pwc-archive/papers-with-abstracts huggingface dataset:
3D ShapeNets: A Deep Representation for Volumetric Shapes
Authors: Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao
Date: 2014-06-22 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Point Cloud Classification, 3D Shape Representation, Object, Object Recognition

3D shape is a crucial but heavily underutilized cue in today's computer
vision systems, mostly due to the lack of a good generic shape representation.
With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft
Kinect), it is becoming increasingly important to have a powerful 3D shape
representation in the loop. Apart from category recognition, recovering full 3D
shapes from view-based 2.5D depth maps is also a critical part of visual
understanding. To this end, we propose to represent a geometric 3D shape as a
probability distribution of binary variables on a 3D voxel grid, using a
Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the
distribution of complex 3D shapes across different object categories and
arbitrary poses from raw CAD data, and discovers hierarchical compositional
part representations automatically. It naturally supports joint object
recognition and shape completion from 2.5D depth maps, and it enables active
object recognition through view planning. To train our 3D deep learning model,
we construct ModelNet -- a large-scale 3D CAD model dataset. Extensive
experiments show that our 3D deep representation enables significant
performance improvement over the-state-of-the-arts in a variety of tasks.
Optimizing Text Quantifiers for Multivariate Loss Functions
Authors: Andrea Esuli, Fabrizio Sebastiani
Date: 2015-02-19 00:00:00
Tasks: Structured Prediction

We address the problem of \emph{quantification}, a supervised learning task
whose goal is, given a class, to estimate the relative frequency (or
\emph{prevalence}) of the class in a dataset of unlabelled items.
Quantification has several applications in data and text mining, such as
estimating the prevalence of positive reviews in a set of reviews of a given
product, or estimating the prevalence of a given support issue in a dataset of
transcripts of phone calls to tech support. So far, quantification has been
addressed by learning a general-purpose classifier, counting the unlabelled
items which have been assigned the class, and tuning the obtained counts
according to some heuristics. In this paper we depart from the tradition of
using general-purpose classifiers, and use instead a supervised learning model
for \emph{structured prediction}, capable of generating classifiers directly
optimized for the (multivariate and non-linear) function used for evaluating
quantification accuracy. The experiments that we have run on 5500 binary
high-dimensional datasets (averaging more than 14,000 documents each) show that
this method is more accurate, more stable, and more efficient than existing,
state-of-the-art quantification methods.
Application of Enhanced-2D-CWT in Topographic Images for Mapping Landslide Risk Areas
Authors: V. V. Vermehren Valenzuela, R. D. Lins, H. M. de Oliveira
Date: 2015-04-15 00:00:00

There has been lately a number of catastrophic events of landslides and
mudslides in the mountainous region of Rio de Janeiro, Brazil. Those were
caused by intense rain in localities where there was unplanned occupation of
slopes of hills and mountains. Thus, it became imperative creating an inventory
of landslide risk areas in densely populated cities. This work presents a way
of demarcating risk areas by using the bidimensional Continuous Wavelet
Transform (2D-CWT) applied to high resolution topographic images of the
mountainous region of Rio de Janeiro.
Improving zero-shot learning by mitigating the hubness problem
Authors: Georgiana Dinu, Angeliki Lazaridou, Marco Baroni
Date: 2014-12-20 00:00:00
Tasks: Image Retrieval, Retrieval, Zero-Shot Learning

The zero-shot paradigm exploits vector-based word representations extracted
from text corpora with unsupervised methods to learn general mapping functions
from other feature spaces onto word space, where the words associated to the
nearest neighbours of the mapped vectors are used as their linguistic labels.
We show that the neighbourhoods of the mapped elements are strongly polluted by
hubs, vectors that tend to be near a high proportion of items, pushing their
correct labels down the neighbour list. After illustrating the problem
empirically, we propose a simple method to correct it by taking the proximity
distribution of potential neighbours across many mapped vectors into account.
We show that this correction leads to consistent improvements in realistic
zero-shot experiments in the cross-lingual, image labeling and image retrieval
domains.
Bridging belief function theory to modern machine learning
Authors: Thomas Burger
Date: 2015-04-15 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, General Classification

