pwc-archive/papers-with-abstracts huggingface dataset:
Self-Paced Learning with Adaptive Deep Visual Embeddings
Authors: Vithursan Thangarasa, Graham W. Taylor
Date: 2018-07-24 00:00:00
Tasks: Fine-Grained Visual Recognition, image-classification, Image Classification, Metric Learning, Scheduling

Selecting the most appropriate data examples to present a deep neural network
(DNN) at different stages of training is an unsolved challenge. Though
practitioners typically ignore this problem, a non-trivial data scheduling
method may result in a significant improvement in both convergence and
generalization performance. In this paper, we introduce Self-Paced Learning
with Adaptive Deep Visual Embeddings (SPL-ADVisE), a novel end-to-end training
protocol that unites self-paced learning (SPL) and deep metric learning (DML).
We leverage the Magnet Loss to train an embedding convolutional neural network
(CNN) to learn a salient representation space. The student CNN classifier
dynamically selects similar instance-level training examples to form a
mini-batch, where the easiness from the cross-entropy loss and the true
diverseness of examples from the learned metric space serve as sample
importance priors. To demonstrate the effectiveness of SPL-ADVisE, we use deep
CNN architectures for the task of supervised image classification on several
coarse- and fine-grained visual recognition datasets. Results show that, across
all datasets, the proposed method converges faster and reaches a higher final
accuracy than other SPL variants, particularly on fine-grained classes.
Multicolumn Networks for Face Recognition
Authors: Weidi Xie, Andrew Zisserman
Date: 2018-07-24 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification, General Classification

The objective of this work is set-based face recognition, i.e. to decide if
two sets of images of a face are of the same person or not. Conventionally, the
set-wise feature descriptor is computed as an average of the descriptors from
individual face images within the set. In this paper, we design a neural
network architecture that learns to aggregate based on both "visual" quality
(resolution, illumination), and "content" quality (relative importance for
discriminative classification). To this end, we propose a Multicolumn Network
(MN) that takes a set of images (the number in the set can vary) as input, and
learns to compute a fix-sized feature descriptor for the entire set. To
encourage high-quality representations, each individual input image is first
weighted by its "visual" quality, determined by a self-quality assessment
module, and followed by a dynamic recalibration based on "content" qualities
relative to the other images within the set. Both of these qualities are learnt
implicitly during training for set-wise classification. Comparing with the
previous state-of-the-art architectures trained with the same dataset
(VGGFace2), our Multicolumn Networks show an improvement of between 2-6% on the
IARPA IJB face recognition benchmarks, and exceed the state of the art for all
methods on these benchmarks.
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
Authors: Jonathon Luiten, Paul Voigtlaender, Bastian Leibe
Date: 2018-07-24 00:00:00
Tasks: Object, One-shot visual object segmentation, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We address semi-supervised video object segmentation, the task of
automatically generating accurate and consistent pixel masks for objects in a
video sequence, given the first-frame ground truth annotations. Towards this
goal, we present the PReMVOS algorithm (Proposal-generation, Refinement and
Merging for Video Object Segmentation). Our method separates this problem into
two steps, first generating a set of accurate object segmentation mask
proposals for each video frame and then selecting and merging these proposals
into accurate and temporally consistent pixel-wise object tracks over a video
sequence in a way which is designed to specifically tackle the difficult
challenges involved with segmenting multiple objects across a video sequence.
Our approach surpasses all previous state-of-the-art results on the DAVIS 2017
video object segmentation benchmark with a J & F mean score of 71.6 on the
test-dev dataset, and achieves first place in both the DAVIS 2018 Video Object
Segmentation Challenge and the YouTube-VOS 1st Large-scale Video Object
Segmentation Challenge.
Robot Imitation through Vision, Kinesthetic and Force Features with Online Adaptation to Changing Environments
Authors: Raul Fernandez-Fernandez, Juan G. Victores, David Estevez, Carlos Balaguer
Date: 2018-07-24 00:00:00
Tasks: Evolutionary Algorithms

