pwc-archive/papers-with-abstracts huggingface dataset:
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration
Authors: Panqu Wang, Isabel Gauthier, Garrison Cottrell
Date: 2016-04-26 00:00:00
Tasks: Object Recognition

Are face and object recognition abilities independent? Although it is
commonly believed that they are, Gauthier et al.(2014) recently showed that
these abilities become more correlated as experience with nonface categories
increases. They argued that there is a single underlying visual ability, v,
that is expressed in performance with both face and nonface categories as
experience grows. Using the Cambridge Face Memory Test and the Vanderbilt
Expertise Test, they showed that the shared variance between Cambridge Face
Memory Test and Vanderbilt Expertise Test performance increases monotonically
as experience increases. Here, we address why a shared resource across
different visual domains does not lead to competition and to an inverse
correlation in abilities? We explain this conundrum using our
neurocomputational model of face and object processing (The Model, TM). Our
results show that, as in the behavioral data, the correlation between
subordinate level face and object recognition accuracy increases as experience
grows. We suggest that different domains do not compete for resources because
the relevant features are shared between faces and objects. The essential power
of experience is to generate a "spreading transform" for faces that generalizes
to objects that must be individuated. Interestingly, when the task of the
network is basic level categorization, no increase in the correlation between
domains is observed. Hence, our model predicts that it is the type of
experience that matters and that the source of the correlation is in the
fusiform face area, rather than in cortical areas that subserve basic level
categorization. This result is consistent with our previous modeling
elucidating why the FFA is recruited for novel domains of expertise (Tong et
al., 2008).
Entities as topic labels: Improving topic interpretability and evaluability combining Entity Linking and Labeled LDA
Authors: Federico Nanni, Pablo Ruiz Fabo
Date: 2016-04-26 00:00:00
Tasks: Descriptive, Entity Linking, Topic Models

In order to create a corpus exploration method providing topics that are
easier to interpret than standard LDA topic models, here we propose combining
two techniques called Entity linking and Labeled LDA. Our method identifies in
an ontology a series of descriptive labels for each document in a corpus. Then
it generates a specific topic for each label. Having a direct relation between
topics and labels makes interpretation easier; using an ontology as background
knowledge limits label ambiguity. As our topics are described with a limited
number of clear-cut labels, they promote interpretability, and this may help
quantitative evaluation. We illustrate the potential of the approach by
applying it in order to define the most relevant topics addressed by each party
in the European Parliament's fifth mandate (1999-2004).
Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior
Authors: Jacob Schrum, Joel Lehman, Sebastian Risi
Date: 2016-04-26 00:00:00

An important challenge in neuroevolution is to evolve complex neural networks
with multiple modes of behavior. Indirect encodings can potentially answer this
challenge. Yet in practice, indirect encodings do not yield effective
multimodal controllers. Thus, this paper introduces novel multimodal extensions
to HyperNEAT, a popular indirect encoding. A previous multimodal HyperNEAT
approach called situational policy geometry assumes that multiple brains
benefit from being embedded within an explicit geometric space. However,
experiments here illustrate that this assumption unnecessarily constrains
evolution, resulting in lower performance. Specifically, this paper introduces
HyperNEAT extensions for evolving many brains without assuming geometric
relationships between them. The resulting Multi-Brain HyperNEAT can exploit
human-specified task divisions to decide when each brain controls the agent, or
can automatically discover when brains should be used, by means of preference
neurons. A further extension called module mutation allows evolution to
discover the number of brains, enabling multimodal behavior with even less
expert knowledge. Experiments in several multimodal domains highlight that
multi-brain approaches are more effective than HyperNEAT without multimodal
extensions, and show that brains without a geometric relation to each other
outperform situational policy geometry. The conclusion is that Multi-Brain
HyperNEAT provides several promising techniques for evolving complex multimodal
behavior.
Scale Normalization
Authors: Henry Z. Lo, Kevin Amaral, Wei Ding
Date: 2016-04-26 00:00:00

