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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Harley, Adam W. Zuo, Yiming Wen, Jing Mangal, Ayush Potdar, Shubhankar Chaudhry, Ritwick Fragkiadaki, Katerina Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos. The method begins with classic handcrafted techniques for segmenting objects using motion cues: we estimate optical flow and camera motion, and conservatively segment regions that appear to be moving independently of the background. Treating these initial segments as pseudo-labels, we learn an ensemble of appearance-based 2D and 3D detectors, under heavy data augmentation. We use this ensemble to detect new instances of the "moving" type, even if they are not moving, and add these as new pseudo-labels. Our method is an expectation-maximization algorithm, where in the expectation step we fire all modules and look for agreement among them, and in the maximization step we re-train the modules to improve this agreement. The constraint of ensemble agreement helps combat contamination of the generated pseudo-labels (during the E step), and data augmentation helps the modules generalize to yetunlabelled data (during the M step). We compare against existing unsupervised object discovery and tracking methods, using challenging videos from CATER and KITTI, and show strong improvements over the state-of-the-art.

关键词： Training Three-dimensional displays Motion segmentation Detectors Pattern recognition Object tracking expectation-maximization algorithms

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A Novel Data Statistical Analysis Method Based on Hyper-Erlang Distribution

A Novel Data Statistical Analysis Method Based on Hyper-Erla...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Dan Yang Mengjun Li College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Selecting the appropriate probability distribution can be a challenging task when using parametric methods for statistical analysis of specific data. To address this challenge, this paper proposes a method of analyzing the data using the Hyper-Erlang distribution. This distribution is a dense subset of all probability distributions on the non-negative real axis and is both easy to analyze and compute. This paper discusses the dense properties of the Hyper-Erlang distribution and the parameter estimation method based on the expectation maximization algorithm, and verifies the validity of the proposed method using the actual income data.

关键词： Parameter estimation Fitting Big Data Probability distribution Task analysis expectation-maximization algorithms Artificial intelligence

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Adults Use Cross-Situational Statistics for Word Learning in a Conservative Way

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2020年第2期12卷 232-242页

作者： Aussems, Suzanne Vogt, Paul Univ Warwick Dept Psychol Coventry CV4 7AL W Midlands England Tilburg Univ Dept Cognit Sci & Artificial Intelligence NL-5000 Tilburg Netherlands

This paper examined how much adults rely on cross-situational information in word learning by comparing their gaze behavior in a word learning task with models of four learning strategies. We manipulated the input type of situations (consecutive versus interleaved) and the co-occurrence frequencies between words and objects so that adult learners could infer correct word-object mappings based on cross-situational information. There are two key findings. First, an exposure-by-exposure analysis of gaze behavior during the word learning procedure revealed that most participants collected sufficient cross-situational information before they developed a preference for one particular word-object mapping, with consecutive as well as interleaved situations. Second, a classification approach in which individual gaze behavior was attributed to different word learning strategies showed that participants relied mostly on a conservative cross-situational learning (XSL) strategy, compared to Associative XSL, Propose-but-Verify, and Random strategies. Adults relied on Conservative XSL when presented with consecutive and interleaved situations, but they shifted toward Associative XSL when presented with interleaved situations.

关键词： Training Task analysis Object recognition Uncertainty Gaze tracking expectation-maximization algorithms Psychology Adults cross-situational word learning (XSL) expectation-maximization (EM) algorithm eye tracking Propose-but-Verify (PbV)

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Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning

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IEEE ACCESS 2020年 8卷 789-801页

作者： Li, Zhi Yang, Liqun Li, Zhoujun Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China

Privacy has become a major concern in data mining as it is utilized in many important applications. Distributed privacy-preserving data mining (DPPDM) is one of the techniques to address this concern, which focuses on protecting private information of members in distributed systems during data mining. As DPPDM is widely discussed in recent works, the semi-supervised manner of learning still draws less attention in this field. In this paper, a mixture-model-based semi-supervised DPPDM method is proposed. By introducing our method, a site in a distributed system is able to initiate a learning process using labeled data of its own and unlabeled data from all the sites. During the process, no individual data of any site is revealed to others, no information about data can be traced back to any specific site, and only the initiating site learns the result. We propose a parameter-masking privacy-preserving expectation-maximization (EM) algorithm and a mixture-model-based semi-supervised learning algorithm as the two main steps of our method. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed method.

