Paper Group ANR 582
Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features. Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decomposition. Spectral Methods from Tensor Networks. Confidence Prediction for Lexicon-Free OCR. Evaluation of the visual odometry methods for semi-dense real-time. …
Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features
| Title | Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features |
| Authors | Xiao Song, Xu Zhao, Tianwei Lin |
| Abstract | Robust features are of vital importance to face spoofing detection, because various situations make feature space extremely complicated to partition. Thus in this paper, two novel and robust features for anti-spoofing are proposed. The first one is a binocular camera based depth feature called Template Face Matched Binocular Depth (TFBD) feature. The second one is a high-level micro-texture based feature called Spatial Pyramid Coding Micro-Texture (SPMT) feature. Novel template face registration algorithm and spatial pyramid coding algorithm are also introduced along with the two novel features. Multi-modal face spoofing detection is implemented based on these two robust features. Experiments are conducted on a widely used dataset and a comprehensive dataset constructed by ourselves. The results reveal that face spoofing detection with the fusion of our proposed features is of strong robustness and time efficiency, meanwhile outperforming other state-of-the-art traditional methods. |
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| Published | 2018-03-13 |
| URL | http://arxiv.org/abs/1803.04722v1 |
| http://arxiv.org/pdf/1803.04722v1.pdf | |
| PWC | https://paperswithcode.com/paper/face-spoofing-detection-by-fusing-binocular |
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Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decomposition
| Title | Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decomposition |
| Authors | Jenn-Bing Ong, Wee-Keong Ng, C. -C. Jay Kuo |
| Abstract | Tensor network decomposition, originated from quantum physics to model entangled many-particle quantum systems, turns out to be a promising mathematical technique to efficiently represent and process big data in parsimonious manner. In this study, we show that tensor networks can systematically partition structured data, e.g. color images, for distributed storage and communication in privacy-preserving manner. Leveraging the sea of big data and metadata privacy, empirical results show that neighbouring subtensors with implicit information stored in tensor network formats cannot be identified for data reconstruction. This technique complements the existing encryption and randomization techniques which store explicit data representation at one place and highly susceptible to adversarial attacks such as side-channel attacks and de-anonymization. Furthermore, we propose a theory for adversarial examples that mislead convolutional neural networks to misclassification using subspace analysis based on singular value decomposition (SVD). The theory is extended to analyze higher-order tensors using tensor-train SVD (TT-SVD); it helps to explain the level of susceptibility of different datasets to adversarial attacks, the structural similarity of different adversarial attacks including global and localized attacks, and the efficacy of different adversarial defenses based on input transformation. An efficient and adaptive algorithm based on robust TT-SVD is then developed to detect strong and static adversarial attacks. |
| Tasks | Tensor Networks |
| Published | 2018-11-20 |
| URL | http://arxiv.org/abs/1812.02622v2 |
| http://arxiv.org/pdf/1812.02622v2.pdf | |
| PWC | https://paperswithcode.com/paper/convolutional-neural-networks-with |
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Spectral Methods from Tensor Networks
| Title | Spectral Methods from Tensor Networks |
| Authors | Ankur Moitra, Alexander S. Wein |
