pwc-archive/papers-with-abstracts huggingface dataset:
Building Proteins in a Day: Efficient 3D Molecular Reconstruction
Authors: Marcus A. Brubaker, Ali Punjani, David J. Fleet
Date: 2015-04-14 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Reconstruction, Stochastic Optimization

Discovering the 3D atomic structure of molecules such as proteins and viruses
is a fundamental research problem in biology and medicine. Electron
Cryomicroscopy (Cryo-EM) is a promising vision-based technique for structure
estimation which attempts to reconstruct 3D structures from 2D images. This
paper addresses the challenging problem of 3D reconstruction from 2D Cryo-EM
images. A new framework for estimation is introduced which relies on modern
stochastic optimization techniques to scale to large datasets. We also
introduce a novel technique which reduces the cost of evaluating the objective
function during optimization by over five orders or magnitude. The net result
is an approach capable of estimating 3D molecular structure from large scale
datasets in about a day on a single workstation.
Efficient Scene Text Localization and Recognition with Local Character Refinement
Authors: Lukáš Neumann, Jiří Matas
Date: 2015-04-14 00:00:00

An unconstrained end-to-end text localization and recognition method is
presented. The method detects initial text hypothesis in a single pass by an
efficient region-based method and subsequently refines the text hypothesis
using a more robust local text model, which deviates from the common assumption
of region-based methods that all characters are detected as connected
components.
  Additionally, a novel feature based on character stroke area estimation is
introduced. The feature is efficiently computed from a region distance map, it
is invariant to scaling and rotations and allows to efficiently detect text
regions regardless of what portion of text they capture.
  The method runs in real time and achieves state-of-the-art text localization
and recognition results on the ICDAR 2013 Robust Reading dataset.
A Multicomponent Approach to Nonrigid Registration of Diffusion Tensor Images
Authors: Mohammed Khader, A. Ben Hamza
Date: 2015-04-08 00:00:00

We propose a nonrigid registration approach for diffusion tensor images using
a multicomponent information-theoretic measure. Explicit orientation
optimization is enabled by incorporating tensor reorientation, which is
necessary for wrapping diffusion tensor images. Experimental results on
diffusion tensor images indicate the feasibility of the proposed approach and a
much better performance compared to the affine registration method based on
mutual information in terms of registration accuracy in the presence of
geometric distortion.
Sample compression schemes for VC classes
Authors: Shay Moran, Amir Yehudayoff
Date: 2015-03-24 00:00:00

Sample compression schemes were defined by Littlestone and Warmuth (1986) as
an abstraction of the structure underlying many learning algorithms. Roughly
speaking, a sample compression scheme of size $k$ means that given an arbitrary
list of labeled examples, one can retain only $k$ of them in a way that allows
to recover the labels of all other examples in the list. They showed that
compression implies PAC learnability for binary-labeled classes, and asked
whether the other direction holds. We answer their question and show that every
concept class $C$ with VC dimension $d$ has a sample compression scheme of size
exponential in $d$. The proof uses an approximate minimax phenomenon for binary
matrices of low VC dimension, which may be of interest in the context of game
theory.
Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
Authors: Fang Wang, Le Kang, Yi Li
Date: 2015-04-14 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Shape Classification, 3D Shape Retrieval, Image Retrieval, Retrieval

Retrieving 3D models from 2D human sketches has received considerable
attention in the areas of graphics, image retrieval, and computer vision.
Almost always in state of the art approaches a large amount of "best views" are
computed for 3D models, with the hope that the query sketch matches one of
these 2D projections of 3D models using predefined features.
  We argue that this two stage approach (view selection -- matching) is
pragmatic but also problematic because the "best views" are subjective and
ambiguous, which makes the matching inputs obscure. This imprecise nature of
matching further makes it challenging to choose features manually. Instead of
relying on the elusive concept of "best views" and the hand-crafted features,
we propose to define our views using a minimalism approach and learn features
for both sketches and views. Specifically, we drastically reduce the number of
views to only two predefined directions for the whole dataset. Then, we learn
two Siamese Convolutional Neural Networks (CNNs), one for the views and one for
the sketches. The loss function is defined on the within-domain as well as the
cross-domain similarities. Our experiments on three benchmark datasets
demonstrate that our method is significantly better than state of the art
approaches, and outperforms them in all conventional metrics.
An active search strategy for efficient object class detection
Authors: Abel Gonzalez-Garcia, Alexander Vezhnevets, Vittorio Ferrari
Date: 2014-12-11 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object

