pwc-archive/papers-with-abstracts huggingface dataset:
Manifold Gaussian Processes for Regression
Authors: Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth
Date: 2014-02-24 00:00:00
Tasks: Gaussian Processes, regression

Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness
assumptions on the structure of the function to be modeled. To model complex
and non-differentiable functions, these smoothness assumptions are often too
restrictive. One way to alleviate this limitation is to find a different
representation of the data by introducing a feature space. This feature space
is often learned in an unsupervised way, which might lead to data
representations that are not useful for the overall regression task. In this
paper, we propose Manifold Gaussian Processes, a novel supervised method that
jointly learns a transformation of the data into a feature space and a GP
regression from the feature space to observed space. The Manifold GP is a full
GP and allows to learn data representations, which are useful for the overall
regression task. As a proof-of-concept, we evaluate our approach on complex
non-smooth functions where standard GPs perform poorly, such as step functions
and robotics tasks with contacts.
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Authors: Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, Yu Qiao
Date: 2016-04-11 00:00:00
Tasks: Code Generation, Face Alignment, Face Detection

Face detection and alignment in unconstrained environment are challenging due
to various poses, illuminations and occlusions. Recent studies show that deep
learning approaches can achieve impressive performance on these two tasks. In
this paper, we propose a deep cascaded multi-task framework which exploits the
inherent correlation between them to boost up their performance. In particular,
our framework adopts a cascaded structure with three stages of carefully
designed deep convolutional networks that predict face and landmark location in
a coarse-to-fine manner. In addition, in the learning process, we propose a new
online hard sample mining strategy that can improve the performance
automatically without manual sample selection. Our method achieves superior
accuracy over the state-of-the-art techniques on the challenging FDDB and WIDER
FACE benchmark for face detection, and AFLW benchmark for face alignment, while
keeps real time performance.
Efficient Globally Optimal 2D-to-3D Deformable Shape Matching
Authors: Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt, Michael M. Bronstein, Daniel Cremers
Date: 2016-01-22 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Shape Retrieval, Retrieval

We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the output is a continuous curve on the surface. We cast the problem as finding the shortest circular path on the product 3-manifold of the surface and the curve. We prove that the optimal matching can be computed in polynomial time with a (worst-case) complexity of $O(mn^2\log(n))$, where $m$ and $n$ denote the number of vertices on the template curve and the 3D shape respectively. We also demonstrate that in practice the runtime is essentially linear in $m\!\cdot\! n$ making it an efficient method for shape analysis and shape retrieval. Quantitative evaluation confirms that the method provides excellent results for sketch-based deformable 3D shape retrieval.
Active Learning for Online Recognition of Human Activities from Streaming Videos
Authors: Rocco De Rosa, Ilaria Gori, Fabio Cuzzolin, Barbara Caputo, Nicolò Cesa-Bianchi
Date: 2016-04-11 00:00:00
Tasks: Active Learning

Recognising human activities from streaming videos poses unique challenges to
learning algorithms: predictive models need to be scalable, incrementally
trainable, and must remain bounded in size even when the data stream is
arbitrarily long. Furthermore, as parameter tuning is problematic in a
streaming setting, suitable approaches should be parameterless, and make no
assumptions on what class labels may occur in the stream. We present here an
approach to the recognition of human actions from streaming data which meets
all these requirements by: (1) incrementally learning a model which adaptively
covers the feature space with simple local classifiers; (2) employing an active
learning strategy to reduce annotation requests; (3) achieving promising
accuracy within a fixed model size. Extensive experiments on standard
benchmarks show that our approach is competitive with state-of-the-art
non-incremental methods, and outperforms the existing active incremental
baselines.
Method of Tibetan Person Knowledge Extraction
Authors: Yuan Sun, Zhen Zhu
Date: 2016-04-11 00:00:00
Tasks: graph construction, Information Retrieval, Question Answering, Retrieval, Unity

Person knowledge extraction is the foundation of the Tibetan knowledge graph
construction, which provides support for Tibetan question answering system,
information retrieval, information extraction and other researches, and
promotes national unity and social stability. This paper proposes a SVM and
template based approach to Tibetan person knowledge extraction. Through
constructing the training corpus, we build the templates based the shallow
parsing analysis of Tibetan syntactic, semantic features and verbs. Using the
training corpus, we design a hierarchical SVM classifier to realize the entity
knowledge extraction. Finally, experimental results prove the method has
greater improvement in Tibetan person knowledge extraction.
Differential Evolution with Generalized Mutation Operator for Parameters Optimization in Gene Selection for Cancer Classification
Authors: H. Sharifi Noghabi, H. Rajabi Mashhadi, K. Shojaei
Date: 2015-10-08 00:00:00
Tasks: Cancer Classification, Evolutionary Algorithms, feature selection, General Classification, global-optimization

