pwc-archive/papers-with-abstracts huggingface dataset:
Switch-LSTMs for Multi-Criteria Chinese Word Segmentation
Authors: Jingjing Gong, Xinchi Chen, Tao Gui, Xipeng Qiu
Date: 2018-12-19 00:00:00
Tasks: Chinese Word Segmentation, Segmentation

Multi-criteria Chinese word segmentation is a promising but challenging task,
which exploits several different segmentation criteria and mines their common
underlying knowledge. In this paper, we propose a flexible multi-criteria
learning for Chinese word segmentation. Usually, a segmentation criterion could
be decomposed into multiple sub-criteria, which are shareable with other
segmentation criteria. The process of word segmentation is a routing among
these sub-criteria. From this perspective, we present Switch-LSTMs to segment
words, which consist of several long short-term memory neural networks (LSTM),
and a switcher to automatically switch the routing among these LSTMs. With
these auto-switched LSTMs, our model provides a more flexible solution for
multi-criteria CWS, which is also easy to transfer the learned knowledge to new
criteria. Experiments show that our model obtains significant improvements on
eight corpora with heterogeneous segmentation criteria, compared to the
previous method and single-criterion learning.
Progressive Data Science: Potential and Challenges
Authors: Cagatay Turkay, Nicola Pezzotti, Carsten Binnig, Hendrik Strobelt, Barbara Hammer, Daniel A. Keim, Jean-Daniel Fekete, Themis Palpanas, Yunhai Wang, Florin Rusu
Date: 2018-12-19 00:00:00

Data science requires time-consuming iterative manual activities. In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iterative trial-and-error processes that could be sped-up significantly by providing quick feedback on the impact of changes. The idea of progressive data science is to compute the results of changes in a progressive manner, returning a first approximation of results quickly and allow iterative refinements until converging to a final result. Enabling the user to interact with the intermediate results allows an early detection of erroneous or suboptimal choices, the guided definition of modifications to the pipeline and their quick assessment. In this paper, we discuss the progressiveness challenges arising in different steps of the data science pipeline. We describe how changes in each step of the pipeline impact the subsequent steps and outline why progressive data science will help to make the process more effective. Computing progressive approximations of outcomes resulting from changes creates numerous research challenges, especially if the changes are made in the early steps of the pipeline. We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.
Accurate Hand Keypoint Localization on Mobile Devices
Authors: Filippos Gouidis, Paschalis Panteleris, Iason Oikonomidis, Antonis Argyros
Date: 2018-12-19 00:00:00
Tasks: Computational Efficiency, Hand Keypoint Localization, Keypoint Estimation

We present a novel approach for 2D hand keypoint localization from regular
color input. The proposed approach relies on an appropriately designed
Convolutional Neural Network (CNN) that computes a set of heatmaps, one per
hand keypoint of interest. Extensive experiments with the proposed method
compare it against state of the art approaches and demonstrate its accuracy and
computational performance on standard, publicly available datasets. The
obtained results demonstrate that the proposed method matches or outperforms
the competing methods in accuracy, but clearly outperforms them in
computational efficiency, making it a suitable building block for applications
that require hand keypoint estimation on mobile devices.
Training Deep Neural Networks with 8-bit Floating Point Numbers
Authors: Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan
Date: 2018-12-19 00:00:00
Proceeding: NeurIPS 2018 12

The state-of-the-art hardware platforms for training Deep Neural Networks
(DNNs) are moving from traditional single precision (32-bit) computations
towards 16 bits of precision -- in large part due to the high energy efficiency
and smaller bit storage associated with using reduced-precision
representations. However, unlike inference, training with numbers represented
with less than 16 bits has been challenging due to the need to maintain
fidelity of the gradient computations during back-propagation. Here we
demonstrate, for the first time, the successful training of DNNs using 8-bit
floating point numbers while fully maintaining the accuracy on a spectrum of
Deep Learning models and datasets. In addition to reducing the data and
computation precision to 8 bits, we also successfully reduce the arithmetic
precision for additions (used in partial product accumulation and weight
updates) from 32 bits to 16 bits through the introduction of a number of key
ideas including chunk-based accumulation and floating point stochastic
rounding. The use of these novel techniques lays the foundation for a new
generation of hardware training platforms with the potential for 2-4x improved
throughput over today's systems.
A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction
Authors: Xiaodan Zhang, Xinbo Gao, Wen Lu, Lihuo He
Date: 2018-12-19 00:00:00

