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学者姓名：黄庆明
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摘要 ：
Organizing webpages into hot topics is one of the key steps to understand the trends from multi-modal web data. To handle this pressing problem, Poisson Deconvolution (PD), a state-of-the-art method, recently is proposed to rank the interestingness of web topics on a similarity graph. Nevertheless, in terms of scalability, PD optimized by expectation-maximization is not sufficiently efficient for a large-scale data set. In this paper, we develop a Stochastic Poisson Deconvolution (SPD) to deal with the large-scale web data sets. Experiments demonstrate the efficacy of the proposed approach in comparison with the state-of-the-art methods on two public data sets and one large-scale synthetic data set. © 2019, Springer Nature Switzerland AG.
关键词 ：
Maximum principle Maximum principle Stochastic systems Stochastic systems
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| GB/T 7714 | Lin, Jinzhong , Pang, Junbiao , Su, Li et al. Accelerating Topic Detection on Web for a Large-Scale Data Set via Stochastic Poisson Deconvolution [C] . 2019 : 590-602 . |
| MLA | Lin, Jinzhong et al. "Accelerating Topic Detection on Web for a Large-Scale Data Set via Stochastic Poisson Deconvolution" . (2019) : 590-602 . |
| APA | Lin, Jinzhong , Pang, Junbiao , Su, Li , Liu, Yugui , Huang, Qingming . Accelerating Topic Detection on Web for a Large-Scale Data Set via Stochastic Poisson Deconvolution . (2019) : 590-602 . |
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摘要 ：
Organizing webpages into interesting topics is one of the key steps to understand the trends from multimodal Web data. The sparse, noisy, and less-constrained user-generated content results in inefficient feature representations. These descriptors unavoidably cause that a detected topic still contains a certain number of the false detected webpages, which further make a topic be less coherent, less interpretable, and less useful. In this paper, we address this problem from a viewpoint interpreting a topic by its prototypes, and present a two-step approach to achieve this goal. Following the detection-by-ranking approach, a sparse Poisson deconvolution is proposed to learn the intratopic similarities between webpages. To find the prototypes, leveraging the intratopic similarities, top-k diverse yet representative prototype webpages are identified from a submodularity function. Experimental results not only show the improved accuracies for the Web topic detection task, but also increase the interpretation of a topic by its prototypes on two public datasets.
关键词 ：
Poisson deconvolution Poisson deconvolution prototype learning (PL) prototype learning (PL) sparsity sparsity submodularity submodularity topic interpretation topic interpretation Web topic detection Web topic detection
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| GB/T 7714 | Pang, Junbiao , Hu, Anjing , Huang, Qingming et al. Increasing Interpretation of Web Topic Detection via Prototype Learning From Sparse Poisson Deconvolution [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2019 , 49 (3) : 1072-1083 . |
| MLA | Pang, Junbiao et al. "Increasing Interpretation of Web Topic Detection via Prototype Learning From Sparse Poisson Deconvolution" . | IEEE TRANSACTIONS ON CYBERNETICS 49 . 3 (2019) : 1072-1083 . |
| APA | Pang, Junbiao , Hu, Anjing , Huang, Qingming , Tian, Qi , Yin, Baocai . Increasing Interpretation of Web Topic Detection via Prototype Learning From Sparse Poisson Deconvolution . | IEEE TRANSACTIONS ON CYBERNETICS , 2019 , 49 (3) , 1072-1083 . |
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摘要 ：
Organizing multimodal Web pages into hot topics is the core step to grasp trends on the Web. However, the less-constrained social media generate noisy user-generated content, which makes a detected topic be less coherent and less interpretable. In this paper, we address this problem by proposing a coupled Poisson deconvolution to jointly handle topic detection and topic description. For the topic detection, the interestingness of a topic is estimated from the similarities refined by the description of topics; for the topic description, the interestingness of topics is leveraged to describe topics. Two processes cyclically detect interesting topics and generate the multimodal description of topics. This is the innovation of this paper, which just likes killing two birds with one stone. Experiments not only show the significantly improved accuracies for the topic detection but also demonstrate the interpretable descriptions for the topic description on two public data sets.
