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"Topic": models, code, and papers

Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery

Jan 06, 2021
Rong Wang, Wei Feng, Qianrong Zhang, Feiping Nie, Zhen Wang, Xuelong Li

In recent years, hyperspectral anomaly detection (HAD) has become an active topic and plays a significant role in military and civilian fields. As a classic HAD method, the collaboration representation-based detector (CRD) has attracted extensive attention and in-depth research. Despite the good performance of CRD method, its computational cost is too high for the widely demanded real-time applications. To alleviate this problem, a novel ensemble and random collaborative representation-based detector (ERCRD) is proposed for HAD. This approach comprises two main steps. Firstly, we propose a random background modeling to replace the sliding dual window strategy used in the original CRD method. Secondly, we can obtain multiple detection results through multiple random background modeling, and these results are further refined to final detection result through ensemble learning. Experiments on four real hyperspectral datasets exhibit the accuracy and efficiency of this proposed ERCRD method compared with ten state-of-the-art HAD methods.

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Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage

Oct 31, 2020
Ella Rabinovich, Hila Gonen, Suzanne Stevenson

A large body of research on gender-linked language has established foundations regarding cross-gender differences in lexical, emotional, and topical preferences, along with their sociological underpinnings. We compile a novel, large and diverse corpus of spontaneous linguistic productions annotated with speakers' gender, and perform a first large-scale empirical study of distinctions in the usage of \textit{figurative language} between male and female authors. Our analyses suggest that (1) idiomatic choices reflect gender-specific lexical and semantic preferences in general language, (2) men's and women's idiomatic usages express higher emotion than their literal language, with detectable, albeit more subtle, differences between male and female authors along the dimension of dominance compared to similar distinctions in their literal utterances, and (3) contextual analysis of idiomatic expressions reveals considerable differences, reflecting subtle divergences in usage environments, shaped by cross-gender communication styles and semantic biases.

* COLING'2020, 12 pages 

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Explainable Automated Fact-Checking for Public Health Claims

Oct 19, 2020
Neema Kotonya, Francesca Toni

Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.

* Accepted to EMNLP 2020. 15 pages, 7 figures, 9 tables. The dataset is available at 

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Learning to Reconstruct and Segment 3D Objects

Oct 19, 2020
Bo Yang

To endow machines with the ability to perceive the real-world in a three dimensional representation as we do as humans is a fundamental and long-standing topic in Artificial Intelligence. Given different types of visual inputs such as images or point clouds acquired by 2D/3D sensors, one important goal is to understand the geometric structure and semantics of the 3D environment. Traditional approaches usually leverage hand-crafted features to estimate the shape and semantics of objects or scenes. However, they are difficult to generalize to novel objects and scenarios, and struggle to overcome critical issues caused by visual occlusions. By contrast, we aim to understand scenes and the objects within them by learning general and robust representations using deep neural networks, trained on large-scale real-world 3D data. To achieve these aims, this thesis makes three core contributions from object-level 3D shape estimation from single or multiple views to scene-level semantic understanding.

* DPhil (PhD) Thesis 2020, University of Oxford 

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Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation

Oct 06, 2020
Allan Pinto, Manuel A. Córdova, Luis G. L. Decker, Jose L. Flores-Campana, Marcos R. Souza, Andreza A. dos Santos, Jhonatas S. Conceição, Henrique F. Gagliardi, Diogo C. Luvizon, Ricardo da S. Torres, Helio Pedrini

Stereo vision is a growing topic in computer vision due to the innumerable opportunities and applications this technology offers for the development of modern solutions, such as virtual and augmented reality applications. To enhance the user's experience in three-dimensional virtual environments, the motion parallax estimation is a promising technique to achieve this objective. In this paper, we propose an algorithm for generating parallax motion effects from a single image, taking advantage of state-of-the-art instance segmentation and depth estimation approaches. This work also presents a comparison against such algorithms to investigate the trade-off between efficiency and quality of the parallax motion effects, taking into consideration a multi-task learning network capable of estimating instance segmentation and depth estimation at once. Experimental results and visual quality assessment indicate that the PyD-Net network (depth estimation) combined with Mask R-CNN or FBNet networks (instance segmentation) can produce parallax motion effects with good visual quality.