Differential Evolution (DE) proved to be one of the most successful
evolutionary algorithms for global optimization purposes in continuous
problems. The core operator in DE is mutation which can provide the algorithm
with both exploration and exploitation. In this article, a new notation for DE
is proposed which has a formula that can be utilized for generating and
extracting novel mutations and by applying this new notation, four novel
mutations are proposed. More importantly, by combining these novel trial vector
generation strategies and four other well-known ones, we proposed Generalized
Mutation Differential Evolution (GMDE) that takes advantage of two mutation
pools that have both explorative and exploitative strategies inside them.
Results and experimental analysis are performed on CEC2005 benchmarks and the
results stated that GMDE is surprisingly competitive and significantly improved
the performance of this algorithm. Finally, GMDE is also applied to parameters
optimization, modification and improvement of a feature selection method for
cancer classification purposes over gene expression microarray profiles.
Beyond Brightness Constancy: Learning Noise Models for Optical Flow
Authors: Dan Rosenbaum, Yair Weiss
Date: 2016-04-11 00:00:00
Tasks: Denoising, Optical Flow Estimation

Optical flow is typically estimated by minimizing a "data cost" and an
optional regularizer. While there has been much work on different regularizers
many modern algorithms still use a data cost that is not very different from
the ones used over 30 years ago: a robust version of brightness constancy or
gradient constancy. In this paper we leverage the recent availability of
ground-truth optical flow databases in order to learn a data cost. Specifically
we take a generative approach in which the data cost models the distribution of
noise after warping an image according to the flow and we measure the
"goodness" of a data cost by how well it matches the true distribution of flow
warp error. Consistent with current practice, we find that robust versions of
gradient constancy are better models than simple brightness constancy but a
learned GMM that models the density of patches of warp error gives a much
better fit than any existing assumption of constancy. This significant
advantage of the GMM is due to an explicit modeling of the spatial structure of
warp errors, a feature which is missing from almost all existing data costs in
optical flow. Finally, we show how a good density model of warp error patches
can be used for optical flow estimation on whole images. We replace the data
cost by the expected patch log-likelihood (EPLL), and show how this cost can be
optimized iteratively using an additional step of denoising the warp error
image. The results of our experiments are promising and show that patch models
with higher likelihood lead to better optical flow estimation.
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Authors: Amir Shahroudy, Jun Liu, Tian-Tsong Ng, Gang Wang
Date: 2016-04-11 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Action Recognition, Action Classification, Action Recognition, General Classification, Skeleton Based Action Recognition

Recent approaches in depth-based human activity analysis achieved outstanding
performance and proved the effectiveness of 3D representation for
classification of action classes. Currently available depth-based and
RGB+D-based action recognition benchmarks have a number of limitations,
including the lack of training samples, distinct class labels, camera views and
variety of subjects. In this paper we introduce a large-scale dataset for RGB+D
human action recognition with more than 56 thousand video samples and 4 million
frames, collected from 40 distinct subjects. Our dataset contains 60 different
action classes including daily, mutual, and health-related actions. In
addition, we propose a new recurrent neural network structure to model the
long-term temporal correlation of the features for each body part, and utilize
them for better action classification. Experimental results show the advantages
of applying deep learning methods over state-of-the-art hand-crafted features
on the suggested cross-subject and cross-view evaluation criteria for our
dataset. The introduction of this large scale dataset will enable the community
to apply, develop and adapt various data-hungry learning techniques for the
task of depth-based and RGB+D-based human activity analysis.
Intelligent information extraction based on artificial neural network
Authors: Ahlam Ansari, Moonish Maknojia, Altamash Shaikh
Date: 2016-04-11 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval

Question Answering System (QAS) is used for information retrieval and natural
language processing (NLP) to reduce human effort. There are numerous QAS based
on the user documents present today, but they all are limited to providing
objective answers and process simple questions only. Complex questions cannot
be answered by the existing QAS, as they require interpretation of the current
and old data as well as the question asked by the user. The above limitations
can be overcome by using deep cases and neural network. Hence we propose a
modified QAS in which we create a deep artificial neural network with
associative memory from text documents. The modified QAS processes the contents
of the text document provided to it and find the answer to even complex
questions in the documents.
Capturing Dynamic Textured Surfaces of Moving Targets
Authors: Ruizhe Wang, Lingyu Wei, Etienne Vouga, Qi-Xing Huang, Duygu Ceylan, Gerard Medioni, Hao Li
Date: 2016-04-11 00:00:00

