pwc-archive/papers-with-abstracts huggingface dataset:
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
Authors: Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, Nicholas Ayache
Date: 2018-07-23 00:00:00
Tasks: Segmentation, Tumor Segmentation

We present an efficient deep learning approach for the challenging task of
tumor segmentation in multisequence MR images. In recent years, Convolutional
Neural Networks (CNN) have achieved state-of-the-art performances in a large
variety of recognition tasks in medical imaging. Because of the considerable
computational cost of CNNs, large volumes such as MRI are typically processed
by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D
patches. In this paper we introduce a CNN-based model which efficiently
combines the advantages of the short-range 3D context and the long-range 2D
context. To overcome the limitations of specific choices of neural network
architectures, we also propose to merge outputs of several cascaded 2D-3D
models by a voxelwise voting strategy. Furthermore, we propose a network
architecture in which the different MR sequences are processed by separate
subnetworks in order to be more robust to the problem of missing MR sequences.
Finally, a simple and efficient algorithm for training large CNN models is
introduced. We evaluate our method on the public benchmark of the BRATS 2017
challenge on the task of multiclass segmentation of malignant brain tumors. Our
method achieves good performances and produces accurate segmentations with
median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854
(enhancing core). Our approach can be naturally applied to various tasks
involving segmentation of lesions or organs.
Recent Advances in Convolutional Neural Network Acceleration
Authors: Qianru Zhang, Meng Zhang, Tinghuan Chen, Zhifei Sun, Yuzhe ma, Bei Yu
Date: 2018-07-23 00:00:00
Tasks: image-classification, Image Classification

In recent years, convolutional neural networks (CNNs) have shown great
performance in various fields such as image classification, pattern
recognition, and multi-media compression. Two of the feature properties, local
connectivity and weight sharing, can reduce the number of parameters and
increase processing speed during training and inference. However, as the
dimension of data becomes higher and the CNN architecture becomes more
complicated, the end-to-end approach or the combined manner of CNN is
computationally intensive, which becomes limitation to CNN's further
implementation. Therefore, it is necessary and urgent to implement CNN in a
faster way. In this paper, we first summarize the acceleration methods that
contribute to but not limited to CNN by reviewing a broad variety of research
papers. We propose a taxonomy in terms of three levels, i.e.~structure level,
algorithm level, and implementation level, for acceleration methods. We also
analyze the acceleration methods in terms of CNN architecture compression,
algorithm optimization, and hardware-based improvement. At last, we give a
discussion on different perspectives of these acceleration and optimization
methods within each level. The discussion shows that the methods in each level
still have large exploration space. By incorporating such a wide range of
disciplines, we expect to provide a comprehensive reference for researchers who
are interested in CNN acceleration.
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations
Authors: Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
Date: 2018-07-23 00:00:00
Tasks: Word Embeddings

Dialog act (DA) recognition is a task that has been widely explored over the years. Recently, most approaches to the task explored different DNN architectures to combine the representations of the words in a segment and generate a segment representation that provides cues for intention. In this study, we explore means to generate more informative segment representations, not only by exploring different network architectures, but also by considering different token representations, not only at the word level, but also at the character and functional levels. At the word level, in addition to the commonly used uncontextualized embeddings, we explore the use of contextualized representations, which provide information concerning word sense and segment structure. Character-level tokenization is important to capture intention-related morphological aspects that cannot be captured at the word level. Finally, the functional level provides an abstraction from words, which shifts the focus to the structure of the segment. We also explore approaches to enrich the segment representation with context information from the history of the dialog, both in terms of the classifications of the surrounding segments and the turn-taking history. This kind of information has already been proved important for the disambiguation of DAs in previous studies. Nevertheless, we are able to capture additional information by considering a summary of the dialog history and a wider turn-taking context. By combining the best approaches at each step, we achieve results that surpass the previous state-of-the-art on generic DA recognition on both SwDA and MRDA, two of the most widely explored corpora for the task. Furthermore, by considering both past and future context, simulating annotation scenario, our approach achieves a performance similar to that of a human annotator on SwDA and surpasses it on MRDA.
Person Search by Multi-Scale Matching
Authors: Xu Lan, Xiatian Zhu, Shaogang Gong
Date: 2018-07-23 00:00:00
Proceeding: ECCV 2018
Tasks: Benchmarking, Human Detection, Person Search