| Abstract | A tensor network is a diagram that specifies a way to “multiply” a collection of tensors together to produce another tensor (or matrix). Many existing algorithms for tensor problems (such as tensor decomposition and tensor PCA), although they are not presented this way, can be viewed as spectral methods on matrices built from simple tensor networks. In this work we leverage the full power of this abstraction to design new algorithms for certain continuous tensor decomposition problems. An important and challenging family of tensor problems comes from orbit recovery, a class of inference problems involving group actions (inspired by applications such as cryo-electron microscopy). Orbit recovery problems over finite groups can often be solved via standard tensor methods. However, for infinite groups, no general algorithms are known. We give a new spectral algorithm based on tensor networks for one such problem: continuous multi-reference alignment over the infinite group SO(2). Our algorithm extends to the more general heterogeneous case. |
| Tasks | Tensor Networks |
| Published | 2018-11-02 |
| URL | http://arxiv.org/abs/1811.00944v1 |
| http://arxiv.org/pdf/1811.00944v1.pdf | |
| PWC | https://paperswithcode.com/paper/spectral-methods-from-tensor-networks |
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Confidence Prediction for Lexicon-Free OCR
| Title | Confidence Prediction for Lexicon-Free OCR |
| Authors | Noam Mor, Lior Wolf |
| Abstract | Having a reliable accuracy score is crucial for real world applications of OCR, since such systems are judged by the number of false readings. Lexicon-based OCR systems, which deal with what is essentially a multi-class classification problem, often employ methods explicitly taking into account the lexicon, in order to improve accuracy. However, in lexicon-free scenarios, filtering errors requires an explicit confidence calculation. In this work we show two explicit confidence measurement techniques, and show that they are able to achieve a significant reduction in misreads on both standard benchmarks and a proprietary dataset. |
| Tasks | Optical Character Recognition |
| Published | 2018-05-28 |
| URL | http://arxiv.org/abs/1805.11161v1 |
| http://arxiv.org/pdf/1805.11161v1.pdf | |
| PWC | https://paperswithcode.com/paper/confidence-prediction-for-lexicon-free-ocr |
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Evaluation of the visual odometry methods for semi-dense real-time
| Title | Evaluation of the visual odometry methods for semi-dense real-time |
| Authors | Haidara Gaoussou, Peng Dewei |
| Abstract | Recent decades have witnessed a significant increase in the use of visual odometry(VO) in the computer vision area. It has also been used in varieties of robotic applications, for example on the Mars Exploration Rovers. This paper, firstly, discusses two popular existing visual odometry approaches, namely LSD-SLAM and ORB-SLAM2 to improve the performance metrics of visual SLAM systems using Umeyama Method. We carefully evaluate the methods referred to above on three different well-known KITTI datasets, EuRoC MAV dataset, and TUM RGB-D dataset to obtain the best results and graphically compare the results to evaluation metrics from different visual odometry approaches. Secondly, we propose an approach running in real-time with a stereo camera, which combines an existing feature-based (indirect) method and an existing feature-less (direct) method matching with accurate semidense direct image alignment and reconstructing an accurate 3D environment directly on pixels that have image gradient. Keywords VO, performance metrics, Umeyama Method, feature-based method, feature-less method & semi-dense real-time. |
| Tasks | Visual Odometry |
| Published | 2018-04-10 |
| URL | http://arxiv.org/abs/1804.03558v2 |
| http://arxiv.org/pdf/1804.03558v2.pdf | |
| PWC | https://paperswithcode.com/paper/evaluation-of-the-visual-odometry-methods-for |
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Lower Bounds for Parallel and Randomized Convex Optimization
| Title | Lower Bounds for Parallel and Randomized Convex Optimization |
| Authors | Jelena Diakonikolas, Cristóbal Guzmán |
| Abstract | We study the question of whether parallelization in the exploration of the feasible set can be used to speed up convex optimization, in the local oracle model of computation. We show that the answer is negative for both deterministic and randomized algorithms applied to essentially any of the interesting geometries and nonsmooth, weakly-smooth, or smooth objective functions. In particular, we show that it is not possible to obtain a polylogarithmic (in the sequential complexity of the problem) number of parallel rounds with a polynomial (in the dimension) number of queries per round. In the majority of these settings and when the dimension of the space is polynomial in the inverse target accuracy, our lower bounds match the oracle complexity of sequential convex optimization, up to at most a logarithmic factor in the dimension, which makes them (nearly) tight. Prior to our work, lower bounds for parallel convex optimization algorithms were only known in a small fraction of the settings considered in this paper, mainly applying to Euclidean ($\ell_2$) and $\ell_\infty$ spaces. Our work provides a more general approach for proving lower bounds in the setting of parallel convex optimization. |