Machine learning is a quickly evolving field which now looks really different
from what it was 15 years ago, when classification and clustering were major
issues. This document proposes several trends to explore the new questions of
modern machine learning, with the strong afterthought that the belief function
framework has a major role to play.
Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videos
Authors: Meng-Che Chuang, Jenq-Neng Hwang, Kresimir Williams, Richard Towler
Date: 2015-04-15 00:00:00
Tasks: Segmentation, Stereo Matching, Stereo Matching Hand

Non-extractive fish abundance estimation with the aid of visual analysis has
drawn increasing attention. Unstable illumination, ubiquitous noise and low
frame rate video capturing in the underwater environment, however, make
conventional tracking methods unreliable. In this paper, we present a multiple
fish tracking system for low-contrast and low-frame-rate stereo videos with the
use of a trawl-based underwater camera system. An automatic fish segmentation
algorithm overcomes the low-contrast issues by adopting a histogram
backprojection approach on double local-thresholded images to ensure an
accurate segmentation on the fish shape boundaries. Built upon a reliable
feature-based object matching method, a multiple-target tracking algorithm via
a modified Viterbi data association is proposed to overcome the poor motion
continuity and frequent entrance/exit of fish targets under low-frame-rate
scenarios. In addition, a computationally efficient block-matching approach
performs successful stereo matching, which enables an automatic fish-body tail
compensation to greatly reduce segmentation error and allows for an accurate
fish length measurement. Experimental results show that an effective and
reliable tracking performance for multiple live fish with underwater stereo
cameras is achieved.
Text Localization in Video Using Multiscale Weber's Local Descriptor
Authors: B. H. Shekar, Smitha M. L.
Date: 2015-04-15 00:00:00

In this paper, we propose a novel approach for detecting the text present in
videos and scene images based on the Multiscale Weber's Local Descriptor
(MWLD). Given an input video, the shots are identified and the key frames are
extracted based on their spatio-temporal relationship. From each key frame, we
detect the local region information using WLD with different radius and
neighborhood relationship of pixel values and hence obtained intensity enhanced
key frames at multiple scales. These multiscale WLD key frames are merged
together and then the horizontal gradients are computed using morphological
operations. The obtained results are then binarized and the false positives are
eliminated based on geometrical properties. Finally, we employ connected
component analysis and morphological dilation operation to determine the text
regions that aids in text localization. The experimental results obtained on
publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset
illustrate that the proposed method can accurately detect and localize texts of
various sizes, fonts and colors in videos.
Fully Convolutional Multi-Class Multiple Instance Learning
Authors: Deepak Pathak, Evan Shelhamer, Jonathan Long, Trevor Darrell
Date: 2014-12-22 00:00:00
Tasks: Multiple Instance Learning, Segmentation, Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Multiple instance learning (MIL) can reduce the need for costly annotation in
tasks such as semantic segmentation by weakening the required degree of
supervision. We propose a novel MIL formulation of multi-class semantic
segmentation learning by a fully convolutional network. In this setting, we
seek to learn a semantic segmentation model from just weak image-level labels.
The model is trained end-to-end to jointly optimize the representation while
disambiguating the pixel-image label assignment. Fully convolutional training
accepts inputs of any size, does not need object proposal pre-processing, and
offers a pixelwise loss map for selecting latent instances. Our multi-class MIL
loss exploits the further supervision given by images with multiple labels. We
evaluate this approach through preliminary experiments on the PASCAL VOC
segmentation challenge.
Posterior contraction of the population polytope in finite admixture models
Authors: XuanLong Nguyen
Date: 2012-06-01 00:00:00
Tasks: Topic Models

