pwc-archive/papers-with-abstracts huggingface dataset:
Weakly Supervised Fine-Grained Image Categorization
Authors: Yu Zhang, Xiu-Shen Wei, Jianxin Wu, Jianfei Cai, Jiangbo Lu, Viet-Anh Nguyen, Minh N. Do
Date: 2015-04-20 00:00:00
Tasks: Fine-Grained Image Classification, Image Categorization, Object

In this paper, we categorize fine-grained images without using any object /
part annotation neither in the training nor in the testing stage, a step
towards making it suitable for deployments. Fine-grained image categorization
aims to classify objects with subtle distinctions. Most existing works heavily
rely on object / part detectors to build the correspondence between object
parts by using object or object part annotations inside training images. The
need for expensive object annotations prevents the wide usage of these methods.
Instead, we propose to select useful parts from multi-scale part proposals in
objects, and use them to compute a global image representation for
categorization. This is specially designed for the annotation-free fine-grained
categorization task, because useful parts have shown to play an important role
in existing annotation-dependent works but accurate part detectors can be
hardly acquired. With the proposed image representation, we can further detect
and visualize the key (most discriminative) parts in objects of different
classes. In the experiment, the proposed annotation-free method achieves better
accuracy than that of state-of-the-art annotation-free and most existing
annotation-dependent methods on two challenging datasets, which shows that it
is not always necessary to use accurate object / part annotations in
fine-grained image categorization.
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition
Authors: Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton von den Hengel
Date: 2015-04-20 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

The introduction of low-cost RGB-D sensors has promoted the research in
skeleton-based human action recognition. Devising a representation suitable for
characterising actions on the basis of noisy skeleton sequences remains a
challenge, however. We here provide two insights into this challenge. First, we
show that the discriminative information of a skeleton sequence usually resides
in a short temporal interval and we propose a simple-but-effective local
descriptor called trajectorylet to capture the static and kinematic information
within this interval. Second, we further propose to encode each trajectorylet
with a discriminative trajectorylet detector set which is selected from a large
number of candidate detectors trained through exemplar-SVMs. The action-level
representation is obtained by pooling trajectorylet encodings. Evaluating on
standard datasets acquired from the Kinect sensor, it is demonstrated that our
method obtains superior results over existing approaches under various
experimental setups.
Illuminating search spaces by mapping elites
Authors: Jean-Baptiste Mouret, Jeff Clune
Date: 2015-04-20 00:00:00

Many fields use search algorithms, which automatically explore a search space
to find high-performing solutions: chemists search through the space of
molecules to discover new drugs; engineers search for stronger, cheaper, safer
designs, scientists search for models that best explain data, etc. The goal of
search algorithms has traditionally been to return the single
highest-performing solution in a search space. Here we describe a new,
fundamentally different type of algorithm that is more useful because it
provides a holistic view of how high-performing solutions are distributed
throughout a search space. It creates a map of high-performing solutions at
each point in a space defined by dimensions of variation that a user gets to
choose. This Multi-dimensional Archive of Phenotypic Elites (MAP-Elites)
algorithm illuminates search spaces, allowing researchers to understand how
interesting attributes of solutions combine to affect performance, either
positively or, equally of interest, negatively. For example, a drug company may
wish to understand how performance changes as the size of molecules and their
cost-to-produce vary. MAP-Elites produces a large diversity of high-performing,
yet qualitatively different solutions, which can be more helpful than a single,
high-performing solution. Interestingly, because MAP-Elites explores more of
the search space, it also tends to find a better overall solution than
state-of-the-art search algorithms. We demonstrate the benefits of this new
algorithm in three different problem domains ranging from producing modular
neural networks to designing simulated and real soft robots. Because MAP-
Elites (1) illuminates the relationship between performance and dimensions of
interest in solutions, (2) returns a set of high-performing, yet diverse
solutions, and (3) improves finding a single, best solution, it will advance
science and engineering.
Beyond Gaussian Pyramid: Multi-skip Feature Stacking for Action Recognition
Authors: Zhenzhong Lan, Ming Lin, Xuanchong Li, Alexander G. Hauptmann, Bhiksha Raj
Date: 2014-11-24 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Event Detection, Temporal Action Localization

