the
outliers in the Kinect motion data. Our analysis shows that overall Kinect 2
has more robust and more accurate tracking of human pose as compared to Kinect
1.
A Person Re-Identification System For Mobile Devices
Authors: George Cushen
Date: 2015-12-13 00:00:00
Tasks: Person Re-Identification

Person re-identification is a critical security task for recognizing a person
across spatially disjoint sensors. Previous work can be computationally
intensive and is mainly based on low-level cues extracted from RGB data and
implemented on a PC for a fixed sensor network (such as traditional CCTV). We
present a practical and efficient framework for mobile devices (such as smart
phones and robots) where high-level semantic soft biometrics are extracted from
RGB and depth data. By combining these cues, our approach attempts to provide
robustness to noise, illumination, and minor variations in clothing. This
mobile approach may be particularly useful for the identification of persons in
areas ill-served by fixed sensors or for tasks where the sensor position and
direction need to dynamically adapt to a target. Results on the BIWI dataset
are preliminary but encouraging. Further evaluation and demonstration of the
system will be available on our website.
Dimensionality-reduced subspace clustering
Authors: Reinhard Heckel, Michael Tschannen, Helmut Bölcskei
Date: 2015-07-25 00:00:00
Tasks: Clustering, Dimensionality Reduction

Subspace clustering refers to the problem of clustering unlabeled
high-dimensional data points into a union of low-dimensional linear subspaces,
whose number, orientations, and dimensions are all unknown. In practice one may
have access to dimensionality-reduced observations of the data only, resulting,
e.g., from undersampling due to complexity and speed constraints on the
acquisition device or mechanism. More pertinently, even if the high-dimensional
data set is available it is often desirable to first project the data points
into a lower-dimensional space and to perform clustering there; this reduces
storage requirements and computational cost. The purpose of this paper is to
quantify the impact of dimensionality reduction through random projection on
the performance of three subspace clustering algorithms, all of which are based
on principles from sparse signal recovery. Specifically, we analyze the
thresholding based subspace clustering (TSC) algorithm, the sparse subspace
clustering (SSC) algorithm, and an orthogonal matching pursuit variant thereof
(SSC-OMP). We find, for all three algorithms, that dimensionality reduction
down to the order of the subspace dimensions is possible without incurring
significant performance degradation. Moreover, these results are order-wise
optimal in the sense that reducing the dimensionality further leads to a
fundamentally ill-posed clustering problem. Our findings carry over to the
noisy case as illustrated through analytical results for TSC and simulations
for SSC and SSC-OMP. Extensive experiments on synthetic and real data
complement our theoretical findings.
Articulated Pose Estimation Using Hierarchical Exemplar-Based Models
Authors: Jiongxin Liu, Yinxiao Li, Peter Allen, Peter Belhumeur
Date: 2015-12-13 00:00:00
Tasks: Pose Estimation

Exemplar-based models have achieved great success on localizing the parts of
semi-rigid objects. However, their efficacy on highly articulated objects such
as humans is yet to be explored. Inspired by hierarchical object representation
and recent application of Deep Convolutional Neural Networks (DCNNs) on human
pose estimation, we propose a novel formulation that incorporates both
hierarchical exemplar-based models and DCNNs in the spatial terms.
Specifically, we obtain more expressive spatial models by assuming independence
between exemplars at different levels in the hierarchy; we also obtain stronger
spatial constraints by inferring the spatial relations between parts at the
same level. As our method strikes a good balance between expressiveness and
strength of spatial models, it is both effective and generalizable, achieving
state-of-the-art results on different benchmarks: Leeds Sports Dataset and
CUB-200-2011.
Unsupervised Temporal Segmentation of Repetitive Human Actions Based on Kinematic Modeling and Frequency Analysis
Authors: Qifei Wang, Gregorij Kurillo, Ferda Ofli, Ruzena Bajcsy
Date: 2015-12-13 00:00:00
Tasks: Clustering, Segmentation

