pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Sparse Bayesian Dictionary Learning
Authors: Igor Fedorov, Bhaskar D. Rao
Date: 2018-04-10 00:00:00
Tasks: Dictionary Learning

This paper addresses the problem of learning dictionaries for multimodal datasets, i.e. datasets collected from multiple data sources. We present an algorithm called multimodal sparse Bayesian dictionary learning (MSBDL). MSBDL leverages information from all available data modalities through a joint sparsity constraint. The underlying framework offers a considerable amount of flexibility to practitioners and addresses many of the shortcomings of existing multimodal dictionary learning approaches. In particular, the procedure includes the automatic tuning of hyperparameters and is unique in that it allows the dictionaries for each data modality to have different cardinality, a significant feature in cases when the dimensionality of data differs across modalities. MSBDL is scalable and can be used in supervised learning settings. Theoretical results relating to the convergence of MSBDL are presented and the numerical results provide evidence of the superior performance of MSBDL on synthetic and real datasets compared to existing methods.
On Regularized Losses for Weakly-supervised CNN Segmentation
Authors: Meng Tang, Federico Perazzi, Abdelaziz Djelouah, Ismail Ben Ayed, Christopher Schroers, Yuri Boykov
Date: 2018-03-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Segmentation, Semantic Segmentation

Minimization of regularized losses is a principled approach to weak
supervision well-established in deep learning, in general. However, it is
largely overlooked in semantic segmentation currently dominated by methods
mimicking full supervision via "fake" fully-labeled training masks (proposals)
generated from available partial input. To obtain such full masks the typical
methods explicitly use standard regularization techniques for "shallow"
segmentation, e.g. graph cuts or dense CRFs. In contrast, we integrate such
standard regularizers directly into the loss functions over partial input. This
approach simplifies weakly-supervised training by avoiding extra MRF/CRF
inference steps or layers explicitly generating full masks, while improving
both the quality and efficiency of training. This paper proposes and
experimentally compares different losses integrating MRF/CRF regularization
terms. We juxtapose our regularized losses with earlier proposal-generation
methods using explicit regularization steps or layers. Our approach achieves
state-of-the-art accuracy in semantic segmentation with near full-supervision
quality.
Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks
Authors: Swapnil Mishra, Marian-Andrei Rizoiu, Lexing Xie
Date: 2018-04-06 00:00:00
Tasks: Articles

Understanding and predicting the popularity of online items is an important
open problem in social media analysis. Considerable progress has been made
recently in data-driven predictions, and in linking popularity to external
promotions. However, the existing methods typically focus on a single source of
external influence, whereas for many types of online content such as YouTube
videos or news articles, attention is driven by multiple heterogeneous sources
simultaneously - e.g. microblogs or traditional media coverage. Here, we
propose RNN-MAS, a recurrent neural network for modeling asynchronous streams.
It is a sequence generator that connects multiple streams of different
granularity via joint inference. We show RNN-MAS not only to outperform the
current state-of-the-art Youtube popularity prediction system by 17%, but also
to capture complex dynamics, such as seasonal trends of unseen influence. We
define two new metrics: promotion score quantifies the gain in popularity from
one unit of promotion for a Youtube video; the loudness level captures the
effects of a particular user tweeting about the video. We use the loudness
level to compare the effects of a video being promoted by a single
highly-followed user (in the top 1% most followed users) against being promoted
by a group of mid-followed users. We find that results depend on the type of
content being promoted: superusers are more successful in promoting Howto and
Gaming videos, whereas the cohort of regular users are more influential for
Activism videos. This work provides more accurate and explainable popularity
predictions, as well as computational tools for content producers and marketers
to allocate resources for promotion campaigns.
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
Authors: Canyi Lu, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, Shuicheng Yan
Date: 2018-04-10 00:00:00

