pwc-archive/papers-with-abstracts huggingface dataset:
Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting
Authors: Kyeong Soo Kim
Date: 2018-10-13 00:00:00
Tasks: Classification, General Classification, Indoor Localization, regression

In this paper, we propose hybrid building/floor classification and
floor-level two-dimensional location coordinates regression using a
single-input and multi-output (SIMO) deep neural network (DNN) for large-scale
indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits
the different nature of the estimation of building/floor and floor-level
location coordinates and uses a different estimation framework for each task
with a dedicated output and hidden layers enabled by SIMO DNN architecture. We
carry out preliminary evaluation of the performance of the hybrid floor
classification and floor-level two-dimensional location coordinates regression
using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere
University of Technology (TUT), Finland, covering a single building with five
floors. Experimental results demonstrate that the proposed SIMO-DNN-based
hybrid classification/regression scheme outperforms existing schemes in terms
of both floor detection rate and mean positioning errors.
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models
Authors: Shudong Hao, Michael J. Paul
Date: 2018-10-13 00:00:00
Proceeding: CL 2020 3
Tasks: Topic Models, Transfer Learning

Probabilistic topic modeling is a popular choice as the first step of crosslingual tasks to enable knowledge transfer and extract multilingual features. While many multilingual topic models have been developed, their assumptions on the training corpus are quite varied, and it is not clear how well the models can be applied under various training conditions. In this paper, we systematically study the knowledge transfer mechanisms behind different multilingual topic models, and through a broad set of experiments with four models on ten languages, we provide empirical insights that can inform the selection and future development of multilingual topic models.
Attention Driven Person Re-identification
Authors: Fan Yang, Ke Yan, Shijian Lu, Huizhu Jia, Xiaodong Xie, Wen Gao
Date: 2018-10-13 00:00:00
Tasks: Person Re-Identification

Person re-identification (ReID) is a challenging task due to arbitrary human
pose variations, background clutters, etc. It has been studied extensively in
recent years, but the multifarious local and global features are still not
fully exploited by either ignoring the interplay between whole-body images and
body-part images or missing in-depth examination of specific body-part images.
In this paper, we propose a novel attention-driven multi-branch network that
learns robust and discriminative human representation from global whole-body
images and local body-part images simultaneously. Within each branch, an
intra-attention network is designed to search for informative and
discriminative regions within the whole-body or body-part images, where
attention is elegantly decomposed into spatial-wise attention and channel-wise
attention for effective and efficient learning. In addition, a novel
inter-attention module is designed which fuses the output of intra-attention
networks adaptively for optimal person ReID. The proposed technique has been
evaluated over three widely used datasets CUHK03, Market-1501 and
DukeMTMC-ReID, and experiments demonstrate its superior robustness and
effectiveness as compared with the state of the arts.
Equivalent Constraints for Two-View Geometry: Pose Solution/Pure Rotation Identification and 3D Reconstruction
Authors: Qi Cai, Yuanxin Wu, Lilian Zhang, Peike Zhang
Date: 2018-10-13 00:00:00
Tasks: 3D Reconstruction, Pose Estimation

Two-view relative pose estimation and structure reconstruction is a classical
problem in computer vision. The typical methods usually employ the singular
value decomposition of the essential matrix to get multiple solutions of the
relative pose, from which the right solution is picked out by reconstructing
the three-dimension (3D) feature points and imposing the constraint of positive
depth. This paper revisits the two-view geometry problem and discovers that the
two-view imaging geometry is equivalently governed by a Pair of new Pose-Only
(PPO) constraints: the same-side constraint and the intersection constraint.
From the perspective of solving equation, the complete pose solutions of the
essential matrix are explicitly derived and we rigorously prove that the
orientation part of the pose can still be recovered in the case of pure
rotation. The PPO constraints are simplified and formulated in the form of
inequalities to directly identify the right pose solution with no need of 3D
reconstruction and the 3D reconstruction can be analytically achieved from the
identified right pose. Furthermore, the intersection inequality also enables a
robust criterion for pure rotation identification. Experiment results validate
the correctness of analyses and the robustness of the derived pose
solution/pure rotation identification and analytical 3D reconstruction.
Exploiting Semantics in Adversarial Training for Image-Level Domain Adaptation
Authors: Pierluigi Zama Ramirez, Alessio Tonioni, Luigi Di Stefano
Date: 2018-10-13 00:00:00
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation, Translation

