pwc-archive/papers-with-abstracts huggingface dataset:
High-frequency crowd insights for public safety and congestion control
Authors: Karthik Nandakumar, Sebastien Blandin, Laura Wynter
Date: 2019-04-23 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We present results from several projects aimed at enabling the real-time
understanding of crowds and their behaviour in the built environment. We make
use of CCTV video cameras that are ubiquitous throughout the developed and
developing world and as such are able to play the role of a reliable sensing
mechanism. We outline the novel methods developed for our crowd insights
engine, and illustrate examples of its use in different contexts in the urban
landscape. Applications of the technology range from maintaining security in
public spaces to quantifying the adequacy of public transport level of service.
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning
Authors: Tianyu Shi, Pin Wang, Xuxin Cheng, Ching-Yao Chan, Ding Huang
Date: 2019-04-23 00:00:00
Tasks: Autonomous Driving, Decision Making, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver. Furthermore, we design two similar Deep Q learning frameworks with quadratic approximator for deciding how to select a comfortable gap and just follow the preceding vehicle. Finally, a polynomial lane change trajectory is generated and Pure Pursuit Control is implemented for path tracking. We demonstrate the effectiveness of this framework in simulation, from both the decision-making and control layers. The proposed architecture also has the potential to be extended to other autonomous driving scenarios.
Link Prediction in Multiplex Networks based on Interlayer Similarity
Authors: Shaghayegh Najari, Mostafa Salehi, Vahid Ranjbar, Mahdi Jalili
Date: 2019-04-23 00:00:00
Tasks: Link Prediction, Prediction

Some networked systems can be better modelled by multilayer structure where
the individual nodes develop relationships in multiple layers. Multilayer
networks with similar nodes across layers are also known as multiplex networks.
This manuscript proposes a novel framework for predicting forthcoming or
missing links in multiplex networks. The link prediction problem in multiplex
networks is how to predict links in one of the layers, taking into account the
structural information of other layers. The proposed link prediction framework
is based on interlayer similarity and proximity-based features extracted from
the layer for which the link prediction is considered. To this end, commonly
used proximity-based features such as Adamic-Adar and Jaccard Coefficient are
considered. These features that have been originally proposed to predict
missing links in monolayer networks, do not require learning, and thus are
simple to compute. The proposed method introduces a systematic approach to take
into account interlayer similarity for the link prediction purpose.
Experimental results on both synthetic and real multiplex networks reveal the
effectiveness of the proposed method and show its superior performance than
state-of-the-art algorithms proposed for the link prediction problem in
multiplex networks.
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
Authors: Jingwen Ye, Yixin Ji, Xinchao Wang, Kairi Ou, Dapeng Tao, Mingli Song
Date: 2019-04-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation, Scene Parsing

In this paper, we investigate a novel deep-model reusing task. Our goal is to
train a lightweight and versatile student model, without human-labelled
annotations, that amalgamates the knowledge and masters the expertise of two
pretrained teacher models working on heterogeneous problems, one on scene
parsing and the other on depth estimation. To this end, we propose an
innovative training strategy that learns the parameters of the student
intertwined with the teachers, achieved by 'projecting' its amalgamated
features onto each teacher's domain and computing the loss. We also introduce
two options to generalize the proposed training strategy to handle three or
more tasks simultaneously. The proposed scheme yields very encouraging results.
As demonstrated on several benchmarks, the trained student model achieves
results even superior to those of the teachers in their own expertise domains
and on par with the state-of-the-art fully supervised models relying on
human-labelled annotations.
T-SVD Based Non-convex Tensor Completion and Robust Principal Component Analysis
Authors: Tao Li, Jinwen Ma
Date: 2019-04-23 00:00:00
Tasks: Denoising, Image Inpainting

Tensor completion and robust principal component analysis have been widely used in machine learning while the key problem relies on the minimization of a tensor rank that is very challenging. A common way to tackle this difficulty is to approximate the tensor rank with the $\ell_1-$norm of singular values based on its Tensor Singular Value Decomposition (T-SVD). Besides, the sparsity of a tensor is also measured by its $\ell_1-$norm. However, the $\ell_1$ penalty is essentially biased and thus the result will deviate. In order to sidestep the bias, we propose a novel non-convex tensor rank surrogate function and a novel non-convex sparsity measure. In this new setting by using the concavity instead of the convexity, a majorization minimization algorithm is further designed for tensor completion and robust principal component analysis. Furthermore, we analyze its theoretical properties. Finally, the experiments on natural and hyperspectral images demonstrate the efficacy and efficiency of our proposed method.
Multi-Task Learning for Argumentation Mining
Authors: Tobias Kahse
Date: 2019-04-23 00:00:00
Tasks: Grapheme-to-Phoneme Conversion, Multi-Task Learning