In this paper, we propose a method for temporal segmentation of human
repetitive actions based on frequency analysis of kinematic parameters,
zero-velocity crossing detection, and adaptive k-means clustering. Since the
human motion data may be captured with different modalities which have
different temporal sampling rate and accuracy (e.g., optical motion capture
systems vs. Microsoft Kinect), we first apply a generic full-body kinematic
model with an unscented Kalman filter to convert the motion data into a unified
representation that is robust to noise. Furthermore, we extract the most
representative kinematic parameters via the primary frequency analysis. The
sequences are segmented based on zero-velocity crossing of the selected
parameters followed by an adaptive k-means clustering to identify the
repetition segments. Experimental results demonstrate that for the motion data
captured by both the motion capture system and the Microsoft Kinect, our
proposed algorithm obtains robust segmentation of repetitive action sequences.
Policy Gradient Methods for Off-policy Control
Authors: Lucas Lehnert, Doina Precup
Date: 2015-12-13 00:00:00
Tasks: Policy Gradient Methods

Off-policy learning refers to the problem of learning the value function of a
way of behaving, or policy, while following a different policy. Gradient-based
off-policy learning algorithms, such as GTD and TDC/GQ, converge even when
using function approximation and incremental updates. However, they have been
developed for the case of a fixed behavior policy. In control problems, one
would like to adapt the behavior policy over time to become more greedy with
respect to the existing value function. In this paper, we present the first
gradient-based learning algorithms for this problem, which rely on the
framework of policy gradient in order to modify the behavior policy. We present
derivations of the algorithms, a convergence theorem, and empirical evidence
showing that they compare favorably to existing approaches.
Stack Exchange Tagger
Authors: Sanket Mehta, Shagun Sodhani
Date: 2015-12-13 00:00:00
Tasks: Classification, General Classification

The goal of our project is to develop an accurate tagger for questions posted
on Stack Exchange. Our problem is an instance of the more general problem of
developing accurate classifiers for large scale text datasets. We are tackling
the multilabel classification problem where each item (in this case, question)
can belong to multiple classes (in this case, tags). We are predicting the tags
(or keywords) for a particular Stack Exchange post given only the question text
and the title of the post. In the process, we compare the performance of
Support Vector Classification (SVC) for different kernel functions, loss
function, etc. We found linear SVC with Crammer Singer technique produces best
results.
Big Data Scaling through Metric Mapping: Exploiting the Remarkable Simplicity of Very High Dimensional Spaces using Correspondence Analysis
Authors: Fionn Murtagh
Date: 2015-12-13 00:00:00

We present new findings in regard to data analysis in very high dimensional
spaces. We use dimensionalities up to around one million. A particular benefit
of Correspondence Analysis is its suitability for carrying out an orthonormal
mapping, or scaling, of power law distributed data. Power law distributed data
are found in many domains. Correspondence factor analysis provides a latent
semantic or principal axes mapping. Our experiments use data from digital
chemistry and finance, and other statistically generated data.
Tracking Idea Flows between Social Groups
Authors: Yangxin Zhong, Shixia Liu, Xiting Wang, Jiannan Xiao, Yangqiu Song
Date: 2015-12-13 00:00:00
Tasks: Clustering, Dynamic Time Warping

In many applications, ideas that are described by a set of words often flow
between different groups. To facilitate users in analyzing the flow, we present
a method to model the flow behaviors that aims at identifying the lead-lag
relationships between word clusters of different user groups. In particular, an
improved Bayesian conditional cointegration based on dynamic time warping is
employed to learn links between words in different groups. A tensor-based
technique is developed to cluster these linked words into different clusters
(ideas) and track the flow of ideas. The main feature of the tensor
representation is that we introduce two additional dimensions to represent both
time and lead-lag relationships. Experiments on both synthetic and real
datasets show that our method is more effective than methods based on
traditional clustering techniques and achieves better accuracy. A case study
was conducted to demonstrate the usefulness of our method in helping users
understand the flow of ideas between different user groups on social media
The Rationale behind the Concept of Goal
Authors: Guido Governatori, Francesco Olivieri, Simone Scannapieco, Antonino Rotolo, Matteo Cristani
Date: 2015-12-13 00:00:00