One of the difficulties of training deep neural networks is caused by
improper scaling between layers. Scaling issues introduce exploding / gradient
problems, and have typically been addressed by careful scale-preserving
initialization. We investigate the value of preserving scale, or isometry,
beyond the initial weights. We propose two methods of maintaing isometry, one
exact and one stochastic. Preliminary experiments show that for both
determinant and scale-normalization effectively speeds up learning. Results
suggest that isometry is important in the beginning of learning, and
maintaining it leads to faster learning.
A Framework for Human Pose Estimation in Videos
Authors: Dong Zhang, Mubarak Shah
Date: 2016-04-26 00:00:00
Tasks: Pose Estimation

In this paper, we present a method to estimate a sequence of human poses in
unconstrained videos. We aim to demonstrate that by using temporal information,
the human pose estimation results can be improved over image based pose
estimation methods. In contrast to the commonly employed graph optimization
formulation, which is NP-hard and needs approximate solutions, we formulate
this problem into a unified two stage tree-based optimization problem for which
an efficient and exact solution exists. Although the proposed method finds an
exact solution, it does not sacrifice the ability to model the spatial and
temporal constraints between body parts in the frames; in fact it models the
{\em symmetric} parts better than the existing methods. The proposed method is
based on two main ideas: `Abstraction' and `Association' to enforce the intra-
and inter-frame body part constraints without inducing extra computational
complexity to the polynomial time solution. Using the idea of `Abstraction', a
new concept of `abstract body part' is introduced to conceptually combine the
symmetric body parts and model them in the tree based body part structure.
Using the idea of `Association', the optimal tracklets are generated for each
abstract body part, in order to enforce the spatiotemporal constraints between
body parts in adjacent frames. A sequence of the best poses is inferred from
the abstract body part tracklets through the tree-based optimization. Finally,
the poses are refined by limb alignment and refinement schemes. We evaluated
the proposed method on three publicly available video based human pose
estimation datasets, and obtained dramatically improved performance compared to
the state-of-the-art methods.
AGM-Style Revision of Beliefs and Intentions from a Database Perspective (Preliminary Version)
Authors: Marc van Zee, Dragan Doder
Date: 2016-04-25 00:00:00

We introduce a logic for temporal beliefs and intentions based on Shoham's
database perspective. We separate strong beliefs from weak beliefs. Strong
beliefs are independent from intentions, while weak beliefs are obtained by
adding intentions to strong beliefs and everything that follows from that. We
formalize coherence conditions on strong beliefs and intentions. We provide
AGM-style postulates for the revision of strong beliefs and intentions. We show
in a representation theorem that a revision operator satisfying our postulates
can be represented by a pre-order on interpretations of the beliefs, together
with a selection function for the intentions.
Neural network-based clustering using pairwise constraints
Authors: Yen-Chang Hsu, Zsolt Kira
Date: 2015-11-19 00:00:00
Tasks: Clustering