Continuous Goal-Directed Actions (CGDA) is a robot imitation framework that encodes actions as the changes they produce on the environment. While it presents numerous advantages with respect to other robot imitation frameworks in terms of generalization and portability, final robot joint trajectories for the execution of actions are not necessarily encoded within the model. This is studied as an optimization problem, and the solution is computed through evolutionary algorithms in simulated environments. Evolutionary algorithms require a large number of evaluations, which had made the use of these algorithms in real world applications very challenging. This paper presents online evolutionary strategies, as a change of paradigm within CGDA execution. Online evolutionary strategies shift and merge motor execution into the planning loop. A concrete online evolutionary strategy, Online Evolved Trajectories (OET), is presented. OET drastically reduces computational times between motor executions, and enables working in real world dynamic environments and/or with human collaboration. Its performance has been measured against Full Trajectory Evolution (FTE) and Incrementally Evolved Trajectories (IET), obtaining the best overall results. Experimental evaluations are performed on the TEO full-sized humanoid robot with "paint" and "iron" actions that together involve vision, kinesthetic and force features.
Convolutional Simplex Projection Network (CSPN) for Weakly Supervised Semantic Segmentation
Authors: Rania Briq, Michael Moeller, Juergen Gall
Date: 2018-07-24 00:00:00
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation has been a subject of increased
interest due to the scarcity of fully annotated images. We introduce a new
approach for solving weakly supervised semantic segmentation with deep
Convolutional Neural Networks (CNNs). The method introduces a novel layer which
applies simplex projection on the output of a neural network using area
constraints of class objects. The proposed method is general and can be
seamlessly integrated into any CNN architecture. Moreover, the projection layer
allows strongly supervised models to be adapted to weakly supervised models
effortlessly by substituting ground truth labels. Our experiments have shown
that applying such an operation on the output of a CNN improves the accuracy of
semantic segmentation in a weakly supervised setting with image-level labels.
Skin disease identification from dermoscopy images using deep convolutional neural network
Authors: Anabik Pal, Sounak Ray, Utpal Garain
Date: 2018-07-24 00:00:00

In this paper, a deep neural network based ensemble method is experimented
for automatic identification of skin disease from dermoscopic images. The
developed algorithm is applied on the task3 of the ISIC 2018 challenge dataset
(Skin Lesion Analysis Towards Melanoma Detection).
Partial Person Re-identification with Alignment and Hallucination
Authors: Sara Iodice, Krystian Mikolajczyk
Date: 2018-07-24 00:00:00
Tasks: Hallucination, Person Re-Identification

Partial person re-identification involves matching pedestrian frames where
only a part of a body is visible in corresponding images. This reflects
practical CCTV surveillance scenario, where full person views are often not
available. Missing body parts make the comparison very challenging due to
significant misalignment and varying scale of the views. We propose Partial
Matching Net (PMN) that detects body joints, aligns partial views and
hallucinates the missing parts based on the information present in the frame
and a learned model of a person. The aligned and reconstructed views are then
combined into a joint representation and used for matching images. We evaluate
our approach and compare to other methods on three different datasets,
demonstrating significant improvements.
An argument in favor of strong scaling for deep neural networks with small datasets
Authors: Renato L. de F. Cunha, Eduardo R. Rodrigues, Matheus Palhares Viana, Dario Augusto Borges Oliveira
Date: 2018-07-24 00:00:00

In recent years, with the popularization of deep learning frameworks and large datasets, researchers have started parallelizing their models in order to train faster. This is crucially important, because they typically explore many hyperparameters in order to find the best ones for their applications. This process is time consuming and, consequently, speeding up training improves productivity. One approach to parallelize deep learning models followed by many researchers is based on weak scaling. The minibatches increase in size as new GPUs are added to the system. In addition, new learning rates schedules have been proposed to fix optimization issues that occur with large minibatch sizes. In this paper, however, we show that the recommendations provided by recent work do not apply to models that lack large datasets. In fact, we argument in favor of using strong scaling for achieving reliable performance in such cases. We evaluated our approach with up to 32 GPUs and show that weak scaling not only does not have the same accuracy as the sequential model, it also fails to converge most of time. Meanwhile, strong scaling has good scalability while having exactly the same accuracy of a sequential implementation.
QUEST: Quadriletral Senary bit Pattern for Facial Expression Recognition
Authors: Monu Verma, Prafulla Saxena, Santosh. K. Vipparthi, Gridhari Singh
Date: 2018-07-24 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Facial expression has a significant role in analyzing human cognitive state.
Deriving an accurate facial appearance representation is a critical task for an
automatic facial expression recognition application. This paper provides a new
feature descriptor named as Quadrilateral Senary bit Pattern for facial
expression recognition. The QUEST pattern encoded the intensity changes by
emphasizing the relationship between neighboring and reference pixels by
dividing them into two quadrilaterals in a local neighborhood. Thus, the
resultant gradient edges reveal the transitional variation information, that
improves the classification rate by discriminating expression classes.
Moreover, it also enhances the capability of the descriptor to deal with
viewpoint variations and illumination changes. The trine relationship in a
quadrilateral structure helps to extract the expressive edges and suppressing
noise elements to enhance the robustness to noisy conditions. The QUEST pattern
generates a six-bit compact code, which improves the efficiency of the FER
system with more discriminability. The effectiveness of the proposed method is
evaluated by conducting several experiments on four benchmark datasets: MMI,
GEMEP-FERA, OULU-CASIA, and ISED. The experimental results show better
performance of the proposed method as compared to existing state-art-the
approaches.
Residual Network based Aggregation Model for Skin Lesion Classification
Authors: Yongsheng Pan, Yong Xia
Date: 2018-07-24 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Lesion Classification, Skin Lesion Classification

