pwc-archive/papers-with-abstracts huggingface dataset:
Active Learning for Interactive Neural Machine Translation of Data Streams
Authors: Álvaro Peris, Francisco Casacuberta
Date: 2018-07-30 00:00:00
Proceeding: CONLL 2018 10
Tasks: Active Learning, Machine Translation, Translation

We study the application of active learning techniques to the translation of
unbounded data streams via interactive neural machine translation. The main
idea is to select, from an unbounded stream of source sentences, those worth to
be supervised by a human agent. The user will interactively translate those
samples. Once validated, these data is useful for adapting the neural machine
translation model.
  We propose two novel methods for selecting the samples to be validated. We
exploit the information from the attention mechanism of a neural machine
translation system. Our experiments show that the inclusion of active learning
techniques into this pipeline allows to reduce the effort required during the
process, while increasing the quality of the translation system. Moreover, it
enables to balance the human effort required for achieving a certain
translation quality. Moreover, our neural system outperforms classical
approaches by a large margin.
Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network
Authors: Yongcheng Liu, Bin Fan, Lingfeng Wang, Jun Bai, Shiming Xiang, Chunhong Pan
Date: 2018-07-30 00:00:00

Semantic labeling for very high resolution (VHR) images in urban areas, is of
significant importance in a wide range of remote sensing applications. However,
many confusing manmade objects and intricate fine-structured objects make it
very difficult to obtain both coherent and accurate labeling results. For this
challenging task, we propose a novel deep model with convolutional neural
networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet).
Specifically, for confusing manmade objects, ScasNet improves the labeling
coherence with sequential global-to-local contexts aggregation. Technically,
multi-scale contexts are captured on the output of a CNN encoder, and then they
are successively aggregated in a self-cascaded manner. Meanwhile, for
fine-structured objects, ScasNet boosts the labeling accuracy with a
coarse-to-fine refinement strategy. It progressively refines the target objects
using the low-level features learned by CNN's shallow layers. In addition, to
correct the latent fitting residual caused by multi-feature fusion inside
ScasNet, a dedicated residual correction scheme is proposed. It greatly
improves the effectiveness of ScasNet. Extensive experimental results on three
public datasets, including two challenging benchmarks, show that ScasNet
achieves the state-of-the-art performance.
Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder
Authors: Jeffrey M. Ede
Date: 2018-07-30 00:00:00
Tasks: Decoder, Playing the Game of 2048

We present an atrous convolutional encoder-decoder trained to denoise
512$\times$512 crops from electron micrographs. It consists of a modified
Xception backbone, atrous convoltional spatial pyramid pooling module and a
multi-stage decoder. Our neural network was trained end-to-end to remove
Poisson noise applied to low-dose ($\ll$ 300 counts ppx) micrographs created
from a new dataset of 17267 2048$\times$2048 high-dose ($>$ 2500 counts ppx)
micrographs and then fine-tuned for ordinary doses (200-2500 counts ppx). Its
performance is benchmarked against bilateral, non-local means, total variation,
wavelet, Wiener and other restoration methods with their default parameters.
Our network outperforms their best mean squared error and structural similarity
index performances by 24.6% and 9.6% for low doses and by 43.7% and 5.5% for
ordinary doses. In both cases, our network's mean squared error has the lowest
variance. Source code and links to our new high-quality dataset and trained
network have been made publicly available at
https://github.com/Jeffrey-Ede/Electron-Micrograph-Denoiser
Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging
Authors: Yaroslav Shmulev, Mikhail Belyaev
Date: 2018-07-30 00:00:00
Tasks: Deep Learning, Prediction