We consider the problem of person search in unconstrained scene images.
Existing methods usually focus on improving the person detection accuracy to
mitigate negative effects imposed by misalignment, mis-detections, and false
alarms resulted from noisy people auto-detection. In contrast to previous
studies, we show that sufficiently reliable person instance cropping is
achievable by slightly improved state-of-the-art deep learning object detectors
(e.g. Faster-RCNN), and the under-studied multi-scale matching problem in
person search is a more severe barrier. In this work, we address this
multi-scale person search challenge by proposing a Cross-Level Semantic
Alignment (CLSA) deep learning approach capable of learning more discriminative
identity feature representations in a unified end-to-end model. This is
realised by exploiting the in-network feature pyramid structure of a deep
neural network enhanced by a novel cross pyramid-level semantic alignment loss
function. This favourably eliminates the need for constructing a
computationally expensive image pyramid and a complex multi-branch network
architecture. Extensive experiments show the modelling advantages and
performance superiority of CLSA over the state-of-the-art person search and
multi-scale matching methods on two large person search benchmarking datasets:
CUHK-SYSU and PRW.
Invisible Steganography via Generative Adversarial Networks
Authors: Ru Zhang, Shiqi Dong, Jianyi Liu
Date: 2018-07-23 00:00:00
Tasks: Image Steganography, Steganalysis

Nowadays, there are plenty of works introducing convolutional neural networks
(CNNs) to the steganalysis and exceeding conventional steganalysis algorithms.
These works have shown the improving potential of deep learning in information
hiding domain. There are also several works based on deep learning to do image
steganography, but these works still have problems in capacity, invisibility
and security. In this paper, we propose a novel CNN architecture named as
\isgan to conceal a secret gray image into a color cover image on the sender
side and exactly extract the secret image out on the receiver side. There are
three contributions in our work: (i) we improve the invisibility by hiding the
secret image only in the Y channel of the cover image; (ii) We introduce the
generative adversarial networks to strengthen the security by minimizing the
divergence between the empirical probability distributions of stego images and
natural images. (iii) In order to associate with the human visual system
better, we construct a mixed loss function which is more appropriate for
steganography to generate more realistic stego images and reveal out more
better secret images. Experiment results show that ISGAN can achieve
start-of-art performances on LFW, Pascal VOC2012 and ImageNet datasets.
MVDepthNet: Real-time Multiview Depth Estimation Neural Network
Authors: Kaixuan Wang, Shaojie Shen
Date: 2018-07-23 00:00:00
Tasks: Data Augmentation, Decoder, Depth Estimation

Although deep neural networks have been widely applied to computer vision
problems, extending them into multiview depth estimation is non-trivial. In
this paper, we present MVDepthNet, a convolutional network to solve the depth
estimation problem given several image-pose pairs from a localized monocular
camera in neighbor viewpoints. Multiview observations are encoded in a cost
volume and then combined with the reference image to estimate the depth map
using an encoder-decoder network. By encoding the information from multiview
observations into the cost volume, our method achieves real-time performance
and the flexibility of traditional methods that can be applied regardless of
the camera intrinsic parameters and the number of images. Geometric data
augmentation is used to train MVDepthNet. We further apply MVDepthNet in a
monocular dense mapping system that continuously estimates depth maps using a
single localized moving camera. Experiments show that our method can generate
depth maps efficiently and precisely.
Explainable Neural Computation via Stack Neural Module Networks
Authors: Ronghang Hu, Jacob Andreas, Trevor Darrell, Kate Saenko
Date: 2018-07-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decision Making, Question Answering, Referring Expression Comprehension

In complex inferential tasks like question answering, machine learning models
must confront two challenges: the need to implement a compositional reasoning
process, and, in many applications, the need for this reasoning process to be
interpretable to assist users in both development and prediction. Existing
models designed to produce interpretable traces of their decision-making
process typically require these traces to be supervised at training time. In
this paper, we present a novel neural modular approach that performs
compositional reasoning by automatically inducing a desired sub-task
decomposition without relying on strong supervision. Our model allows linking
different reasoning tasks though shared modules that handle common routines
across tasks. Experiments show that the model is more interpretable to human
evaluators compared to other state-of-the-art models: users can better
understand the model's underlying reasoning procedure and predict when it will
succeed or fail based on observing its intermediate outputs.
Iterative Interaction Training for Segmentation Editing Networks
Authors: Gustav Bredell, Christine Tanner, Ender Konukoglu
Date: 2018-07-23 00:00:00
Tasks: Interactive Segmentation, Segmentation

Automatic segmentation has great potential to facilitate morphological
measurements while simultaneously increasing efficiency. Nevertheless often
users want to edit the segmentation to their own needs and will need different
tools for this. There has been methods developed to edit segmentations of
automatic methods based on the user input, primarily for binary segmentations.
Here however, we present an unique training strategy for convolutional neural
networks (CNNs) trained on top of an automatic method to enable interactive
segmentation editing that is not limited to binary segmentation. By utilizing a
robot-user during training, we closely mimic realistic use cases to achieve
optimal editing performance. In addition, we show that an increase of the
iterative interactions during the training process up to ten improves the
segmentation editing performance substantially. Furthermore, we compare our
segmentation editing CNN (interCNN) to state-of-the-art interactive
segmentation algorithms and show a superior or on par performance.
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks
Authors: Minjun Li, Hao-Zhi Huang, Lin Ma, Wei Liu, Tong Zhang, Yu-Gang Jiang
Date: 2018-07-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