Multi-task learning has recently become a very active field in deep learning
research. In contrast to learning a single task in isolation, multiple tasks
are learned at the same time, thereby utilizing the training signal of related
tasks to improve the performance on the respective machine learning tasks.
Related work shows various successes in different domains when applying this
paradigm and this thesis extends the existing empirical results by evaluating
multi-task learning in four different scenarios: argumentation mining,
epistemic segmentation, argumentation component segmentation, and
grapheme-to-phoneme conversion. We show that multi-task learning can, indeed,
improve the performance compared to single-task learning in all these
scenarios, but may also hurt the performance. Therefore, we investigate the
reasons for successful and less successful applications of this paradigm and
find that dataset properties such as entropy or the size of the label inventory
are good indicators for a potential multi-task learning success and that
multi-task learning is particularly useful if the task at hand suffers from
data sparsity, i.e. a lack of training data. Moreover, multi-task learning is
particularly effective for long input sequences in our experiments. We have
observed this trend in all evaluated scenarios. Finally, we develop a highly
configurable and extensible sequence tagging framework which supports
multi-task learning to conduct our empirical experiments and to aid future
research regarding the multi-task learning paradigm and natural language
processing.
A Novel Multi-layer Framework for Tiny Obstacle Discovery
Authors: Feng Xue, Anlong Ming, Menghan Zhou, Yu Zhou
Date: 2019-04-23 00:00:00

For tiny obstacle discovery in a monocular image, edge is a fundamental visual element. Nevertheless, because of various reasons, e.g., noise and similar color distribution with background, it is still difficult to detect the edges of tiny obstacles at long distance. In this paper, we propose an obstacle-aware discovery method to recover the missing contours of these obstacles, which helps to obtain obstacle proposals as much as possible. First, by using visual cues in monocular images, several multi-layer regions are elaborately inferred to reveal the distances from the camera. Second, several novel obstacle-aware occlusion edge maps are constructed to well capture the contours of tiny obstacles, which combines cues from each layer. Third, to ensure the existence of the tiny obstacle proposals, the maps from all layers are used for proposals extraction. Finally, based on these proposals containing tiny obstacles, a novel obstacle-aware regressor is proposed to generate an obstacle occupied probability map with high confidence. The convincing experimental results with comparisons on the Lost and Found dataset demonstrate the effectiveness of our approach, achieving around 9.5% improvement on the accuracy than FPHT and PHT, it even gets comparable performance to MergeNet. Moreover, our method outperforms the state-of-the-art algorithms and significantly improves the discovery ability for tiny obstacles at long distance.
Learning Feature Sparse Principal Components
Authors: Lai Tian, Feiping Nie, Xuelong. Li
Date: 2019-04-23 00:00:00
Tasks: feature selection

This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously. Existing optimization methods for FSPCA require data distribution assumptions and are lack of global convergence guarantee. Though the general FSPCA problem is NP-hard, we show that, for a low-rank covariance, FSPCA can be solved globally (Algorithm 1). Then, we propose another strategy (Algorithm 2) to solve FSPCA for the general covariance by iteratively building a carefully designed proxy. We prove theoretical guarantees on approximation and convergence for the new algorithms. Experimental results show the promising performance of the new algorithms compared with the state-of-the-arts on both synthetic and real-world datasets.
Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization
Authors: Shing-Jiuan Liu, Ronald Y. Chang, Feng-Tsun Chien
Date: 2019-04-23 00:00:00
Tasks: Dimensionality Reduction, Indoor Localization

Device-free Wi-Fi indoor localization has received significant attention as a key enabling technology for many Internet of Things (IoT) applications. Machine learning-based location estimators, such as the deep neural network (DNN), carry proven potential in achieving high-precision localization performance by automatically learning discriminative features from the noisy wireless signal measurements. However, the inner workings of DNNs are not transparent and not adequately understood especially in the indoor localization application. In this paper, we provide quantitative and visual explanations for the DNN learning process as well as the critical features that DNN has learned during the process. Toward this end, we propose to use several visualization techniques, including: 1) dimensionality reduction visualization, to project the high-dimensional feature space to the 2D space to facilitate visualization and interpretation, and 2) visual analytics and information visualization, to quantify relative contributions of each feature with the proposed feature manipulation procedures. The results provide insightful views and plausible explanations of the DNN in device-free Wi-Fi indoor localization using channel state information (CSI) fingerprints.
REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments
Authors: Yuankai Qi, Qi Wu, Peter Anderson, Xin Wang, William Yang Wang, Chunhua Shen, Anton Van Den Hengel
Date: 2019-04-23 00:00:00
Proceeding: CVPR 2020 6
Tasks: Referring Expression, Vision and Language Navigation

