pwc-archive/papers-with-abstracts huggingface dataset:
GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB
Authors: Franziska Mueller, Florian Bernard, Oleksandr Sotnychenko, Dushyant Mehta, Srinath Sridhar, Dan Casas, Christian Theobalt
Date: 2017-12-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image-to-Image Translation, Translation

We address the highly challenging problem of real-time 3D hand tracking based
on a monocular RGB-only sequence. Our tracking method combines a convolutional
neural network with a kinematic 3D hand model, such that it generalizes well to
unseen data, is robust to occlusions and varying camera viewpoints, and leads
to anatomically plausible as well as temporally smooth hand motions. For
training our CNN we propose a novel approach for the synthetic generation of
training data that is based on a geometrically consistent image-to-image
translation network. To be more specific, we use a neural network that
translates synthetic images to "real" images, such that the so-generated images
follow the same statistical distribution as real-world hand images. For
training this translation network we combine an adversarial loss and a
cycle-consistency loss with a geometric consistency loss in order to preserve
geometric properties (such as hand pose) during translation. We demonstrate
that our hand tracking system outperforms the current state-of-the-art on
challenging RGB-only footage.
Adaptive Quantization for Deep Neural Network
Authors: Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung, Pascal Frossard
Date: 2017-12-04 00:00:00
Tasks: Quantization

In recent years Deep Neural Networks (DNNs) have been rapidly developed in
various applications, together with increasingly complex architectures. The
performance gain of these DNNs generally comes with high computational costs
and large memory consumption, which may not be affordable for mobile platforms.
Deep model quantization can be used for reducing the computation and memory
costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we
propose an optimization framework for deep model quantization. First, we
propose a measurement to estimate the effect of parameter quantization errors
in individual layers on the overall model prediction accuracy. Then, we propose
an optimization process based on this measurement for finding optimal
quantization bit-width for each layer. This is the first work that
theoretically analyse the relationship between parameter quantization errors of
individual layers and model accuracy. Our new quantization algorithm
outperforms previous quantization optimization methods, and achieves 20-40%
higher compression rate compared to equal bit-width quantization at the same
model prediction accuracy.
Vprop: Variational Inference using RMSprop
Authors: Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal
Date: 2017-12-04 00:00:00
Tasks: Deep Learning, Variational Inference

Many computationally-efficient methods for Bayesian deep learning rely on
continuous optimization algorithms, but the implementation of these methods
requires significant changes to existing code-bases. In this paper, we propose
Vprop, a method for Gaussian variational inference that can be implemented with
two minor changes to the off-the-shelf RMSprop optimizer. Vprop also reduces
the memory requirements of Black-Box Variational Inference by half. We derive
Vprop using the conjugate-computation variational inference method, and
establish its connections to Newton's method, natural-gradient methods, and
extended Kalman filters. Overall, this paper presents Vprop as a principled,
computationally-efficient, and easy-to-implement method for Bayesian deep
learning.
A Unified View-Graph Selection Framework for Structure from Motion
Authors: Rajvi Shah, Visesh Chari, P. J. Narayanan
Date: 2017-08-03 00:00:00

View-graph is an essential input to large-scale structure from motion (SfM)
pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on
the input view-graph. Inconsistent or inaccurate edges can lead to inferior or
wrong reconstruction. Most SfM methods remove `undesirable' images and pairs
using several, fixed heuristic criteria, and propose tailor-made solutions to
achieve specific reconstruction objectives such as efficiency, accuracy, or
disambiguation. In contrast to these disparate solutions, we propose a single
optimization framework that can be used to achieve these different
reconstruction objectives with task-specific cost modeling. We also construct a
very efficient network-flow based formulation for its approximate solution. The
abstraction brought on by this selection mechanism separates the challenges
specific to datasets and reconstruction objectives from the standard SfM
pipeline and improves its generalization. This paper demonstrates the
application of the proposed view-graph framework with standard SfM pipeline for
two particular use-cases, (i) accurate and ghost-free reconstructions of highly
ambiguous datasets using costs based on disambiguation priors, and (ii)
accurate and efficient reconstruction of large-scale Internet datasets using
costs based on commonly used priors.
Chord Generation from Symbolic Melody Using BLSTM Networks
Authors: Hyungui Lim, Seungyeon Rhyu, Kyogu Lee
Date: 2017-12-04 00:00:00