Performance achievable by modern deep learning approaches are directly
related to the amount of data used at training time. Unfortunately, the
annotation process is notoriously tedious and expensive, especially for
pixel-wise tasks like semantic segmentation. Recent works have proposed to rely
on synthetically generated imagery to ease the training set creation. However,
models trained on these kind of data usually under-perform on real images due
to the well known issue of domain shift. We address this problem by learning a
domain-to-domain image translation GAN to shrink the gap between real and
synthetic images. Peculiarly to our method, we introduce semantic constraints
into the generation process to both avoid artifacts and guide the synthesis. To
prove the effectiveness of our proposal, we show how a semantic segmentation
CNN trained on images from the synthetic GTA dataset adapted by our method can
improve performance by more than 16% mIoU with respect to the same model
trained on synthetic images.
Overview of CAIL2018: Legal Judgment Prediction Competition
Authors: Haoxi Zhong, Chaojun Xiao, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang, Jianfeng Xu
Date: 2018-10-13 00:00:00
Tasks: Articles, Prediction

In this paper, we give an overview of the Legal Judgment Prediction (LJP)
competition at Chinese AI and Law challenge (CAIL2018). This competition
focuses on LJP which aims to predict the judgment results according to the
given facts. Specifically, in CAIL2018 , we proposed three subtasks of LJP for
the contestants, i.e., predicting relevant law articles, charges and prison
terms given the fact descriptions. CAIL2018 has attracted several hundreds
participants (601 teams, 1, 144 contestants from 269 organizations). In this
paper, we provide a detailed overview of the task definition, related works,
outstanding methods and competition results in CAIL2018.
Characterising epithelial tissues using persistent entropy
Authors: N. Atienza, L. M. Escudero, M. J. Jimenez, M. Soriano-Trigueros
Date: 2018-10-13 00:00:00

In this paper, we apply persistent entropy, a novel topological statistic,
for characterization of images of epithelial tissues. We have found out that
persistent entropy is able to summarize topological and geometric information
encoded by \alpha-complexes and persistent homology. After using some
statistical tests, we can guarantee the existence of significant differences in
the studied tissues.
Categorical Aspects of Parameter Learning
Authors: Bart Jacobs
Date: 2018-10-13 00:00:00

Parameter learning is the technique for obtaining the probabilistic
parameters in conditional probability tables in Bayesian networks from tables
with (observed) data --- where it is assumed that the underlying graphical
structure is known. There are basically two ways of doing so, referred to as
maximal likelihood estimation (MLE) and as Bayesian learning. This paper
provides a categorical analysis of these two techniques and describes them in
terms of basic properties of the multiset monad M, the distribution monad D and
the Giry monad G. In essence, learning is about the reltionships between
multisets (used for counting) on the one hand and probability distributions on
the other. These relationsips will be described as suitable natural
transformations.
Efficient Multi-level Correlating for Visual Tracking
Authors: Yipeng Ma, Chun Yuan, Peng Gao, Fei Wang
Date: 2018-10-13 00:00:00
Tasks: Visual Tracking