In this paper, we consider the Tensor Robust Principal Component Analysis
(TRPCA) problem, which aims to exactly recover the low-rank and sparse
components from their sum. Our model is based on the recently proposed
tensor-tensor product (or t-product). Induced by the t-product, we first
rigorously deduce the tensor spectral norm, tensor nuclear norm, and tensor
average rank, and show that the tensor nuclear norm is the convex envelope of
the tensor average rank within the unit ball of the tensor spectral norm. These
definitions, their relationships and properties are consistent with matrix
cases. Equipped with the new tensor nuclear norm, we then solve the TRPCA
problem by solving a convex program and provide the theoretical guarantee for
the exact recovery. Our TRPCA model and recovery guarantee include matrix RPCA
as a special case. Numerical experiments verify our results, and the
applications to image recovery and background modeling problems demonstrate the
effectiveness of our method.
Graph Matching with Anchor Nodes: A Learning Approach
Authors: Nan Hu, Raif M. Rustamov, Leonidas Guibas
Date: 2018-04-10 00:00:00
Proceeding: CVPR 2013 6
Tasks: Graph Matching

In this paper, we consider the weighted graph matching problem with partially
disclosed correspondences between a number of anchor nodes. Our construction
exploits recently introduced node signatures based on graph Laplacians, namely
the Laplacian family signature (LFS) on the nodes, and the pairwise heat kernel
map on the edges. In this paper, without assuming an explicit form of
parametric dependence nor a distance metric between node signatures, we
formulate an optimization problem which incorporates the knowledge of anchor
nodes. Solving this problem gives us an optimized proximity measure specific to
the graphs under consideration. Using this as a first order compatibility term,
we then set up an integer quadratic program (IQP) to solve for a near optimal
graph matching. Our experiments demonstrate the superior performance of our
approach on randomly generated graphs and on two widely-used image sequences,
when compared with other existing signature and adjacency matrix based graph
matching methods.
Stable and Informative Spectral Signatures for Graph Matching
Authors: Nan Hu, Raif M. Rustamov, Leonidas Guibas
Date: 2013-04-04 00:00:00
Proceeding: CVPR 2014 6
Tasks: Graph Matching, Informativeness

In this paper, we consider the approximate weighted graph matching problem
and introduce stable and informative first and second order compatibility terms
suitable for inclusion into the popular integer quadratic program formulation.
Our approach relies on a rigorous analysis of stability of spectral signatures
based on the graph Laplacian. In the case of the first order term, we derive an
objective function that measures both the stability and informativeness of a
given spectral signature. By optimizing this objective, we design new spectral
node signatures tuned to a specific graph to be matched. We also introduce the
pairwise heat kernel distance as a stable second order compatibility term; we
justify its plausibility by showing that in a certain limiting case it
converges to the classical adjacency matrix-based second order compatibility
function. We have tested our approach on a set of synthetic graphs, the
widely-used CMU house sequence, and a set of real images. These experiments
show the superior performance of our first and second order compatibility terms
as compared with the commonly used ones.
The iNaturalist Species Classification and Detection Dataset
Authors: Grant Van Horn, Oisin Mac Aodha, Yang song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, Serge Belongie
Date: 2017-07-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, image-classification, Image Classification

Existing image classification datasets used in computer vision tend to have a
uniform distribution of images across object categories. In contrast, the
natural world is heavily imbalanced, as some species are more abundant and
easier to photograph than others. To encourage further progress in challenging
real world conditions we present the iNaturalist species classification and
detection dataset, consisting of 859,000 images from over 5,000 different
species of plants and animals. It features visually similar species, captured
in a wide variety of situations, from all over the world. Images were collected
with different camera types, have varying image quality, feature a large class
imbalance, and have been verified by multiple citizen scientists. We discuss
the collection of the dataset and present extensive baseline experiments using
state-of-the-art computer vision classification and detection models. Results
show that current non-ensemble based methods achieve only 67% top one
classification accuracy, illustrating the difficulty of the dataset.
Specifically, we observe poor results for classes with small numbers of
training examples suggesting more attention is needed in low-shot learning.
Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification
Authors: Xin Yi, Ekta Walia, Paul Babyn
Date: 2018-04-10 00:00:00
Tasks: General Classification, Generative Adversarial Network, image-classification, Image Classification