关键词 ：
Multimodal description Multimodal description Poisson deconvolution (PD) Poisson deconvolution (PD) topic coherent topic coherent topic description topic description topic detection on Web topic detection on Web
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| GB/T 7714 | Pang, Junbiao , Tao, Fei , Huang, Qingming et al. Two Birds With One Stone: A Coupled Poisson Deconvolution for Detecting and Describing Topics From Multimodal Web Data [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2019 , 30 (8) : 2397-2409 . |
| MLA | Pang, Junbiao et al. "Two Birds With One Stone: A Coupled Poisson Deconvolution for Detecting and Describing Topics From Multimodal Web Data" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30 . 8 (2019) : 2397-2409 . |
| APA | Pang, Junbiao , Tao, Fei , Huang, Qingming , Tian, Qi , Yin, Baocai . Two Birds With One Stone: A Coupled Poisson Deconvolution for Detecting and Describing Topics From Multimodal Web Data . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2019 , 30 (8) , 2397-2409 . |
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摘要 ：
Bus arrival time prediction intends to improve the level of the services provided by transportation agencies. Intuitively, many stochastic factors affect the predictability of the arrival time, e.g., weather and local events. Moreover, the arrival time prediction for a current station is closely correlated with that of multiple passed stations. Motivated by the observations above, this paper proposes to exploit the long-range dependencies among the multiple time steps for bus arrival prediction via recurrent neural network (RNN). Concretely, RNN with long short-term memory block is used to "correct" the prediction for a station by the correlated multiple passed stations. During the correlation among multiple stations, one-hot coding is introduced to fuse heterogeneous information into a unified vector space. Therefore, the proposed framework leverages the dynamic measurements (i.e., historical trajectory data) and the static observations (i.e., statistics of the infrastructure) for bus arrival time prediction. In order to fairly compare with the state-of-the-art methods, to the best of our knowledge, we have released the largest data set for this task. The experimental results demonstrate the superior performances of our approach on this data set.
关键词 ：
Bus arriving time prediction Bus arriving time prediction heterogenous measurement heterogenous measurement long-range dependencies long-range dependencies multi-step-ahead prediction multi-step-ahead prediction recurrent neural network recurrent neural network
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| GB/T 7714 | Pang, Junbiao , Huang, Jing , Du, Yong et al. Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2019 , 20 (9) : 3283-3293 . |
| MLA | Pang, Junbiao et al. "Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 20 . 9 (2019) : 3283-3293 . |
| APA | Pang, Junbiao , Huang, Jing , Du, Yong , Yu, Haitao , Huang, Qingming , Yin, Baocai . Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2019 , 20 (9) , 3283-3293 . |
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摘要 ：
To quickly grasp what interesting topics are happening on web, it is challenge to discover and describe topics from User-Generated Content (UGC) data. Describing topics by probable keywords and prototype images is an efficient human-machine interaction to help person quickly grasp a topic. However, except for the challenges from web topic detection, mining the multi-media description is a challenge task that the conventional approaches can barely handle: (1) noises from non-informative short texts or images due to less-constrained UGC; and (2) even for these informative images, the gaps between visual concepts and social ones. This paper addresses above challenges from the perspective of background similarity remove, and proposes a two-step approach to mining the multi-media description from noisy data. First, we utilize a devcovolution model to strip the similarities among non-informative words/images during web topic detection. Second, the background-removed similarities are reconstructed to identify the probable keywords and prototype images during topic description. By removing background similarities, we can generate coherent and informative multi-media description for a topic. Experiments show that the proposed method produces a high quality description on two public datasets. (C) 2017 Elsevier B.V. All rights reserved.
关键词 ：
Background similarity Background similarity Multi-modal description Multi-modal description Poisson deconvolution Poisson deconvolution Topic description Topic description Topic detection Topic detection User-Generated Content User-Generated Content
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| GB/T 7714 | Pang, Junbiao , Tao, Fei , Li, Liang et al. A two-step approach to describing web topics via probable keywords and prototype images from background-removed similarities [J]. | NEUROCOMPUTING , 2018 , 275 : 478-487 . |
| MLA | Pang, Junbiao et al. "A two-step approach to describing web topics via probable keywords and prototype images from background-removed similarities" . | NEUROCOMPUTING 275 (2018) : 478-487 . |
| APA | Pang, Junbiao , Tao, Fei , Li, Liang , Huang, Qingming , Yin, Baocai , Tian, Qi . A two-step approach to describing web topics via probable keywords and prototype images from background-removed similarities . | NEUROCOMPUTING , 2018 , 275 , 478-487 . |
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摘要 ：
As increasing volumes of urban data are being available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based policies. In particular, taxi trip is an important urban sensor that provides unprecedented insights into many aspects of a city, from economic activity, human mobility to land development. However, analyzing these data presents many challenges, e.g., sparse data for fine-grained patterns, and the regularity submerged by seemingly random data. Inspired by above challenges, we focus on Pick-Up (PU)/Drop-Off (DO) points from taxi trips, and propose a fine-grained approach to unveil a set of low spatio-temporal patterns from the regularity-discovered intensity. The proposed method is conceptually simple yet efficient, by leveraging point process to handle sparsity of points, and by decomposing point intensities into the low-rank regularity and the factorized basis patterns, our approach enables domain experts to discover patterns that are previously unattainable for them, from a case study motivated by traffic engineers.