关键词： Data privacy distributed computing expectation-maximization algorithms graph theory semisupervised learning

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Weighted and Class-Specific Maximum Mean Discrepancy for Unsupervised Domain Adaptation

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IEEE TRANSACTIONS ON MULTIMEDIA 2020年第9期22卷 2420-2433页

作者： Yan, Hongliang Li, Zhetao Wang, Qilong Li, Peihua Xu, Yong Zuo, Wangmeng Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Xiangtan Univ Key Lab Hunan Prov Internet Things & Informat Sec Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Coll Informat Engn Xiangtan 411105 Hunan Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Harbin Inst Technol Biocomp Res Ctr Shenzhen 518055 Peoples R China

Although maximum mean discrepancy (MMD) has achieved great success in unsupervised domain adaptation (UDA), most of existing UDA methods ignore the issue of class weight bias across domains, which is ubiquitous and evidently gives rise to the degradation of UDA performance. In this work, we propose two improved MMD metrics, i.e., weighted MMD (WMMD) and class-specific MMD (CMMD), to alleviate the adverse effect caused by the changes of class prior distributions between source and target domains. In WMMD, class-specific auxiliary weights are deployed to reweigh the source samples. In CMMD, we calculate the MMD for each class of source and target samples. Since the class labels of target samples are unknown for UDA problem, we present a classification expectation-maximization algorithm to estimate the pseudo-labels of target samples on the fly and update the model parameters using estimated labels. The proposed methods can be flexibly incorporated into deep convolutional neural networks to form WMMD and CMMD based domain adaptation networks, which we called WDAN and CDAN, respectively. By combining WMMD with CMMD, we present a CWMMD based domain adaptation network (CWDAN) to further improve classification performance. Experiments show that, both WMMD and CMMD benefit the classification accuracy, and our CWDAN can achieve compelling UDA performance in comparison with MMD and the state-of-the-art UDA methods.

关键词： Measurement Adaptation models Airplanes Gallium nitride Task analysis Generative adversarial networks Degradation Image recognition unsupervised domain adaption convolutional neural network expectation-maximization algorithms

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From yield history to productivity zone identification with hidden Markov random fields

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PRECISION AGRICULTURE 2020年第4期21卷 762-781页

作者： Layton, Alex Krogmeier, James V. Ault, Aaron Buckmaster, Dennis R. Purdue Univ Sch Elect & Comp Engn 465 Northwestern Ave W Lafayette IN 47907 USA Purdue Univ Sch Agr & Biol Engn 225 S Univ St W Lafayette IN 47907 USA

Modern precision agriculture equipment enables site-specific management by allowing different treatments for different parts of a field. This ability to subdivide the field calls for identifying management zones. A compromise between treating a field uniformly and treating every plant individually is needed, as the former does not maximize yields and the latter is often impractical. This work presents an algorithm for inferring the yield productivity zones (YPZ) for a field based on yield data from multiple years. The algorithm uses a hidden Markov random field model (HMRF) to find regions of the field which likely correspond to the same underlying yield distribution (i.e., productivity zones). These regions are modeled to be the same every year, but their distributions (i.e., yield characteristics) are allowed to vary with time to account for year-to-year variability (from e.g., weather effects, differing crops or crop varieties). The zone assignments and distributions are estimated using stochastic expectation maximization (SEM) and the maximizer of the posterior marginals (MPM). The underlying assumption of the model and algorithm is that the yields corresponding to a given YPZ will behave similarly and therefore derive from the same probability distribution. YPZs are useful inputs for determining management zones. An advantage of this method is that it is able to run with only the yield data which are automatically collected during harvest. Also, this method requires no crop specific calibration or configuration or normalization of the data by year.

关键词： Hidden Markov models Image segmentation expectation-maximization algorithms Monte Carlo methods Yield Productivity zone Management zone

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Serialized Interacting Mixed Membership Stochastic Block Model

Serialized Interacting Mixed Membership Stochastic Block Mod...

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IEEE International Conference on Data Mining (ICDM)

作者： Gaël Poux-médard Julien Velcin Sabine Loudcher ERIC Lab Université de Lyon Lyon France

Last years have seen a regain of interest for the use of stochastic block modeling (SBM) in recommender systems. These models are seen as a flexible alternative to tensor decomposition techniques that are able to handle labeled data. Recent works proposed to tackle discrete recommendation problems via SBMs by considering larger contexts as input data and by adding second order interactions between contexts’ related elements. In this work, we show that these models are all special cases of a single global framework: the Serialized Interacting Mixed membership Stochastic Block Model (SIMSBM). It allows to model an arbitrarily large context as well as an arbitrarily high order of interactions. We demonstrate that SIMSBM generalizes several recent SBM-based baselines. Besides, we demonstrate that our formulation allows for an increased predictive power on five real-world datasets. 1

关键词： Tensors Systematics Stochastic processes Predictive models Data models Data mining expectation-maximization algorithms

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A Self-limiting Hawkes Process: Interpretation, Estimation, and Use in Crime Modeling 8

A Self-limiting Hawkes Process: Interpretation, Estimation, ...

