pwc-archive/papers-with-abstracts huggingface dataset:
Cross-lingual Language Model Pretraining
Authors: Guillaume Lample, Alexis Conneau
Date: 2019-01-22 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Language Modeling, Language Modelling, Machine Translation, model, Natural Language Understanding, Translation, Unsupervised Machine Translation

Recent studies have demonstrated the efficiency of generative pretraining for
English natural language understanding. In this work, we extend this approach
to multiple languages and show the effectiveness of cross-lingual pretraining.
We propose two methods to learn cross-lingual language models (XLMs): one
unsupervised that only relies on monolingual data, and one supervised that
leverages parallel data with a new cross-lingual language model objective. We
obtain state-of-the-art results on cross-lingual classification, unsupervised
and supervised machine translation. On XNLI, our approach pushes the state of
the art by an absolute gain of 4.9% accuracy. On unsupervised machine
translation, we obtain 34.3 BLEU on WMT'16 German-English, improving the
previous state of the art by more than 9 BLEU. On supervised machine
translation, we obtain a new state of the art of 38.5 BLEU on WMT'16
Romanian-English, outperforming the previous best approach by more than 4 BLEU.
Our code and pretrained models will be made publicly available.
Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach
Authors: Mingyang Geng, Suning Shang, Bo Ding, Huaimin Wang, Pengfei Zhang, Lei Zhang
Date: 2019-01-22 00:00:00
Tasks: Depth And Camera Motion, Depth Estimation, Image Reconstruction, Motion Estimation, Pose Estimation

The RGB-D camera maintains a limited range for working and is hard to
accurately measure the depth information in a far distance. Besides, the RGB-D
camera will easily be influenced by strong lighting and other external factors,
which will lead to a poor accuracy on the acquired environmental depth
information. Recently, deep learning technologies have achieved great success
in the visual SLAM area, which can directly learn high-level features from the
visual inputs and improve the estimation accuracy of the depth information.
Therefore, deep learning technologies maintain the potential to extend the
source of the depth information and improve the performance of the SLAM system.
However, the existing deep learning-based methods are mainly supervised and
require a large amount of ground-truth depth data, which is hard to acquire
because of the realistic constraints. In this paper, we first present an
unsupervised learning framework, which not only uses image reconstruction for
supervising but also exploits the pose estimation method to enhance the
supervised signal and add training constraints for the task of monocular depth
and camera motion estimation. Furthermore, we successfully exploit our
unsupervised learning framework to assist the traditional ORB-SLAM system when
the initialization module of ORB-SLAM method could not match enough features.
Qualitative and quantitative experiments have shown that our unsupervised
learning framework performs the depth estimation task comparable to the
supervised methods and outperforms the previous state-of-the-art approach by
$13.5\%$ on KITTI dataset. Besides, our unsupervised learning framework could
significantly accelerate the initialization process of ORB-SLAM system and
effectively improve the accuracy on environmental mapping in strong lighting
and weak texture scenes.
Ego-motion Sensor for Unmanned Aerial Vehicles Based on a Single-Board Computer
Authors: Gaël Écorchard, Adam Heinrich, Libor Přeučil
Date: 2019-01-22 00:00:00
Tasks: Optical Flow Estimation

This paper describes the design and implementation of a ground-related
odometry sensor suitable for micro aerial vehicles. The sensor is based on a
ground-facing camera and a single-board Linux-based embedded computer with a
multimedia System on a Chip (SoC). The SoC features a hardware video encoder
which is used to estimate the optical flow online. The optical flow is then
used in combination with a distance sensor to estimate the vehicle's velocity.
The proposed sensor is compared to a similar existing solution and evaluated in
both indoor and outdoor environments.
Minimal penalties and the slope heuristics: a survey
Authors: Sylvain Arlot
Date: 2019-01-22 00:00:00
Tasks: Survey

Birg{\'e} and Massart proposed in 2001 the slope heuristics as a way to choose optimally from data an unknown multiplicative constant in front of a penalty. It is built upon the notion of minimal penalty, and it has been generalized since to some "minimal-penalty algorithms". This paper reviews the theoretical results obtained for such algorithms, with a self-contained proof in the simplest framework, precise proof ideas for further generalizations, and a few new results. Explicit connections are made with residual-variance estimators-with an original contribution on this topic, showing that for this task the slope heuristics performs almost as well as a residual-based estimator with the best model choice-and some classical algorithms such as L-curve or elbow heuristics, Mallows' C p , and Akaike's FPE. Practical issues are also addressed, including two new practical definitions of minimal-penalty algorithms that are compared on synthetic data to previously-proposed definitions. Finally, several conjectures and open problems are suggested as future research directions.
Super-Trajectories: A Compact Yet Rich Video Representation
Authors: Ijaz Akhter, Cheong Loong Fah, Richard Hartley
Date: 2019-01-22 00:00:00
Tasks: Position, Superpixels

