pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Adapter RGBT Tracking
Authors: Chenglong Li, Andong Lu, Aihua Zheng, Zhengzheng Tu, Jin Tang
Date: 2019-07-17 00:00:00
Tasks: Visual Tracking

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms.
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
Authors: Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, Wieland Brendel
Date: 2019-07-17 00:00:00
Tasks: Autonomous Driving, Benchmarking, Data Augmentation, Instance Segmentation, Object, object-detection, Object Detection, Robust Object Detection

The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. We here provide an easy-to-use benchmark to assess how object detection models perform when image quality degrades. The three resulting benchmark datasets, termed Pascal-C, Coco-C and Cityscapes-C, contain a large variety of image corruptions. We show that a range of standard object detection models suffer a severe performance loss on corrupted images (down to 30--60\% of the original performance). However, a simple data augmentation trick---stylizing the training images---leads to a substantial increase in robustness across corruption type, severity and dataset. We envision our comprehensive benchmark to track future progress towards building robust object detection models. Benchmark, code and data are publicly available.
Remaining Useful Lifetime Prediction via Deep Domain Adaptation
Authors: Paulo R. de O. da Costa, Alp Akcay, Yingqian Zhang, Uzay Kaymak
Date: 2019-07-17 00:00:00
Tasks: Domain Adaptation, Management, Prediction, Time Series, Time Series Analysis

In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven prediction methods assume that training (source) and testing (target) condition monitoring data have similar distributions. However, due to different operating conditions, fault modes, noise and equipment updates distribution shift exists across different data domains. This shift reduces the performance of predictive models previously built to specific conditions when no observed run-to-failure data is available for retraining. To address this issue, this paper proposes a new data-driven approach for domain adaptation in prognostics using Long Short-Term Neural Networks (LSTM). We use a time window approach to extract temporal information from time-series data in a source domain with observed RUL values and a target domain containing only sensor information. We propose a Domain Adversarial Neural Network (DANN) approach to learn domain-invariant features that can be used to predict the RUL in the target domain. The experimental results show that the proposed method can provide more reliable RUL predictions under datasets with different operating conditions and fault modes. These results suggest that the proposed method offers a promising approach to performing domain adaptation in practical PHM applications.
AVDNet: A Small-Sized Vehicle Detection Network for Aerial Visual Data
Authors: Murari Mandal, Manal Shah, Prashant Meena, Sanhita Devi, Santosh Kumar Vipparthi
Date: 2019-07-17 00:00:00
Tasks: vehicle detection

Detection of small-sized targets in aerial views is a challenging task due to the smallness of vehicle size, complex background, and monotonic object appearances. In this letter, we propose a one-stage vehicle detection network (AVDNet) to robustly detect small-sized vehicles in aerial scenes. In AVDNet, we introduced ConvRes residual blocks at multiple scales to alleviate the problem of vanishing features for smaller objects caused because of the inclusion of deeper convolutional layers. These residual blocks, along with enlarged output feature map, ensure the robust representation of the salient features for small sized objects. Furthermore, we proposed a recurrent-feature aware visualization (RFAV) technique to analyze the network behavior. We also created a new airborne image data set (ABD) by annotating 1396 new objects in 79 aerial images for our experiments. The effectiveness of AVDNet is validated on VEDAI, DLR- 3K, DOTA, and the combined (VEDAI, DLR-3K, DOTA, and ABD) data set. Experimental results demonstrate the significant performance improvement of the proposed method over state-of-the-art detection techniques in terms of mAP, computation, and space complexity.
Improving Outbreak Detection with Stacking of Statistical Surveillance Methods
Authors: Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz
Date: 2019-07-17 00:00:00
Tasks: BIG-bench Machine Learning