We study the posterior contraction behavior of the latent population
structure that arises in admixture models as the amount of data increases. We
adopt the geometric view of admixture models - alternatively known as topic
models - as a data generating mechanism for points randomly sampled from the
interior of a (convex) population polytope, whose extreme points correspond to
the population structure variables of interest. Rates of posterior contraction
are established with respect to Hausdorff metric and a minimum matching
Euclidean metric defined on polytopes. Tools developed include posterior
asymptotics of hierarchical models and arguments from convex geometry.
Relax, no need to round: integrality of clustering formulations
Authors: Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar, Ravishankar Krishnaswamy, Soledad Villar, Rachel Ward
Date: 2014-08-18 00:00:00
Tasks: Clustering

We study exact recovery conditions for convex relaxations of point cloud
clustering problems, focusing on two of the most common optimization problems
for unsupervised clustering: $k$-means and $k$-median clustering. Motivations
for focusing on convex relaxations are: (a) they come with a certificate of
optimality, and (b) they are generic tools which are relatively parameter-free,
not tailored to specific assumptions over the input. More precisely, we
consider the distributional setting where there are $k$ clusters in
$\mathbb{R}^m$ and data from each cluster consists of $n$ points sampled from a
symmetric distribution within a ball of unit radius. We ask: what is the
minimal separation distance between cluster centers needed for convex
relaxations to exactly recover these $k$ clusters as the optimal integral
solution? For the $k$-median linear programming relaxation we show a tight
bound: exact recovery is obtained given arbitrarily small pairwise separation
$\epsilon > 0$ between the balls. In other words, the pairwise center
separation is $\Delta > 2+\epsilon$. Under the same distributional model, the
$k$-means LP relaxation fails to recover such clusters at separation as large
as $\Delta = 4$. Yet, if we enforce PSD constraints on the $k$-means LP, we get
exact cluster recovery at center separation $\Delta > 2\sqrt2(1+\sqrt{1/m})$.
In contrast, common heuristics such as Lloyd's algorithm (a.k.a. the $k$-means
algorithm) can fail to recover clusters in this setting; even with arbitrarily
large cluster separation, k-means++ with overseeding by any constant factor
fails with high probability at exact cluster recovery. To complement the
theoretical analysis, we provide an experimental study of the recovery
guarantees for these various methods, and discuss several open problems which
these experiments suggest.
Norm-Based Capacity Control in Neural Networks
Authors: Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
Date: 2015-02-27 00:00:00

We investigate the capacity, convexity and characterization of a general
family of norm-constrained feed-forward networks.
Background Subtraction via Generalized Fused Lasso Foreground Modeling
Authors: Bo Xin, Yuan Tian, Yizhou Wang, Wen Gao
Date: 2015-04-14 00:00:00
Proceeding: CVPR 2015 6
Tasks: Foreground Segmentation

Background Subtraction (BS) is one of the key steps in video analysis. Many
background models have been proposed and achieved promising performance on
public data sets. However, due to challenges such as illumination change,
dynamic background etc. the resulted foreground segmentation often consists of
holes as well as background noise. In this regard, we consider generalized
fused lasso regularization to quest for intact structured foregrounds. Together
with certain assumptions about the background, such as the low-rank assumption
or the sparse-composition assumption (depending on whether pure background
frames are provided), we formulate BS as a matrix decomposition problem using
regularization terms for both the foreground and background matrices. Moreover,
under the proposed formulation, the two generally distinctive background
assumptions can be solved in a unified manner. The optimization was carried out
via applying the augmented Lagrange multiplier (ALM) method in such a way that
a fast parametric-flow algorithm is used for updating the foreground matrix.
Experimental results on several popular BS data sets demonstrate the advantage
of the proposed model compared to state-of-the-arts.
Probabilistic Clustering of Time-Evolving Distance Data
Authors: Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir S. Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch
Date: 2015-04-14 00:00:00
Tasks: Clustering