Nowadays, a lot of scientific efforts are concentrated on the diagnosis of
Alzheimer's Disease (AD) applying deep learning methods to neuroimaging data.
Even for 2017, there were published more than a hundred papers dedicated to AD
diagnosis, whereas only a few works considered a problem of mild cognitive
impairments (MCI) conversion to the AD. However, the conversion prediction is
an important problem since approximately 15% of patients with MCI converges to
the AD every year. In the current work, we are focusing on the conversion
prediction using brain Magnetic Resonance Imaging and clinical data, such as
demographics, cognitive assessments, genetic, and biochemical markers. First of
all, we applied state-of-the-art deep learning algorithms on the neuroimaging
data and compared these results with two machine learning algorithms that we
fit using the clinical data. As a result, the models trained on the clinical
data outperform the deep learning algorithms applied to the MR images. To
explore the impact of neuroimaging further, we trained a deep feed-forward
embedding using similarity learning with Histogram loss on all available MRIs
and obtained 64-dimensional vector representation of neuroimaging data. The use
of learned representation from the deep embedding allowed to increase the
quality of prediction based on the neuroimaging. Finally, the current results
on this dataset show that the neuroimaging does affect conversion prediction,
however, cannot noticeably increase the quality of the prediction. The best
results of predicting MCI-to-AD conversion are provided by XGBoost algorithm
trained on the clinical and embedding data. The resulting accuracy is 0.76 +-
0.01 and the area under the ROC curve - 0.86 +- 0.01.
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles
Authors: Tao Li, Lei Lin, Minsoo Choi, Kaiming Fu, Siyuan Gong, Jian Wang
Date: 2018-07-30 00:00:00
Tasks: Autonomous Vehicles, Opinion Mining, Self-Driving Cars, Sentiment Analysis

With one billion monthly viewers, and millions of users discussing and
sharing opinions, comments below YouTube videos are rich sources of data for
opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset,
a freely-available collections of more than 50,000 YouTube comments and
metadata below autonomous vehicle (AV)-related videos. We describe its creation
process, its content and data format, and discuss its possible usages.
Especially, we do a case study of the first self-driving car fatality to
evaluate the dataset, and show how we can use this dataset to better understand
public attitudes toward self-driving cars and public reactions to the accident.
Future developments of the dataset are also discussed.
Deep Hybrid Real and Synthetic Training for Intrinsic Decomposition
Authors: Sai Bi, Nima Khademi Kalantari, Ravi Ramamoorthi
Date: 2018-07-30 00:00:00
Tasks: Intrinsic Image Decomposition

Intrinsic image decomposition is the process of separating the reflectance
and shading layers of an image, which is a challenging and underdetermined
problem. In this paper, we propose to systematically address this problem using
a deep convolutional neural network (CNN). Although deep learning (DL) has been
recently used to handle this application, the current DL methods train the
network only on synthetic images as obtaining ground truth reflectance and
shading for real images is difficult. Therefore, these methods fail to produce
reasonable results on real images and often perform worse than the non-DL
techniques. We overcome this limitation by proposing a novel hybrid approach to
train our network on both synthetic and real images. Specifically, in addition
to directly supervising the network using synthetic images, we train the
network by enforcing it to produce the same reflectance for a pair of images of
the same real-world scene with different illuminations. Furthermore, we improve
the results by incorporating a bilateral solver layer into our system during
both training and test stages. Experimental results show that our approach
produces better results than the state-of-the-art DL and non-DL methods on
various synthetic and real datasets both visually and numerically.
Training Neural Machine Translation using Word Embedding-based Loss
Authors: Katsuki Chousa, Katsuhito Sudoh, Satoshi Nakamura
Date: 2018-07-30 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation, Word Similarity

In neural machine translation (NMT), the computational cost at the output
layer increases with the size of the target-side vocabulary. Using a
limited-size vocabulary instead may cause a significant decrease in translation
quality. This trade-off is derived from a softmax-based loss function that
handles in-dictionary words independently, in which word similarity is not
considered. In this paper, we propose a novel NMT loss function that includes
word similarity in forms of distances in a word embedding space. The proposed
loss function encourages an NMT decoder to generate words close to their
references in the embedding space; this helps the decoder to choose similar
acceptable words when the actual best candidates are not included in the
vocabulary due to its size limitation. In experiments using ASPEC
Japanese-to-English and IWSLT17 English-to-French data sets, the proposed
method showed improvements against a standard NMT baseline in both datasets;
especially with IWSLT17 En-Fr, it achieved up to +1.72 in BLEU and +1.99 in
METEOR. When the target-side vocabulary was very limited to 1,000 words, the
proposed method demonstrated a substantial gain, +1.72 in METEOR with ASPEC
Ja-En.
CAKE: Compact and Accurate K-dimensional representation of Emotion
Authors: Corentin Kervadec, Valentin Vielzeuf, Stéphane Pateux, Alexis Lechervy, Frédéric Jurie
Date: 2018-07-30 00:00:00
Tasks: Emotion Recognition, Facial Expression Recognition (FER)