This paper presents a neural network-based end-to-end clustering framework.
We design a novel strategy to utilize the contrastive criteria for pushing
data-forming clusters directly from raw data, in addition to learning a feature
embedding suitable for such clustering. The network is trained with weak
labels, specifically partial pairwise relationships between data instances. The
cluster assignments and their probabilities are then obtained at the output
layer by feed-forwarding the data. The framework has the interesting
characteristic that no cluster centers need to be explicitly specified, thus
the resulting cluster distribution is purely data-driven and no distance
metrics need to be predefined. The experiments show that the proposed approach
beats the conventional two-stage method (feature embedding with k-means) by a
significant margin. It also compares favorably to the performance of the
standard cross entropy loss for classification. Robustness analysis also shows
that the method is largely insensitive to the number of clusters. Specifically,
we show that the number of dominant clusters is close to the true number of
clusters even when a large k is used for clustering.
Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning
Authors: Zhaoxiang Zang, Zhao Li, Junying Wang, Zhiping Dan
Date: 2016-04-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As a genetics-based machine learning technique, zeroth-level classifier
system (ZCS) is based on a discounted reward reinforcement learning algorithm,
bucket-brigade algorithm, which optimizes the discounted total reward received
by an agent but is not suitable for all multi-step problems, especially
large-size ones. There are some undiscounted reinforcement learning methods
available, such as R-learning, which optimize the average reward per time step.
In this paper, R-learning is used as the reinforcement learning employed by
ZCS, to replace its discounted reward reinforcement learning approach, and
tournament selection is used to replace roulette wheel selection in ZCS. The
modification results in classifier systems that can support long action chains,
and thus is able to solve large multi-step problems.
Efficient Splitting-based Method for Global Image Smoothing
Authors: Youngjung Kim, Dongbo Min, Bumsub Ham, Kwanghoon Sohn
Date: 2016-04-26 00:00:00
Tasks: image smoothing

Edge-preserving smoothing (EPS) can be formulated as minimizing an objective
function that consists of data and prior terms. This global EPS approach shows
better smoothing performance than a local one that typically has a form of
weighted averaging, at the price of high computational cost. In this paper, we
introduce a highly efficient splitting-based method for global EPS that
minimizes the objective function of ${l_2}$ data and prior terms (possibly
non-smooth and non-convex) in linear time. Different from previous
splitting-based methods that require solving a large linear system, our
approach solves an equivalent constrained optimization problem, resulting in a
sequence of 1D sub-problems. This enables linear time solvers for
weighted-least squares and -total variation problems. Our solver converges
quickly, and its runtime is even comparable to state-of-the-art local EPS
approaches. We also propose a family of fast iteratively re-weighted algorithms
using a non-convex prior term. Experimental results demonstrate the
effectiveness and flexibility of our approach in a range of computer vision and
image processing tasks.
Real-time Action Recognition with Enhanced Motion Vector CNNs
Authors: Bowen Zhang, Li-Min Wang, Zhe Wang, Yu Qiao, Hanli Wang
Date: 2016-04-26 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

The deep two-stream architecture exhibited excellent performance on video
based action recognition. The most computationally expensive step in this
approach comes from the calculation of optical flow which prevents it to be
real-time. This paper accelerates this architecture by replacing optical flow
with motion vector which can be obtained directly from compressed videos
without extra calculation. However, motion vector lacks fine structures, and
contains noisy and inaccurate motion patterns, leading to the evident
degradation of recognition performance. Our key insight for relieving this
problem is that optical flow and motion vector are inherent correlated.
Transferring the knowledge learned with optical flow CNN to motion vector CNN
can significantly boost the performance of the latter. Specifically, we
introduce three strategies for this, initialization transfer, supervision
transfer and their combination. Experimental results show that our method
achieves comparable recognition performance to the state-of-the-art, while our
method can process 390.7 frames per second, which is 27 times faster than the
original two-stream method.
TEMPO: Feature-Endowed Teichmüller Extremal Mappings of Point Clouds
Authors: Ting Wei Meng, Gary Pui-Tung Choi, Lok Ming Lui
Date: 2015-11-20 00:00:00
Tasks: General Classification

In recent decades, the use of 3D point clouds has been widespread in computer
industry. The development of techniques in analyzing point clouds is
increasingly important. In particular, mapping of point clouds has been a
challenging problem. In this paper, we develop a discrete analogue of the
Teichm\"{u}ller extremal mappings, which guarantee uniform conformality
distortions, on point cloud surfaces. Based on the discrete analogue, we
propose a novel method called TEMPO for computing Teichm\"{u}ller extremal
mappings between feature-endowed point clouds. Using our proposed method, the
Teichm\"{u}ller metric is introduced for evaluating the dissimilarity of point
clouds. Consequently, our algorithm enables accurate recognition and
classification of point clouds. Experimental results demonstrate the
effectiveness of our proposed method.
Online Influence Maximization in Non-Stationary Social Networks
Authors: Yixin Bao, Xiaoke Wang, Zhi Wang, Chuan Wu, Francis C. M. Lau
Date: 2016-04-26 00:00:00
Tasks: Marketing