We propose a new video representation in terms of an over-segmentation of
dense trajectories covering the whole video. Trajectories are often used to
encode long-temporal information in several computer vision applications.
Similar to temporal superpixels, a temporal slice of super-trajectories are
superpixels, but the later contains more information because it maintains the
long dense pixel-wise tracking information as well. The main challenge in using
trajectories for any application, is the accumulation of tracking error in the
trajectory construction. For our problem, this results in disconnected
superpixels. We exploit constraints for edges in addition to trajectory based
color and position similarity. Analogous to superpixels as a preprocessing tool
for images, the proposed representation has its applications for videos,
especially in trajectory based video analysis.
Multiobjective Coverage Path Planning: Enabling Automated Inspection of Complex, Real-World Structures
Authors: Kai Olav Ellefsen, Herman A. Lepikson, Jan C. Albiez
Date: 2019-01-22 00:00:00

An important open problem in robotic planning is the autonomous generation of
3D inspection paths -- that is, planning the best path to move a robot along in
order to inspect a target structure. We recently suggested a new method for
planning paths allowing the inspection of complex 3D structures, given a
triangular mesh model of the structure. The method differs from previous
approaches in its emphasis on generating and considering also plans that result
in imperfect coverage of the inspection target. In many practical tasks, one
would accept imperfections in coverage if this results in a substantially more
energy efficient inspection path. The key idea is using a multiobjective
evolutionary algorithm to optimize the energy usage and coverage of inspection
plans simultaneously - and the result is a set of plans exploring the different
ways to balance the two objectives. We here test our method on a set of
inspection targets with large variation in size and complexity, and compare its
performance with two state-of-the-art methods for complete coverage path
planning. The results strengthen our confidence in the ability of our method to
generate good inspection plans for different types of targets. The method's
advantage is most clearly seen for real-world inspection targets, since
traditional complete coverage methods have no good way of generating plans for
structures with hidden parts. Multiobjective evolution, by optimizing energy
usage and coverage together ensures a good balance between the two - both when
100% coverage is feasible, and when large parts of the object are hidden.
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
Authors: Xiangxiang Chu, Bo Zhang, Hailong Ma, Ruijun Xu, Qingyuan Li
Date: 2019-01-22 00:00:00
Proceeding: arXiv 2019 1
Tasks: Neural Architecture Search, Reinforcement Learning, Reinforcement Learning (RL), Super-Resolution

Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle super-resolution with a multi-objective approach. We also propose an elastic search tactic at both micro and macro level, based on a hybrid controller that profits from evolutionary computation and reinforcement learning. Quantitative experiments help us to draw a conclusion that our generated models dominate most of the state-of-the-art methods with respect to the individual FLOPS.
Perils of Zero-Interaction Security in the Internet of Things
Authors: Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick
Date: 2019-01-22 00:00:00

The Internet of Things (IoT) demands authentication systems which can provide
both security and usability. Recent research utilizes the rich sensing
capabilities of smart devices to build security schemes operating without human
interaction, such as zero-interaction pairing (ZIP) and zero-interaction
authentication (ZIA). Prior work proposed a number of ZIP and ZIA schemes and
reported promising results. However, those schemes were often evaluated under
conditions which do not reflect realistic IoT scenarios. In addition, drawing
any comparison among the existing schemes is impossible due to the lack of a
common public dataset and unavailability of scheme implementations.
  In this paper, we address these challenges by conducting the first
large-scale comparative study of ZIP and ZIA schemes, carried out under
realistic conditions. We collect and release the most comprehensive dataset in
the domain to date, containing over 4250 hours of audio recordings and 1
billion sensor readings from three different scenarios, and evaluate five
state-of-the-art schemes based on these data. Our study reveals that the
effectiveness of the existing proposals is highly dependent on the scenario
they are used in. In particular, we show that these schemes are subject to
error rates between 0.6% and 52.8%.
RPC: A Large-Scale Retail Product Checkout Dataset
Authors: Xiu-Shen Wei, Quan Cui, Lei Yang, Peng Wang, Lingqiao Liu
Date: 2019-01-22 00:00:00