Numerous models describing the human emotional states have been built by the
psychology community. Alongside, Deep Neural Networks (DNN) are reaching
excellent performances and are becoming interesting features extraction tools
in many computer vision tasks.Inspired by works from the psychology community,
we first study the link between the compact two-dimensional representation of
the emotion known as arousal-valence, and discrete emotion classes (e.g. anger,
happiness, sadness, etc.) used in the computer vision community. It enables to
assess the benefits -- in terms of discrete emotion inference -- of adding an
extra dimension to arousal-valence (usually named dominance). Building on these
observations, we propose CAKE, a 3-dimensional representation of emotion
learned in a multi-domain fashion, achieving accurate emotion recognition on
several public datasets. Moreover, we visualize how emotions boundaries are
organized inside DNN representations and show that DNNs are implicitly learning
arousal-valence-like descriptions of emotions. Finally, we use the CAKE
representation to compare the quality of the annotations of different public
datasets.
Persistence Atlas for Critical Point Variability in Ensembles
Authors: Guillaume Favelier, Noura Faraj, Brian Summa, Julien Tierny
Date: 2018-07-30 00:00:00
Tasks: Clustering

This paper presents a new approach for the visualization and analysis of the
spatial variability of features of interest represented by critical points in
ensemble data. Our framework, called Persistence Atlas, enables the
visualization of the dominant spatial patterns of critical points, along with
statistics regarding their occurrence in the ensemble. The persistence atlas
represents in the geometrical domain each dominant pattern in the form of a
confidence map for the appearance of critical points. As a by-product, our
method also provides 2-dimensional layouts of the entire ensemble, highlighting
the main trends at a global level. Our approach is based on the new notion of
Persistence Map, a measure of the geometrical density in critical points which
leverages the robustness to noise of topological persistence to better
emphasize salient features. We show how to leverage spectral embedding to
represent the ensemble members as points in a low-dimensional Euclidean space,
where distances between points measure the dissimilarities between critical
point layouts and where statistical tasks, such as clustering, can be easily
carried out. Further, we show how the notion of mandatory critical point can be
leveraged to evaluate for each cluster confidence regions for the appearance of
critical points. Most of the steps of this framework can be trivially
parallelized and we show how to efficiently implement them. Extensive
experiments demonstrate the relevance of our approach. The accuracy of the
confidence regions provided by the persistence atlas is quantitatively
evaluated and compared to a baseline strategy using an off-the-shelf clustering
approach. We illustrate the importance of the persistence atlas in a variety of
real-life datasets, where clear trends in feature layouts are identified and
analyzed.
Hard-Aware Point-to-Set Deep Metric for Person Re-identification
Authors: Rui Yu, Zhiyong Dou, Song Bai, Zhao-Xiang Zhang, Yongchao Xu, Xiang Bai
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Metric Learning, Person Re-Identification, Triplet