We present a novel probabilistic clustering model for objects that are
represented via pairwise distances and observed at different time points. The
proposed method utilizes the information given by adjacent time points to find
the underlying cluster structure and obtain a smooth cluster evolution. This
approach allows the number of objects and clusters to differ at every time
point, and no identification on the identities of the objects is needed.
Further, the model does not require the number of clusters being specified in
advance -- they are instead determined automatically using a Dirichlet process
prior. We validate our model on synthetic data showing that the proposed method
is more accurate than state-of-the-art clustering methods. Finally, we use our
dynamic clustering model to analyze and illustrate the evolution of brain
cancer patients over time.
Inferring Social Status and Rich Club Effects in Enterprise Communication Networks
Authors: Yuxiao Dong, Jie Tang, Nitesh Chawla, Tiancheng Lou, Yang Yang, Bai Wang
Date: 2014-04-14 00:00:00
Tasks: Position

Social status, defined as the relative rank or position that an individual
holds in a social hierarchy, is known to be among the most important motivating
forces in social behaviors. In this paper, we consider the notion of status
from the perspective of a position or title held by a person in an enterprise.
We study the intersection of social status and social networks in an
enterprise. We study whether enterprise communication logs can help reveal how
social interactions and individual status manifest themselves in social
networks. To that end, we use two enterprise datasets with three communication
channels --- voice call, short message, and email --- to demonstrate the
social-behavioral differences among individuals with different status. We have
several interesting findings and based on these findings we also develop a
model to predict social status. On the individual level, high-status
individuals are more likely to be spanned as structural holes by linking to
people in parts of the enterprise networks that are otherwise not well
connected to one another. On the community level, the principle of homophily,
social balance and clique theory generally indicate a "rich club" maintained by
high-status individuals, in the sense that this community is much more
connected, balanced and dense. Our model can predict social status of
individuals with 93% accuracy.
Temporal ordering of clinical events
Authors: Azad Dehghan
Date: 2015-04-14 00:00:00

This report describes a minimalistic set of methods engineered to anchor
clinical events onto a temporal space. Specifically, we describe methods to
extract clinical events (e.g., Problems, Treatments and Tests), temporal
expressions (i.e., time, date, duration, and frequency), and temporal links
(e.g., Before, After, Overlap) between events and temporal entities. These
methods are developed and validated using high quality datasets.
From Captions to Visual Concepts and Back
Authors: Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C. Platt, C. Lawrence Zitnick, Geoffrey Zweig
Date: 2014-11-18 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Captioning, Language Modeling, Language Modelling, Multiple Instance Learning, Re-Ranking, Sentence

This paper presents a novel approach for automatically generating image
descriptions: visual detectors, language models, and multimodal similarity
models learnt directly from a dataset of image captions. We use multiple
instance learning to train visual detectors for words that commonly occur in
captions, including many different parts of speech such as nouns, verbs, and
adjectives. The word detector outputs serve as conditional inputs to a
maximum-entropy language model. The language model learns from a set of over
400,000 image descriptions to capture the statistics of word usage. We capture
global semantics by re-ranking caption candidates using sentence-level features
and a deep multimodal similarity model. Our system is state-of-the-art on the
official Microsoft COCO benchmark, producing a BLEU-4 score of 29.1%. When
human judges compare the system captions to ones written by other people on our
held-out test set, the system captions have equal or better quality 34% of the
time.
Learning to Compare Image Patches via Convolutional Neural Networks
Authors: Sergey Zagoruyko, Nikos Komodakis
Date: 2015-04-14 00:00:00
Proceeding: CVPR 2015 6

In this paper we show how to learn directly from image data (i.e., without
resorting to manually-designed features) a general similarity function for
comparing image patches, which is a task of fundamental importance for many
computer vision problems. To encode such a function, we opt for a CNN-based
model that is trained to account for a wide variety of changes in image
appearance. To that end, we explore and study multiple neural network
architectures, which are specifically adapted to this task. We show that such
an approach can significantly outperform the state-of-the-art on several
problems and benchmark datasets.
A data-based classification of Slavic languages: Indices of qualitative variation applied to grapheme frequencies
Authors: Michaela Koscová, Ján Macutek, Emmerich Kelih
Date: 2015-04-14 00:00:00
Tasks: General Classification