Most state-of-the-art action feature extractors involve differential
operators, which act as highpass filters and tend to attenuate low frequency
action information. This attenuation introduces bias to the resulting features
and generates ill-conditioned feature matrices. The Gaussian Pyramid has been
used as a feature enhancing technique that encodes scale-invariant
characteristics into the feature space in an attempt to deal with this
attenuation. However, at the core of the Gaussian Pyramid is a convolutional
smoothing operation, which makes it incapable of generating new features at
coarse scales. In order to address this problem, we propose a novel feature
enhancing technique called Multi-skIp Feature Stacking (MIFS), which stacks
features extracted using a family of differential filters parameterized with
multiple time skips and encodes shift-invariance into the frequency space. MIFS
compensates for information lost from using differential operators by
recapturing information at coarse scales. This recaptured information allows us
to match actions at different speeds and ranges of motion. We prove that MIFS
enhances the learnability of differential-based features exponentially. The
resulting feature matrices from MIFS have much smaller conditional numbers and
variances than those from conventional methods. Experimental results show
significantly improved performance on challenging action recognition and event
detection tasks. Specifically, our method exceeds the state-of-the-arts on
Hollywood2, UCF101 and UCF50 datasets and is comparable to state-of-the-arts on
HMDB51 and Olympics Sports datasets. MIFS can also be used as a speedup
strategy for feature extraction with minimal or no accuracy cost.
DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets
Authors: Sukrit Shankar, Vikas K. Garg, Roberto Cipolla
Date: 2015-04-19 00:00:00
Proceeding: CVPR 2015 6
Tasks: Attribute, Image Retrieval

Most of the approaches for discovering visual attributes in images demand
significant supervision, which is cumbersome to obtain. In this paper, we aim
to discover visual attributes in a weakly supervised setting that is commonly
encountered with contemporary image search engines. Deep Convolutional Neural
Networks (CNNs) have enjoyed remarkable success in vision applications
recently. However, in a weakly supervised scenario, widely used CNN training
procedures do not learn a robust model for predicting multiple attribute labels
simultaneously. The primary reason is that the attributes highly co-occur
within the training data. To ameliorate this limitation, we propose
Deep-Carving, a novel training procedure with CNNs, that helps the net
efficiently carve itself for the task of multiple attribute prediction. During
training, the responses of the feature maps are exploited in an ingenious way
to provide the net with multiple pseudo-labels (for training images) for
subsequent iterations. The process is repeated periodically after a fixed
number of iterations, and enables the net carve itself iteratively for
efficiently disentangling features. Additionally, we contribute a
noun-adjective pairing inspired Natural Scenes Attributes Dataset to the
research community, CAMIT - NSAD, containing a number of co-occurring
attributes within a noun category. We describe, in detail, salient aspects of
this dataset. Our experiments on CAMIT-NSAD and the SUN Attributes Dataset,
with weak supervision, clearly demonstrate that the Deep-Carved CNNs
consistently achieve considerable improvement in the precision of attribute
prediction over popular baseline methods.
Score Function Features for Discriminative Learning
Authors: Majid Janzamin, Hanie Sedghi, Anima Anandkumar
Date: 2014-12-19 00:00:00

Feature learning forms the cornerstone for tackling challenging learning
problems in domains such as speech, computer vision and natural language
processing. In this paper, we consider a novel class of matrix and
tensor-valued features, which can be pre-trained using unlabeled samples. We
present efficient algorithms for extracting discriminative information, given
these pre-trained features and labeled samples for any related task. Our class
of features are based on higher-order score functions, which capture local
variations in the probability density function of the input. We establish a
theoretical framework to characterize the nature of discriminative information
that can be extracted from score-function features, when used in conjunction
with labeled samples. We employ efficient spectral decomposition algorithms (on
matrices and tensors) for extracting discriminative components. The advantage
of employing tensor-valued features is that we can extract richer
discriminative information in the form of an overcomplete representations.
Thus, we present a novel framework for employing generative models of the input
for discriminative learning.
Exploring Bayesian Models for Multi-level Clustering of Hierarchically Grouped Sequential Data
Authors: Adway Mitra
Date: 2015-04-19 00:00:00
Tasks: Clustering

A wide range of Bayesian models have been proposed for data that is divided
hierarchically into groups. These models aim to cluster the data at different
levels of grouping, by assigning a mixture component to each datapoint, and a
mixture distribution to each group. Multi-level clustering is facilitated by
the sharing of these components and distributions by the groups. In this paper,
we introduce the concept of Degree of Sharing (DoS) for the mixture components
and distributions, with an aim to analyze and classify various existing models.
Next we introduce a generalized hierarchical Bayesian model, of which the
existing models can be shown to be special cases. Unlike most of these models,
our model takes into account the sequential nature of the data, and various
other temporal structures at different levels while assigning mixture
components and distributions. We show one specialization of this model aimed at
hierarchical segmentation of news transcripts, and present a Gibbs Sampling
based inference algorithm for it. We also show experimentally that the proposed
model outperforms existing models for the same task.
Gradual Classical Logic for Attributed Objects - Extended in Re-Presentation
Authors: Ryuta Arisaka
Date: 2015-04-19 00:00:00