Person re-identification (re-ID) is a highly challenging task due to large
variations of pose, viewpoint, illumination, and occlusion. Deep metric
learning provides a satisfactory solution to person re-ID by training a deep
network under supervision of metric loss, e.g., triplet loss. However, the
performance of deep metric learning is greatly limited by traditional sampling
methods. To solve this problem, we propose a Hard-Aware Point-to-Set (HAP2S)
loss with a soft hard-mining scheme. Based on the point-to-set triplet loss
framework, the HAP2S loss adaptively assigns greater weights to harder samples.
Several advantageous properties are observed when compared with other
state-of-the-art loss functions: 1) Accuracy: HAP2S loss consistently achieves
higher re-ID accuracies than other alternatives on three large-scale benchmark
datasets; 2) Robustness: HAP2S loss is more robust to outliers than other
losses; 3) Flexibility: HAP2S loss does not rely on a specific weight function,
i.e., different instantiations of HAP2S loss are equally effective. 4)
Generality: In addition to person re-ID, we apply the proposed method to
generic deep metric learning benchmarks including CUB-200-2011 and Cars196, and
also achieve state-of-the-art results.
Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes
Authors: Xianyan Jia, Shutao Song, wei he, Yangzihao Wang, Haidong Rong, Feihu Zhou, Liqiang Xie, Zhenyu Guo, Yuanzhou Yang, Liwei Yu, Tiegang Chen, Guangxiao Hu, Shaohuai Shi, Xiaowen Chu
Date: 2018-07-30 00:00:00
Tasks: GPU, Playing the Game of 2048

Synchronized stochastic gradient descent (SGD) optimizers with data
parallelism are widely used in training large-scale deep neural networks.
Although using larger mini-batch sizes can improve the system scalability by
reducing the communication-to-computation ratio, it may hurt the generalization
ability of the models. To this end, we build a highly scalable deep learning
training system for dense GPU clusters with three main contributions: (1) We
propose a mixed-precision training method that significantly improves the
training throughput of a single GPU without losing accuracy. (2) We propose an
optimization approach for extremely large mini-batch size (up to 64k) that can
train CNN models on the ImageNet dataset without losing accuracy. (3) We
propose highly optimized all-reduce algorithms that achieve up to 3x and 11x
speedup on AlexNet and ResNet-50 respectively than NCCL-based training on a
cluster with 1024 Tesla P40 GPUs. On training ResNet-50 with 90 epochs, the
state-of-the-art GPU-based system with 1024 Tesla P100 GPUs spent 15 minutes
and achieved 74.9\% top-1 test accuracy, and another KNL-based system with 2048
Intel KNLs spent 20 minutes and achieved 75.4\% accuracy. Our training system
can achieve 75.8\% top-1 test accuracy in only 6.6 minutes using 2048 Tesla P40
GPUs. When training AlexNet with 95 epochs, our system can achieve 58.7\% top-1
test accuracy within 4 minutes, which also outperforms all other existing
systems.
Multi-Fiber Networks for Video Recognition
Authors: Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Classification, Action Recognition, Video Recognition

In this paper, we aim to reduce the computational cost of spatio-temporal
deep neural networks, making them run as fast as their 2D counterparts while
preserving state-of-the-art accuracy on video recognition benchmarks. To this
end, we present the novel Multi-Fiber architecture that slices a complex neural
network into an ensemble of lightweight networks or fibers that run through the
network. To facilitate information flow between fibers we further incorporate
multiplexer modules and end up with an architecture that reduces the
computational cost of 3D networks by an order of magnitude, while increasing
recognition performance at the same time. Extensive experimental results show
that our multi-fiber architecture significantly boosts the efficiency of
existing convolution networks for both image and video recognition tasks,
achieving state-of-the-art performance on UCF-101, HMDB-51 and Kinetics
datasets. Our proposed model requires over 9x and 13x less computations than
the I3D and R(2+1)D models, respectively, yet providing higher accuracy.
End-to-End Deep Kronecker-Product Matching for Person Re-identification
Authors: Yantao Shen, Tong Xiao, Hongsheng Li, Shuai Yi, Xiaogang Wang
Date: 2018-07-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Person Re-Identification

