pwc-archive/papers-with-abstracts huggingface dataset:
Single Reference Image based Scene Relighting via Material Guided Filtering
Authors: Xin Jin, Yannan Li, Ningning Liu, Xiao-Dong Li, Xianggang Jiang, Chaoen Xiao, Shiming Ge
Date: 2017-08-23 00:00:00
Tasks: Image Relighting

Image relighting is to change the illumination of an image to a target
illumination effect without known the original scene geometry, material
information and illumination condition. We propose a novel outdoor scene
relighting method, which needs only a single reference image and is based on
material constrained layer decomposition. Firstly, the material map is
extracted from the input image. Then, the reference image is warped to the
input image through patch match based image warping. Lastly, the input image is
relit using material constrained layer decomposition. The experimental results
reveal that our method can produce similar illumination effect as that of the
reference image on the input image using only a single reference image.
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition
Authors: Soheil Khorram, Zakaria Aldeneh, Dimitrios Dimitriadis, Melvin McInnis, Emily Mower Provost
Date: 2017-08-23 00:00:00
Tasks: Emotion Recognition

The goal of continuous emotion recognition is to assign an emotion value to
every frame in a sequence of acoustic features. We show that incorporating
long-term temporal dependencies is critical for continuous emotion recognition
tasks. To this end, we first investigate architectures that use dilated
convolutions. We show that even though such architectures outperform previously
reported systems, the output signals produced from such architectures undergo
erratic changes between consecutive time steps. This is inconsistent with the
slow moving ground-truth emotion labels that are obtained from human
annotators. To deal with this problem, we model a downsampled version of the
input signal and then generate the output signal through upsampling. Not only
does the resulting downsampling/upsampling network achieve good performance, it
also generates smooth output trajectories. Our method yields the best known
audio-only performance on the RECOLA dataset.
Scale-invariant unconstrained online learning
Authors: Wojciech Kotłowski
Date: 2017-08-23 00:00:00

We consider a variant of online convex optimization in which both the
instances (input vectors) and the comparator (weight vector) are unconstrained.
We exploit a natural scale invariance symmetry in our unconstrained setting:
the predictions of the optimal comparator are invariant under any linear
transformation of the instances. Our goal is to design online algorithms which
also enjoy this property, i.e. are scale-invariant. We start with the case of
coordinate-wise invariance, in which the individual coordinates (features) can
be arbitrarily rescaled. We give an algorithm, which achieves essentially
optimal regret bound in this setup, expressed by means of a coordinate-wise
scale-invariant norm of the comparator. We then study general invariance with
respect to arbitrary linear transformations. We first give a negative result,
showing that no algorithm can achieve a meaningful bound in terms of
scale-invariant norm of the comparator in the worst case. Next, we compliment
this result with a positive one, providing an algorithm which "almost" achieves
the desired bound, incurring only a logarithmic overhead in terms of the norm
of the instances.
Non-linear Convolution Filters for CNN-based Learning
Authors: Georgios Zoumpourlis, Alexandros Doumanoglou, Nicholas Vretos, Petros Daras
Date: 2017-08-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: image-classification, Image Classification

During the last years, Convolutional Neural Networks (CNNs) have achieved
state-of-the-art performance in image classification. Their architectures have
largely drawn inspiration by models of the primate visual system. However,
while recent research results of neuroscience prove the existence of non-linear
operations in the response of complex visual cells, little effort has been
devoted to extend the convolution technique to non-linear forms. Typical
convolutional layers are linear systems, hence their expressiveness is limited.
To overcome this, various non-linearities have been used as activation
functions inside CNNs, while also many pooling strategies have been applied. We
address the issue of developing a convolution method in the context of a
computational model of the visual cortex, exploring quadratic forms through the
Volterra kernels. Such forms, constituting a more rich function space, are used
as approximations of the response profile of visual cells. Our proposed
second-order convolution is tested on CIFAR-10 and CIFAR-100. We show that a
network which combines linear and non-linear filters in its convolutional
layers, can outperform networks that use standard linear filters with the same
architecture, yielding results competitive with the state-of-the-art on these
datasets.
Application of a Convolutional Neural Network for image classification to the analysis of collisions in High Energy Physics
Authors: Celia Fernández Madrazo, Ignacio Heredia Cacha, Lara Lloret Iglesias, Jesús Marco de Lucas
Date: 2017-08-23 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification

The application of deep learning techniques using convolutional neural
networks to the classification of particle collisions in High Energy Physics is
explored. An intuitive approach to transform physical variables, like momenta
of particles and jets, into a single image that captures the relevant
information, is proposed. The idea is tested using a well known deep learning
framework on a simulation dataset, including leptonic ttbar events and the
corresponding background at 7 TeV from the CMS experiment at LHC, available as
Open Data. This initial test shows competitive results when compared to more
classical approaches, like those using feedforward neural networks.
Bayesian Learning of Clique Tree Structure
Authors: Cetin Savkli, J. Ryan Carr, Philip Graff, Lauren Kennell
Date: 2017-08-23 00:00:00
Tasks: Anomaly Detection, Clustering, Tree Decomposition

The problem of categorical data analysis in high dimensions is considered. A
discussion of the fundamental difficulties of probability modeling is provided,
and a solution to the derivation of high dimensional probability distributions
based on Bayesian learning of clique tree decomposition is presented. The main
contributions of this paper are an automated determination of the optimal
clique tree structure for probability modeling, the resulting derived
probability distribution, and a corresponding unified approach to clustering
and anomaly detection based on the probability distribution.
CNN-Based Prediction of Frame-Level Shot Importance for Video Summarization
Authors: Mohaiminul Al Nahian, A. S. M. Iftekhar, Mohammad Tariqul Islam, S. M. Mahbubur Rahman, Dimitrios Hatzinakos
Date: 2017-08-23 00:00:00
Tasks: Video Summarization

In the Internet, ubiquitous presence of redundant, unedited, raw videos has
made video summarization an important problem. Traditional methods of video
summarization employ a heuristic set of hand-crafted features, which in many
cases fail to capture subtle abstraction of a scene. This paper presents a deep
learning method that maps the context of a video to the importance of a scene
similar to that is perceived by humans. In particular, a convolutional neural
network (CNN)-based architecture is proposed to mimic the frame-level shot
importance for user-oriented video summarization. The weights and biases of the
CNN are trained extensively through off-line processing, so that it can provide
the importance of a frame of an unseen video almost instantaneously.
Experiments on estimating the shot importance is carried out using the publicly
available database TVSum50. It is shown that the performance of the proposed
network is substantially better than that of commonly referred feature-based
methods for estimating the shot importance in terms of mean absolute error,
absolute error variance, and relative F-measure.
Statistical Selection of CNN-Based Audiovisual Features for Instantaneous Estimation of Human Emotional States
Authors: Ramesh Basnet, Mohammad Tariqul Islam, Tamanna Howlader, S. M. Mahbubur Rahman, Dimitrios Hatzinakos
Date: 2017-08-23 00:00:00
Tasks: Prediction, regression

Automatic prediction of continuous-level emotional state requires selection
of suitable affective features to develop a regression system based on
supervised machine learning. This paper investigates the performance of
features statistically learned using convolutional neural networks for
instantaneously predicting the continuous dimensions of emotional states.
Features with minimum redundancy and maximum relevancy are chosen by using the
mutual information-based selection process. The performance of frame-by-frame
prediction of emotional state using the moderate length features as proposed in
this paper is evaluated on spontaneous and naturalistic human-human
conversation of RECOLA database. Experimental results show that the proposed
model can be used for instantaneous prediction of emotional state with an
accuracy higher than traditional audio or video features that are used for
affective computation.
DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows
Authors: Jason Kuen, Xiangfei Kong, Gang Wang, Yap-Peng Tan
Date: 2016-11-17 00:00:00
Tasks: General Classification

Deluge Networks (DelugeNets) are deep neural networks which efficiently
facilitate massive cross-layer information inflows from preceding layers to
succeeding layers. The connections between layers in DelugeNets are established
through cross-layer depthwise convolutional layers with learnable filters,
acting as a flexible yet efficient selection mechanism. DelugeNets can
propagate information across many layers with greater flexibility and utilize
network parameters more effectively compared to ResNets, whilst being more
efficient than DenseNets. Remarkably, a DelugeNet model with just model
complexity of 4.31 GigaFLOPs and 20.2M network parameters, achieve
classification errors of 3.76% and 19.02% on CIFAR-10 and CIFAR-100 dataset
respectively. Moreover, DelugeNet-122 performs competitively to ResNet-200 on
ImageNet dataset, despite costing merely half of the computations needed by the
latter.
The Unconstrained Ear Recognition Challenge
Authors: Žiga Emeršič, Dejan Štepec, Vitomir Štruc, Peter Peer, Anjith George, Adil Ahmad, Elshibani Omar, Terrance E. Boult, Reza Safdari, Yuxiang Zhou, Stefanos Zafeiriou, Dogucan Yaman, Fevziye I. Eyiokur, Hazim K. Ekenel
Date: 2017-08-23 00:00:00
Tasks: Benchmarking, Person Recognition