Learning fine-grained details is a key issue in image aesthetic assessment. Most of the previous methods extract the fine-grained details via random cropping strategy, which may undermine the integrity of semantic information. Extensive studies show that humans perceive fine-grained details with a mixture of foveal vision and peripheral vision. Fovea has the highest possible visual acuity and is responsible for seeing the details. The peripheral vision is used for perceiving the broad spatial scene and selecting the attended regions for the fovea. Inspired by these observations, we propose a Gated Peripheral-Foveal Convolutional Neural Network (GPF-CNN). It is a dedicated double-subnet neural network, i.e. a peripheral subnet and a foveal subnet. The former aims to mimic the functions of peripheral vision to encode the holistic information and provide the attended regions. The latter aims to extract fine-grained features on these key regions. Considering that the peripheral vision and foveal vision play different roles in processing different visual stimuli, we further employ a gated information fusion (GIF) network to weight their contributions. The weights are determined through the fully connected layers followed by a sigmoid function. We conduct comprehensive experiments on the standard AVA and Photo.net datasets for unified aesthetic prediction tasks: (i) aesthetic quality classification; (ii) aesthetic score regression; and (iii) aesthetic score distribution prediction. The experimental results demonstrate the effectiveness of the proposed method.
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
Authors: Binbin Xu, Wenbin Li, Dimos Tzoumanikas, Michael Bloesch, Andrew Davison, Stefan Leutenegger
Date: 2018-12-19 00:00:00
Tasks: CPU, Instance Segmentation, Object, Object SLAM, Semantic Segmentation, Semantic SLAM

We propose a new multi-instance dynamic RGB-D SLAM system using an
object-level octree-based volumetric representation. It can provide robust
camera tracking in dynamic environments and at the same time, continuously
estimate geometric, semantic, and motion properties for arbitrary objects in
the scene. For each incoming frame, we perform instance segmentation to detect
objects and refine mask boundaries using geometric and motion information.
Meanwhile, we estimate the pose of each existing moving object using an
object-oriented tracking method and robustly track the camera pose against the
static scene. Based on the estimated camera pose and object poses, we associate
segmented masks with existing models and incrementally fuse corresponding
colour, depth, semantic, and foreground object probabilities into each object
model. In contrast to existing approaches, our system is the first system to
generate an object-level dynamic volumetric map from a single RGB-D camera,
which can be used directly for robotic tasks. Our method can run at 2-3 Hz on a
CPU, excluding the instance segmentation part. We demonstrate its effectiveness
by quantitatively and qualitatively testing it on both synthetic and real-world
sequences.
On Lazy Training in Differentiable Programming
Authors: Lenaic Chizat, Edouard Oyallon, Francis Bach
Date: 2018-12-19 00:00:00
Proceeding: NeurIPS 2019 12

In a series of recent theoretical works, it was shown that strongly over-parameterized neural networks trained with gradient-based methods could converge exponentially fast to zero training loss, with their parameters hardly varying. In this work, we show that this "lazy training" phenomenon is not specific to over-parameterized neural networks, and is due to a choice of scaling, often implicit, that makes the model behave as its linearization around the initialization, thus yielding a model equivalent to learning with positive-definite kernels. Through a theoretical analysis, we exhibit various situations where this phenomenon arises in non-convex optimization and we provide bounds on the distance between the lazy and linearized optimization paths. Our numerical experiments bring a critical note, as we observe that the performance of commonly used non-linear deep convolutional neural networks in computer vision degrades when trained in the lazy regime. This makes it unlikely that "lazy training" is behind the many successes of neural networks in difficult high dimensional tasks.
Dynamic Programming Approach to Template-based OCR
Authors: M. A. Povolotskiy, D. V. Tropin
Date: 2018-12-19 00:00:00
Tasks: Optical Character Recognition (OCR), Position