Generating a chord progression from a monophonic melody is a challenging
problem because a chord progression requires a series of layered notes played
simultaneously. This paper presents a novel method of generating chord
sequences from a symbolic melody using bidirectional long short-term memory
(BLSTM) networks trained on a lead sheet database. To this end, a group of
feature vectors composed of 12 semitones is extracted from the notes in each
bar of monophonic melodies. In order to ensure that the data shares uniform key
and duration characteristics, the key and the time signatures of the vectors
are normalized. The BLSTM networks then learn from the data to incorporate the
temporal dependencies to produce a chord progression. Both quantitative and
qualitative evaluations are conducted by comparing the proposed method with the
conventional HMM and DNN-HMM based approaches. Proposed model achieves 23.8%
and 11.4% performance increase from the other models, respectively. User
studies further confirm that the chord sequences generated by the proposed
method are preferred by listeners.
Sparse Recovery from Extreme Eigenvalues Deviation Inequalities
Authors: Sandrine Dallaporta, Yohann de Castro
Date: 2016-04-05 00:00:00
Tasks: compressed sensing

This article provides a new toolbox to derive sparse recovery guarantees from
small deviations on extreme singular values or extreme eigenvalues obtained in
Random Matrix Theory. This work is based on Restricted Isometry Constants
(RICs) which are a pivotal notion in Compressed Sensing and High-Dimensional
Statistics as these constants finely assess how a linear operator is
conditioned on the set of sparse vectors and hence how it performs in SRSR.
While it is an open problem to construct deterministic matrices with apposite
RICs, one can prove that such matrices exist using random matrices models. In
this paper, we show upper bounds on RICs for Gaussian and Rademacher matrices
using state-of-the-art small deviation estimates on their extreme eigenvalues.
This allows us to derive a lower bound on the probability of getting SRSR. One
benefit of this paper is a direct and explicit derivation of upper bounds on
RICs and lower bounds on SRSR from small deviations on the extreme eigenvalues
given by Random Matrix theory.
NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs
Authors: Paolo Meloni, Alessandro Capotondi, Gianfranco Deriu, Michele Brian, Francesco Conti, Davide Rossi, Luigi Raffo, Luca Benini
Date: 2017-12-04 00:00:00
Tasks: CPU, speech-recognition, Speech Recognition

Deep convolutional neural networks (CNNs) obtain outstanding results in tasks
that require human-level understanding of data, like image or speech
recognition. However, their computational load is significant, motivating the
development of CNN-specialized accelerators. This work presents NEURAghe, a
flexible and efficient hardware/software solution for the acceleration of CNNs
on Zynq SoCs. NEURAghe leverages the synergistic usage of Zynq ARM cores and of
a powerful and flexible Convolution-Specific Processor deployed on the
reconfigurable logic. The Convolution-Specific Processor embeds both a
convolution engine and a programmable soft core, releasing the ARM processors
from most of the supervision duties and allowing the accelerator to be
controlled by software at an ultra-fine granularity. This methodology opens the
way for cooperative heterogeneous computing: while the accelerator takes care
of the bulk of the CNN workload, the ARM cores can seamlessly execute
hard-to-accelerate parts of the computational graph, taking advantage of the
NEON vector engines to further speed up computation. Through the companion
NeuDNN SW stack, NEURAghe supports end-to-end CNN-based classification with a
peak performance of 169 Gops/s, and an energy efficiency of 17 Gops/W. Thanks
to our heterogeneous computing model, our platform improves upon the
state-of-the-art, achieving a frame rate of 5.5 fps on the end-to-end execution
of VGG-16, and 6.6 fps on ResNet-18.
Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals
Authors: Girish Keshav Palshikar, Sachin Pawar, Saheb Chourasia, Nitin Ramrakhiyani
Date: 2017-12-04 00:00:00
Tasks: Clustering, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentence, Sentence Classification, Text Summarization