In this paper we present the results of the Unconstrained Ear Recognition
Challenge (UERC), a group benchmarking effort centered around the problem of
person recognition from ear images captured in uncontrolled conditions. The
goal of the challenge was to assess the performance of existing ear recognition
techniques on a challenging large-scale dataset and identify open problems that
need to be addressed in the future. Five groups from three continents
participated in the challenge and contributed six ear recognition techniques
for the evaluation, while multiple baselines were made available for the
challenge by the UERC organizers. A comprehensive analysis was conducted with
all participating approaches addressing essential research questions pertaining
to the sensitivity of the technology to head rotation, flipping, gallery size,
large-scale recognition and others. The top performer of the UERC was found to
ensure robust performance on a smaller part of the dataset (with 180 subjects)
regardless of image characteristics, but still exhibited a significant
performance drop when the entire dataset comprising 3,704 subjects was used for
testing.
A Neural Network Approach for Mixing Language Models
Authors: Youssef Oualil, Dietrich Klakow
Date: 2017-08-23 00:00:00
Tasks: Text Compression

The performance of Neural Network (NN)-based language models is steadily
improving due to the emergence of new architectures, which are able to learn
different natural language characteristics. This paper presents a novel
framework, which shows that a significant improvement can be achieved by
combining different existing heterogeneous models in a single architecture.
This is done through 1) a feature layer, which separately learns different
NN-based models and 2) a mixture layer, which merges the resulting model
features. In doing so, this architecture benefits from the learning
capabilities of each model with no noticeable increase in the number of model
parameters or the training time. Extensive experiments conducted on the Penn
Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
significant reduction of the perplexity when compared to state-of-the-art
feedforward as well as recurrent neural network architectures.
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Authors: Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan
Date: 2016-12-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Domain Adaptation, Generative Adversarial Network, Unsupervised Domain Adaptation

Collecting well-annotated image datasets to train modern machine learning
algorithms is prohibitively expensive for many tasks. One appealing alternative
is rendering synthetic data where ground-truth annotations are generated
automatically. Unfortunately, models trained purely on rendered images often
fail to generalize to real images. To address this shortcoming, prior work
introduced unsupervised domain adaptation algorithms that attempt to map
representations between the two domains or learn to extract features that are
domain-invariant. In this work, we present a new approach that learns, in an
unsupervised manner, a transformation in the pixel space from one domain to the
other. Our generative adversarial network (GAN)-based method adapts
source-domain images to appear as if drawn from the target domain. Our approach
not only produces plausible samples, but also outperforms the state-of-the-art
on a number of unsupervised domain adaptation scenarios by large margins.
Finally, we demonstrate that the adaptation process generalizes to object
classes unseen during training.
Incremental Learning of Object Detectors without Catastrophic Forgetting
Authors: Konstantin Shmelkov, Cordelia Schmid, Karteek Alahari
Date: 2017-08-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Incremental Learning, Object, object-detection, Object Detection

Despite their success for object detection, convolutional neural networks are
ill-equipped for incremental learning, i.e., adapting the original model
trained on a set of classes to additionally detect objects of new classes, in
the absence of the initial training data. They suffer from "catastrophic
forgetting" - an abrupt degradation of performance on the original set of
classes, when the training objective is adapted to the new classes. We present
a method to address this issue, and learn object detectors incrementally, when
neither the original training data nor annotations for the original classes in
the new training set are available. The core of our proposed solution is a loss
function to balance the interplay between predictions on the new classes and a
new distillation loss which minimizes the discrepancy between responses for old
classes from the original and the updated networks. This incremental learning
can be performed multiple times, for a new set of classes in each step, with a
moderate drop in performance compared to the baseline network trained on the
ensemble of data. We present object detection results on the PASCAL VOC 2007
and COCO datasets, along with a detailed empirical analysis of the approach.
Contrastive-center loss for deep neural networks
Authors: Ce Qi, Fei Su
Date: 2017-07-24 00:00:00
Tasks: Classification, Face Recognition, General Classification, image-classification, Image Classification

The deep convolutional neural network(CNN) has significantly raised the
performance of image classification and face recognition. Softmax is usually
used as supervision, but it only penalizes the classification loss. In this
paper, we propose a novel auxiliary supervision signal called contrastivecenter
loss, which can further enhance the discriminative power of the features, for
it learns a class center for each class. The proposed contrastive-center loss
simultaneously considers intra-class compactness and inter-class separability,
by penalizing the contrastive values between: (1)the distances of training
samples to their corresponding class centers, and (2)the sum of the distances
of training samples to their non-corresponding class centers. Experiments on
different datasets demonstrate the effectiveness of contrastive-center loss.
Exploiting Convolution Filter Patterns for Transfer Learning
Authors: Mehmet Aygün, Yusuf Aytar, Hazim Kemal Ekenel
Date: 2017-08-23 00:00:00
Tasks: Transfer Learning