Melanoma is a curable aggressive skin cancer if detected early. Typically,
the diagnosis involves initial screening with subsequent biopsy and
histopathological examination if necessary. Computer aided diagnosis offers an
objective score that is independent of clinical experience and the potential to
lower the workload of a dermatologist. In the recent past, success of deep
learning algorithms in the field of general computer vision has motivated
successful application of supervised deep learning methods in computer aided
melanoma recognition. However, large quantities of labeled images are required
to make further improvements on the supervised method. A good annotation
generally requires clinical and histological confirmation, which requires
significant effort. In an attempt to alleviate this constraint, we propose to
use categorical generative adversarial network to automatically learn the
feature representation of dermoscopy images in an unsupervised and
semi-supervised manner. Thorough experiments on ISIC 2016 skin lesion chal-
lenge demonstrate that the proposed feature learning method has achieved an
average precision score of 0.424 with only 140 labeled images. Moreover, the
proposed method is also capable of generating real-world like dermoscopy
images.
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Authors: Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
Date: 2018-01-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Quality Assessment, SSIM, Video Quality Assessment

While it is nearly effortless for humans to quickly assess the perceptual
similarity between two images, the underlying processes are thought to be quite
complex. Despite this, the most widely used perceptual metrics today, such as
PSNR and SSIM, are simple, shallow functions, and fail to account for many
nuances of human perception. Recently, the deep learning community has found
that features of the VGG network trained on ImageNet classification has been
remarkably useful as a training loss for image synthesis. But how perceptual
are these so-called "perceptual losses"? What elements are critical for their
success? To answer these questions, we introduce a new dataset of human
perceptual similarity judgments. We systematically evaluate deep features
across different architectures and tasks and compare them with classic metrics.
We find that deep features outperform all previous metrics by large margins on
our dataset. More surprisingly, this result is not restricted to
ImageNet-trained VGG features, but holds across different deep architectures
and levels of supervision (supervised, self-supervised, or even unsupervised).
Our results suggest that perceptual similarity is an emergent property shared
across deep visual representations.
Distributable Consistent Multi-Object Matching
Authors: Nan Hu, Qi-Xing Huang, Boris Thibert, Leonidas Guibas
Date: 2016-11-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object

In this paper we propose an optimization-based framework to multiple object
matching. The framework takes maps computed between pairs of objects as input,
and outputs maps that are consistent among all pairs of objects. The central
idea of our approach is to divide the input object collection into overlapping
sub-collections and enforce map consistency among each sub-collection. This
leads to a distributed formulation, which is scalable to large-scale datasets.
We also present an equivalence condition between this decoupled scheme and the
original scheme. Experiments on both synthetic and real-world datasets show
that our framework is competitive against state-of-the-art multi-object
matching techniques.
French Word Recognition through a Quick Survey on Recurrent Neural Networks Using Long-Short Term Memory RNN-LSTM
Authors: Saman Sarraf
Date: 2018-04-10 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

Optical character recognition (OCR) is a fundamental problem in computer
vision. Research studies have shown significant progress in classifying printed
characters using deep learning-based methods and topologies. Among current
algorithms, recurrent neural networks with long-short term memory blocks called
RNN-LSTM have provided the highest performance in terms of accuracy rate. Using
the top 5,000 French words collected from the internet including all signs and
accents, RNN-LSTM models were trained and tested for several cases. Six fonts
were used to generate OCR samples and an additional dataset that included all
samples from these six fonts was prepared for training and testing purposes.
The trained RNN-LSTM models were tested and achieved the accuracy rates of
99.98798% and 99.91889% for edit distance and sequence error, respectively. An
accurate preprocessing followed by height normalization (standardization
methods in deep learning) enabled the RNN-LSTM model to be trained in the most
efficient way. This machine learning work also revealed the robustness of
RNN-LSTM topology to recognize printed characters.
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
Authors: Mykhaylo Andriluka, Umar Iqbal, Eldar Insafutdinov, Leonid Pishchulin, Anton Milan, Juergen Gall, Bernt Schiele
Date: 2017-10-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Activity Recognition, Multi-Person Pose Estimation, Pose Estimation, Pose Tracking, Retrieval