Epidemiologists use a variety of statistical algorithms for the early detection of outbreaks. The practical usefulness of such methods highly depends on the trade-off between the detection rate of outbreaks and the chances of raising a false alarm. Recent research has shown that the use of machine learning for the fusion of multiple statistical algorithms improves outbreak detection. Instead of relying only on the binary output (alarm or no alarm) of the statistical algorithms, we propose to make use of their p-values for training a fusion classifier. In addition, we also show that adding additional features and adapting the labeling of an epidemic period may further improve performance. For comparison and evaluation, a new measure is introduced which captures the performance of an outbreak detection method with respect to a low rate of false alarms more precisely than previous works. Our results on synthetic data show that it is challenging to improve the performance with a trainable fusion method based on machine learning. In particular, the use of a fusion classifier that is only based on binary outputs of the statistical surveillance methods can make the overall performance worse than directly using the underlying algorithms. However, the use of p-values and additional information for the learning is promising, enabling to identify more valuable patterns to detect outbreaks.
OGNet: Salient Object Detection with Output-guided Attention Module
Authors: Shiping Zhu, Lanyun Zhu
Date: 2019-07-17 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Attention mechanisms are widely used in salient object detection models based on deep learning, which can effectively promote the extraction and utilization of useful information by neural networks. However, most of the existing attention modules used in salient object detection are input with the processed feature map itself, which easily leads to the problem of `blind overconfidence'. In this paper, instead of applying the widely used self-attention module, we present an output-guided attention module built with multi-scale outputs to overcome the problem of `blind overconfidence'. We also construct a new loss function, the intractable area F-measure loss function, which is based on the F-measure of the hard-to-handle area to improve the detection effect of the model in the edge areas and confusing areas of an image. Extensive experiments and abundant ablation studies are conducted to evaluate the effect of our methods and to explore the most suitable structure for the model. Tests on several data sets show that our model performs very well, even though it is very lightweight.
$t$-$k$-means: A Robust and Stable $k$-means Variant
Authors: Yiming Li, Yang Zhang, Qingtao Tang, Weipeng Huang, Yong Jiang, Shu-Tao Xia
Date: 2019-07-17 00:00:00
Tasks: Clustering

$k$-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing. However, due to the thin-tailed property of the Gaussian distribution, $k$-means algorithm suffers from relatively poor performance on the dataset containing heavy-tailed data or outliers. Besides, standard $k$-means algorithm also has relatively weak stability, $i.e.$ its results have a large variance, which reduces its credibility. In this paper, we propose a robust and stable $k$-means variant, dubbed the $t$-$k$-means, as well as its fast version to alleviate those problems. Theoretically, we derive the $t$-$k$-means and analyze its robustness and stability from the aspect of the loss function and the expression of the clustering center, respectively. Extensive experiments are also conducted, which verify the effectiveness and efficiency of the proposed method. The code for reproducing main results is available at \url{https://github.com/THUYimingLi/t-k-means}.
SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking
Authors: Hwaran Lee, Jinsik Lee, Tae-Yoon Kim
Date: 2019-07-17 00:00:00
Proceeding: ACL 2019 7
Tasks: Goal-Oriented Dialog, Multi-domain Dialogue State Tracking

In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn. Previous neural approaches have modeled domain- and slot-dependent belief trackers, and have difficulty in adding new slot-values, resulting in lack of flexibility of domain ontology configurations. In this paper, we propose a new approach to universal and scalable belief tracker, called slot-utterance matching belief tracker (SUMBT). The model learns the relations between domain-slot-types and slot-values appearing in utterances through attention mechanisms based on contextual semantic vectors. Furthermore, the model predicts slot-value labels in a non-parametric way. From our experiments on two dialog corpora, WOZ 2.0 and MultiWOZ, the proposed model showed performance improvement in comparison with slot-dependent methods and achieved the state-of-the-art joint accuracy.
Block based Singular Value Decomposition approach to matrix factorization for recommender systems
Authors: Prasad Bhavana, Vikas Kumar, Vineet Padmanabhan
Date: 2019-07-17 00:00:00
Tasks: GPU, Recommendation Systems