One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language. Overcoming this challenge requires the ability to perform a wide variety of complex tasks in response to multifarious instructions from humans. In the hope that it might drive progress towards more flexible and powerful human interactions with robots, we propose a dataset of varied and complex robot tasks, described in natural language, in terms of objects visible in a large set of real images. Given an instruction, success requires navigating through a previously-unseen environment to identify an object. This represents a practical challenge, but one that closely reflects one of the core visual problems in robotics. Several state-of-the-art vision-and-language navigation, and referring-expression models are tested to verify the difficulty of this new task, but none of them show promising results because there are many fundamental differences between our task and previous ones. A novel Interactive Navigator-Pointer model is also proposed that provides a strong baseline on the task. The proposed model especially achieves the best performance on the unseen test split, but still leaves substantial room for improvement compared to the human performance.
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification
Authors: Xiang Gao, Wei Hu, Zongming Guo
Date: 2019-04-23 00:00:00
Tasks: General Classification, Graph Learning, Node Classification, valid

Graph Convolutional Neural Networks (GCNNs) are generalizations of CNNs to graph-structured data, in which convolution is guided by the graph topology. In many cases where graphs are unavailable, existing methods manually construct graphs or learn task-driven adaptive graphs. In this paper, we propose Graph Learning Neural Networks (GLNNs), which exploit the optimization of graphs (the adjacency matrix in particular) from both data and tasks. Leveraging on spectral graph theory, we propose the objective of graph learning from a sparsity constraint, properties of a valid adjacency matrix as well as a graph Laplacian regularizer via maximum a posteriori estimation. The optimization objective is then integrated into the loss function of the GCNN, which adapts the graph topology to not only labels of a specific task but also the input data. Experimental results show that our proposed GLNN outperforms state-of-the-art approaches over widely adopted social network datasets and citation network datasets for semi-supervised classification.
Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attention
Authors: John Brandt
Date: 2019-04-23 00:00:00
Tasks: Classification, Crop Classification, General Classification, Management

Land use classification of low resolution spatial imagery is one of the most
extensively researched fields in remote sensing. Despite significant
advancements in satellite technology, high resolution imagery lacks global
coverage and can be prohibitively expensive to procure for extended time
periods. Accurately classifying land use change without high resolution imagery
offers the potential to monitor vital aspects of global development agenda
including climate smart agriculture, drought resistant crops, and sustainable
land management. Utilizing a combination of capsule layers and long-short term
memory layers with distributed attention, the present paper achieves
state-of-the-art accuracy on temporal crop type classification at a 30x30m
resolution with Sentinel 2 imagery.
Siamese Attentional Keypoint Network for High Performance Visual Tracking
Authors: Peng Gao, Ruyue Yuan, Fei Wang, Liyi Xiao, Hamido Fujita, Yan Zhang
Date: 2019-04-23 00:00:00
Tasks: Visual Tracking, Vocal Bursts Intensity Prediction

In this paper, we investigate the impacts of three main aspects of visual tracking, i.e., the backbone network, the attentional mechanism, and the detection component, and propose a Siamese Attentional Keypoint Network, dubbed SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese lightweight hourglass network is specially designed for visual tracking. It takes advantage of the benefits of the repeated bottom-up and top-down inference to capture more global and local contextual information at multiple scales. Secondly, a novel cross-attentional module is utilized to leverage both channel-wise and spatial intermediate attentional information, which can enhance both discriminative and localization capabilities of feature maps. Thirdly, a keypoints detection approach is invented to trace any target object by detecting the top-left corner point, the centroid point, and the bottom-right corner point of its bounding box. Therefore, our SATIN tracker not only has a strong capability to learn more effective object representations, but also is computational and memory storage efficiency, either during the training or testing stages. To the best of our knowledge, we are the first to propose this approach. Without bells and whistles, experimental results demonstrate that our approach achieves state-of-the-art performance on several recent benchmark datasets, at a speed far exceeding 27 frames per second.
Lung Nodule Classification using Deep Local-Global Networks
Authors: Mundher Al-Shabi, Boon Leong Lan, Wai Yee Chan, Kwan-Hoong Ng, Maxine Tan
Date: 2019-04-23 00:00:00
Tasks: Classification, Computed Tomography (CT), General Classification, Lung Nodule Classification, Transfer Learning