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| Published | 2018-11-05 |
| URL | https://arxiv.org/abs/1811.01903v3 |
| https://arxiv.org/pdf/1811.01903v3.pdf | |
| PWC | https://paperswithcode.com/paper/lower-bounds-for-parallel-and-randomized |
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Double Adaptive Stochastic Gradient Optimization
| Title | Double Adaptive Stochastic Gradient Optimization |
| Authors | Kin Gutierrez, Jin Li, Cristian Challu, Artur Dubrawski |
| Abstract | Adaptive moment methods have been remarkably successful in deep learning optimization, particularly in the presence of noisy and/or sparse gradients. We further the advantages of adaptive moment techniques by proposing a family of double adaptive stochastic gradient methods~\textsc{DASGrad}. They leverage the complementary ideas of the adaptive moment algorithms widely used by deep learning community, and recent advances in adaptive probabilistic algorithms.We analyze the theoretical convergence improvements of our approach in a stochastic convex optimization setting, and provide empirical validation of our findings with convex and non convex objectives. We observe that the benefits of~\textsc{DASGrad} increase with the model complexity and variability of the gradients, and we explore the resulting utility in extensions of distribution-matching multitask learning. |
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| Published | 2018-11-06 |
| URL | http://arxiv.org/abs/1811.02525v1 |
| http://arxiv.org/pdf/1811.02525v1.pdf | |
| PWC | https://paperswithcode.com/paper/double-adaptive-stochastic-gradient |
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A Survey on Non-rigid 3D Shape Analysis
| Title | A Survey on Non-rigid 3D Shape Analysis |
| Authors | Hamid Laga |
| Abstract | Shape is an important physical property of natural and manmade 3D objects that characterizes their external appearances. Understanding differences between shapes and modeling the variability within and across shape classes, hereinafter referred to as \emph{shape analysis}, are fundamental problems to many applications, ranging from computer vision and computer graphics to biology and medicine. This chapter provides an overview of some of the recent techniques that studied the shape of 3D objects that undergo non-rigid deformations including bending and stretching. Recent surveys that covered some aspects such classification, retrieval, recognition, and rigid or nonrigid registration, focused on methods that use shape descriptors. Descriptors, however, provide abstract representations that do not enable the exploration of shape variability. In this chapter, we focus on recent techniques that treated the shape of 3D objects as points in some high dimensional space where paths describe deformations. Equipping the space with a suitable metric enables the quantification of the range of deformations of a given shape, which in turn enables (1) comparing and classifying 3D objects based on their shape, (2) computing smooth deformations, i.e. geodesics, between pairs of objects, and (3) modeling and exploring continuous shape variability in a collection of 3D models. This article surveys and classifies recent developments in this field, outlines fundamental issues, discusses their potential applications in computer vision and graphics, and highlights opportunities for future research. Our primary goal is to bridge the gap between various techniques that have been often independently proposed by different communities including mathematics and statistics, computer vision and graphics, and medical image analysis. |
| Tasks | 3D Shape Analysis |
| Published | 2018-12-25 |
| URL | http://arxiv.org/abs/1812.10111v1 |
| http://arxiv.org/pdf/1812.10111v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-survey-on-non-rigid-3d-shape-analysis |
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ChangeMyView Through Concessions: Do Concessions Increase Persuasion?