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8th IEEE International Conference on Big Data (Big Data)

作者： Olinde, Jack Short, Martin B. Georgia Inst Technol Sch Math Atlanta GA 30332 USA

ISBN: (纸本)9781728162515

We introduce a self-limiting Hawkes process, building off of the standard Hawkes process and self-correcting point process in the literature. The self-limiting Hawkes process is intended to model situations in which competing forces - one driven by self-excitation and one by the tendency to block events from occurring - can be effectively captured by considering a single stochastic intensity. The example presented here is urban crime. We show that under some circumstances the parameters of the self-limiting Hawkes process can be well-estimated by adapting existing techniques. We apply these techniques to crime data, showing that the self-limiting Hawkes process is a plausible alternative to the standard Hawkes process for modeling crime.

关键词： Crime expectation-maximization algorithms Hawkes process maximum likelihood estimation stochastic point processes

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Dense Syn-Net: Inter-Modal and Self-Guided Deep Learned PET-MR Reconstruction

Dense Syn-Net: Inter-Modal and Self-Guided Deep Learned PET-...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Guillaume Corda-D’Incan Julia A. Schnabel Andrew J. Reader School of Biomedical Engineering and Imaging Sciences King’s College London UK Technical University of Munich Germany Helmholtz Center Munich Germany

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

We present a new framework for PET-MR reconstruction using deep learning. The proposed network named Dense Syn-Net is a new version of the synergistic network Syn-Net. This method is built on two model-based image reconstruction algorithms, the maximum a posteriori expectation-maximization algorithm for PET and the Landweber algorithm for MR, that we fully connect to each other. To avoid the use of handcrafted regularisations, the gradient of the priors for PET and MR are learned from training data along with regularisation strengths for both modalities. Two major modifications of the original Syn-Net have been introduced: i) iteration-dependent targets are used to ensure that the output of every module matches the corresponding iteration of the reconstruction of high quality data, ii) all the previous PET and MR estimates are used to guide the regularisation in a given module allowing both self and inter-modality guidance. Results on 2D simulated data show that Dense-Syn-Net outperforms conventional independent PET and MR reconstruction algorithms. For MR, our method offers improvements compared to deep learned independent methods and synergistic PET-MR reconstruction with mutually weighted quadratic priors. For PET reconstruction, our network shows greater robustness towards mismatches than MR-guided methods by better preserving modality-unique features. Dense Syn-Net improved global image reconstruction accuracy compared to Syn-Net, however the latter performs better for regions of mismatch. Future work will need to focus on assessing the performance of the network on 3D real data and performing ablation studies to investigate the need of fully connections.

关键词： Deep learning Three-dimensional displays Training data Reconstruction algorithms Robustness expectation-maximization algorithms Image reconstruction

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Multirate Dynamic Process Monitoring Based on Multirate Linear Gaussian State-Space Model

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2019年第4期16卷 1708-1719页

作者： Cong, Ya Zhou, Le Song, Zhihuan Ge, Zhiqiang Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Sci & Technol Sch Automat & Elect Engn Hangzhou 310023 Zhejiang Peoples R China

Multivariate statistical process monitoring (MSPM) has been widely used in modern industries and most of traditional MSPM methods are developed using uniformly sampled measurements. However, process variables are often sampled with different rates in practical industries. On the other hand, most of the industries are dynamic processes in which the measurements are highly autocorrelated. Thus, it is difficult to build a dynamic process model with incomplete data sets in multirate processes. In this paper, a multirate linear Gaussian state-space model is exploited to deal with the above issues. Both the offline model training and online process monitoring schemes are developed in the present of incomplete multirate process data sets. The proposed method is validated through a numerical example and the Tennessee Eastman benchmark process. Note to Practitioners Process monitoring is essential to industrial process maintenance and safety. Typical monitoring methods focus on uniformly sampled variables which are rarely satisfied in practical industries. On the other hand, most industries are dynamic processes which bring more difficulties to process monitoring with multirate variables. This paper proposed a new scheme for multirate dynamic process monitoring based on the multirate linear Gaussian state-space model. To fully implement this approach: 1) the multirate process data need to be well divided into multiblocks. Each of the block should contain at least one variable;2) the amount of latent variable's dimension needs to be properly determined;and 3) the sampling rates of the process cannot be extremely distinguished otherwise will lead to a poor model performance. Two cases studied on both numerical example and Tennessee Eastman benchmark are given to validate the efficiency of the proposed algorithm on multirate dynamic process monitoring.

关键词： State-space methods Kalman filters expectation-maximization algorithms Probabilistic logic State-space methods Dynamic process monitoring expectation maximization (EM) multirate linear Gaussian state-space (MLGSS) model multirate processes monitoring probabilistic modeling

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