Correlation filter (CF) based tracking algorithms have demonstrated favorable
performance recently. Nevertheless, the top performance trackers always employ
complicated optimization methods which constraint their real-time applications.
How to accelerate the tracking speed while retaining the tracking accuracy is a
significant issue. In this paper, we propose a multi-level CF-based tracking
approach named MLCFT which further explores the potential capacity of CF with
two-stage detection: primal detection and oriented re-detection. The cascaded
detection scheme is simple but competent to prevent model drift and accelerate
the speed. An effective fusion method based on relative entropy is introduced
to combine the complementary features extracted from deep and shallow layers of
convolutional neural networks (CNN). Moreover, a novel online model update
strategy is utilized in our tracker, which enhances the tracking performance
further. Experimental results demonstrate that our proposed approach
outperforms the most state-of-the-art trackers while tracking at speed of
exceeded 16 frames per second on challenging benchmarks.
Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors
Authors: Zhiwei Li, Huanfeng Shen, Qing Cheng, Yuhao Liu, Shucheng You, Zongyi He
Date: 2018-10-13 00:00:00
Tasks: Cloud Detection

Cloud detection is an important preprocessing step for the precise
application of optical satellite imagery. In this paper, we propose a deep
learning based cloud detection method named multi-scale convolutional feature
fusion (MSCFF) for remote sensing images of different sensors. In the network
architecture of MSCFF, the symmetric encoder-decoder module, which provides
both local and global context by densifying feature maps with trainable
convolutional filter banks, is utilized to extract multi-scale and high-level
spatial features. The feature maps of multiple scales are then up-sampled and
concatenated, and a novel multi-scale feature fusion module is designed to fuse
the features of different scales for the output. The two output feature maps of
the network are cloud and cloud shadow maps, which are in turn fed to binary
classifiers outside the model to obtain the final cloud and cloud shadow mask.
The MSCFF method was validated on hundreds of globally distributed optical
satellite images, with spatial resolutions ranging from 0.5 to 50 m, including
Landsat-5/7/8, Gaofen-1/2/4, Sentinel-2, Ziyuan-3, CBERS-04, Huanjing-1, and
collected high-resolution images exported from Google Earth. The experimental
results show that MSCFF achieves a higher accuracy than the traditional
rule-based cloud detection methods and the state-of-the-art deep learning
models, especially in bright surface covered areas. The effectiveness of MSCFF
means that it has great promise for the practical application of cloud
detection for multiple types of medium and high-resolution remote sensing
images. Our established global high-resolution cloud detection validation
dataset has been made available online.
Point Cloud GAN
Authors: Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabas Poczos, Ruslan Salakhutdinov
Date: 2018-10-13 00:00:00
Tasks: Object Recognition

Generative Adversarial Networks (GAN) can achieve promising performance on
learning complex data distributions on different types of data. In this paper,
we first show a straightforward extension of existing GAN algorithm is not
applicable to point clouds, because the constraint required for discriminators
is undefined for set data. We propose a two fold modification to GAN algorithm
for learning to generate point clouds (PC-GAN). First, we combine ideas from
hierarchical Bayesian modeling and implicit generative models by learning a
hierarchical and interpretable sampling process. A key component of our method
is that we train a posterior inference network for the hidden variables.
Second, instead of using only state-of-the-art Wasserstein GAN objective, we
propose a sandwiching objective, which results in a tighter Wasserstein
distance estimate than the commonly used dual form. Thereby, PC-GAN defines a
generic framework that can incorporate many existing GAN algorithms. We
validate our claims on ModelNet40 benchmark dataset. Using the distance between
generated point clouds and true meshes as metric, we find that PC-GAN trained
by the sandwiching objective achieves better results on test data than the
existing methods. Moreover, as a byproduct, PC- GAN learns versatile latent
representations of point clouds, which can achieve competitive performance with
other unsupervised learning algorithms on object recognition task. Lastly, we
also provide studies on generating unseen classes of objects and transforming
image to point cloud, which demonstrates the compelling generalization
capability and potentials of PC-GAN.
Mixture of Expert/Imitator Networks: Scalable Semi-supervised Learning Framework
Authors: Shun Kiyono, Jun Suzuki, Kentaro Inui
Date: 2018-10-13 00:00:00
Tasks: General Classification, text-classification, Text Classification