Human poses and motions are important cues for analysis of videos with people
and there is strong evidence that representations based on body pose are highly
effective for a variety of tasks such as activity recognition, content
retrieval and social signal processing. In this work, we aim to further advance
the state of the art by establishing "PoseTrack", a new large-scale benchmark
for video-based human pose estimation and articulated tracking, and bringing
together the community of researchers working on visual human analysis. The
benchmark encompasses three competition tracks focusing on i) single-frame
multi-person pose estimation, ii) multi-person pose estimation in videos, and
iii) multi-person articulated tracking. To facilitate the benchmark and
challenge we collect, annotate and release a new %large-scale benchmark dataset
that features videos with multiple people labeled with person tracks and
articulated pose. A centralized evaluation server is provided to allow
participants to evaluate on a held-out test set. We envision that the proposed
benchmark will stimulate productive research both by providing a large and
representative training dataset as well as providing a platform to objectively
evaluate and compare the proposed methods. The benchmark is freely accessible
at https://posetrack.net.
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model
Authors: Baris Gecer, Binod Bhattarai, Josef Kittler, Tae-Kyun Kim
Date: 2018-04-10 00:00:00
Proceeding: ECCV 2018 9
Tasks: Domain Adaptation, Face Generation, Face Recognition, Face Verification, Style Transfer

We propose a novel end-to-end semi-supervised adversarial framework to
generate photorealistic face images of new identities with wide ranges of
expressions, poses, and illuminations conditioned by a 3D morphable model.
Previous adversarial style-transfer methods either supervise their networks
with large volume of paired data or use unpaired data with a highly
under-constrained two-way generative framework in an unsupervised fashion. We
introduce pairwise adversarial supervision to constrain two-way domain
adaptation by a small number of paired real and synthetic images for training
along with the large volume of unpaired data. Extensive qualitative and
quantitative experiments are performed to validate our idea. Generated face
images of new identities contain pose, lighting and expression diversity and
qualitative results show that they are highly constraint by the synthetic input
image while adding photorealism and retaining identity information. We combine
face images generated by the proposed method with the real data set to train
face recognition algorithms. We evaluated the model on two challenging data
sets: LFW and IJB-A. We observe that the generated images from our framework
consistently improves over the performance of deep face recognition network
trained with Oxford VGG Face dataset and achieves comparable results to the
state-of-the-art.
Deep Learning for Digital Text Analytics: Sentiment Analysis
Authors: Reshma U, Barathi Ganesh H. B, Mandar Kale, Prachi Mankame, Gouri Kulkarni
Date: 2018-04-10 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Sentiment Analysis

In today's scenario, imagining a world without negativity is something very
unrealistic, as bad NEWS spreads more virally than good ones. Though it seems
impractical in real life, this could be implemented by building a system using
Machine Learning and Natural Language Processing techniques in identifying the
news datum with negative shade and filter them by taking only the news with
positive shade (good news) to the end user. In this work, around two lakhs
datum have been trained and tested using a combination of rule-based and data
driven approaches. VADER along with a filtration method has been used as an
annotating tool followed by statistical Machine Learning approach that have
used Document Term Matrix (representation) and Support Vector Machine
(classification). Deep Learning algorithms then came into picture to make this
system reliable (Doc2Vec) which finally ended up with Convolutional Neural
Network(CNN) that yielded better results than the other experimented modules.
It showed up a training accuracy of 96%, while a test accuracy of (internal and
external news datum) above 85% was obtained.
Seeded Graph Matching
Authors: Donniell E. Fishkind, Sancar Adali, Heather G. Patsolic, Lingyao Meng, Digvijay Singh, Vince Lyzinski, Carey E. Priebe
Date: 2012-09-03 00:00:00
Tasks: Graph Matching