The paper proposes a fresh look at the concept of goal and advances that
motivational attitudes like desire, goal and intention are just facets of the
broader notion of (acceptable) outcome. We propose to encode the preferences of
an agent as sequences of "alternative acceptable outcomes". We then study how
the agent's beliefs and norms can be used to filter the mental attitudes out of
the sequences of alternative acceptable outcomes. Finally, we formalise such
intuitions in a novel Modal Defeasible Logic and we prove that the resulting
formalisation is computationally feasible.
Deep Tracking: Visual Tracking Using Deep Convolutional Networks
Authors: Meera Hahn, Si Chen, Afshin Dehghan
Date: 2015-12-13 00:00:00
Tasks: Visual Tracking

In this paper, we study a discriminatively trained deep convolutional network
for the task of visual tracking. Our tracker utilizes both motion and
appearance features that are extracted from a pre-trained dual stream deep
convolution network. We show that the features extracted from our dual-stream
network can provide rich information about the target and this leads to
competitive performance against state of the art tracking methods on a visual
tracking benchmark.
Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance
Authors: Mauricio Santillana, Andre Nguyen, Tamara Louie, Anna Zink, Josh Gray, Iyue Sung, John S. Brownstein
Date: 2015-12-13 00:00:00
Tasks: BIG-bench Machine Learning

Accurate real-time monitoring systems of influenza outbreaks help public
health officials make informed decisions that may help save lives. We show that
information extracted from cloud-based electronic health records databases, in
combination with machine learning techniques and historical epidemiological
information, have the potential to accurately and reliably provide near
real-time regional predictions of flu outbreaks in the United States.
Action Recognition with Image Based CNN Features
Authors: Mahdyar Ravanbakhsh, Hossein Mousavi, Mohammad Rastegari, Vittorio Murino, Larry S. Davis
Date: 2015-12-13 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Most of human actions consist of complex temporal compositions of more simple
actions. Action recognition tasks usually relies on complex handcrafted
structures as features to represent the human action model. Convolutional
Neural Nets (CNN) have shown to be a powerful tool that eliminate the need for
designing handcrafted features. Usually, the output of the last layer in CNN (a
layer before the classification layer -known as fc7) is used as a generic
feature for images. In this paper, we show that fc7 features, per se, can not
get a good performance for the task of action recognition, when the network is
trained only on images. We present a feature structure on top of fc7 features,
which can capture the temporal variation in a video. To represent the temporal
components, which is needed to capture motion information, we introduced a
hierarchical structure. The hierarchical model enables to capture sub-actions
from a complex action. At the higher levels of the hierarchy, it represents a
coarse capture of action sequence and lower levels represent fine action
elements. Furthermore, we introduce a method for extracting key-frames using
binary coding of each frame in a video, which helps to improve the performance
of our hierarchical model. We experimented our method on several action
datasets and show that our method achieves superior results compared to other
state-of-the-arts methods.
RNN Fisher Vectors for Action Recognition and Image Annotation
Authors: Guy Lev, Gil Sadeh, Benjamin Klein, Lior Wolf
Date: 2015-12-12 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Transfer Learning

Recurrent Neural Networks (RNNs) have had considerable success in classifying
and predicting sequences. We demonstrate that RNNs can be effectively used in
order to encode sequences and provide effective representations. The
methodology we use is based on Fisher Vectors, where the RNNs are the
generative probabilistic models and the partial derivatives are computed using
backpropagation. State of the art results are obtained in two central but
distant tasks, which both rely on sequences: video action recognition and image
annotation. We also show a surprising transfer learning result from the task of
image annotation to the task of video action recognition.