Our understanding about things is conceptual. By stating that we reason about
objects, it is in fact not the objects but concepts referring to them that we
manipulate. Now, so long just as we acknowledge infinitely extending notions
such as space, time, size, colour, etc, - in short, any reasonable quality -
into which an object is subjected, it becomes infeasible to affirm atomicity in
the concept referring to the object. However, formal/symbolic logics typically
presume atomic entities upon which other expressions are built. Can we reflect
our intuition about the concept onto formal/symbolic logics at all? I assure
that we can, but the usual perspective about the atomicity needs inspected. In
this work, I present gradual logic which materialises the observation that we
cannot tell apart whether a so-regarded atomic entity is atomic or is just
atomic enough not to be considered non-atomic. The motivation is to capture
certain phenomena that naturally occur around concepts with attributes,
including presupposition and contraries. I present logical particulars of the
logic, which is then mapped onto formal semantics. Two linguistically
interesting semantics will be considered. Decidability is shown.
Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval
Authors: Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan
Date: 2015-01-26 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Retrieval, Multi-Label Image Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity

With the rapid growth of web images, hashing has received increasing
interests in large scale image retrieval. Research efforts have been devoted to
learning compact binary codes that preserve semantic similarity based on
labels. However, most of these hashing methods are designed to handle simple
binary similarity. The complex multilevel semantic structure of images
associated with multiple labels have not yet been well explored. Here we
propose a deep semantic ranking based method for learning hash functions that
preserve multilevel semantic similarity between multi-label images. In our
approach, deep convolutional neural network is incorporated into hash functions
to jointly learn feature representations and mappings from them to hash codes,
which avoids the limitation of semantic representation power of hand-crafted
features. Meanwhile, a ranking list that encodes the multilevel similarity
information is employed to guide the learning of such deep hash functions. An
effective scheme based on surrogate loss is used to solve the intractable
optimization problem of nonsmooth and multivariate ranking measures involved in
the learning procedure. Experimental results show the superiority of our
proposed approach over several state-of-the-art hashing methods in term of
ranking evaluation metrics when tested on multi-label image datasets.
Compressing Neural Networks with the Hashing Trick
Authors: Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen
Date: 2015-04-19 00:00:00

As deep nets are increasingly used in applications suited for mobile devices,
a fundamental dilemma becomes apparent: the trend in deep learning is to grow
models to absorb ever-increasing data set sizes; however mobile devices are
designed with very little memory and cannot store such large models. We present
a novel network architecture, HashedNets, that exploits inherent redundancy in
neural networks to achieve drastic reductions in model sizes. HashedNets uses a
low-cost hash function to randomly group connection weights into hash buckets,
and all connections within the same hash bucket share a single parameter value.
These parameters are tuned to adjust to the HashedNets weight sharing
architecture with standard backprop during training. Our hashing procedure
introduces no additional memory overhead, and we demonstrate on several
benchmark data sets that HashedNets shrink the storage requirements of neural
networks substantially while mostly preserving generalization performance.
Supervised Discrete Hashing
Authors: Fumin Shen, Chunhua Shen, Wei Liu, Heng Tao Shen
Date: 2015-03-05 00:00:00
Proceeding: CVPR 2015 6

This paper has been withdrawn by the authour.
A Chasm Between Identity and Equivalence Testing with Conditional Queries
Authors: Jayadev Acharya, Clément L. Canonne, Gautam Kamath
Date: 2014-11-26 00:00:00

A recent model for property testing of probability distributions (Chakraborty
et al., ITCS 2013, Canonne et al., SICOMP 2015) enables tremendous savings in
the sample complexity of testing algorithms, by allowing them to condition the
sampling on subsets of the domain. In particular, Canonne, Ron, and Servedio
(SICOMP 2015) showed that, in this setting, testing identity of an unknown
distribution $D$ (whether $D=D^\ast$ for an explicitly known $D^\ast$) can be
done with a constant number of queries, independent of the support size $n$ --
in contrast to the required $\Omega(\sqrt{n})$ in the standard sampling model.
It was unclear whether the same stark contrast exists for the case of testing
equivalence, where both distributions are unknown. While Canonne et al.
established a $\mathrm{poly}(\log n)$-query upper bound for equivalence
testing, very recently brought down to $\tilde O(\log\log n)$ by Falahatgar et
al. (COLT 2015), whether a dependence on the domain size $n$ is necessary was
still open, and explicitly posed by Fischer at the Bertinoro Workshop on
Sublinear Algorithms (2014). We show that any testing algorithm for equivalence
must make $\Omega(\sqrt{\log\log n})$ queries in the conditional sampling
model. This demonstrates a gap between identity and equivalence testing, absent
in the standard sampling model (where both problems have sampling complexity
$n^{\Theta(1)}$).
  We also obtain results on the query complexity of uniformity testing and
support-size estimation with conditional samples. We answer a question of
Chakraborty et al. (ITCS 2013) showing that non-adaptive uniformity testing
indeed requires $\Omega(\log n)$ queries in the conditional model. For the
related problem of support-size estimation, we provide both adaptive and
non-adaptive algorithms, with query complexities $\mathrm{poly}(\log\log n)$
and $\mathrm{poly}(\log n)$, respectively.
Online Inference for Relation Extraction with a Reduced Feature Set
Authors: Maxim Rabinovich, Cédric Archambeau
Date: 2015-04-18 00:00:00
Tasks: Relation, Relation Extraction, Variational Inference