Object class detectors typically apply a window classifier to all the windows
in a large set, either in a sliding window manner or using object proposals. In
this paper, we develop an active search strategy that sequentially chooses the
next window to evaluate based on all the information gathered before. This
results in a substantial reduction in the number of classifier evaluations and
in a more elegant approach in general. Our search strategy is guided by two
forces. First, we exploit context as the statistical relation between the
appearance of a window and its location relative to the object, as observed in
the training set. This enables to jump across distant regions in the image
(e.g. observing a sky region suggests that cars might be far below) and is done
efficiently in a Random Forest framework. Second, we exploit the score of the
classifier to attract the search to promising areas surrounding a highly scored
window, and to keep away from areas near low scored ones. Our search strategy
can be applied on top of any classifier as it treats it as a black-box. In
experiments with R-CNN on the challenging SUN2012 dataset, our method matches
the detection accuracy of evaluating all windows independently, while
evaluating 9x fewer windows.
The jump set under geometric regularisation. Part 1: Basic technique and first-order denoising
Authors: Tuomo Valkonen
Date: 2014-07-06 00:00:00
Tasks: Denoising

Let $u \in \mbox{BV}(\Omega)$ solve the total variation denoising problem
with $L^2$-squared fidelity and data $f$. Caselles et al. [Multiscale Model.
Simul. 6 (2008), 879--894] have shown the containment $\mathcal{H}^{m-1}(J_u
\setminus J_f)=0$ of the jump set $J_u$ of $u$ in that of $f$. Their proof
unfortunately depends heavily on the co-area formula, as do many results in
this area, and as such is not directly extensible to higher-order,
curvature-based, and other advanced geometric regularisers, such as total
generalised variation (TGV) and Euler's elastica. These have received increased
attention in recent times due to their better practical regularisation
properties compared to conventional total variation or wavelets. We prove
analogous jump set containment properties for a general class of regularisers.
We do this with novel Lipschitz transformation techniques, and do not require
the co-area formula. In the present Part 1 we demonstrate the general technique
on first-order regularisers, while in Part 2 we will extend it to higher-order
regularisers. In particular, we concentrate in this part on TV and, as a
novelty, Huber-regularised TV. We also demonstrate that the technique would
apply to non-convex TV models as well as the Perona-Malik anisotropic
diffusion, if these approaches were well-posed to begin with.
Harnessing Natural Fluctuations: Analogue Computer for Efficient Socially Maximal Decision Making
Authors: Song-Ju Kim, Makoto Naruse, Masashi Aono
Date: 2015-04-14 00:00:00
Tasks: Decision Making

Each individual handles many tasks of finding the most profitable option from
a set of options that stochastically provide rewards. Our society comprises a
collection of such individuals, and the society is expected to maximise the
total rewards, while the individuals compete for common rewards. Such
collective decision making is formulated as the `competitive multi-armed bandit
problem (CBP)', requiring a huge computational cost. Herein, we demonstrate a
prototype of an analog computer that efficiently solves CBPs by exploiting the
physical dynamics of numerous fluids in coupled cylinders. This device enables
the maximisation of the total rewards for the society without paying the
conventionally required computational cost; this is because the fluids estimate
the reward probabilities of the options for the exploitation of past knowledge
and generate random fluctuations for the exploration of new knowledge. Our
results suggest that to optimise the social rewards, the utilisation of
fluid-derived natural fluctuations is more advantageous than applying
artificial external fluctuations. Our analog computing scheme is expected to
trigger further studies for harnessing the huge computational power of natural
phenomena for resolving a wide variety of complex problems in modern
information society.
Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn?
Authors: Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco, Lorenzo Natale
Date: 2015-04-13 00:00:00
Tasks: Image Retrieval, Object Recognition, Retrieval

The ability to visually recognize objects is a fundamental skill for robotics
systems. Indeed, a large variety of tasks involving manipulation, navigation or
interaction with other agents, deeply depends on the accurate understanding of
the visual scene. Yet, at the time being, robots are lacking good visual
perceptual systems, which often become the main bottleneck preventing the use
of autonomous agents for real-world applications.
  Lately in computer vision, systems that learn suitable visual representations
and based on multi-layer deep convolutional networks are showing remarkable
performance in tasks such as large-scale visual recognition and image
retrieval. To this regard, it is natural to ask whether such remarkable
performance would generalize also to the robotic setting.
  In this paper we investigate such possibility, while taking further steps in
developing a computational vision system to be embedded on a robotic platform,
the iCub humanoid robot. In particular, we release a new dataset ({\sc
iCubWorld28}) that we use as a benchmark to address the question: {\it how many
objects can iCub recognize?} Our study is developed in a learning framework
which reflects the typical visual experience of a humanoid robot like the iCub.
Experiments shed interesting insights on the strength and weaknesses of current
computer vision approaches applied in real robotic settings.
Automated Analysis and Prediction of Job Interview Performance
Authors: Iftekhar Naim, M. Iftekhar Tanveer, Daniel Gildea, Mohammed, Hoque
Date: 2015-04-14 00:00:00
Tasks: Prediction