* 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 1621-1625 
* 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates 

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Ranky : An Approach to Solve Distributed SVD on Large Sparse Matrices

Sep 21, 2020
Resul Tugay, Sule Gunduz Oguducu

Singular Value Decomposition (SVD) is a well studied research topic in many fields and applications from data mining to image processing. Data arising from these applications can be represented as a matrix where it is large and sparse. Most existing algorithms are used to calculate singular values, left and right singular vectors of a large-dense matrix but not large and sparse matrix. Even if they can find SVD of a large matrix, calculation of large-dense matrix has high time complexity due to sequential algorithms. Distributed approaches are proposed for computing SVD of large matrices. However, rank of the matrix is still being a problem when solving SVD with these distributed algorithms. In this paper we propose Ranky, set of methods to solve rank problem on large and sparse matrices in a distributed manner. Experimental results show that the Ranky approach recovers singular values, singular left and right vectors of a given large and sparse matrix with negligible error.

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Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

Sep 06, 2020
Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng

Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature, since MNAR data are ubiquitous in modern recommender systems. Missing-at-random (MAR) data, namely randomized controlled trials (RCTs), are usually required by most previous counterfactual learning methods. However, the execution of RCTs is extraordinarily expensive in practice. To circumvent the use of RCTs, we build an information theoretic counterfactual variational information bottleneck (CVIB), as an alternative for debiasing learning without RCTs. By separating the task-aware mutual information term in the original information bottleneck Lagrangian into factual and counterfactual parts, we derive a contrastive information loss and an additional output confidence penalty, which facilitates balanced learning between the factual and counterfactual domains. Empirical evaluation on real-world datasets shows that our CVIB significantly enhances both shallow and deep models, which sheds light on counterfactual learning in recommendation that goes beyond RCTs.

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Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial

Aug 31, 2020
Jussi Tohka, Mark van Gils

Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., have seen enormously increased interest recently. This development is thanks to the increase in data availability as well as advances in artificial intelligence and machine learning research. Highly promising research examples are published daily. However, at the same time, there are some unrealistic expectations with regards to the requirements for reliable development and objective validation that is needed in healthcare settings. These expectations may lead to unmet schedules and disappointments (or non-uptake) at the end-user side. It is the aim of this tutorial to provide practical guidance on how to assess performance reliably and efficiently and avoid common traps. Instead of giving a list of do's and don't s, this tutorial tries to build a better understanding behind these do's and don't s and presents both the most relevant performance evaluation criteria as well as how to compute them. Along the way, we will indicate common mistakes and provide references discussing various topics more in-depth.

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Partially Conditioned Generative Adversarial Networks

Jul 06, 2020
Francisco J. Ibarrola, Nishant Ravikumar, Alejandro F. Frangi

Generative models are undoubtedly a hot topic in Artificial Intelligence, among which the most common type is Generative Adversarial Networks (GANs). These architectures let one synthesise artificial datasets by implicitly modelling the underlying probability distribution of a real-world training dataset. With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset. From a practical standpoint, however, one might desire to generate data conditioned on partial information. That is, only a subset of the ancillary conditioning variables might be of interest when synthesising data. In this work, we argue that standard Conditional GANs are not suitable for such a task and propose a new Adversarial Network architecture and training strategy to deal with the ensuing problems. Experiments illustrating the value of the proposed approach in digit and face image synthesis under partial conditioning information are presented, showing that the proposed method can effectively outperform the standard approach under these circumstances.

* 10 pages, 9 figures 

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Exemplar Loss for Siamese Network in Visual Tracking

Jun 20, 2020
Shuo Chang, YiFan Zhang, Sai Huang, Yuanyuan Yao, Zhiyong Feng

Visual tracking plays an important role in perception system, which is a crucial part of intelligent transportation. Recently, Siamese network is a hot topic for visual tracking to estimate moving targets' trajectory, due to its superior accuracy and simple framework. In general, Siamese tracking algorithms, supervised by logistic loss and triplet loss, increase the value of inner product between exemplar template and positive sample while reduce the value of inner product with background sample. However, the distractors from different exemplars are not considered by mentioned loss functions, which limit the feature models' discrimination. In this paper, a new exemplar loss integrated with logistic loss is proposed to enhance the feature model's discrimination by reducing inner products among exemplars. Without the bells and whistles, the proposed algorithm outperforms the methods supervised by logistic loss or triplet loss. Numerical results suggest that the newly developed algorithm achieves comparable performance in public benchmarks.

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