We recognize that the skin lesion diagnosis is an essential and challenging
sub-task in Image classification, in which the Fisher vector (FV) encoding
algorithm and deep convolutional neural network (DCNN) are two of the most
successful techniques. Since the joint use of FV and DCNN has demonstrated
proven success, the joint techniques could have discriminatory power on skin
lesion diagnosis as well. To this hypothesis, we propose the aggregation
algorithm for skin lesion diagnosis that utilize the residual network to
extract the local features and the Fisher vector method to aggregate the local
features to image-level representation. We applied our algorithm on the
International Skin Imaging Collaboration 2018 (ISIC2018) challenge and only
focus on the third task, i.e., the disease classification.
Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition
Authors: Huajie Jiang, Ruiping Wang, Shiguang Shan, Xilin Chen
Date: 2018-07-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Dictionary Learning, Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize objects of novel classes without
any training samples of specific classes, which is achieved by exploiting the
semantic information and auxiliary datasets. Recently most ZSL approaches focus
on learning visual-semantic embeddings to transfer knowledge from the auxiliary
datasets to the novel classes. However, few works study whether the semantic
information is discriminative or not for the recognition task. To tackle such
problem, we propose a coupled dictionary learning approach to align the
visual-semantic structures using the class prototypes, where the discriminative
information lying in the visual space is utilized to improve the less
discriminative semantic space. Then, zero-shot recognition can be performed in
different spaces by the simple nearest neighbor approach using the learned
class prototypes. Extensive experiments on four benchmark datasets show the
effectiveness of the proposed approach.
Decision Variance in Online Learning
Authors: Sattar Vakili, Alexis Boukouvalas, Qing Zhao
Date: 2018-07-24 00:00:00

Online learning has traditionally focused on the expected rewards. In this
paper, a risk-averse online learning problem under the performance measure of
the mean-variance of the rewards is studied. Both the bandit and full
information settings are considered. The performance of several existing
policies is analyzed, and new fundamental limitations on risk-averse learning
is established. In particular, it is shown that although a logarithmic
distribution-dependent regret in time $T$ is achievable (similar to the
risk-neutral problem), the worst-case (i.e. minimax) regret is lower bounded by
$\Omega(T)$ (in contrast to the $\Omega(\sqrt{T})$ lower bound in the
risk-neutral problem). This sharp difference from the risk-neutral counterpart
is caused by the the variance in the player's decisions, which, while absent in
the regret under the expected reward criterion, contributes to excess
mean-variance due to the non-linearity of this risk measure. The role of the
decision variance in regret performance reflects a risk-averse player's desire
for robust decisions and outcomes.
ISIC 2017 Skin Lesion Segmentation Using Deep Encoder-Decoder Network
Authors: Ngoc-Quang Nguyen
Date: 2018-07-24 00:00:00
Tasks: Data Augmentation, Decoder, Lesion Segmentation, Skin Lesion Segmentation

This paper summarizes our method and validation results for part 1 of the
ISBI Challenge 2018. Our algorithm makes use of deep encoder-decoder network
and novel skin lesion data augmentation to segment the challenge objective.
Besides, we also propose an effective testing strategy by applying multi-model
comparison.
Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations
Authors: Allard Hendriksen, Rianne de Heide, Peter Grünwald
Date: 2018-07-24 00:00:00
Tasks: Two-sample testing

It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary probability theory, of three different mathematical meanings that various authors give to this claim: (1) stopping rule independence, (2) posterior calibration and (3) (semi-) frequentist robustness to optional stopping. We then prove theorems to the effect that these claims do indeed hold in a general measure-theoretic setting. For claims of type (2) and (3), such results are new. By allowing for non-integrable measures based on improper priors, we obtain particularly strong results for the practically important case of models with nuisance parameters satisfying a group invariance (such as location or scale). We also discuss the practical relevance of (1)--(3), and conclude that whether Bayes factor methods actually perform well under optional stopping crucially depends on details of models, priors and the goal of the analysis.
Learning Human Poses from Actions
Authors: Aditya Arun, C. V. Jawahar, M. Pawan Kumar
Date: 2018-07-24 00:00:00