Over recent years, emerging interest has occurred in integrating computer
vision technology into the retail industry. Automatic checkout (ACO) is one of
the critical problems in this area which aims to automatically generate the
shopping list from the images of the products to purchase. The main challenge
of this problem comes from the large scale and the fine-grained nature of the
product categories as well as the difficulty for collecting training images
that reflect the realistic checkout scenarios due to continuous update of the
products. Despite its significant practical and research value, this problem is
not extensively studied in the computer vision community, largely due to the
lack of a high-quality dataset. To fill this gap, in this work we propose a new
dataset to facilitate relevant research. Our dataset enjoys the following
characteristics: (1) It is by far the largest dataset in terms of both product
image quantity and product categories. (2) It includes single-product images
taken in a controlled environment and multi-product images taken by the
checkout system. (3) It provides different levels of annotations for the
check-out images. Comparing with the existing datasets, ours is closer to the
realistic setting and can derive a variety of research problems. Besides the
dataset, we also benchmark the performance on this dataset with various
approaches. The dataset and related resources can be found at
\url{https://rpc-dataset.github.io/}.
Fast and Robust Shortest Paths on Manifolds Learned from Data
Authors: Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober
Date: 2019-01-22 00:00:00
Tasks: Metric Learning

We propose a fast, simple and robust algorithm for computing shortest paths
and distances on Riemannian manifolds learned from data. This amounts to
solving a system of ordinary differential equations (ODEs) subject to boundary
conditions. Here standard solvers perform poorly because they require
well-behaved Jacobians of the ODE, and usually, manifolds learned from data
imply unstable and ill-conditioned Jacobians. Instead, we propose a fixed-point
iteration scheme for solving the ODE that avoids Jacobians. This enhances the
stability of the solver, while reduces the computational cost. In experiments
involving both Riemannian metric learning and deep generative models we
demonstrate significant improvements in speed and stability over both
general-purpose state-of-the-art solvers as well as over specialized solvers.
DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features
Authors: Rong Kang, Jieqi Shi, Xueming Li, Yang Liu, Xiao Liu
Date: 2019-01-22 00:00:00
Tasks: GPU, Simultaneous Localization and Mapping

As the foundation of driverless vehicle and intelligent robots, Simultaneous
Localization and Mapping(SLAM) has attracted much attention these days.
However, non-geometric modules of traditional SLAM algorithms are limited by
data association tasks and have become a bottleneck preventing the development
of SLAM. To deal with such problems, many researchers seek to Deep Learning for
help. But most of these studies are limited to virtual datasets or specific
environments, and even sacrifice efficiency for accuracy. Thus, they are not
practical enough.
  We propose DF-SLAM system that uses deep local feature descriptors obtained
by the neural network as a substitute for traditional hand-made features.
Experimental results demonstrate its improvements in efficiency and stability.
DF-SLAM outperforms popular traditional SLAM systems in various scenes,
including challenging scenes with intense illumination changes. Its versatility
and mobility fit well into the need for exploring new environments. Since we
adopt a shallow network to extract local descriptors and remain others the same
as original SLAM systems, our DF-SLAM can still run in real-time on GPU.
Unsupervised Automated Event Detection using an Iterative Clustering based Segmentation Approach
Authors: Deepak K. Gupta, Rohit K. Shrivastava, Suhas Phadke, Jeroen Goudswaard
Date: 2019-01-22 00:00:00
Tasks: Clustering, Event Detection, Image Segmentation, Semantic Segmentation

A class of vision problems, less commonly studied, consists of detecting
objects in imagery obtained from physics-based experiments. These objects can
span in 4D (x, y, z, t) and are visible as disturbances (caused due to physical
phenomena) in the image with background distribution being approximately
uniform. Such objects, occasionally referred to as `events', can be considered
as high energy blobs in the image. Unlike the images analyzed in conventional
vision problems, very limited features are associated with such events, and
their shape, size and count can vary significantly. This poses a challenge on
the use of pre-trained models obtained from supervised approaches.
  In this paper, we propose an unsupervised approach involving iterative
clustering based segmentation (ICS) which can detect target objects (events) in
real-time. In this approach, a test image is analyzed over several cycles, and
one event is identified per cycle. Each cycle consists of the following steps:
(1) image segmentation using a modified k-means clustering method, (2)
elimination of empty (with no events) segments based on statistical analysis of
each segment, (3) merging segments that overlap (correspond to same event), and
(4) selecting the strongest event. These four steps are repeated until all the
events have been identified. The ICS approach consists of a few
hyper-parameters that have been chosen based on statistical study performed
over a set of test images. The applicability of ICS method is demonstrated on
several 2D and 3D test examples.
Reducing the Model Variance of a Rectal Cancer Segmentation Network
Authors: Joohyung Lee, Ji Eun Oh, Min Ju Kim, Bo Yun Hur, Dae Kyung Sohn
Date: 2019-01-22 00:00:00
Tasks: Data Augmentation, Image Segmentation, Medical Image Segmentation, Multi-Task Learning, Segmentation, Semantic Segmentation