The Ord's graph is a simple graphical method for displaying frequency
distributions of data or theoretical distributions in the two-dimensional
plane. Its coordinates are proportions of the first three moments, either
empirical or theoretical ones. A modification of the Ord's graph based on
proportions of indices of qualitative variation is presented. Such a
modification makes the graph applicable also to data of categorical character.
In addition, the indices are normalized with values between 0 and 1, which
enables comparing data files divided into different numbers of categories. Both
the original and the new graph are used to display grapheme frequencies in
eleven Slavic languages. As the original Ord's graph requires an assignment of
numbers to the categories, graphemes were ordered decreasingly according to
their frequencies. Data were taken from parallel corpora, i.e., we work with
grapheme frequencies from a Russian novel and its translations to ten other
Slavic languages. Then, cluster analysis is applied to the graph coordinates.
While the original graph yields results which are not linguistically
interpretable, the modification reveals meaningful relations among the
languages.
A Solution for Multi-Alignment by Transformation Synchronisation
Authors: Florian Bernard, Johan Thunberg, Peter Gemmar, Frank Hertel, Andreas Husch, Jorge Goncalves
Date: 2014-10-30 00:00:00
Proceeding: CVPR 2015 6

The alignment of a set of objects by means of transformations plays an
important role in computer vision. Whilst the case for only two objects can be
solved globally, when multiple objects are considered usually iterative methods
are used. In practice the iterative methods perform well if the relative
transformations between any pair of objects are free of noise. However, if only
noisy relative transformations are available (e.g. due to missing data or wrong
correspondences) the iterative methods may fail.
  Based on the observation that the underlying noise-free transformations can
be retrieved from the null space of a matrix that can directly be obtained from
pairwise alignments, this paper presents a novel method for the synchronisation
of pairwise transformations such that they are transitively consistent.
  Simulations demonstrate that for noisy transformations, a large proportion of
missing data and even for wrong correspondence assignments the method delivers
encouraging results.
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
Authors: Ayan Chakrabarti, Ying Xiong, Steven J. Gortler, Todd Zickler
Date: 2014-11-18 00:00:00
Proceeding: CVPR 2015 6
Tasks: valid

We introduce a multi-scale framework for low-level vision, where the goal is
estimating physical scene values from image data---such as depth from stereo
image pairs. The framework uses a dense, overlapping set of image regions at
multiple scales and a "local model," such as a slanted-plane model for stereo
disparity, that is expected to be valid piecewise across the visual field.
Estimation is cast as optimization over a dichotomous mixture of variables,
simultaneously determining which regions are inliers with respect to the local
model (binary variables) and the correct co-ordinates in the local model space
for each inlying region (continuous variables). When the regions are organized
into a multi-scale hierarchy, optimization can occur in an efficient and
parallel architecture, where distributed computational units iteratively
perform calculations and share information through sparse connections between
parents and children. The framework performs well on a standard benchmark for
binocular stereo, and it produces a distributional scene representation that is
appropriate for combining with higher-level reasoning and other low-level cues.
Towards Verifiably Ethical Robot Behaviour
Authors: Louise A. Dennis, Michael Fisher, Alan F. T. Winfield
Date: 2015-04-14 00:00:00
Tasks: Decision Making

Ensuring that autonomous systems work ethically is both complex and
difficult. However, the idea of having an additional `governor' that assesses
options the system has, and prunes them to select the most ethical choices is
well understood. Recent work has produced such a governor consisting of a
`consequence engine' that assesses the likely future outcomes of actions then
applies a Safety/Ethical logic to select actions. Although this is appealing,
it is impossible to be certain that the most ethical options are actually
taken. In this paper we extend and apply a well-known agent verification
approach to our consequence engine, allowing us to verify the correctness of
its ethical decision-making.