pwc-archive/papers-with-abstracts huggingface dataset:
Towards Multi-pose Guided Virtual Try-on Network
Authors: Haoye Dong, Xiaodan Liang, Bochao Wang, Hanjiang Lai, Jia Zhu, Jian Yin
Date: 2019-02-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: Fashion Synthesis, Generative Adversarial Network, Human Parsing, Image-to-Image Translation, Virtual Try-on

Virtual try-on system under arbitrary human poses has huge application
potential, yet raises quite a lot of challenges, e.g. self-occlusions, heavy
misalignment among diverse poses, and diverse clothes textures. Existing
methods aim at fitting new clothes into a person can only transfer clothes on
the fixed human pose, but still show unsatisfactory performances which often
fail to preserve the identity, lose the texture details, and decrease the
diversity of poses. In this paper, we make the first attempt towards multi-pose
guided virtual try-on system, which enables transfer clothes on a person image
under diverse poses. Given an input person image, a desired clothes image, and
a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON)
can generate a new person image after fitting the desired clothes into the
input image and manipulating human poses. Our MG-VTON is constructed in three
stages: 1) a desired human parsing map of the target image is synthesized to
match both the desired pose and the desired clothes shape; 2) a deep Warping
Generative Adversarial Network (Warp-GAN) warps the desired clothes appearance
into the synthesized human parsing map and alleviates the misalignment problem
between the input human pose and desired human pose; 3) a refinement render
utilizing multi-pose composition masks recovers the texture details of clothes
and removes some artifacts. Extensive experiments on well-known datasets and
our newly collected largest virtual try-on benchmark demonstrate that our
MG-VTON significantly outperforms all state-of-the-art methods both
qualitatively and quantitatively with promising multi-pose virtual try-on
performances.
DPOD: 6D Pose Object Detector and Refiner
Authors: Sergey Zakharov, Ivan Shugurov, Slobodan Ilic
Date: 2019-02-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Object Detection, 6D Pose Estimation, 6D Pose Estimation using RGB, Object, object-detection, Object Detection, Pose Estimation

In this paper we present a novel deep learning method for 3D object detection and 6D pose estimation from RGB images. Our method, named DPOD (Dense Pose Object Detector), estimates dense multi-class 2D-3D correspondence maps between an input image and available 3D models. Given the correspondences, a 6DoF pose is computed via PnP and RANSAC. An additional RGB pose refinement of the initial pose estimates is performed using a custom deep learning-based refinement scheme. Our results and comparison to a vast number of related works demonstrate that a large number of correspondences is beneficial for obtaining high-quality 6D poses both before and after refinement. Unlike other methods that mainly use real data for training and do not train on synthetic renderings, we perform evaluation on both synthetic and real training data demonstrating superior results before and after refinement when compared to all recent detectors. While being precise, the presented approach is still real-time capable.
Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors
Authors: Ke Sun, Zhanxing Zhu, Zhouchen Lin
Date: 2019-02-28 00:00:00

Most previous works usually explained adversarial examples from several
specific perspectives, lacking relatively integral comprehension about this
problem. In this paper, we present a systematic study on adversarial examples
from three aspects: the amount of training data, task-dependent and
model-specific factors. Particularly, we show that adversarial generalization
(i.e. test accuracy on adversarial examples) for standard training requires
more data than standard generalization (i.e. test accuracy on clean examples);
and uncover the global relationship between generalization and robustness with
respect to the data size especially when data is augmented by generative
models. This reveals the trade-off correlation between standard generalization
and robustness in limited training data regime and their consistency when data
size is large enough. Furthermore, we explore how different task-dependent and
model-specific factors influence the vulnerability of deep neural networks by
extensive empirical analysis. Relevant recommendations on defense against
adversarial attacks are provided as well. Our results outline a potential path
towards the luminous and systematic understanding of adversarial examples.
MassFace: an efficient implementation using triplet loss for face recognition
Authors: Yule Li
Date: 2019-02-28 00:00:00
Tasks: Face Recognition, Triplet

In this paper we present an efficient implementation using triplet loss for
face recognition. We conduct the practical experiment to analyze the factors
that influence the training of triplet loss. All models are trained on
CASIA-Webface dataset and tested on LFW. We analyze the experiment results and
give some insights to help others balance the factors when they apply triplet
loss to their own problem especially for face recognition task. Code has been
released in https://github.com/yule-li/MassFace.
Global Vectors for Node Representations
Authors: Robin Brochier, Adrien Guille, Julien Velcin
Date: 2019-02-28 00:00:00
Tasks: Network Embedding