Recent studies on unsupervised image-to-image translation have made a
remarkable progress by training a pair of generative adversarial networks with
a cycle-consistent loss. However, such unsupervised methods may generate
inferior results when the image resolution is high or the two image domains are
of significant appearance differences, such as the translations between
semantic layouts and natural images in the Cityscapes dataset. In this paper,
we propose novel Stacked Cycle-Consistent Adversarial Networks (SCANs) by
decomposing a single translation into multi-stage transformations, which not
only boost the image translation quality but also enable higher resolution
image-to-image translations in a coarse-to-fine manner. Moreover, to properly
exploit the information from the previous stage, an adaptive fusion block is
devised to learn a dynamic integration of the current stage's output and the
previous stage's output. Experiments on multiple datasets demonstrate that our
proposed approach can improve the translation quality compared with previous
single-stage unsupervised methods.
Particle Filtering Methods for Stochastic Optimization with Application to Large-Scale Empirical Risk Minimization
Authors: Bin Liu
Date: 2018-07-23 00:00:00
Tasks: Binary Classification, Scheduling, Stochastic Optimization

There is a recent interest in developing statistical filtering methods for stochastic optimization (FSO) by leveraging a probabilistic perspective of incremental proximity methods (IPMs). The existent FSO methods are derived based on the Kalman filter (KF) and extended KF (EKF). Different from classical stochastic optimization methods such as the stochastic gradient descent (SGD) and typical IPMs, such KF-type algorithms possess a desirable property, namely they do not require pre-scheduling of the learning rate for convergence. However, on the other side, they have inherent limitations inherited from the nature of KF mechanisms. It is a consensus that the class of particle filters (PFs) outperforms the KF and its variants remarkably for nonlinear and/or non-Gaussian statistical filtering tasks. Hence, it is natural to ask if the FSO methods can benefit from the PF theory to get around of limitations of the KF-type stochastic optimization methods. We provide an affirmative answer to the aforementioned question by developing two PF based stochastic optimizers (PFSOs). For performance evaluation, we apply them to address nonlinear least-square fitting using simulated data sets and empirical risk minimization for binary classification using real data sets. Experimental results demonstrate that PFSOs outperform remarkably existent methods in terms of numerical stability, convergence speed, and flexibility in handling different types of loss functions.
Improving Deep Models of Person Re-identification for Cross-Dataset Usage
Authors: Sergey Rodionov, Alexey Potapov, Hugo Latapie, Enzo Fenoglio, Maxim Peterson
Date: 2018-07-23 00:00:00
Tasks: Deep Learning, Person Re-Identification, Triplet

Person re-identification (Re-ID) is the task of matching humans across
cameras with non-overlapping views that has important applications in visual
surveillance. Like other computer vision tasks, this task has gained much with
the utilization of deep learning methods. However, existing solutions based on
deep learning are usually trained and tested on samples taken from same
datasets, while in practice one need to deploy Re-ID systems for new sets of
cameras for which labeled data is unavailable. Here, we mitigate this problem
for one state-of-the-art model, namely, metric embedding trained with the use
of the triplet loss function, although our results can be extended to other
models. The contribution of our work consists in developing a method of
training the model on multiple datasets, and a method for its online
practically unsupervised fine-tuning. These methods yield up to 19.1%
improvement in Rank-1 score in the cross-dataset evaluation.
Implementing Neural Turing Machines
Authors: Mark Collier, Joeran Beel
Date: 2018-07-23 00:00:00

Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural
Networks, a new class of recurrent neural networks which decouple computation
from memory by introducing an external memory unit. NTMs have demonstrated
superior performance over Long Short-Term Memory Cells in several sequence
learning tasks. A number of open source implementations of NTMs exist but are
unstable during training and/or fail to replicate the reported performance of
NTMs. This paper presents the details of our successful implementation of a
NTM. Our implementation learns to solve three sequential learning tasks from
the original NTM paper. We find that the choice of memory contents
initialization scheme is crucial in successfully implementing a NTM. Networks
with memory contents initialized to small constant values converge on average 2
times faster than the next best memory contents initialization scheme.
Git Loss for Deep Face Recognition
Authors: Alessandro Calefati, Muhammad Kamran Janjua, Shah Nawaz, Ignazio Gallo
Date: 2018-07-23 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification

Convolutional Neural Networks (CNNs) have been widely used in computer vision
tasks, such as face recognition and verification, and have achieved
state-of-the-art results due to their ability to capture discriminative deep
features. Conventionally, CNNs have been trained with softmax as supervision
signal to penalize the classification loss. In order to further enhance the
discriminative capability of deep features, we introduce a joint supervision
signal, Git loss, which leverages on softmax and center loss functions. The aim
of our loss function is to minimize the intra-class variations as well as
maximize the inter-class distances. Such minimization and maximization of deep
features are considered ideal for face recognition task. We perform experiments
on two popular face recognition benchmarks datasets and show that our proposed
loss function achieves maximum separability between deep face features of
different identities and achieves state-of-the-art accuracy on two major face
recognition benchmark datasets: Labeled Faces in the Wild (LFW) and YouTube
Faces (YTF). However, it should be noted that the major objective of Git loss
is to achieve maximum separability between deep features of divergent
identities.
Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs
Authors: Tomer Galanti, Sagie Benaim, Lior Wolf
Date: 2018-07-23 00:00:00

The recent empirical success of unsupervised cross-domain mapping algorithms, between two domains that share common characteristics, is not well-supported by theoretical justifications. This lacuna is especially troubling, given the clear ambiguity in such mappings. We work with adversarial training methods based on IPMs and derive a novel risk bound, which upper bounds the risk between the learned mapping $h$ and the target mapping $y$, by a sum of three terms: (i) the risk between $h$ and the most distant alternative mapping that was learned by the same cross-domain mapping algorithm, (ii) the minimal discrepancy between the target domain and the domain obtained by applying a hypothesis $h^*$ on the samples of the source domain, where $h^*$ is a hypothesis selectable by the same algorithm. The bound is directly related to Occam's razor and encourages the selection of the minimal architecture that supports a small mapping discrepancy and (iii) an approximation error term that decreases as the complexity of the class of discriminators increases and is empirically shown to be small. The bound leads to multiple algorithmic consequences, including a method for hyperparameters selection and for early stopping in cross-domain mapping GANs. We also demonstrate a novel capability for unsupervised learning of estimating confidence in the mapping of every specific sample.
A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images
Authors: Yutong Xie, Jianpeng Zhang, Yong Xia
Date: 2018-07-23 00:00:00
Tasks: General Classification, Lesion Classification, Skin Lesion Classification

A multi-level deep ensemble (MLDE) model that can be trained in an 'end to
end' manner is proposed for skin lesion classification in dermoscopy images. In
this model, four pre-trained ResNet-50 networks are used to characterize the
multiscale information of skin lesions and are combined by using an adaptive
weighting scheme that can be learned during the error back propagation. The
proposed MLDE model achieved an average AUC value of 86.5% on the ISIC-skin
2018 official validation dataset, which is substantially higher than the
average AUC values achieved by each of four ResNet-50 networks.
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
Authors: Kripasindhu Sarkar, Basavaraj Hampiholi, Kiran varanasi, Didier Stricker
Date: 2018-07-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Object Classification, General Classification

We present a novel global representation of 3D shapes, suitable for the
application of 2D CNNs. We represent 3D shapes as multi-layered height-maps
(MLH) where at each grid location, we store multiple instances of height maps,
thereby representing 3D shape detail that is hidden behind several layers of
occlusion. We provide a novel view merging method for combining view dependent
information (Eg. MLH descriptors) from multiple views. Because of the ability
of using 2D CNNs, our method is highly memory efficient in terms of input
resolution compared to the voxel based input. Together with MLH descriptors and
our multi view merging, we achieve the state-of-the-art result in
classification on ModelNet dataset.
AceKG: A Large-scale Knowledge Graph for Academic Data Mining
Authors: Ruijie Wang, Yuchen Yan, Jialu Wang, Yuting Jia, Ye Zhang, Wei-Nan Zhang, Xinbing Wang
Date: 2018-07-23 00:00:00
Tasks: Community Detection, Entity Alignment, Knowledge Graphs, Link Prediction, Representation Learning

Most existing knowledge graphs (KGs) in academic domains suffer from problems
of insufficient multi-relational information, name ambiguity and improper data
format for large-scale machine processing. In this paper, we present AceKG, a
new large-scale KG in academic domain. AceKG not only provides clean academic
information, but also offers a large-scale benchmark dataset for researchers to
conduct challenging data mining projects including link prediction, community
detection and scholar classification. Specifically, AceKG describes 3.13
billion triples of academic facts based on a consistent ontology, including
necessary properties of papers, authors, fields of study, venues and
institutes, as well as the relations among them. To enrich the proposed
knowledge graph, we also perform entity alignment with existing databases and
rule-based inference. Based on AceKG, we conduct experiments of three typical
academic data mining tasks and evaluate several state-of- the-art knowledge
embedding and network representation learning approaches on the benchmark
datasets built from AceKG. Finally, we discuss several promising research
directions that benefit from AceKG.