In this paper we propose a dynamic programming solution to the template-based
recognition task in OCR case. We formulate a problem of optimal position search
for complex objects consisting of parts forming a sequence. We limit the
distance between every two adjacent elements with predefined upper and lower
thresholds. We choose the sum of penalties for each part in given position as a
function to be minimized. We show that such a choice of restrictions allows a
faster algorithm to be used than the one for the general form of deformation
penalties. We named this algorithm Dynamic Squeezeboxes Packing (DSP) and
applied it to solve the two OCR problems: text fields extraction from an image
of document Visual Inspection Zone (VIZ) and license plate segmentation. The
quality and the performance of resulting solutions were experimentally proved
to meet the requirements of the state-of-the-art industrial recognition
systems.
An Empirical Study of Generative Models with Encoders
Authors: Paul K. Rubenstein, Yunpeng Li, Dominik Roblek
Date: 2018-12-19 00:00:00

Generative adversarial networks (GANs) are capable of producing high quality
image samples. However, unlike variational autoencoders (VAEs), GANs lack
encoders that provide the inverse mapping for the generators, i.e., encode
images back to the latent space. In this work, we consider adversarially
learned generative models that also have encoders. We evaluate models based on
their ability to produce high quality samples and reconstructions of real
images. Our main contributions are twofold: First, we find that the baseline
Bidirectional GAN (BiGAN) can be improved upon with the addition of an
autoencoder loss, at the expense of an extra hyper-parameter to tune. Second,
we show that comparable performance to BiGAN can be obtained by simply training
an encoder to invert the generator of a normal GAN.
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation
Authors: Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, Pheng-Ann Heng
Date: 2018-12-19 00:00:00
Tasks: Cardiac Segmentation, Domain Adaptation, Medical Image Generation, Medical Image Segmentation, Segmentation

Deep convolutional networks have demonstrated the state-of-the-art
performance on various medical image computing tasks. Leveraging images from
different modalities for the same analysis task holds clinical benefits.
However, the generalization capability of deep models on test data with
different distributions remain as a major challenge. In this paper, we propose
the PnPAdaNet (plug-and-play adversarial domain adaptation network) for
adapting segmentation networks between different modalities of medical images,
e.g., MRI and CT. We propose to tackle the significant domain shift by aligning
the feature spaces of source and target domains in an unsupervised manner.
Specifically, a domain adaptation module flexibly replaces the early encoder
layers of the source network, and the higher layers are shared between domains.
With adversarial learning, we build two discriminators whose inputs are
respectively multi-level features and predicted segmentation masks. We have
validated our domain adaptation method on cardiac structure segmentation in
unpaired MRI and CT. The experimental results with comprehensive ablation
studies demonstrate the excellent efficacy of our proposed PnP-AdaNet.
Moreover, we introduce a novel benchmark on the cardiac dataset for the task of
unsupervised cross-modality domain adaptation. We will make our code and
database publicly available, aiming to promote future studies on this
challenging yet important research topic in medical imaging.
An Empirical Evaluation of Sketched SVD and its Application to Leverage Score Ordering
Authors: Hui Han Chin, Paul Pu Liang
Date: 2018-12-19 00:00:00
Tasks: image-classification, Image Classification, Sentiment Analysis