Performance appraisal (PA) is an important HR process to periodically measure
and evaluate every employee's performance vis-a-vis the goals established by
the organization. A PA process involves purposeful multi-step multi-modal
communication between employees, their supervisors and their peers, such as
self-appraisal, supervisor assessment and peer feedback. Analysis of the
structured data and text produced in PA is crucial for measuring the quality of
appraisals and tracking actual improvements. In this paper, we apply text
mining techniques to produce insights from PA text. First, we perform sentence
classification to identify strengths, weaknesses and suggestions of
improvements found in the supervisor assessments and then use clustering to
discover broad categories among them. Next we use multi-class multi-label
classification techniques to match supervisor assessments to predefined broad
perspectives on performance. Finally, we propose a short-text summarization
technique to produce a summary of peer feedback comments for a given employee
and compare it with manual summaries. All techniques are illustrated using a
real-life dataset of supervisor assessment and peer feedback text produced
during the PA of 4528 employees in a large multi-national IT company.
End-to-End Relation Extraction using Markov Logic Networks
Authors: Sachin Pawar, Pushpak Bhattacharya, Girish K. Palshikar
Date: 2017-12-04 00:00:00
Tasks: Relation, Relation Extraction, Sentence

The task of end-to-end relation extraction consists of two sub-tasks: i)
identifying entity mentions along with their types and ii) recognizing semantic
relations among the entity mention pairs. %Identifying entity mentions along
with their types and recognizing semantic relations among the entity mentions,
are two very important problems in Information Extraction. It has been shown
that for better performance, it is necessary to address these two sub-tasks
jointly. We propose an approach for simultaneous extraction of entity mentions
and relations in a sentence, by using inference in Markov Logic Networks (MLN).
We learn three different classifiers : i) local entity classifier, ii) local
relation classifier and iii) "pipeline" relation classifier which uses
predictions of the local entity classifier. Predictions of these classifiers
may be inconsistent with each other. We represent these predictions along with
some domain knowledge using weighted first-order logic rules in an MLN and
perform joint inference over the MLN to obtain a global output with minimum
inconsistencies. Experiments on the ACE (Automatic Content Extraction) 2004
dataset demonstrate that our approach of joint extraction using MLNs
outperforms the baselines of individual classifiers. Our end-to-end relation
extraction performance is better than 2 out of 3 previous results reported on
the ACE 2004 dataset.
Multiframe Motion Coupling for Video Super Resolution
Authors: Jonas Geiping, Hendrik Dirks, Daniel Cremers, Michael Moeller
Date: 2016-11-23 00:00:00
Tasks: Motion Estimation, Super-Resolution, Video Super-Resolution