We present a computational framework for automatically quantifying verbal and
nonverbal behaviors in the context of job interviews. The proposed framework is
trained by analyzing the videos of 138 interview sessions with 69
internship-seeking undergraduates at the Massachusetts Institute of Technology
(MIT). Our automated analysis includes facial expressions (e.g., smiles, head
gestures, facial tracking points), language (e.g., word counts, topic
modeling), and prosodic information (e.g., pitch, intonation, and pauses) of
the interviewees. The ground truth labels are derived by taking a weighted
average over the ratings of 9 independent judges. Our framework can
automatically predict the ratings for interview traits such as excitement,
friendliness, and engagement with correlation coefficients of 0.75 or higher,
and can quantify the relative importance of prosody, language, and facial
expressions. By analyzing the relative feature weights learned by the
regression models, our framework recommends to speak more fluently, use less
filler words, speak as "we" (vs. "I"), use more unique words, and smile more.
We also find that the students who were rated highly while answering the first
interview question were also rated highly overall (i.e., first impression
matters). Finally, our MIT Interview dataset will be made available to other
researchers to further validate and expand our findings.
Material Recognition in the Wild with the Materials in Context Database
Authors: Sean Bell, Paul Upchurch, Noah Snavely, Kavita Bala
Date: 2014-12-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Material Recognition, Segmentation

Recognizing materials in real-world images is a challenging task. Real-world
materials have rich surface texture, geometry, lighting conditions, and
clutter, which combine to make the problem particularly difficult. In this
paper, we introduce a new, large-scale, open dataset of materials in the wild,
the Materials in Context Database (MINC), and combine this dataset with deep
learning to achieve material recognition and segmentation of images in the
wild.
  MINC is an order of magnitude larger than previous material databases, while
being more diverse and well-sampled across its 23 categories. Using MINC, we
train convolutional neural networks (CNNs) for two tasks: classifying materials
from patches, and simultaneous material recognition and segmentation in full
images. For patch-based classification on MINC we found that the best
performing CNN architectures can achieve 85.2% mean class accuracy. We convert
these trained CNN classifiers into an efficient fully convolutional framework
combined with a fully connected conditional random field (CRF) to predict the
material at every pixel in an image, achieving 73.1% mean class accuracy. Our
experiments demonstrate that having a large, well-sampled dataset such as MINC
is crucial for real-world material recognition and segmentation.
Deep Visual-Semantic Alignments for Generating Image Descriptions
Authors: Andrej Karpathy, Li Fei-Fei
Date: 2014-12-07 00:00:00
Proceeding: CVPR 2015 6
Tasks: Cross-Modal Retrieval, Image Captioning, Image-to-Text Retrieval, Retrieval, Sentence

We present a model that generates natural language descriptions of images and
their regions. Our approach leverages datasets of images and their sentence
descriptions to learn about the inter-modal correspondences between language
and visual data. Our alignment model is based on a novel combination of
Convolutional Neural Networks over image regions, bidirectional Recurrent
Neural Networks over sentences, and a structured objective that aligns the two
modalities through a multimodal embedding. We then describe a Multimodal
Recurrent Neural Network architecture that uses the inferred alignments to
learn to generate novel descriptions of image regions. We demonstrate that our
alignment model produces state of the art results in retrieval experiments on
Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated
descriptions significantly outperform retrieval baselines on both full images
and on a new dataset of region-level annotations.
HHCART: An Oblique Decision Tree
Authors: D. C. Wickramarachchi, B. L. Robertson, M. Reale, C. J. Price, J. Brown
Date: 2015-04-14 00:00:00
Tasks: General Classification

Decision trees are a popular technique in statistical data classification.
They recursively partition the feature space into disjoint sub-regions until
each sub-region becomes homogeneous with respect to a particular class. The
basic Classification and Regression Tree (CART) algorithm partitions the
feature space using axis parallel splits. When the true decision boundaries are
not aligned with the feature axes, this approach can produce a complicated
boundary structure. Oblique decision trees use oblique decision boundaries to
potentially simplify the boundary structure. The major limitation of this
approach is that the tree induction algorithm is computationally expensive. In
this article we present a new decision tree algorithm, called HHCART. The
method utilizes a series of Householder matrices to reflect the training data
at each node during the tree construction. Each reflection is based on the
directions of the eigenvectors from each classes' covariance matrix.
Considering axis parallel splits in the reflected training data provides an
efficient way of finding oblique splits in the unreflected training data.
Experimental results show that the accuracy and size of the HHCART trees are
comparable with some benchmark methods in the literature. The appealing feature
of HHCART is that it can handle both qualitative and quantitative features in
the same oblique split.
Consensus based Detection in the Presence of Data Falsification Attacks
Authors: Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney
Date: 2015-04-14 00:00:00