In this paper, we introduce a new regularization technique for transfer
learning. The aim of the proposed approach is to capture statistical
relationships among convolution filters learned from a well-trained network and
transfer this knowledge to another network. Since convolution filters of the
prevalent deep Convolutional Neural Network (CNN) models share a number of
similar patterns, in order to speed up the learning procedure, we capture such
correlations by Gaussian Mixture Models (GMMs) and transfer them using a
regularization term. We have conducted extensive experiments on the CIFAR10,
Places2, and CMPlaces datasets to assess generalizability, task
transferability, and cross-model transferability of the proposed approach,
respectively. The experimental results show that the feature representations
have efficiently been learned and transferred through the proposed statistical
regularization scheme. Moreover, our method is an architecture independent
approach, which is applicable for a variety of CNN architectures.
In search of inliers: 3d correspondence by local and global voting
Authors: Anders Glent Buch, Yang Yang, Norbert Krüger, Henrik Gordon Petersen
Date: 2017-08-23 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Object Detection, object-detection, Object Detection

We present a method for finding correspondence between 3D models. From an
initial set of feature correspondences, our method uses a fast voting scheme to
separate the inliers from the outliers. The novelty of our method lies in the
use of a combination of local and global constraints to determine if a vote
should be cast. On a local scale, we use simple, low-level geometric
invariants. On a global scale, we apply covariant constraints for finding
compatible correspondences. We guide the sampling for collecting voters by
downward dependencies on previous voting stages. All of this together results
in an accurate matching procedure. We evaluate our algorithm by controlled and
comparative testing on different datasets, giving superior performance compared
to state of the art methods. In a final experiment, we apply our method for 3D
object detection, showing potential use of our method within higher-level
vision.
Pose Estimation using Local Structure-Specific Shape and Appearance Context
Authors: Anders Glent Buch, Dirk Kraft, Joni-Kristian Kamarainen, Henrik Gordon Petersen, Norbert Krüger
Date: 2017-08-23 00:00:00
Tasks: Pose Estimation

We address the problem of estimating the alignment pose between two models
using structure-specific local descriptors. Our descriptors are generated using
a combination of 2D image data and 3D contextual shape data, resulting in a set
of semi-local descriptors containing rich appearance and shape information for
both edge and texture structures. This is achieved by defining feature space
relations which describe the neighborhood of a descriptor. By quantitative
evaluations, we show that our descriptors provide high discriminative power
compared to state of the art approaches. In addition, we show how to utilize
this for the estimation of the alignment pose between two point sets. We
present experiments both in controlled and real-life scenarios to validate our
approach.
Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities
Authors: Adrien Todeschini, Xenia Miscouridou, François Caron
Date: 2016-02-05 00:00:00

We propose a novel statistical model for sparse networks with overlapping
community structure. The model is based on representing the graph as an
exchangeable point process, and naturally generalizes existing probabilistic
models with overlapping block-structure to the sparse regime. Our construction
builds on vectors of completely random measures, and has interpretable
parameters, each node being assigned a vector representing its level of
affiliation to some latent communities. We develop methods for simulating this
class of random graphs, as well as to perform posterior inference. We show that
the proposed approach can recover interpretable structure from two real-world
networks and can handle graphs with thousands of nodes and tens of thousands of
edges.
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Authors: Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, Fabio Roli
Date: 2017-08-23 00:00:00
Tasks: General Classification

Deep neural networks have been widely adopted in recent years, exhibiting
impressive performances in several application domains. It has however been
shown that they can be fooled by adversarial examples, i.e., images altered by
a barely-perceivable adversarial noise, carefully crafted to mislead
classification. In this work, we aim to evaluate the extent to which
robot-vision systems embodying deep-learning algorithms are vulnerable to
adversarial examples, and propose a computationally efficient countermeasure to
mitigate this threat, based on rejecting classification of anomalous inputs. We
then provide a clearer understanding of the safety properties of deep networks
through an intuitive empirical analysis, showing that the mapping learned by
such networks essentially violates the smoothness assumption of learning
algorithms. We finally discuss the main limitations of this work, including the
creation of real-world adversarial examples, and sketch promising research
directions.
Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables
Authors: L. Azzimonti, G. Corani, M. Zaffalon
Date: 2017-08-23 00:00:00
Tasks: General Classification

We present a novel approach for estimating conditional probability tables,
based on a joint, rather than independent, estimate of the conditional
distributions belonging to the same table. We derive exact analytical
expressions for the estimators and we analyse their properties both
analytically and via simulation. We then apply this method to the estimation of
parameters in a Bayesian network. Given the structure of the network, the
proposed approach better estimates the joint distribution and significantly
improves the classification performance with respect to traditional approaches.