Social networks have been popular platforms for information propagation. An
important use case is viral marketing: given a promotion budget, an advertiser
can choose some influential users as the seed set and provide them free or
discounted sample products; in this way, the advertiser hopes to increase the
popularity of the product in the users' friend circles by the world-of-mouth
effect, and thus maximizes the number of users that information of the
production can reach. There has been a body of literature studying the
influence maximization problem. Nevertheless, the existing studies mostly
investigate the problem on a one-off basis, assuming fixed known influence
probabilities among users, or the knowledge of the exact social network
topology. In practice, the social network topology and the influence
probabilities are typically unknown to the advertiser, which can be varying
over time, i.e., in cases of newly established, strengthened or weakened social
ties. In this paper, we focus on a dynamic non-stationary social network and
design a randomized algorithm, RSB, based on multi-armed bandit optimization,
to maximize influence propagation over time. The algorithm produces a sequence
of online decisions and calibrates its explore-exploit strategy utilizing
outcomes of previous decisions. It is rigorously proven to achieve an
upper-bounded regret in reward and applicable to large-scale social networks.
Practical effectiveness of the algorithm is evaluated using both synthetic and
real-world datasets, which demonstrates that our algorithm outperforms previous
stationary methods under non-stationary conditions.
Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding
Authors: Huazheng Wang, Fei Tian, Bin Gao, Jiang Bian, Tie-Yan Liu
Date: 2015-05-29 00:00:00

Intelligence Quotient (IQ) Test is a set of standardized questions designed
to evaluate human intelligence. Verbal comprehension questions appear very
frequently in IQ tests, which measure human's verbal ability including the
understanding of the words with multiple senses, the synonyms and antonyms, and
the analogies among words. In this work, we explore whether such tests can be
solved automatically by artificial intelligence technologies, especially the
deep learning technologies that are recently developed and successfully applied
in a number of fields. However, we found that the task was quite challenging,
and simply applying existing technologies (e.g., word embedding) could not
achieve a good performance, mainly due to the multiple senses of words and the
complex relations among words. To tackle these challenges, we propose a novel
framework consisting of three components. First, we build a classifier to
recognize the specific type of a verbal question (e.g., analogy,
classification, synonym, or antonym). Second, we obtain distributed
representations of words and relations by leveraging a novel word embedding
method that considers the multi-sense nature of words and the relational
knowledge among words (or their senses) contained in dictionaries. Third, for
each type of questions, we propose a specific solver based on the obtained
distributed word representations and relation representations. Experimental
results have shown that the proposed framework can not only outperform existing
methods for solving verbal comprehension questions but also exceed the average
performance of the Amazon Mechanical Turk workers involved in the study. The
results indicate that with appropriate uses of the deep learning technologies
we might be a further step closer to the human intelligence.
Mutual Transformation of Information and Knowledge
Authors: Olegs Verhodubs
Date: 2016-04-26 00:00:00

Information and knowledge are transformable into each other. Information
transformation into knowledge by the example of rule generation from OWL (Web
Ontology Language) ontology has been shown during the development of the SWES
(Semantic Web Expert System). The SWES is expected as an expert system for
searching OWL ontologies from the Web, generating rules from the found
ontologies and supplementing the SWES knowledge base with these rules. The
purpose of this paper is to show knowledge transformation into information by
the example of ontology generation from rules.
An Empirical Study into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation
Authors: Thomas A. Lampert, André Stumpf, Pierre Gançarski
Date: 2013-07-01 00:00:00
Tasks: Object, object-detection, Object Detection