关键词 ：
fine-grained pattern fine-grained pattern low-rank regularity low-rank regularity matrix factorization matrix factorization point process point process Spatio-temporal pattern Spatio-temporal pattern taxis trip taxis trip
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| GB/T 7714 | Pang, Junbiao , Huang, Jing , Yang, Xue et al. Discovering Fine-Grained Spatial Pattern From Taxi Trips: Where Point Process Meets Matrix Decomposition and Factorization [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2018 , 19 (10) : 3208-3219 . |
| MLA | Pang, Junbiao et al. "Discovering Fine-Grained Spatial Pattern From Taxi Trips: Where Point Process Meets Matrix Decomposition and Factorization" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 19 . 10 (2018) : 3208-3219 . |
| APA | Pang, Junbiao , Huang, Jing , Yang, Xue , Wang, Zuyun , Yu, Haitao , Huang, Qingming et al. Discovering Fine-Grained Spatial Pattern From Taxi Trips: Where Point Process Meets Matrix Decomposition and Factorization . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2018 , 19 (10) , 3208-3219 . |
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摘要 ：
Recently top performing cross-media topic detection employs Similarity Diffusion Process (SDP) to rank the interestingness of topics from a large number of candidates. SDP models the polysemous phenomenon from short and less-constrained user-generated data by assuming the similarities between two multi-media data should be divided into intersected topics. The noise in SDP plays an important role to explain the generation of the similarity. However, it is unclear what kind of noise is more appropriate for different modalities in cross media: SDP under different noises should has the lower false positives when topics are successfully detected. In this paper, we provide an in depth analysis of two types of noises (Poisson and Gaussian) for this task. In the evaluation, we observe that the combination of Poisson noise and topic sizes performs best while Gaussian noise has a faster optimization speed than that of Poisson one.
关键词 ：
Deconvolution Deconvolution Gaussian noise Gaussian noise Poisson noise Poisson noise Similarity Diffusion Process Similarity Diffusion Process Unsupervised ranking Unsupervised ranking
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| GB/T 7714 | Pang, Junbiao , Huang, Jing , Zhang, Weigang et al. Justify role of Similarity Diffusion Process in cross-media topic ranking: an empirical evaluation [J]. | MULTIMEDIA TOOLS AND APPLICATIONS , 2017 , 76 (23) : 25145-25157 . |
| MLA | Pang, Junbiao et al. "Justify role of Similarity Diffusion Process in cross-media topic ranking: an empirical evaluation" . | MULTIMEDIA TOOLS AND APPLICATIONS 76 . 23 (2017) : 25145-25157 . |
| APA | Pang, Junbiao , Huang, Jing , Zhang, Weigang , Huang, Qingming , Yin, Baocai . Justify role of Similarity Diffusion Process in cross-media topic ranking: an empirical evaluation . | MULTIMEDIA TOOLS AND APPLICATIONS , 2017 , 76 (23) , 25145-25157 . |
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摘要 ：
Inspired by the photometric invariance of color space, this paper proposes a simple yet powerful descriptor for object detection and recognition, called Rotative Maximal Pattern (RMP). The effectiveness of RMP comes from the two components: Rotatable Couple Templates (RCTs) with max pooling, and Normalized Histogram Intersection (NHI) with the theoretical guarantee. More concretely, RCTs are the combination of two templates to code the possible rotations. NHI serves as the similarity between two color histograms. We have conducted extensive experiments on INRIA pedestrian and Pascal VOC2007 data sets for object detection tasks; we also show that our approach leads to a promising performance on Caltech 101, Scene 15, UIUCsport and Stanford 40 action data sets. (C) 2017 Published by Elsevier Inc.