Person re-identification aims to robustly measure similarities between person
images. The significant variation of person poses and viewing angles challenges
for accurate person re-identification. The spatial layout and correspondences
between query person images are vital information for tackling this problem but
are ignored by most state-of-the-art methods. In this paper, we propose a novel
Kronecker Product Matching module to match feature maps of different persons in
an end-to-end trainable deep neural network. A novel feature soft warping
scheme is designed for aligning the feature maps based on matching results,
which is shown to be crucial for achieving superior accuracy. The multi-scale
features based on hourglass-like networks and self-residual attention are also
exploited to further boost the re-identification performance. The proposed
approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and
DukeMTMC datasets, which demonstrates the effectiveness and generalization
ability of our proposed approach.
Deep Group-shuffling Random Walk for Person Re-identification
Authors: Yantao Shen, Hongsheng Li, Tong Xiao, Shuai Yi, Dapeng Chen, Xiaogang Wang
Date: 2018-07-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Person Re-Identification, Retrieval

Person re-identification aims at finding a person of interest in an image
gallery by comparing the probe image of this person with all the gallery
images. It is generally treated as a retrieval problem, where the affinities
between the probe image and gallery images (P2G affinities) are used to rank
the retrieved gallery images. However, most existing methods only consider P2G
affinities but ignore the affinities between all the gallery images (G2G
affinity). Some frameworks incorporated G2G affinities into the testing
process, which is not end-to-end trainable for deep neural networks. In this
paper, we propose a novel group-shuffling random walk network for fully
utilizing the affinity information between gallery images in both the training
and testing processes. The proposed approach aims at end-to-end refining the
P2G affinities based on G2G affinity information with a simple yet effective
matrix operation, which can be integrated into deep neural networks. Feature
grouping and group shuffle are also proposed to apply rich supervisions for
learning better person features. The proposed approach outperforms
state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets by
large margins, which demonstrate the effectiveness of our approach.
Human Motion Analysis with Deep Metric Learning
Authors: Huseyin Coskun, David Joseph Tan, Sailesh Conjeti, Nassir Navab, Federico Tombari
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Dynamic Time Warping, Metric Learning, Retrieval, Triplet

Effectively measuring the similarity between two human motions is necessary
for several computer vision tasks such as gait analysis, person identi-
fication and action retrieval. Nevertheless, we believe that traditional
approaches such as L2 distance or Dynamic Time Warping based on hand-crafted
local pose metrics fail to appropriately capture the semantic relationship
across motions and, as such, are not suitable for being employed as metrics
within these tasks. This work addresses this limitation by means of a
triplet-based deep metric learning specifically tailored to deal with human
motion data, in particular with the prob- lem of varying input size and
computationally expensive hard negative mining due to motion pair alignment.
Specifically, we propose (1) a novel metric learn- ing objective based on a
triplet architecture and Maximum Mean Discrepancy; as well as, (2) a novel deep
architecture based on attentive recurrent neural networks. One benefit of our
objective function is that it enforces a better separation within the learned
embedding space of the different motion categories by means of the associated
distribution moments. At the same time, our attentive recurrent neural network
allows processing varying input sizes to a fixed size of embedding while
learning to focus on those motion parts that are semantically distinctive. Our
ex- periments on two different datasets demonstrate significant improvements
over conventional human motion metrics.
Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots
Authors: Xin Ye, Zhe Lin, Haoxiang Li, Shibin Zheng, Yezhou Yang
Date: 2018-07-30 00:00:00
Tasks: Deep Reinforcement Learning, Object, Object Recognition, Reinforcement Learning, Visual Navigation

We study the problem of learning a navigation policy for a robot to actively
search for an object of interest in an indoor environment solely from its
visual inputs. While scene-driven visual navigation has been widely studied,
prior efforts on learning navigation policies for robots to find objects are
limited. The problem is often more challenging than target scene finding as the
target objects can be very small in the view and can be in an arbitrary pose.
We approach the problem from an active perceiver perspective, and propose a
novel framework that integrates a deep neural network based object recognition
module and a deep reinforcement learning based action prediction mechanism. To
validate our method, we conduct experiments on both a simulation dataset
(AI2-THOR) and a real-world environment with a physical robot. We further
propose a new decaying reward function to learn the control policy specific to
the object searching task. Experimental results validate the efficacy of our
method, which outperforms competing methods in both average trajectory length
and success rate.