In preoperative imaging, the demarcation of rectal cancer with magnetic resonance images provides an important basis for cancer staging and treatment planning. Recently, deep learning has greatly improved the state-of-the-art method in automatic segmentation. However, limitations in data availability in the medical field can cause large variance and consequent overfitting to medical image segmentation networks. In this study, we propose methods to reduce the model variance of a rectal cancer segmentation network by adding a rectum segmentation task and performing data augmentation; the geometric correlation between the rectum and rectal cancer motivated the former approach. Moreover, we propose a method to perform a bias-variance analysis within an arbitrary region-of-interest (ROI) of a segmentation network, which we applied to assess the efficacy of our approaches in reducing model variance. As a result, adding a rectum segmentation task reduced the model variance of the rectal cancer segmentation network within tumor regions by a factor of 0.90; data augmentation further reduced the variance by a factor of 0.89. These approaches also reduced the training duration by a factor of 0.96 and a further factor of 0.78, respectively. Our approaches will improve the quality of rectal cancer staging by increasing the accuracy of its automatic demarcation and by providing rectum boundary information since rectal cancer staging requires the demarcation of both rectum and rectal cancer. Besides such clinical benefits, our method also enables segmentation networks to be assessed with bias-variance analysis within an arbitrary ROI, such as a cancerous region.
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Authors: Guang-Neng Hu, Yu Zhang, Qiang Yang
Date: 2019-01-22 00:00:00
Tasks: Collaborative Filtering, Movie Recommendation, Recommendation Systems, Transfer Learning

Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
Authors: Haimeng Zhao, Peiyuan Liao
Date: 2019-01-22 00:00:00
Tasks: Image Compression, MS-SSIM, Neural Architecture Search, SSIM

We introduce ADMM-pruned Compressive AutoEncoder (CAE-ADMM) that uses
Alternative Direction Method of Multipliers (ADMM) to optimize the trade-off
between distortion and efficiency of lossy image compression. Specifically,
ADMM in our method is to promote sparsity to implicitly optimize the bitrate,
different from entropy estimators used in the previous research. The
experiments on public datasets show that our method outperforms the original
CAE and some traditional codecs in terms of SSIM/MS-SSIM metrics, at reasonable
inference speed.
A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go
Authors: Chang-Shing Lee, Mei-Hui Wang, Li-Chuang Chen, Yusuke Nojima, Tzong-Xiang Huang, Jinseok Woo, Naoyuki Kubota, Eri Sato-Shimokawara, Toru Yamaguchi
Date: 2019-01-22 00:00:00
Tasks: Game of Go

This paper applies a genetic algorithm and fuzzy markup language to construct
a human and smart machine cooperative learning system on game of Go. The
genetic fuzzy markup language (GFML)-based Robot Agent can work on various
kinds of robots, including Palro, Pepper, and TMUs robots. We use the
parameters of FAIR open source Darkforest and OpenGo AI bots to construct the
knowledge base of Open Go Darkforest (OGD) cloud platform for student learning
on the Internet. In addition, we adopt the data from AlphaGo Master sixty
online games as the training data to construct the knowledge base and rule base
of the co-learning system. First, the Darkforest predicts the win rate based on
various simulation numbers and matching rates for each game on OGD platform,
then the win rate of OpenGo is as the final desired output. The experimental
results show that the proposed approach can improve knowledge base and rule
base of the prediction ability based on Darkforest and OpenGo AI bot with
various simulation numbers.
Towards Learning to Imitate from a Single Video Demonstration
Authors: Glen Berseth, Florian Golemo, Christopher Pal
Date: 2019-01-22 00:00:00
Tasks: Imitation Learning, One-Shot Learning, Reinforcement Learning, Reinforcement Learning (RL)

Agents that can learn to imitate given video observation -- \emph{without direct access to state or action information} are more applicable to learning in the natural world. However, formulating a reinforcement learning (RL) agent that facilitates this goal remains a significant challenge. We approach this challenge using contrastive training to learn a reward function comparing an agent's behaviour with a single demonstration. We use a Siamese recurrent neural network architecture to learn rewards in space and time between motion clips while training an RL policy to minimize this distance. Through experimentation, we also find that the inclusion of multi-task data and additional image encoding losses improve the temporal consistency of the learned rewards and, as a result, significantly improves policy learning. We demonstrate our approach on simulated humanoid, dog, and raptor agents in 2D and a quadruped and a humanoid in 3D. We show that our method outperforms current state-of-the-art techniques in these environments and can learn to imitate from a single video demonstration.