Purpose: Lung nodules have very diverse shapes and sizes, which makes
classifying them as benign/malignant a challenging problem. In this paper, we
propose a novel method to predict the malignancy of nodules that have the
capability to analyze the shape and size of a nodule using a global feature
extractor, as well as the density and structure of the nodule using a local
feature extractor. Methods: We propose to use Residual Blocks with a 3x3 kernel
size for local feature extraction, and Non-Local Blocks to extract the global
features. The Non-Local Block has the ability to extract global features
without using a huge number of parameters. The key idea behind the Non-Local
Block is to apply matrix multiplications between features on the same feature
maps. Results: We trained and validated the proposed method on the LIDC-IDRI
dataset which contains 1,018 computed tomography (CT) scans. We followed a
rigorous procedure for experimental setup namely, 10-fold cross-validation and
ignored the nodules that had been annotated by less than 3 radiologists. The
proposed method achieved state-of-the-art results with AUC=95.62%, while
significantly outperforming other baseline methods. Conclusions: Our proposed
Deep Local-Global network has the capability to accurately extract both local
and global features. Our new method outperforms state-of-the-art architecture
including Densenet and Resnet with transfer learning.
Bounds in Query Learning
Authors: Hunter Chase, James Freitag
Date: 2019-04-23 00:00:00

We introduce new combinatorial quantities for concept classes, and prove
lower and upper bounds for learning complexity in several models of query
learning in terms of various combinatorial quantities. Our approach is flexible
and powerful enough to enough to give new and very short proofs of the
efficient learnability of several prominent examples (e.g. regular languages
and regular $\omega$-languages), in some cases also producing new bounds on the
number of queries. In the setting of equivalence plus membership queries, we
give an algorithm which learns a class in polynomially many queries whenever
any such algorithm exists.
  We also study equivalence query learning in a randomized model, producing new
bounds on the expected number of queries required to learn an arbitrary
concept. Many of the techniques and notions of dimension draw inspiration from
or are related to notions from model theory, and these connections are
explained. We also use techniques from query learning to mildly improve a
result of Laskowski regarding compression schemes.
Semi-Cyclic Stochastic Gradient Descent
Authors: Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar
Date: 2019-04-23 00:00:00
Tasks: Federated Learning

We consider convex SGD updates with a block-cyclic structure, i.e. where each
cycle consists of a small number of blocks, each with many samples from a
possibly different, block-specific, distribution. This situation arises, e.g.,
in Federated Learning where the mobile devices available for updates at
different times during the day have different characteristics. We show that
such block-cyclic structure can significantly deteriorate the performance of
SGD, but propose a simple approach that allows prediction with the same
performance guarantees as for i.i.d., non-cyclic, sampling.
Learning Actor Relation Graphs for Group Activity Recognition
Authors: Jianchao Wu, Li-Min Wang, Li Wang, Jie Guo, Gangshan Wu
Date: 2019-04-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Activity Recognition, Group Activity Recognition, Relation

Modeling relation between actors is important for recognizing group activity
in a multi-person scene. This paper aims at learning discriminative relation
between actors efficiently using deep models. To this end, we propose to build
a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture
the appearance and position relation between actors. Thanks to the Graph
Convolutional Network, the connections in ARG could be automatically learned
from group activity videos in an end-to-end manner, and the inference on ARG
could be efficiently performed with standard matrix operations. Furthermore, in
practice, we come up with two variants to sparsify ARG for more effective
modeling in videos: spatially localized ARG and temporal randomized ARG. We
perform extensive experiments on two standard group activity recognition
datasets: the Volleyball dataset and the Collective Activity dataset, where
state-of-the-art performance is achieved on both datasets. We also visualize
the learned actor graphs and relation features, which demonstrate that the
proposed ARG is able to capture the discriminative relation information for
group activity recognition.
Stochastic Primal-Dual Algorithms with Faster Convergence than $O(1/\sqrt{T})$ for Problems without Bilinear Structure
Authors: Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang
Date: 2019-04-23 00:00:00

Previous studies on stochastic primal-dual algorithms for solving min-max problems with faster convergence heavily rely on the bilinear structure of the problem, which restricts their applicability to a narrowed range of problems. The main contribution of this paper is the design and analysis of new stochastic primal-dual algorithms that use a mixture of stochastic gradient updates and a logarithmic number of deterministic dual updates for solving a family of convex-concave problems with no bilinear structure assumed. Faster convergence rates than $O(1/\sqrt{T})$ with $T$ being the number of stochastic gradient updates are established under some mild conditions of involved functions on the primal and the dual variable. For example, for a family of problems that enjoy a weak strong convexity in terms of the primal variable and has a strongly concave function of the dual variable, the convergence rate of the proposed algorithm is $O(1/T)$. We also investigate the effectiveness of the proposed algorithms for learning robust models and empirical AUC maximization.