Most network embedding algorithms consist in measuring co-occurrences of
nodes via random walks then learning the embeddings using Skip-Gram with
Negative Sampling. While it has proven to be a relevant choice, there are
alternatives, such as GloVe, which has not been investigated yet for network
embedding. Even though SGNS better handles non co-occurrence than GloVe, it has
a worse time-complexity. In this paper, we propose a matrix factorization
approach for network embedding, inspired by GloVe, that better handles non
co-occurrence with a competitive time-complexity. We also show how to extend
this model to deal with networks where nodes are documents, by simultaneously
learning word, node and document representations. Quantitative evaluations show
that our model achieves state-of-the-art performance, while not being so
sensitive to the choice of hyper-parameters. Qualitatively speaking, we show
how our model helps exploring a network of documents by generating
complementary network-oriented and content-oriented keywords.
3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
Authors: Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni, Antonio de Marvao, Stuart A. Cook, Declan P. O'Regan, Daniel Rueckert
Date: 2019-02-28 00:00:00
Tasks: Cardiac Segmentation, LV Segmentation, Segmentation

Accurate segmentation of heart structures imaged by cardiac MR is key for the
quantitative analysis of pathology. High-resolution 3D MR sequences enable
whole-heart structural imaging but are time-consuming, expensive to acquire and
they often require long breath holds that are not suitable for patients.
Consequently, multiplanar breath-hold 2D cine sequences are standard practice
but are disadvantaged by lack of whole-heart coverage and low through-plane
resolution. To address this, we propose a conditional variational autoencoder
architecture able to learn a generative model of 3D high-resolution left
ventricular (LV) segmentations which is conditioned on three 2D LV
segmentations of one short-axis and two long-axis images. By only employing
these three 2D segmentations, our model can efficiently reconstruct the 3D
high-resolution LV segmentation of a subject. When evaluated on 400 unseen
healthy volunteers, our model yielded an average Dice score of $87.92 \pm 0.15$
and outperformed competing architectures.
Salient object detection on hyperspectral images using features learned from unsupervised segmentation task
Authors: Nevrez Imamoglu, Guanqun Ding, Yuming Fang, Asako Kanezaki, Toru Kouyama, Ryosuke Nakamura
Date: 2019-02-28 00:00:00
Tasks: Clustering, Image Segmentation, Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Semantic Segmentation, Unsupervised Image Segmentation

Various saliency detection algorithms from color images have been proposed to
mimic eye fixation or attentive object detection response of human observers
for the same scenes. However, developments on hyperspectral imaging systems
enable us to obtain redundant spectral information of the observed scenes from
the reflected light source from objects. A few studies using low-level features
on hyperspectral images demonstrated that salient object detection can be
achieved. In this work, we proposed a salient object detection model on
hyperspectral images by applying manifold ranking (MR) on self-supervised
Convolutional Neural Network (CNN) features (high-level features) from
unsupervised image segmentation task. Self-supervision of CNN continues until
clustering loss or saliency maps converges to a defined error between each
iteration. Finally, saliency estimations is done as the saliency map at last
iteration when the self-supervision procedure terminates with convergence.
Experimental evaluations demonstrated that proposed saliency detection
algorithm on hyperspectral images is outperforming state-of-the-arts
hyperspectral saliency models including the original MR based saliency model.
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization
Authors: Yeonwoo Jeong, Yoonsung Kim, Hyun Oh Song
Date: 2019-02-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Combinatorial Optimization, Quantization, Representation Learning

We develop hierarchically quantized efficient embedding representations for
similarity-based search and show that this representation provides not only the
state of the art performance on the search accuracy but also provides several
orders of speed up during inference. The idea is to hierarchically quantize the
representation so that the quantization granularity is greatly increased while
maintaining the accuracy and keeping the computational complexity low. We also
show that the problem of finding the optimal sparse compound hash code
respecting the hierarchical structure can be optimized in polynomial time via
minimum cost flow in an equivalent flow network. This allows us to train the
method end-to-end in a mini-batch stochastic gradient descent setting. Our
experiments on Cifar100 and ImageNet datasets show the state of the art search
accuracy while providing several orders of magnitude search speedup
respectively over exhaustive linear search over the dataset.
Better, Faster, Stronger Sequence Tagging Constituent Parsers
Authors: David Vilares, Mostafa Abdou, Anders Søgaard
Date: 2019-02-28 00:00:00
Proceeding: NAACL 2019 6
Tasks: Multi-Task Learning, Sentence