Access to web-scale corpora is gradually bringing robust automatic knowledge
base creation and extension within reach. To exploit these large
unannotated---and extremely difficult to annotate---corpora, unsupervised
machine learning methods are required. Probabilistic models of text have
recently found some success as such a tool, but scalability remains an obstacle
in their application, with standard approaches relying on sampling schemes that
are known to be difficult to scale. In this report, we therefore present an
empirical assessment of the sublinear time sparse stochastic variational
inference (SSVI) scheme applied to RelLDA. We demonstrate that online inference
leads to relatively strong qualitative results but also identify some of its
pathologies---and those of the model---which will need to be overcome if SSVI
is to be used for large-scale relation extraction.
Understanding the Fisher Vector: a multimodal part model
Authors: David Novotný, Diane Larlus, Florent Perronnin, Andrea Vedaldi
Date: 2015-04-18 00:00:00
Tasks: model, object-detection, Object Detection

Fisher Vectors and related orderless visual statistics have demonstrated
excellent performance in object detection, sometimes superior to established
approaches such as the Deformable Part Models. However, it remains unclear how
these models can capture complex appearance variations using visual codebooks
of limited sizes and coarse geometric information. In this work, we propose to
interpret Fisher-Vector-based object detectors as part-based models. Through
the use of several visualizations and experiments, we show that this is a
useful insight to explain the good performance of the model. Furthermore, we
reveal for the first time several interesting properties of the FV, including
its ability to work well using only a small subset of input patches and visual
words. Finally, we discuss the relation of the FV and DPM detectors, pointing
out differences and commonalities between them.
Time Resolution Dependence of Information Measures for Spiking Neurons: Atoms, Scaling, and Universality
Authors: Sarah E. Marzen, Michael R. DeWeese, James P. Crutchfield
Date: 2015-04-18 00:00:00
Tasks: Model Selection

The mutual information between stimulus and spike-train response is commonly
used to monitor neural coding efficiency, but neuronal computation broadly
conceived requires more refined and targeted information measures of
input-output joint processes. A first step towards that larger goal is to
develop information measures for individual output processes, including
information generation (entropy rate), stored information (statistical
complexity), predictable information (excess entropy), and active information
accumulation (bound information rate). We calculate these for spike trains
generated by a variety of noise-driven integrate-and-fire neurons as a function
of time resolution and for alternating renewal processes. We show that their
time-resolution dependence reveals coarse-grained structural properties of
interspike interval statistics; e.g., $\tau$-entropy rates that diverge less
quickly than the firing rate indicate interspike interval correlations. We also
find evidence that the excess entropy and regularized statistical complexity of
different types of integrate-and-fire neurons are universal in the
continuous-time limit in the sense that they do not depend on mechanism
details. This suggests a surprising simplicity in the spike trains generated by
these model neurons. Interestingly, neurons with gamma-distributed ISIs and
neurons whose spike trains are alternating renewal processes do not fall into
the same universality class. These results lead to two conclusions. First, the
dependence of information measures on time resolution reveals mechanistic
details about spike train generation. Second, information measures can be used
as model selection tools for analyzing spike train processes.
A Knowledge-poor Pronoun Resolution System for Turkish
Authors: Dilek Küçük, Meltem Turhan Yöndem
Date: 2015-04-18 00:00:00

A pronoun resolution system which requires limited syntactic knowledge to
identify the antecedents of personal and reflexive pronouns in Turkish is
presented. As in its counterparts for languages like English, Spanish and
French, the core of the system is the constraints and preferences determined
empirically. In the evaluation phase, it performed considerably better than the
baseline algorithm used for comparison. The system is significant for its being
the first fully specified knowledge-poor computational framework for pronoun
resolution in Turkish where Turkish possesses different structural properties
from the languages for which knowledge-poor systems had been developed.
On the consistency of Multithreshold Entropy Linear Classifier
Authors: Wojciech Marian Czarnecki
Date: 2015-04-18 00:00:00

Multithreshold Entropy Linear Classifier (MELC) is a recent classifier idea
which employs information theoretic concept in order to create a multithreshold
maximum margin model. In this paper we analyze its consistency over
multithreshold linear models and show that its objective function upper bounds
the amount of misclassified points in a similar manner like hinge loss does in
support vector machines. For further confirmation we also conduct some
numerical experiments on five datasets.