The idea of video super resolution is to use different view points of a
single scene to enhance the overall resolution and quality. Classical energy
minimization approaches first establish a correspondence of the current frame
to all its neighbors in some radius and then use this temporal information for
enhancement. In this paper, we propose the first variational super resolution
approach that computes several super resolved frames in one batch optimization
procedure by incorporating motion information between the high-resolution image
frames themselves. As a consequence, the number of motion estimation problems
grows linearly in the number of frames, opposed to a quadratic growth of
classical methods and temporal consistency is enforced naturally. We use
infimal convolution regularization as well as an automatic parameter balancing
scheme to automatically determine the reliability of the motion information and
reweight the regularization locally. We demonstrate that our approach yields
state-of-the-art results and even is competitive with machine learning
approaches.
Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis
Authors: Dat Thanh Tran, Alexandros Iosifidis, Juho Kanniainen, Moncef Gabbouj
Date: 2017-12-04 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Financial time-series forecasting has long been a challenging problem because
of the inherently noisy and stochastic nature of the market. In the
High-Frequency Trading (HFT), forecasting for trading purposes is even a more
challenging task since an automated inference system is required to be both
accurate and fast. In this paper, we propose a neural network layer
architecture that incorporates the idea of bilinear projection as well as an
attention mechanism that enables the layer to detect and focus on crucial
temporal information. The resulting network is highly interpretable, given its
ability to highlight the importance and contribution of each temporal instance,
thus allowing further analysis on the time instances of interest. Our
experiments in a large-scale Limit Order Book (LOB) dataset show that a
two-hidden-layer network utilizing our proposed layer outperforms by a large
margin all existing state-of-the-art results coming from much deeper
architectures while requiring far fewer computations.
Face Translation between Images and Videos using Identity-aware CycleGAN
Authors: Zhiwu Huang, Bernhard Kratzwald, Danda Pani Paudel, Jiqing Wu, Luc van Gool
Date: 2017-12-04 00:00:00
Tasks: Image-to-Image Translation, Translation, Video Prediction

This paper presents a new problem of unpaired face translation between images
and videos, which can be applied to facial video prediction and enhancement. In
this problem there exist two major technical challenges: 1) designing a robust
translation model between static images and dynamic videos, and 2) preserving
facial identity during image-video translation. To address such two problems,
we generalize the state-of-the-art image-to-image translation network
(Cycle-Consistent Adversarial Networks) to the image-to-video/video-to-image
translation context by exploiting a image-video translation model and an
identity preservation model. In particular, we apply the state-of-the-art
Wasserstein GAN technique to the setting of image-video translation for better
convergence, and we meanwhile introduce a face verificator to ensure the
identity. Experiments on standard image/video face datasets demonstrate the
effectiveness of the proposed model in both terms of qualitative and
quantitative evaluations.
Leaf Identification Using a Deep Convolutional Neural Network
Authors: Christoph Wick, Frank Puppe
Date: 2017-12-04 00:00:00
Tasks: Data Augmentation, General Classification, Transfer Learning

Convolutional neural networks (CNNs) have become popular especially in
computer vision in the last few years because they achieved outstanding
performance on different tasks, such as image classifications. We propose a
nine-layer CNN for leaf identification using the famous Flavia and Foliage
datasets. Usually the supervised learning of deep CNNs requires huge datasets
for training. However, the used datasets contain only a few examples per plant
species. Therefore, we apply data augmentation and transfer learning to prevent
our network from overfitting. The trained CNNs achieve recognition rates above
99% on the Flavia and Foliage datasets, and slightly outperform current methods
for leaf classification.
An interpretable latent variable model for attribute applicability in the Amazon catalogue
Authors: Tammo Rukat, Dustin Lange, Cédric Archambeau
Date: 2017-11-30 00:00:00
Tasks: Attribute

Learning attribute applicability of products in the Amazon catalog (e.g.,
predicting that a shoe should have a value for size, but not for battery-type
at scale is a challenge. The need for an interpretable model is contingent on
(1) the lack of ground truth training data, (2) the need to utilise prior
information about the underlying latent space and (3) the ability to understand
the quality of predictions on new, unseen data. To this end, we develop the
MaxMachine, a probabilistic latent variable model that learns distributed
binary representations, associated to sets of features that are likely to
co-occur in the data. Layers of MaxMachines can be stacked such that higher
layers encode more abstract information. Any set of variables can be clamped to
encode prior information. We develop fast sampling based posterior inference.
Preliminary results show that the model improves over the baseline in 17 out of
19 product groups and provides qualitatively reasonable predictions.
Composite Quantization
Authors: Jingdong Wang, Ting Zhang
Date: 2017-12-04 00:00:00
Tasks: Quantization