The power of randomized algorithms in numerical methods have led to fast
solutions which use the Singular Value Decomposition (SVD) as a core routine.
However, given the large data size of modern and the modest runtime of SVD,
most practical algorithms would require some form of approximation, such as
sketching, when running SVD. While these approximation methods satisfy many
theoretical guarantees, we provide the first algorithmic implementations for
sketch-and-solve SVD problems on real-world, large-scale datasets. We provide a
comprehensive empirical evaluation of these algorithms and provide guidelines
on how to ensure accurate deployment to real-world data. As an application of
sketched SVD, we present Sketched Leverage Score Ordering, a technique for
determining the ordering of data in the training of neural networks. Our
technique is based on the distributed computation of leverage scores using
random projections. These computed leverage scores provide a flexible and
efficient method to determine the optimal ordering of training data without
manual intervention or annotations. We present empirical results on an
extensive set of experiments across image classification, language sentiment
analysis, and multi-modal sentiment analysis. Our method is faster compared to
standard randomized projection algorithms and shows improvements in convergence
and results.
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning
Authors: Mirko Polato, Fabio Aiolli
Date: 2018-12-19 00:00:00
Tasks: feature selection, Relational Reasoning

A large body of research is currently investigating on the connection between
machine learning and game theory. In this work, game theory notions are
injected into a preference learning framework. Specifically, a preference
learning problem is seen as a two-players zero-sum game. An algorithm is
proposed to incrementally include new useful features into the hypothesis. This
can be particularly important when dealing with a very large number of
potential features like, for instance, in relational learning and rule
extraction. A game theoretical analysis is used to demonstrate the convergence
of the algorithm. Furthermore, leveraging on the natural analogy between
features and rules, the resulting models can be easily interpreted by humans.
An extensive set of experiments on classification tasks shows the effectiveness
of the proposed method in terms of interpretability and feature selection
quality, with accuracy at the state-of-the-art.
Hierarchical Macro Strategy Model for MOBA Game AI
Authors: Bin Wu, Qiang Fu, Jing Liang, Peng Qu, Xiaoqian Li, Liang Wang, Wei Liu, Wei Yang, Yongsheng Liu
Date: 2018-12-19 00:00:00

The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS
games provide partially observable gaming environments, where agents interact
with one another in an action space much larger than that of GO. Mastering RTS
games requires both strong macro strategies and delicate micro level execution.
Recently, great progress has been made in micro level execution, while complete
solutions for macro strategies are still lacking. In this paper, we propose a
novel learning-based Hierarchical Macro Strategy model for mastering MOBA
games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy
model, agents explicitly make macro strategy decisions and further guide their
micro level execution. Moreover, each of the agents makes independent strategy
decisions, while simultaneously communicating with the allies through
leveraging a novel imitated cross-agent communication mechanism. We perform
comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena
(MOBA) game. Our 5-AI team achieves a 48% winning rate against human player
teams which are ranked top 1% in the player ranking system.
Fast Adjustable Threshold For Uniform Neural Network Quantization (Winning solution of LPIRC-II)
Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev
Date: 2018-12-19 00:00:00
Tasks: Quantization

Neural network quantization procedure is the necessary step for porting of neural networks to mobile devices. Quantization allows accelerating the inference, reducing memory consumption and model size. It can be performed without fine-tuning using calibration procedure (calculation of parameters necessary for quantization), or it is possible to train the network with quantization from scratch. Training with quantization from scratch on the labeled data is rather long and resource-consuming procedure. Quantization of network without fine-tuning leads to accuracy drop because of outliers which appear during the calibration. In this article we suggest to simplify the quantization procedure significantly by introducing the trained scale factors for quantization thresholds. It allows speeding up the process of quantization with fine-tuning up to 8 epochs as well as reducing the requirements to the set of train images. By our knowledge, the proposed method allowed us to get the first public available quantized version of MNAS without significant accuracy reduction - 74.8% vs 75.3% for original full-precision network. Model and code are ready for use and available at: https://github.com/agoncharenko1992/FAT-fast_adjustable_threshold.
Removing rain streaks by a linear model
Authors: Yinglong Wang, Shuaicheng Liu, Bing Zeng
Date: 2018-12-19 00:00:00
Tasks: model, Rain Removal