We present an end-to-end system for reconstructing complete watertight and
textured models of moving subjects such as clothed humans and animals, using
only three or four handheld sensors. The heart of our framework is a new
pairwise registration algorithm that minimizes, using a particle swarm
strategy, an alignment error metric based on mutual visibility and occlusion.
We show that this algorithm reliably registers partial scans with as little as
15% overlap without requiring any initial correspondences, and outperforms
alternative global registration algorithms. This registration algorithm allows
us to reconstruct moving subjects from free-viewpoint video produced by
consumer-grade sensors, without extensive sensor calibration, constrained
capture volume, expensive arrays of cameras, or templates of the subject
geometry.
Adaptive Object Detection Using Adjacency and Zoom Prediction
Authors: Yongxi Lu, Tara Javidi, Svetlana Lazebnik
Date: 2015-12-24 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, Prediction

State-of-the-art object detection systems rely on an accurate set of region
proposals. Several recent methods use a neural network architecture to
hypothesize promising object locations. While these approaches are
computationally efficient, they rely on fixed image regions as anchors for
predictions. In this paper we propose to use a search strategy that adaptively
directs computational resources to sub-regions likely to contain objects.
Compared to methods based on fixed anchor locations, our approach naturally
adapts to cases where object instances are sparse and small. Our approach is
comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach
while using two orders of magnitude fewer anchors on average. Code is publicly
available.
Symbolic Knowledge Extraction using Łukasiewicz Logics
Authors: Carlos Leandro
Date: 2016-04-11 00:00:00
Tasks: Descriptive, Translation

This work describes a methodology that combines logic-based systems and
connectionist systems. Our approach uses finite truth-valued {\L}ukasiewicz
logic, wherein every connective can be defined by a neuron in an artificial
network. This allowed the injection of first-order formulas into a network
architecture, and also simplified symbolic rule extraction. For that we trained
a neural networks using the Levenderg-Marquardt algorithm, where we restricted
the knowledge dissemination in the network structure. This procedure reduces
neural network plasticity without drastically damaging the learning
performance, thus making the descriptive power of produced neural networks
similar to the descriptive power of {\L}ukasiewicz logic language and
simplifying the translation between symbolic and connectionist structures. We
used this method for reverse engineering truth table and in extraction of
formulas from real data sets.
Validation of Matching
Authors: Ya Le, Eric Bax, Nicola Barbieri, David Garcia Soriano, Jitesh Mehta, James Li
Date: 2014-10-31 00:00:00
Tasks: Entity Resolution

We introduce a technique to compute probably approximately correct (PAC)
bounds on precision and recall for matching algorithms. The bounds require some
verified matches, but those matches may be used to develop the algorithms. The
bounds can be applied to network reconciliation or entity resolution
algorithms, which identify nodes in different networks or values in a data set
that correspond to the same entity. For network reconciliation, the bounds do
not require knowledge of the network generation process.
Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop
Authors: Yin Cui, Feng Zhou, Yuanqing Lin, Serge Belongie
Date: 2015-12-16 00:00:00
Proceeding: CVPR 2016 6
Tasks: Fine-Grained Visual Categorization, Metric Learning

Existing fine-grained visual categorization methods often suffer from three
challenges: lack of training data, large number of fine-grained categories, and
high intraclass vs. low inter-class variance. In this work we propose a generic
iterative framework for fine-grained categorization and dataset bootstrapping
that handles these three challenges. Using deep metric learning with humans in
the loop, we learn a low dimensional feature embedding with anchor points on
manifolds for each category. These anchor points capture intra-class variances
and remain discriminative between classes. In each round, images with high
confidence scores from our model are sent to humans for labeling. By comparing
with exemplar images, labelers mark each candidate image as either a "true
positive" or a "false positive". True positives are added into our current
dataset and false positives are regarded as "hard negatives" for our metric
learning model. Then the model is retrained with an expanded dataset and hard
negatives for the next round. To demonstrate the effectiveness of the proposed
framework, we bootstrap a fine-grained flower dataset with 620 categories from
Instagram images. The proposed deep metric learning scheme is evaluated on both
our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show
significant performance gain using dataset bootstrapping and demonstrate
state-of-the-art results achieved by the proposed deep metric learning methods.
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
Authors: Michael Bloodgood, Benjamin Strauss
Date: 2016-02-25 00:00:00
Tasks: Anomaly Detection, Optical Character Recognition, Optical Character Recognition (OCR), Transliteration