This paper studies the compact coding approach to approximate nearest
neighbor search. We introduce a composite quantization framework. It uses the
composition of several ($M$) elements, each of which is selected from a
different dictionary, to accurately approximate a $D$-dimensional vector, thus
yielding accurate search, and represents the data vector by a short code
composed of the indices of the selected elements in the corresponding
dictionaries. Our key contribution lies in introducing a near-orthogonality
constraint, which makes the search efficiency is guaranteed as the cost of the
distance computation is reduced to $O(M)$ from $O(D)$ through a distance table
lookup scheme. The resulting approach is called near-orthogonal composite
quantization. We theoretically justify the equivalence between near-orthogonal
composite quantization and minimizing an upper bound of a function formed by
jointly considering the quantization error and the search cost according to a
generalized triangle inequality. We empirically show the efficacy of the
proposed approach over several benchmark datasets. In addition, we demonstrate
the superior performances in other three applications: combination with
inverted multi-index, quantizing the query for mobile search, and inner-product
similarity search.
Enhanced Characterness for Text Detection in the Wild
Authors: Aarushi Agrawal, Prerana Mukherjee, Siddharth Srivastava, Brejesh lall
Date: 2017-12-04 00:00:00
Tasks: Text Detection, Text Spotting

Text spotting is an interesting research problem as text may appear at any
random place and may occur in various forms. Moreover, ability to detect text
opens the horizons for improving many advanced computer vision problems. In
this paper, we propose a novel language agnostic text detection method
utilizing edge enhanced Maximally Stable Extremal Regions in natural scenes by
defining strong characterness measures. We show that a simple combination of
characterness cues help in rejecting the non text regions. These regions are
further fine-tuned for rejecting the non-textual neighbor regions.
Comprehensive evaluation of the proposed scheme shows that it provides
comparative to better generalization performance to the traditional methods for
this task.
Object Classification using Ensemble of Local and Deep Features
Authors: Siddharth Srivastava, Prerana Mukherjee, Brejesh lall, Kamlesh Jaiswal
Date: 2017-12-04 00:00:00
Tasks: Classification, General Classification, Object

In this paper we propose an ensemble of local and deep features for object
classification. We also compare and contrast effectiveness of feature
representation capability of various layers of convolutional neural network. We
demonstrate with extensive experiments for object classification that the
representation capability of features from deep networks can be complemented
with information captured from local features. We also find out that features
from various deep convolutional networks encode distinctive characteristic
information. We establish that, as opposed to conventional practice,
intermediate layers of deep networks can augment the classification
capabilities of features obtained from fully connected layers.
Will People Like Your Image? Learning the Aesthetic Space
Authors: Katharina Schwarz, Patrick Wieschollek, Hendrik P. A. Lensch
Date: 2016-11-16 00:00:00

Rating how aesthetically pleasing an image appears is a highly complex matter
and depends on a large number of different visual factors. Previous work has
tackled the aesthetic rating problem by ranking on a 1-dimensional rating
scale, e.g., incorporating handcrafted attributes. In this paper, we propose a
rather general approach to automatically map aesthetic pleasingness with all
its complexity into an "aesthetic space" to allow for a highly fine-grained
resolution. In detail, making use of deep learning, our method directly learns
an encoding of a given image into this high-dimensional feature space
resembling visual aesthetics. Additionally to the mentioned visual factors,
differences in personal judgments have a large impact on the likeableness of a
photograph. Nowadays, online platforms allow users to "like" or favor certain
content with a single click. To incorporate a huge diversity of people, we make
use of such multi-user agreements and assemble a large data set of 380K images
(AROD) with associated meta information and derive a score to rate how visually
pleasing a given photo is. We validate our derived model of aesthetics in a
user study. Further, without any extra data labeling or handcrafted features,
we achieve state-of-the art accuracy on the AVA benchmark data set. Finally, as
our approach is able to predict the aesthetic quality of any arbitrary image or
video, we demonstrate our results on applications for resorting photo
collections, capturing the best shot on mobile devices and aesthetic key-frame
extraction from videos.