With the abundance of data in recent years, interesting challenges are posed in the area of recommender systems. Producing high quality recommendations with scalability and performance is the need of the hour. Singular Value Decomposition(SVD) based recommendation algorithms have been leveraged to produce better results. In this paper, we extend the SVD technique further for scalability and performance in the context of 1) multi-threading 2) multiple computational units (with the use of Graphical Processing Units) and 3) distributed computation. We propose block based matrix factorization (BMF) paired with SVD. This enabled us to take advantage of SVD over basic matrix factorization(MF) while taking advantage of parallelism and scalability through BMF. We used Compute Unified Device Architecture (CUDA) platform and related hardware for leveraging Graphical Processing Unit (GPU) along with block based SVD to demonstrate the advantages in terms of performance and memory.
Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation
Authors: Risheng Liu, Long Ma, Yuxi Zhang, Xin Fan, Zhongxuan Luo
Date: 2019-07-17 00:00:00
Tasks: Face Detection, Single Image Haze Removal

Enhancing visual qualities for underexposed images is an extensively concerned task that plays important roles in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we in this work develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to solve the underexposed image correction task. More concretely, we propose a new perspective to formulate this task as an energy-inspired model with advanced hybrid priors. A propagation procedure navigated by the hybrid priors is well designed for simultaneously propagating the reflectance and illumination toward desired results. We conduct extensive experiments to verify the necessity of integrating both underlying principles (i.e., with knowledge) and distributions (i.e., from data) as navigated deep propagation. Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments. Additionally, we execute the task of face detection to further verify the naturalness and practical value of underexposed image correction. What's more, we employ our method to single image haze removal whose experimental results further demonstrate its superiorities.
Towards Markerless Grasp Capture
Authors: Samarth Brahmbhatt, Charles C. Kemp, James Hays
Date: 2019-07-17 00:00:00
Tasks: Diversity, Hand Pose Estimation, Object, Pose Estimation

Humans excel at grasping objects and manipulating them. Capturing human grasps is important for understanding grasping behavior and reconstructing it realistically in Virtual Reality (VR). However, grasp capture - capturing the pose of a hand grasping an object, and orienting it w.r.t. the object - is difficult because of the complexity and diversity of the human hand, and occlusion. Reflective markers and magnetic trackers traditionally used to mitigate this difficulty introduce undesirable artifacts in images and can interfere with natural grasping behavior. We present preliminary work on a completely marker-less algorithm for grasp capture from a video depicting a grasp. We show how recent advances in 2D hand pose estimation can be used with well-established optimization techniques. Uniquely, our algorithm can also capture hand-object contact in detail and integrate it in the grasp capture process. This is work in progress, find more details at https://contactdb. cc.gatech.edu/grasp_capture.html.
The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search
Authors: Martin Aumüller, Matteo Ceccarello
Date: 2019-07-17 00:00:00
Tasks: Benchmarking, Diversity

This paper reconsiders common benchmarking approaches to nearest neighbor search. It is shown that the concept of local intrinsic dimensionality (LID) allows to choose query sets of a wide range of difficulty for real-world datasets. Moreover, the effect of different LID distributions on the running time performance of implementations is empirically studied. To this end, different visualization concepts are introduced that allow to get a more fine-grained overview of the inner workings of nearest neighbor search principles. The paper closes with remarks about the diversity of datasets commonly used for nearest neighbor search benchmarking. It is shown that such real-world datasets are not diverse: results on a single dataset predict results on all other datasets well.
Feature Selection via Mutual Information: New Theoretical Insights
Authors: Mario Beraha, Alberto Maria Metelli, Matteo Papini, Andrea Tirinzoni, Marcello Restelli
Date: 2019-07-17 00:00:00
Tasks: feature selection, regression

Mutual information has been successfully adopted in filter feature-selection methods to assess both the relevancy of a subset of features in predicting the target variable and the redundancy with respect to other variables. However, existing algorithms are mostly heuristic and do not offer any guarantee on the proposed solution. In this paper, we provide novel theoretical results showing that conditional mutual information naturally arises when bounding the ideal regression/classification errors achieved by different subsets of features. Leveraging on these insights, we propose a novel stopping condition for backward and forward greedy methods which ensures that the ideal prediction error using the selected feature subset remains bounded by a user-specified threshold. We provide numerical simulations to support our theoretical claims and compare to common heuristic methods.
DeepNC: Deep Generative Network Completion
Authors: Cong Tran, Won-Yong Shin, Andreas Spitz, Michael Gertz
Date: 2019-07-17 00:00:00
Tasks: Link Prediction