The current success of deep neural networks (DNNs) in an increasingly broad
range of tasks involving artificial intelligence strongly depends on the
quality and quantity of labeled training data. In general, the scarcity of
labeled data, which is often observed in many natural language processing
tasks, is one of the most important issues to be addressed. Semi-supervised
learning (SSL) is a promising approach to overcoming this issue by
incorporating a large amount of unlabeled data. In this paper, we propose a
novel scalable method of SSL for text classification tasks. The unique property
of our method, Mixture of Expert/Imitator Networks, is that imitator networks
learn to "imitate" the estimated label distribution of the expert network over
the unlabeled data, which potentially contributes a set of features for the
classification. Our experiments demonstrate that the proposed method
consistently improves the performance of several types of baseline DNNs. We
also demonstrate that our method has the more data, better performance property
with promising scalability to the amount of unlabeled data.
Learning to Globally Edit Images with Textual Description
Authors: Hai Wang, Jason D. Williams, SingBing Kang
Date: 2018-10-13 00:00:00
Tasks: Generative Adversarial Network

We show how we can globally edit images using textual instructions: given a
source image and a textual instruction for the edit, generate a new image
transformed under this instruction. To tackle this novel problem, we develop
three different trainable models based on RNN and Generative Adversarial
Network (GAN). The models (bucket, filter bank, and end-to-end) differ in how
much expert knowledge is encoded, with the most general version being purely
end-to-end. To train these systems, we use Amazon Mechanical Turk to collect
textual descriptions for around 2000 image pairs sampled from several datasets.
Experimental results evaluated on our dataset validate our approaches. In
addition, given that the filter bank model is a good compromise between
generality and performance, we investigate it further by replacing RNN with
Graph RNN, and show that Graph RNN improves performance. To the best of our
knowledge, this is the first computational photography work on global image
editing that is purely based on free-form textual instructions.
Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
Authors: Sorour Mohajerani, Thomas A. Krammer, Parvaneh Saeedi
Date: 2018-10-13 00:00:00
Tasks: Cloud Detection, Deep Learning

This paper presents a deep-learning based framework for addressing the
problem of accurate cloud detection in remote sensing images. This framework
benefits from a Fully Convolutional Neural Network (FCN), which is capable of
pixel-level labeling of cloud regions in a Landsat 8 image. Also, a
gradient-based identification approach is proposed to identify and exclude
regions of snow/ice in the ground truths of the training set. We show that
using the hybrid of the two methods (threshold-based and deep-learning)
improves the performance of the cloud identification process without the need
to manually correct automatically generated ground truths. In average the
Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.
Pose Estimation for Objects with Rotational Symmetry
Authors: Enric Corona, Kaustav Kundu, Sanja Fidler
Date: 2018-10-13 00:00:00
Tasks: Pose Estimation

Pose estimation is a widely explored problem, enabling many robotic tasks
such as grasping and manipulation. In this paper, we tackle the problem of pose
estimation for objects that exhibit rotational symmetry, which are common in
man-made and industrial environments. In particular, our aim is to infer poses
for objects not seen at training time, but for which their 3D CAD models are
available at test time. Previous work has tackled this problem by learning to
compare captured views of real objects with the rendered views of their 3D CAD
models, by embedding them in a joint latent space using neural networks. We
show that sidestepping the issue of symmetry in this scenario during training
leads to poor performance at test time. We propose a model that reasons about
rotational symmetry during training by having access to only a small set of
symmetry-labeled objects, whereby exploiting a large collection of unlabeled
CAD models. We demonstrate that our approach significantly outperforms a
naively trained neural network on a new pose dataset containing images of tools
and hardware.
CPNet: A Context Preserver Convolutional Neural Network for Detecting Shadows in Single RGB Images
Authors: Sorour Mohajerani, Parvaneh Saeedi
Date: 2018-10-13 00:00:00
Tasks: Detecting Shadows