Given two graphs, the graph matching problem is to align the two vertex sets
so as to minimize the number of adjacency disagreements between the two graphs.
The seeded graph matching problem is the graph matching problem when we are
first given a partial alignment that we are tasked with completing. In this
paper, we modify the state-of-the-art approximate graph matching algorithm
"FAQ" of Vogelstein et al. (2015) to make it a fast approximate seeded graph
matching algorithm, adapt its applicability to include graphs with differently
sized vertex sets, and extend the algorithm so as to provide, for each
individual vertex, a nomination list of likely matches. We demonstrate the
effectiveness of our algorithm via simulation and real data experiments;
indeed, knowledge of even a few seeds can be extremely effective when our
seeded graph matching algorithm is used to recover a naturally existing
alignment that is only partially observed.
Audio-Visual Scene Analysis with Self-Supervised Multisensory Features
Authors: Andrew Owens, Alexei A. Efros
Date: 2018-04-10 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Recognition, Audio Source Separation, Sound Source Localization, Temporal Action Localization

The thud of a bouncing ball, the onset of speech as lips open -- when visual
and audio events occur together, it suggests that there might be a common,
underlying event that produced both signals. In this paper, we argue that the
visual and audio components of a video signal should be modeled jointly using a
fused multisensory representation. We propose to learn such a representation in
a self-supervised way, by training a neural network to predict whether video
frames and audio are temporally aligned. We use this learned representation for
three applications: (a) sound source localization, i.e. visualizing the source
of sound in a video; (b) audio-visual action recognition; and (c) on/off-screen
audio source separation, e.g. removing the off-screen translator's voice from a
foreign official's speech. Code, models, and video results are available on our
webpage: http://andrewowens.com/multisensory
Testing Identity of Multidimensional Histograms
Authors: Ilias Diakonikolas, Daniel M. Kane, John Peebles
Date: 2018-04-10 00:00:00
Tasks: Two-sample testing

We investigate the problem of identity testing for multidimensional histogram
distributions. A distribution $p: D \rightarrow \mathbb{R}_+$, where $D
\subseteq \mathbb{R}^d$, is called a $k$-histogram if there exists a partition
of the domain into $k$ axis-aligned rectangles such that $p$ is constant within
each such rectangle. Histograms are one of the most fundamental nonparametric
families of distributions and have been extensively studied in computer science
and statistics. We give the first identity tester for this problem with {\em
sub-learning} sample complexity in any fixed dimension and a nearly-matching
sample complexity lower bound.
  In more detail, let $q$ be an unknown $d$-dimensional $k$-histogram
distribution in fixed dimension $d$, and $p$ be an explicitly given
$d$-dimensional $k$-histogram. We want to correctly distinguish, with
probability at least $2/3$, between the case that $p = q$ versus $\|p-q\|_1
\geq \epsilon$. We design an algorithm for this hypothesis testing problem with
sample complexity $O((\sqrt{k}/\epsilon^2) 2^{d/2} \log^{2.5 d}(k/\epsilon))$
that runs in sample-polynomial time. Our algorithm is robust to model
misspecification, i.e., succeeds even if $q$ is only promised to be {\em close}
to a $k$-histogram. Moreover, for $k = 2^{\Omega(d)}$, we show a sample
complexity lower bound of $(\sqrt{k}/\epsilon^2) \cdot \Omega(\log(k)/d)^{d-1}$
when $d\geq 2$. That is, for any fixed dimension $d$, our upper and lower
bounds are nearly matching. Prior to our work, the sample complexity of the
$d=1$ case was well-understood, but no algorithm with sub-learning sample
complexity was known, even for $d=2$. Our new upper and lower bounds have
interesting conceptual implications regarding the relation between learning and
testing in this setting.