Although agreement between annotators has been studied in the past from a
statistical viewpoint, little work has attempted to quantify the extent to
which this phenomenon affects the evaluation of computer vision (CV) object
detection algorithms. Many researchers utilise ground truth (GT) in experiments
and more often than not this GT is derived from one annotator's opinion. How
does the difference in opinion affect an algorithm's evaluation? Four examples
of typical CV problems are chosen, and a methodology is applied to each to
quantify the inter-annotator variance and to offer insight into the mechanisms
behind agreement and the use of GT. It is found that when detecting linear
objects annotator agreement is very low. The agreement in object position,
linear or otherwise, can be partially explained through basic image properties.
Automatic object detectors are compared to annotator agreement and it is found
that a clear relationship exists. Several methods for calculating GTs from a
number of annotations are applied and the resulting differences in the
performance of the object detectors are quantified. It is found that the rank
of a detector is highly dependent upon the method used to form the GT. It is
also found that although the STAPLE and LSML GT estimation methods appear to
represent the mean of the performance measured using the individual
annotations, when there are few annotations, or there is a large variance in
them, these estimates tend to degrade. Furthermore, one of the most commonly
adopted annotation combination methods--consensus voting--accentuates more
obvious features, which results in an overestimation of the algorithm's
performance. Finally, it is concluded that in some datasets it may not be
possible to state with any confidence that one algorithm outperforms another
when evaluating upon one GT and a method for calculating confidence bounds is
discussed.
A New Approach in Persian Handwritten Letters Recognition Using Error Correcting Output Coding
Authors: Maziar Kazemi, Muhammad Yousefnezhad, Saber Nourian
Date: 2016-04-26 00:00:00
Tasks: Binary Classification, Classification, feature selection, General Classification

Classification Ensemble, which uses the weighed polling of outputs, is the
art of combining a set of basic classifiers for generating high-performance,
robust and more stable results. This study aims to improve the results of
identifying the Persian handwritten letters using Error Correcting Output
Coding (ECOC) ensemble method. Furthermore, the feature selection is used to
reduce the costs of errors in our proposed method. ECOC is a method for
decomposing a multi-way classification problem into many binary classification
tasks; and then combining the results of the subtasks into a hypothesized
solution to the original problem. Firstly, the image features are extracted by
Principal Components Analysis (PCA). After that, ECOC is used for
identification the Persian handwritten letters which it uses Support Vector
Machine (SVM) as the base classifier. The empirical results of applying this
ensemble method using 10 real-world data sets of Persian handwritten letters
indicate that this method has better results in identifying the Persian
handwritten letters than other ensemble methods and also single
classifications. Moreover, by testing a number of different features, this
paper found that we can reduce the additional cost in feature selection stage
by using this method.
A novel mutation operator based on the union of fitness and design spaces information for Differential Evolution
Authors: H. Sharifi Noghabi, H. Rajabi Mashhadi, K. Shojaei
Date: 2015-10-08 00:00:00
Tasks: Evolutionary Algorithms, global-optimization

Differential Evolution (DE) is one of the most successful and powerful
evolutionary algorithms for global optimization problem. The most important
operator in this algorithm is mutation operator which parents are selected
randomly to participate in it. Recently, numerous papers are tried to make this
operator more intelligent by selection of parents for mutation intelligently.
The intelligent selection for mutation vectors is performed by applying design
space (also known as decision space) criterion or fitness space criterion,
however, in both cases, half of valuable information of the problem space is
disregarded. In this article, a Universal Differential Evolution (UDE) is
proposed which takes advantage of both design and fitness spaces criteria for
intelligent selection of mutation vectors. The experimental analysis on UDE are
performed on CEC2005 benchmarks and the results stated that UDE significantly
improved the performance of differential evolution in comparison with other
methods that only use one criterion for intelligent selection.