This paper considers the problem of detection in distributed networks in the
presence of data falsification (Byzantine) attacks. Detection approaches
considered in the paper are based on fully distributed consensus algorithms,
where all of the nodes exchange information only with their neighbors in the
absence of a fusion center. In such networks, we characterize the negative
effect of Byzantines on the steady-state and transient detection performance of
the conventional consensus based detection algorithms. To address this issue,
we study the problem from the network designer's perspective. More
specifically, we first propose a distributed weighted average consensus
algorithm that is robust to Byzantine attacks. We show that, under reasonable
assumptions, the global test statistic for detection can be computed locally at
each node using our proposed consensus algorithm. We exploit the statistical
distribution of the nodes' data to devise techniques for mitigating the
influence of data falsifying Byzantines on the distributed detection system.
Since some parameters of the statistical distribution of the nodes' data might
not be known a priori, we propose learning based techniques to enable an
adaptive design of the local fusion or update rules.
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
Authors: Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
Date: 2015-04-14 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Retrieval, Quantization, Retrieval, Triplet

Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
Clustering Assisted Fundamental Matrix Estimation
Authors: Hao Wu, Yi Wan
Date: 2015-04-14 00:00:00
Tasks: 3D Reconstruction, Clustering

In computer vision, the estimation of the fundamental matrix is a basic
problem that has been extensively studied. The accuracy of the estimation
imposes a significant influence on subsequent tasks such as the camera
trajectory determination and 3D reconstruction. In this paper we propose a new
method for fundamental matrix estimation that makes use of clustering a group
of 4D vectors. The key insight is the observation that among the 4D vectors
constructed from matching pairs of points obtained from the SIFT algorithm,
well-defined cluster points tend to be reliable inliers suitable for
fundamental matrix estimation. Based on this, we utilizes a recently proposed
efficient clustering method through density peaks seeking and propose a new
clustering assisted method. Experimental results show that the proposed
algorithm is faster and more accurate than currently commonly used methods.
Hot Swapping for Online Adaptation of Optimization Hyperparameters
Authors: Kevin Bache, Dennis Decoste, Padhraic Smyth
Date: 2014-12-20 00:00:00

We describe a general framework for online adaptation of optimization
hyperparameters by `hot swapping' their values during learning. We investigate
this approach in the context of adaptive learning rate selection using an
explore-exploit strategy from the multi-armed bandit literature. Experiments on
a benchmark neural network show that the hot swapping approach leads to
consistently better solutions compared to well-known alternatives such as
AdaDelta and stochastic gradient with exhaustive hyperparameter search.
Tractable Query Answering and Optimization for Extensions of Weakly-Sticky Datalog+-
Authors: Mostafa Milani, Leopoldo Bertossi
Date: 2015-04-13 00:00:00

We consider a semantic class, weakly-chase-sticky (WChS), and a syntactic
subclass, jointly-weakly-sticky (JWS), of Datalog+- programs. Both extend that
of weakly-sticky (WS) programs, which appear in our applications to data
quality. For WChS programs we propose a practical, polynomial-time query
answering algorithm (QAA). We establish that the two classes are closed under
magic-sets rewritings. As a consequence, QAA can be applied to the optimized
programs. QAA takes as inputs the program (including the query) and semantic
information about the "finiteness" of predicate positions. For the syntactic
subclasses JWS and WS of WChS, this additional information is computable.
Self-informed neural network structure learning
Authors: David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov
Date: 2014-12-20 00:00:00
Tasks: Object Recognition

We study the problem of large scale, multi-label visual recognition with a
large number of possible classes. We propose a method for augmenting a trained
neural network classifier with auxiliary capacity in a manner designed to
significantly improve upon an already well-performing model, while minimally
impacting its computational footprint. Using the predictions of the network
itself as a descriptor for assessing visual similarity, we define a
partitioning of the label space into groups of visually similar entities. We
then augment the network with auxilliary hidden layer pathways with
connectivity only to these groups of label units. We report a significant
improvement in mean average precision on a large-scale object recognition task
with the augmented model, while increasing the number of multiply-adds by less
than 3%.