Automatic detection of shadow regions in an image is a difficult task due to
the lack of prior information about the illumination source and the dynamic of
the scene objects. To address this problem, in this paper, a deep-learning
based segmentation method is proposed that identifies shadow regions at the
pixel-level in a single RGB image. We exploit a novel Convolutional Neural
Network (CNN) architecture to identify and extract shadow features in an
end-to-end manner. This network preserves learned contexts during the training
and observes the entire image to detect global and local shadow patterns
simultaneously. The proposed method is evaluated on two publicly available
datasets of SBU and UCF. We have improved the state-of-the-art Balanced Error
Rate (BER) on these datasets by 22\% and 14\%, respectively.
Hierarchical Game-Theoretic Planning for Autonomous Vehicles
Authors: Jaime F. Fisac, Eli Bronstein, Elis Stefansson, Dorsa Sadigh, S. Shankar Sastry, Anca D. Dragan
Date: 2018-10-13 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making, Trajectory Planning

The actions of an autonomous vehicle on the road affect and are affected by
those of other drivers, whether overtaking, negotiating a merge, or avoiding an
accident. This mutual dependence, best captured by dynamic game theory, creates
a strong coupling between the vehicle's planning and its predictions of other
drivers' behavior, and constitutes an open problem with direct implications on
the safety and viability of autonomous driving technology. Unfortunately,
dynamic games are too computationally demanding to meet the real-time
constraints of autonomous driving in its continuous state and action space. In
this paper, we introduce a novel game-theoretic trajectory planning algorithm
for autonomous driving, that enables real-time performance by hierarchically
decomposing the underlying dynamic game into a long-horizon "strategic" game
with simplified dynamics and full information structure, and a short-horizon
"tactical" game with full dynamics and a simplified information structure. The
value of the strategic game is used to guide the tactical planning, implicitly
extending the planning horizon, pushing the local trajectory optimization
closer to global solutions, and, most importantly, quantitatively accounting
for the autonomous vehicle and the human driver's ability and incentives to
influence each other. In addition, our approach admits non-deterministic models
of human decision-making, rather than relying on perfectly rational
predictions. Our results showcase richer, safer, and more effective autonomous
behavior in comparison to existing techniques.
A Model for Auto-Programming for General Purposes
Authors: Juyang Weng
Date: 2018-10-12 00:00:00
Tasks: model

The Universal Turing Machine (TM) is a model for VonNeumann computers ---
general-purpose computers. A human brain can inside-skull-automatically learn a
universal TM so that he acts as a general-purpose computer and writes a
computer program for any practical purposes. It is unknown whether a machine
can accomplish the same. This theoretical work shows how the Developmental
Network (DN) can accomplish this. Unlike a traditional TM, the TM learned by DN
is a super TM --- Grounded, Emergent, Natural, Incremental, Skulled, Attentive,
Motivated, and Abstractive (GENISAMA). A DN is free of any central controller
(e.g., Master Map, convolution, or error back-propagation). Its learning from a
teacher TM is one transition observation at a time, immediate, and error-free
until all its neurons have been initialized by early observed teacher
transitions. From that point on, the DN is no longer error-free but is always
optimal at every time instance in the sense of maximal likelihood, conditioned
on its limited computational resources and the learning experience. This letter
also extends the Church-Turing thesis to automatic programming for general
purposes and sketchily proved it.
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
Authors: Mounica Maddela, Wei Xu
Date: 2018-10-12 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Lexical Simplification

Current lexical simplification approaches rely heavily on heuristics and
corpus level features that do not always align with human judgment. We create a
human-rated word-complexity lexicon of 15,000 English words and propose a novel
neural readability ranking model with a Gaussian-based feature vectorization
layer that utilizes these human ratings to measure the complexity of any given
word or phrase. Our model performs better than the state-of-the-art systems for
different lexical simplification tasks and evaluation datasets. Additionally,
we also produce SimplePPDB++, a lexical resource of over 10 million simplifying
paraphrase rules, by applying our model to the Paraphrase Database (PPDB).