Sequence tagging models for constituent parsing are faster, but less accurate than other types of parsers. In this work, we address the following weaknesses of such constituent parsers: (a) high error rates around closing brackets of long constituents, (b) large label sets, leading to sparsity, and (c) error propagation arising from greedy decoding. To effectively close brackets, we train a model that learns to switch between tagging schemes. To reduce sparsity, we decompose the label set and use multi-task learning to jointly learn to predict sublabels. Finally, we mitigate issues from greedy decoding through auxiliary losses and sentence-level fine-tuning with policy gradient. Combining these techniques, we clearly surpass the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebanks, and reduce their parsing time even further. On the SPMRL datasets, we observe even greater improvements across the board, including a new state of the art on Basque, Hebrew, Polish and Swedish.
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
Authors: Andrés F. López-Lopera, ST John, Nicolas Durrande
Date: 2019-02-28 00:00:00
Tasks: Point Processes

Gaussian process (GP) modulated Cox processes are widely used to model point
patterns. Existing approaches require a mapping (link function) between the
unconstrained GP and the positive intensity function. This commonly yields
solutions that do not have a closed form or that are restricted to specific
covariance functions. We introduce a novel finite approximation of GP-modulated
Cox processes where positiveness conditions can be imposed directly on the GP,
with no restrictions on the covariance function. Our approach can also ensure
other types of inequality constraints (e.g. monotonicity, convexity), resulting
in more versatile models that can be used for other classes of point processes
(e.g. renewal processes). We demonstrate on both synthetic and real-world data
that our framework accurately infers the intensity functions. Where
monotonicity is a feature of the process, our ability to include this in the
inference improves results.
Learning partially ranked data based on graph regularization
Authors: Kento Nakamura, Keisuke Yano, Fumiyasu Komaki
Date: 2019-02-28 00:00:00
Tasks: parameter estimation

Ranked data appear in many different applications, including voting and
consumer surveys. There often exhibits a situation in which data are partially
ranked. Partially ranked data is thought of as missing data. This paper
addresses parameter estimation for partially ranked data under a (possibly)
non-ignorable missing mechanism. We propose estimators for both complete
rankings and missing mechanisms together with a simple estimation procedure.
Our estimation procedure leverages a graph regularization in conjunction with
the Expectation-Maximization algorithm. Our estimation procedure is
theoretically guaranteed to have the convergence properties. We reduce a
modeling bias by allowing a non-ignorable missing mechanism. In addition, we
avoid the inherent complexity within a non-ignorable missing mechanism by
introducing a graph regularization. The experimental results demonstrate that
the proposed estimators work well under non-ignorable missing mechanisms.
Extended Gaze Following: Detecting Objects in Videos Beyond the Camera Field of View
Authors: Benoit Massé, Stéphane Lathuilière, Pablo Mesejo, Radu Horaud
Date: 2019-02-28 00:00:00
Tasks: Decoder

In this paper we address the problems of detecting objects of interest in a
video and of estimating their locations, solely from the gaze directions of
people present in the video. Objects can be indistinctly located inside or
outside the camera field of view. We refer to this problem as extended gaze
following. The contributions of the paper are the followings. First, we propose
a novel spatial representation of the gaze directions adopting a top-view
perspective. Second, we develop several convolutional encoder/decoder networks
to predict object locations and compare them with heuristics and with classical
learning-based approaches. Third, in order to train the proposed models, we
generate a very large number of synthetic scenarios employing a probabilistic
formulation. Finally, our methodology is empirically validated using a publicly
available dataset.
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data
Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco Lorenzi
Date: 2019-02-28 00:00:00
Tasks: blind source separation, Gaussian Processes

We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic trajectories, the temporal sources are defined as monotonic and time-reparametrized Gaussian Processes. To account for the non-stationarity of brain images, we model the spatial sources as sparse codes convolved at multiple scales. The method was tested on synthetic data favourably comparing with standard blind source separation approaches. The application on large-scale imaging data from a clinical study allows to disentangle differential temporal progression patterns mapping brain regions key to neurodegeneration, while revealing a disease-specific time scale associated to the clinical diagnosis.
Dynamic Multi-path Neural Network
Authors: Yingcheng Su, Shunfeng Zhou, Yi-Chao Wu, Tian Su, Ding Liang, Jiaheng Liu, Dixin Zheng, Yingxu Wang, Junjie Yan, Xiaolin Hu
Date: 2019-02-28 00:00:00