Removing rain streaks from a single image continues to draw attentions today
in outdoor vision systems. In this paper, we present an efficient method to
remove rain streaks. First, the location map of rain pixels needs to be known
as precisely as possible, to which we implement a relatively accurate detection
of rain streaks by utilizing two characteristics of rain streaks.The key
component of our method is to represent the intensity of each detected rain
pixel using a linear model: $p=\alpha s + \beta$, where $p$ is the observed
intensity of a rain pixel and $s$ represents the intensity of the background
(i.e., before rain-affected). To solve $\alpha$ and $\beta$ for each detected
rain pixel, we concentrate on a window centered around it and form an
$L_2$-norm cost function by considering all detected rain pixels within the
window, where the corresponding rain-removed intensity of each detected rain
pixel is estimated by some neighboring non-rain pixels. By minimizing this cost
function, we determine $\alpha$ and $\beta$ so as to construct the final
rain-removed pixel intensity. Compared with several state-of-the-art works, our
proposed method can remove rain streaks from a single color image much more
efficiently - it offers not only a better visual quality but also a speed-up of
several times to one degree of magnitude.
Deep Global-Relative Networks for End-to-End 6-DoF Visual Localization and Odometry
Authors: Yimin Lin, Zhaoxiang Liu, Jianfeng Huang, Chaopeng Wang, Guoguang Du, Jinqiang Bai, Shiguo Lian, Bill Huang
Date: 2018-12-19 00:00:00
Tasks: Autonomous Navigation, Pose Estimation, Robot Navigation, Visual Localization, Visual Odometry

Although a wide variety of deep neural networks for robust Visual Odometry (VO) can be found in the literature, they are still unable to solve the drift problem in long-term robot navigation. Thus, this paper aims to propose novel deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative and global networks based on Recurrent Convolutional Neural Networks (RCNNs) to improve the monocular localization accuracy. Indeed, the relative sub-networks are implemented to smooth the VO trajectory, while global subnetworks are designed to avoid drift problem. All the parameters are jointly optimized using Cross Transformation Constraints (CTC), which represents temporal geometric consistency of the consecutive frames, and Mean Square Error (MSE) between the predicted pose and ground truth. The experimental results on both indoor and outdoor datasets show that our method outperforms other state-of-the-art learning-based VO methods in terms of pose accuracy.
Crack Detection Using Enhanced Thresholding on UAV based Collected Images
Authors: Q. Zhu, T. H. Dinh, V. T. Hoang, M. D. Phung, Q. P. Ha
Date: 2018-12-19 00:00:00

This paper proposes a thresholding approach for crack detection in an
unmanned aerial vehicle (UAV) based infrastructure inspection system. The
proposed algorithm performs recursively on the intensity histogram of UAV-taken
images to exploit their crack-pixels appearing at the low intensity interval. A
quantified criterion of interclass contrast is proposed and employed as an
object cost and stop condition for the recursive process. Experiments on
different datasets show that our algorithm outperforms different segmentation
approaches to accurately extract crack features of some commercial buildings.
Self-Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers
Authors: Artaches Ambartsoumian, Fred Popowich
Date: 2018-12-19 00:00:00
Proceeding: WS 2018 10
Tasks: Sentiment Analysis

Sentiment Analysis has seen much progress in the past two decades. For the
past few years, neural network approaches, primarily RNNs and CNNs, have been
the most successful for this task. Recently, a new category of neural networks,
self-attention networks (SANs), have been created which utilizes the attention
mechanism as the basic building block. Self-attention networks have been shown
to be effective for sequence modeling tasks, while having no recurrence or
convolutions. In this work we explore the effectiveness of the SANs for
sentiment analysis. We demonstrate that SANs are superior in performance to
their RNN and CNN counterparts by comparing their classification accuracy on
six datasets as well as their model characteristics such as training speed and
memory consumption. Finally, we explore the effects of various SAN
modifications such as multi-head attention as well as two methods of
incorporating sequence position information into SANs.