| Title | ChangeMyView Through Concessions: Do Concessions Increase Persuasion? |
| Authors | Elena Musi, Debanjan Ghosh, Smaranda Muresan |
| Abstract | In discourse studies concessions are considered among those argumentative strategies that increase persuasion. We aim to empirically test this hypothesis by calculating the distribution of argumentative concessions in persuasive vs. non-persuasive comments from the ChangeMyView subreddit. This constitutes a challenging task since concessions are not always part of an argument. Drawing from a theoretically-informed typology of concessions, we conduct an annotation task to label a set of polysemous lexical markers as introducing an argumentative concession or not and we observe their distribution in threads that achieved and did not achieve persuasion. For the annotation, we used both expert and novice annotators. With the ultimate goal of conducting the study on large datasets, we present a self-training method to automatically identify argumentative concessions using linguistically motivated features. We achieve a moderate F1 of 57.4% on the development set and 46.0% on the test set via the self-training method. These results are comparable to state of the art results on similar tasks of identifying explicit discourse connective types from the Penn Discourse Treebank. Our findings from the manual labeling and the classification experiments indicate that the type of argumentative concessions we investigated is almost equally likely to be used in winning and losing arguments from the ChangeMyView dataset. While this result seems to contradict theoretical assumptions, we provide some reasons for this discrepancy related to the ChangeMyView subreddit. |
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| Published | 2018-06-08 |
| URL | http://arxiv.org/abs/1806.03223v1 |
| http://arxiv.org/pdf/1806.03223v1.pdf | |
| PWC | https://paperswithcode.com/paper/changemyview-through-concessions-do |
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The Expressive Power of Parameterized Quantum Circuits
| Title | The Expressive Power of Parameterized Quantum Circuits |
| Authors | Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao |
| Abstract | Parameterized quantum circuits (PQCs) have been broadly used as a hybrid quantum-classical machine learning scheme to accomplish generative tasks. However, whether PQCs have better expressive power than classical generative neural networks, such as restricted or deep Boltzmann machines, remains an open issue. In this paper, we prove that PQCs with a simple structure already outperform any classical neural network for generative tasks, unless the polynomial hierarchy collapses. Our proof builds on known results from tensor networks and quantum circuits (in particular, instantaneous quantum polynomial circuits). In addition, PQCs equipped with ancillary qubits for post-selection have even stronger expressive power than those without post-selection. We employ them as an application for Bayesian learning, since it is possible to learn prior probabilities rather than assuming they are known. We expect that it will find many more applications in semi-supervised learning where prior distributions are normally assumed to be unknown. Lastly, we conduct several numerical experiments using the Rigetti Forest platform to demonstrate the performance of the proposed Bayesian quantum circuit. |
| Tasks | Tensor Networks |
| Published | 2018-10-29 |
| URL | http://arxiv.org/abs/1810.11922v1 |
| http://arxiv.org/pdf/1810.11922v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-expressive-power-of-parameterized-quantum |
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Extracting Parallel Paragraphs from Common Crawl
| Title | Extracting Parallel Paragraphs from Common Crawl |
| Authors | Jakub Kúdela, Irena Holubová, Ondřej Bojar |
| Abstract | Most of the current methods for mining parallel texts from the web assume that web pages of web sites share same structure across languages. We believe that there still exists a non-negligible amount of parallel data spread across sources not satisfying this assumption. We propose an approach based on a combination of bivec (a bilingual extension of word2vec) and locality-sensitive hashing which allows us to efficiently identify pairs of parallel segments located anywhere on pages of a given web domain, regardless their structure. We validate our method on realigning segments from a large parallel corpus. Another experiment with real-world data provided by Common Crawl Foundation confirms that our solution scales to hundreds of terabytes large set of web-crawled data. |
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| Published | 2018-04-27 |
| URL | http://arxiv.org/abs/1804.10413v1 |
| http://arxiv.org/pdf/1804.10413v1.pdf | |
| PWC | https://paperswithcode.com/paper/extracting-parallel-paragraphs-from-common |
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Performance Evaluation of 3D Correspondence Grouping Algorithms
| Title | Performance Evaluation of 3D Correspondence Grouping Algorithms |
| Authors | Jiaqi Yang, Ke Xian, Yang Xiao, Zhiguo Cao |