Although deeper and larger neural networks have achieved better performance,
the complex network structure and increasing computational cost cannot meet the
demands of many resource-constrained applications. Existing methods usually
choose to execute or skip an entire specific layer, which can only alter the
depth of the network. In this paper, we propose a novel method called Dynamic
Multi-path Neural Network (DMNN), which provides more path selection choices in
terms of network width and depth during inference. The inference path of the
network is determined by a controller, which takes into account both previous
state and object category information. The proposed method can be easily
incorporated into most modern network architectures. Experimental results on
ImageNet and CIFAR-100 demonstrate the superiority of our method on both
efficiency and overall classification accuracy. To be specific, DMNN-101
significantly outperforms ResNet-101 with an encouraging 45.1% FLOPs reduction,
and DMNN-50 performs comparably to ResNet-101 while saving 42.1% parameters.
Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification
Authors: Bolei Xu, Jingxin Liu, Xianxu Hou, Bozhi Liu, Jon Garibaldi, Ian O. Ellis, Andy Green, Linlin Shen, Guoping Qiu
Date: 2019-02-28 00:00:00
Tasks: Cancer Classification, General Classification

One issue with computer based histopathology image analysis is that the size
of the raw image is usually very large. Taking the raw image as input to the
deep learning model would be computationally expensive while resizing the raw
image to low resolution would incur information loss. In this paper, we present
a novel deep hybrid attention approach to breast cancer classification. It
first adaptively selects a sequence of coarse regions from the raw image by a
hard visual attention algorithm, and then for each such region it is able to
investigate the abnormal parts based on a soft-attention mechanism. A recurrent
network is then built to make decisions to classify the image region and also
to predict the location of the image region to be investigated at the next time
step. As the region selection process is non-differentiable, we optimize the
whole network through a reinforcement approach to learn an optimal policy to
classify the regions. Based on this novel Look, Investigate and Classify
approach, we only need to process a fraction of the pixels in the raw image
resulting in significant saving in computational resources without sacrificing
performances. Our approach is evaluated on a public breast cancer
histopathology database, where it demonstrates superior performance to the
state-of-the-art deep learning approaches, achieving around 96\% classification
accuracy while only 15% of raw pixels are used.
A security steganography scheme based on hdr image
Authors: Wei Gao, Yongqing Huo, Yan Qiao
Date: 2019-02-28 00:00:00
Tasks: Image Steganography

It is widely recognized that the image format is crucial to steganography for
that each individual format has its unique properities. Nowadays, the most
famous approach of digital image steganography is to combine a well-defined
distortion function with efficient practical codes such as STC. And numerous
researches are concentrated on spatial domain and jpeg domain. However, whether
in spatial domain or jpeg domain, high payload (e.g., 0.5 bit per pixel) is not
secure enough. In this paper, we propose a novel adaptive steganography scheme
based on 32-bit HDR (High dynamic range) format and Norm IEEE 754. Experiments
show that the steganographic method can achieve satisfactory security under
payload from 0.3bpp to 0.5bpp.
A comparative evaluation of novelty detection algorithms for discrete sequences
Authors: Rémi Domingues, Pietro Michiardi, Jérémie Barlet, Maurizio Filippone
Date: 2019-02-28 00:00:00
Tasks: Fraud Detection, Intrusion Detection, Novelty Detection

The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of the novelty detection problem applied to discrete sequences. The objective of this study is to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case. These recommendations rely on extensive novelty detection experiments based on a variety of public datasets in addition to novel industrial datasets. We also perform thorough scalability and memory usage tests resulting in new supplementary insights of the methods' performance, key selection criterion to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints.
High dynamic range image forensics using cnn
Authors: Yongqing Huo, Xiaofeng Zhu
Date: 2019-02-28 00:00:00
Tasks: Image Forensics, inverse tone mapping, Inverse-Tone-Mapping, Tone Mapping, Vocal Bursts Intensity Prediction

High dynamic range (HDR) imaging has recently drawn much attention in
multimedia community. In this paper, we proposed a HDR image forensics method
based on convolutional neural network (CNN).To our best knowledge, this is the
first time to apply deep learning method on HDR image forensics. The proposed
algorithm uses CNN to distinguish HDR images generated by multiple low dynamic
range (LDR) images from that expanded by single LDR image using inverse tone
mapping (iTM). To do this, we learn the change of statistical characteristics
extracted by the proposed CNN architectures and classify two kinds of HDR
images. Comparision results with some traditional statistical characteristics
shows efficiency of the proposed method in HDR image source identification.