关键词 ：
Max pooling Max pooling Object detection Object detection Object recognition Object recognition Photometric invariance Photometric invariance Self similarity Self similarity Translation invariance Translation invariance
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| GB/T 7714 | Pang, Junbiao , Huang, Jing , Qin, Lei et al. Rotative maximal pattern: A local coloring descriptor for object classification and recognition [J]. | INFORMATION SCIENCES , 2017 , 405 : 190-206 . |
| MLA | Pang, Junbiao et al. "Rotative maximal pattern: A local coloring descriptor for object classification and recognition" . | INFORMATION SCIENCES 405 (2017) : 190-206 . |
| APA | Pang, Junbiao , Huang, Jing , Qin, Lei , Zhang, Weigang , Qing, Laiyun , Huang, Qingming et al. Rotative maximal pattern: A local coloring descriptor for object classification and recognition . | INFORMATION SCIENCES , 2017 , 405 , 190-206 . |
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摘要 ：
In web topic detection, detecting "hot" topics from enormous User- Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from noisy images and short texts; and 2) uncertain roles of modalities where visual content is either highly or weakly relevant to textual cues due to less-constrained data. In this paper, following the detection by ranking approach, we address the problem by learning a robust shared representation from multiple, noisy and complementary features, and integrating both textual and visual graphs into a k-NearestNeighbor Similarity Graph (k-N(2)SG). Then Non-negative Matrix Factorization using Random walk (NMFR) is introduced to generate topic candidates. An efficient fusion of multiple graphs is then done by a Latent Poisson Deconvolution (LPD) which consists of a poisson deconvolution with sparse basis similarities for each edge. Experiments show significantly improved accuracy of the proposed approach in comparison with the state-of-the-art methods on two public data sets.
关键词 ：
Cross Media Cross Media Latent Poisson Deconvolution Latent Poisson Deconvolution Multi-view Learning Multi-view Learning Similarity Cascade Similarity Cascade Topic Detection Topic Detection
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| GB/T 7714 | Tao, Fei , Pang, Junbiao , Zhang, Chunjie et al. ROBUST LATENT POISSON DECONVOLUTION FROM MULTIPLE IMPERFECT FEATURES FOR WEB TOPIC DETECTION [C] . 2016 . |
| MLA | Tao, Fei et al. "ROBUST LATENT POISSON DECONVOLUTION FROM MULTIPLE IMPERFECT FEATURES FOR WEB TOPIC DETECTION" . (2016) . |
| APA | Tao, Fei , Pang, Junbiao , Zhang, Chunjie , Li, Liang , Su, Li , Zhang, Weigang et al. ROBUST LATENT POISSON DECONVOLUTION FROM MULTIPLE IMPERFECT FEATURES FOR WEB TOPIC DETECTION . (2016) . |
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摘要 ：
Convolutional Neural Networks (CNNs) have delivered impressive state-of-the-art performances for many vision tasks, while the computation costs of these networks during test-time are notorious. Empirical results have discovered that CNNs have learned the redundant representations both within and across different layers. When CNNs are applied for binary classification, we investigate a method to exploit this redundancy across layers, and construct a cascade of classifiers which explicitly balances classification accuracy and hierarchical feature extraction costs. Our method cost-sensitively selects feature points across several layers from trained networks and embeds non-expensive yet discriminative features into a cascade. Experiments on binary classification demonstrate that our framework leads to drastic test-time improvements, e.g., possible 47.2x speedup for TRECVID upper body detection, 2.82x speedup for Pascal VOC2007 People detection, 3.72x for INRIA Person detection with less than 0.5% drop in accuracies of the original networks.
关键词 ：
accelerate accelerate binary classification binary classification cascade cascade Convolutional Neural Networks Convolutional Neural Networks cost-sensitive cost-sensitive feature selection feature selection
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| GB/T 7714 | Pang, Junbiao , Lin, Huihuang , Su, Li et al. ACCELERATE CONVOLUTIONAL NEURAL NETWORKS FOR BINARY CLASSIFICATION VIA CASCADING COST-SENSITIVE FEATURE [C] . 2016 : 1037-1041 . |
| MLA | Pang, Junbiao et al. "ACCELERATE CONVOLUTIONAL NEURAL NETWORKS FOR BINARY CLASSIFICATION VIA CASCADING COST-SENSITIVE FEATURE" . (2016) : 1037-1041 . |
| APA | Pang, Junbiao , Lin, Huihuang , Su, Li , Zhang, Chunjie , Zhang, Weigang , Duan, Lijuan et al. ACCELERATE CONVOLUTIONAL NEURAL NETWORKS FOR BINARY CLASSIFICATION VIA CASCADING COST-SENSITIVE FEATURE . (2016) : 1037-1041 . |
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