Most network data are collected from partially observable networks with both missing nodes and missing edges, for example, due to limited resources and privacy settings specified by users on social media. Thus, it stands to reason that inferring the missing parts of the networks by performing network completion should precede downstream applications. However, despite this need, the recovery of missing nodes and edges in such incomplete networks is an insufficiently explored problem due to the modeling difficulty, which is much more challenging than link prediction that only infers missing edges. In this paper, we present DeepNC, a novel method for inferring the missing parts of a network based on a deep generative model of graphs. Specifically, our method first learns a likelihood over edges via an autoregressive generative model, and then identifies the graph that maximizes the learned likelihood conditioned on the observable graph topology. Moreover, we propose a computationally efficient DeepNC algorithm that consecutively finds individual nodes that maximize the probability in each node generation step, as well as an enhanced version using the expectation-maximization algorithm. The runtime complexities of both algorithms are shown to be almost linear in the number of nodes in the network. We empirically demonstrate the superiority of DeepNC over state-of-the-art network completion approaches.
CLaRO: a Data-driven CNL for Specifying Competency Questions
Authors: C. Maria Keet, Zola Mahlaza, Mary-Jane Antia
Date: 2019-07-17 00:00:00
Tasks: Sentence

Competency Questions (CQs) for an ontology and similar artefacts aim to provide insights into the contents of an ontology and to demarcate its scope. The absence of a controlled natural language, tooling and automation to support the authoring of CQs has hampered their effective use in ontology development and evaluation. The few question templates that exists are based on informal analyses of a small number of CQs and have limited coverage of question types and sentence constructions. We aim to fill this gap by proposing a template-based CNL to author CQs, called CLaRO. For its design, we exploited a new dataset of 234 CQs that had been processed automatically into 106 patterns, which we analysed and used to design a template-based CNL, with an additional CNL model and XML serialisation. The CNL was evaluated with a subset of questions from the original dataset and with two sets of newly sourced CQs. The coverage of CLaRO, with its 93 main templates and 41 linguistic variants, is about 90% for unseen questions. CLaRO has the potential to facilitate streamlining formalising ontology content requirements and, given that about one third of the competency questions in the test sets turned out to be invalid questions, assist in writing good questions.
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
Authors: Erico Tjoa, Cuntai Guan
Date: 2019-07-17 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Survey

Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning. Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the deep learning is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide "obviously" interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that (1) clinicians and practitioners can subsequently approach these methods with caution, (2) insights into interpretability will be born with more considerations for medical practices, and (3) initiatives to push forward data-based, mathematically- and technically-grounded medical education are encouraged.
Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences
Authors: Yen-Hao Huang, Yi-Hsin Chen, Fernando Henrique Calderon Alvarado, Ssu-Rui Lee, Shu-I Wu, Yuwen Lai, Yi-Shin Chen
Date: 2019-07-17 00:00:00

Most previous studies on automatic recognition model for bipolar disorder (BD) were based on both social media and linguistic features. The present study investigates the possibility of adopting only language-based features, namely the syntax and morpheme collocation. We also examine the effect of gender on the results considering gender has long been recognized as an important modulating factor for mental disorders, yet it received little attention in previous linguistic models. The present study collects Twitter posts 3 months prior to the self-disclosure by 349 BD users (231 female, 118 male). We construct a set of syntactic patterns in terms of the word usage based on graph pattern construction and pattern attention mechanism. The factors examined are gender differences, syntactic patterns, and bipolar recognition performance. The performance indicates our F1 scores reach over 91% and outperform several baselines, including those using TF-IDF, LIWC and pre-trained language models (ELMO and BERT). The contributions of the present study are: (1) The features are contextualized, domain-agnostic, and purely linguistic. (2) The performance of BD recognition is improved by gender-enriched linguistic pattern features, which are constructed with gender differences in language usage.