Many important forms of data are stored digitally in XML format. Errors can
occur in the textual content of the data in the fields of the XML. Fixing these
errors manually is time-consuming and expensive, especially for large amounts
of data. There is increasing interest in the research, development, and use of
automated techniques for assisting with data cleaning. Electronic dictionaries
are an important form of data frequently stored in XML format that frequently
have errors introduced through a mixture of manual typographical entry errors
and optical character recognition errors. In this paper we describe methods for
flagging statistical anomalies as likely errors in electronic dictionaries
stored in XML format. We describe six systems based on different sources of
information. The systems detect errors using various signals in the data
including uncommon characters, text length, character-based language models,
word-based language models, tied-field length ratios, and tied-field
transliteration models. Four of the systems detect errors based on expectations
automatically inferred from content within elements of a single field type. We
call these single-field systems. Two of the systems detect errors based on
correspondence expectations automatically inferred from content within elements
of multiple related field types. We call these tied-field systems. For each
system, we provide an intuitive analysis of the type of error that it is
successful at detecting. Finally, we describe two larger-scale evaluations
using crowdsourcing with Amazon's Mechanical Turk platform and using the
annotations of a domain expert. The evaluations consistently show that the
systems are useful for improving the efficiency with which errors in XML
electronic dictionaries can be detected.
Natural Language Object Retrieval
Authors: Ronghang Hu, Huazhe Xu, Marcus Rohrbach, Jiashi Feng, Kate Saenko, Trevor Darrell
Date: 2015-11-13 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Captioning, Image Retrieval, Object, Phrase Grounding, Referring Expression Comprehension, Retrieval, Transfer Learning

In this paper, we address the task of natural language object retrieval, to
localize a target object within a given image based on a natural language query
of the object. Natural language object retrieval differs from text-based image
retrieval task as it involves spatial information about objects within the
scene and global scene context. To address this issue, we propose a novel
Spatial Context Recurrent ConvNet (SCRC) model as scoring function on candidate
boxes for object retrieval, integrating spatial configurations and global
scene-level contextual information into the network. Our model processes query
text, local image descriptors, spatial configurations and global context
features through a recurrent network, outputs the probability of the query text
conditioned on each candidate box as a score for the box, and can transfer
visual-linguistic knowledge from image captioning domain to our task.
Experimental results demonstrate that our method effectively utilizes both
local and global information, outperforming previous baseline methods
significantly on different datasets and scenarios, and can exploit large scale
vision and language datasets for knowledge transfer.
Knowledge Extraction and Knowledge Integration governed by Łukasiewicz Logics
Authors: Carlos Leandro
Date: 2016-04-11 00:00:00
Tasks: Descriptive

The development of machine learning in particular and artificial intelligent
in general has been strongly conditioned by the lack of an appropriate
interface layer between deduction, abduction and induction. In this work we
extend traditional algebraic specification methods in this direction. Here we
assume that such interface for AI emerges from an adequate Neural-Symbolic
integration. This integration is made for universe of discourse described on a
Topos governed by a many-valued {\L}ukasiewicz logic. Sentences are integrated
in a symbolic knowledge base describing the problem domain, codified using a
graphic-based language, wherein every logic connective is defined by a neuron
in an artificial network. This allows the integration of first-order formulas
into a network architecture as background knowledge, and simplifies symbolic
rule extraction from trained networks. For the train of such neural networks we
changed the Levenderg-Marquardt algorithm, restricting the knowledge
dissemination in the network structure using soft crystallization. This
procedure reduces neural network plasticity without drastically damaging the
learning performance, allowing the emergence of symbolic patterns. This makes
the descriptive power of produced neural networks similar to the descriptive
power of {\L}ukasiewicz logic language, reducing the information lost on
translation between symbolic and connectionist structures. We tested this
method on the extraction of knowledge from specified structures. For it, we
present the notion of fuzzy state automata, and we use automata behaviour to
infer its structure. We use this type of automata on the generation of models
for relations specified as symbolic background knowledge.
Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
Authors: Iacopo Masi, Anh Tuan Tran, Jatuporn Toy Leksut, Tal Hassner, Gerard Medioni
Date: 2016-03-23 00:00:00
Tasks: Face Recognition, Face Verification

Face recognition capabilities have recently made extraordinary leaps. Though
this progress is at least partially due to ballooning training set sizes --
huge numbers of face images downloaded and labeled for identity -- it is not
clear if the formidable task of collecting so many images is truly necessary.
We propose a far more accessible means of increasing training data sizes for
face recognition systems. Rather than manually harvesting and labeling more
faces, we simply synthesize them. We describe novel methods of enriching an
existing dataset with important facial appearance variations by manipulating
the faces it contains. We further apply this synthesis approach when matching
query images represented using a standard convolutional neural network. The
effect of training and testing with synthesized images is extensively tested on
the LFW and IJB-A (verification and identification) benchmarks and Janus CS2.
The performances obtained by our approach match state of the art results
reported by systems trained on millions of downloaded images.