| Abstract | This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences. A good correspondence grouping algorithm is desired to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall. Towards this rule, we deploy the experiments on three benchmarks respectively addressing shape retrieval, 3D object recognition and point cloud registration scenarios. The variety in application context brings a rich category of nuisances including noise, varying point densities, clutter, occlusion and partial overlaps. It also results to different ratios of inliers and correspondence distributions for comprehensive evaluation. Based on the quantitative outcomes, we give a summarization of the merits/demerits of the evaluated algorithms from both performance and efficiency perspectives. |
| Tasks | 3D Object Recognition, Object Recognition, Point Cloud Registration |
| Published | 2018-04-06 |
| URL | http://arxiv.org/abs/1804.02085v1 |
| http://arxiv.org/pdf/1804.02085v1.pdf | |
| PWC | https://paperswithcode.com/paper/performance-evaluation-of-3d-correspondence |
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Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration
| Title | Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration |
| Authors | Jan Rühaak, Thomas Polzin, Stefan Heldmann, Ivor J. A. Simpson, Heinz Handels, Jan Modersitzki, Mattias P. Heinrich |
| Abstract | We present a novel algorithm for the registration of pulmonary CT scans. Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework. The detection of keypoint correspondences enables robustness against large deformations by jointly optimizing over a large number of potential discrete displacements, whereas the dense continuous registration achieves subvoxel alignment with smooth transformations. Both steps are driven by the same normalized gradient fields data term. We employ curvature regularization and a volume change control mechanism to prevent foldings of the deformation grid and restrict the determinant of the Jacobian to physiologically meaningful values. Keypoint correspondences are integrated into the dense registration by a quadratic penalty with adaptively determined weight. Using a parallel matrix-free derivative calculation scheme, a runtime of about 5 min was realized on a standard PC. The proposed algorithm ranks first in the EMPIRE10 challenge on pulmonary image registration. Moreover, it achieves an average landmark distance of 0.82 mm on the DIR-Lab COPD database, thereby improving upon the state of the art in accuracy by 15%. Our algorithm is the first to reach the inter-observer variability in landmark annotation on this dataset. |
| Tasks | Image Registration |
| Published | 2018-07-02 |
| URL | http://arxiv.org/abs/1807.00467v1 |
| http://arxiv.org/pdf/1807.00467v1.pdf | |
| PWC | https://paperswithcode.com/paper/estimation-of-large-motion-in-lung-ct-by |
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Brain-Inspired Stigmergy Learning
| Title | Brain-Inspired Stigmergy Learning |
| Authors | Xing Hsu, Zhifeng Zhao, Rongpeng Li, Honggang Zhang |
| Abstract | Stigmergy has proved its great superiority in terms of distributed control, robustness and adaptability, thus being regarded as an ideal solution for large-scale swarm control problems. Based on new discoveries on astrocytes in regulating synaptic transmission in the brain, this paper has mapped stigmergy mechanism into the interaction between synapses and investigated its characteristics and advantages. Particularly, we have divided the interaction between synapses which are not directly connected into three phases and proposed a stigmergic learning model. In this model, the state change of a stigmergy agent will expand its influence to affect the states of others. The strength of the interaction is determined by the level of neural activity as well as the distance between stigmergy agents. Inspired by the morphological and functional changes in astrocytes during environmental enrichment, it is likely that the regulation of distance between stigmergy agents plays a critical role in the stigmergy learning process. Simulation results have verified its importance and indicated that the well-regulated distance between stigmergy agents can help to obtain stigmergy learning gain. |
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| Published | 2018-11-20 |
| URL | http://arxiv.org/abs/1811.08210v1 |
| http://arxiv.org/pdf/1811.08210v1.pdf | |
| PWC | https://paperswithcode.com/paper/brain-inspired-stigmergy-learning |
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Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
| Title | Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification |
| Authors | Maayan Frid-Adar, Eyal Klang, Michal Amitai, Jacob Goldberger, Hayit Greenspan |
| Abstract | In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity. |
| Tasks | Computed Tomography (CT), Data Augmentation |
| Published | 2018-01-08 |
| URL | http://arxiv.org/abs/1801.02385v1 |
| http://arxiv.org/pdf/1801.02385v1.pdf | |
| PWC | https://paperswithcode.com/paper/synthetic-data-augmentation-using-gan-for |
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