We consider the task of learning to estimate human pose in still images. In
order to avoid the high cost of full supervision, we propose to use a diverse
data set, which consists of two types of annotations: (i) a small number of
images are labeled using the expensive ground-truth pose; and (ii) other images
are labeled using the inexpensive action label. As action information helps
narrow down the pose of a human, we argue that this approach can help reduce
the cost of training without significantly affecting the accuracy. To
demonstrate this we design a probabilistic framework that employs two
distributions: (i) a conditional distribution to model the uncertainty over the
human pose given the image and the action; and (ii) a prediction distribution,
which provides the pose of an image without using any action information. We
jointly estimate the parameters of the two aforementioned distributions by
minimizing their dissimilarity coefficient, as measured by a task-specific loss
function. During both training and testing, we only require an efficient
sampling strategy for both the aforementioned distributions. This allows us to
use deep probabilistic networks that are capable of providing accurate pose
estimates for previously unseen images. Using the MPII data set, we show that
our approach outperforms baseline methods that either do not use the diverse
annotations or rely on pointwise estimates of the pose.
Feature Fusion through Multitask CNN for Large-scale Remote Sensing Image Segmentation
Authors: Shihao Sun, Lei Yang, Wenjie Liu, Ruirui Li
Date: 2018-07-24 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In recent years, Fully Convolutional Networks (FCN) has been widely used in
various semantic segmentation tasks, including multi-modal remote sensing
imagery. How to fuse multi-modal data to improve the segmentation performance
has always been a research hotspot. In this paper, a novel end-toend fully
convolutional neural network is proposed for semantic segmentation of natural
color, infrared imagery and Digital Surface Models (DSM). It is based on a
modified DeepUNet and perform the segmentation in a multi-task way. The
channels are clustered into groups and processed on different task pipelines.
After a series of segmentation and fusion, their shared features and private
features are successfully merged together. Experiment results show that the
feature fusion network is efficient. And our approach achieves good performance
in ISPRS Semantic Labeling Contest (2D).
CaricatureShop: Personalized and Photorealistic Caricature Sketching
Authors: Xiaoguang Han, Kangcheng Hou, Dong Du, Yuda Qiu, Yizhou Yu, Kun Zhou, Shuguang Cui
Date: 2018-07-24 00:00:00
Tasks: Caricature, Face Model

In this paper, we propose the first sketching system for interactively
personalized and photorealistic face caricaturing. Input an image of a human
face, the users can create caricature photos by manipulating its facial feature
curves. Our system firstly performs exaggeration on the recovered 3D face model
according to the edited sketches, which is conducted by assigning the laplacian
of each vertex a scaling factor. To construct the mapping between 2D sketches
and a vertex-wise scaling field, a novel deep learning architecture is
developed. With the obtained 3D caricature model, two images are generated, one
obtained by applying 2D warping guided by the underlying 3D mesh deformation
and the other obtained by re-rendering the deformed 3D textured model. These
two images are then seamlessly integrated to produce our final output. Due to
the severely stretching of meshes, the rendered texture is of blurry
appearances. A deep learning approach is exploited to infer the missing details
for enhancing these blurry regions. Moreover, a relighting operation is
invented to further improve the photorealism of the result. Both quantitative
and qualitative experiment results validated the efficiency of our sketching
system and the superiority of our proposed techniques against existing methods.
Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series
Authors: Axel Brando, Jose A. Rodríguez-Serrano, Mauricio Ciprian, Roberto Maestre, Jordi Vitrià
Date: 2018-07-24 00:00:00
Tasks: Deep Learning

Deep Learning is a consolidated, state-of-the-art Machine Learning tool to
fit a function when provided with large data sets of examples. However, in
regression tasks, the straightforward application of Deep Learning models
provides a point estimate of the target. In addition, the model does not take
into account the uncertainty of a prediction. This represents a great
limitation for tasks where communicating an erroneous prediction carries a
risk. In this paper we tackle a real-world problem of forecasting impending
financial expenses and incomings of customers, while displaying predictable
monetary amounts on a mobile app. In this context, we investigate if we would
obtain an advantage by applying Deep Learning models with a Heteroscedastic
model of the variance of a network's output. Experimentally, we achieve a
higher accuracy than non-trivial baselines. More importantly, we introduce a
mechanism to discard low-confidence predictions, which means that they will not
be visible to users. This should help enhance the user experience of our
product.
Collective Matrix Completion
Authors: Mokhtar Z. Alaya, Olga Klopp
Date: 2018-07-24 00:00:00
Tasks: Matrix Completion

Matrix completion aims to reconstruct a data matrix based on observations of a small number of its entries. Usually in matrix completion a single matrix is considered, which can be, for example, a rating matrix in recommendation system. However, in practical situations, data is often obtained from multiple sources which results in a collection of matrices rather than a single one. In this work, we consider the problem of collective matrix completion with multiple and heterogeneous matrices, which can be count, binary, continuous, etc. We first investigate the setting where, for each source, the matrix entries are sampled from an exponential family distribution. Then, we relax the assumption of exponential family distribution for the noise and we investigate the distribution-free case. In this setting, we do not assume any specific model for the observations. The estimation procedures are based on minimizing the sum of a goodness-of-fit term and the nuclear norm penalization of the whole collective matrix. We prove that the proposed estimators achieve fast rates of convergence under the two considered settings and we corroborate our results with numerical experiments.