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International Conference on Computer Vision (ICCV)

作者： Haoye Dong Xiaodan Liang Xiaohui Shen Bochao Wang Hanjiang Lai Jia Zhu Zhiting Hu Jian Yin School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R.China School of Intelligent Systems Engineering Sun Yat-sen University ByteDance AI Lab. School of Computer Science South China Normal University Guangzhou Key Laboratory of Big Data and Intelligent Education Carnegie Mellon University

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Virtual try-on systems under arbitrary human poses have significant application potential, yet also raise extensive challenges, such as self-occlusions, heavy misalignment among different poses, and complex clothes textures. Existing virtual try-on methods can only transfer clothes given a fixed human pose, and still show unsatisfactory performances, often failing to preserve person identity or texture details, and with limited pose diversity. This paper makes the first attempt towards a multi-pose guided virtual try-on system, which enables clothes to transfer onto a person with diverse poses. Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-On Network (MG-VTON) generates a new person image after fitting the desired clothes into the person and manipulating the pose. MG-VTON is constructed with three stages: 1) a conditional human parsing network is proposed that matches both the desired pose and the desired clothes shape; 2) a deep Warping Generative Adversarial Network (Warp-GAN) that warps the desired clothes appearance into the synthesized human parsing map and alleviates the misalignment problem between the input human pose and the desired one; 3) a refinement render network recovers the texture details of clothes and removes artifacts, based on multi-pose composition masks. Extensive experiments on commonly-used datasets and our newly-collected largest virtual try-on benchmark demonstrate that our MG-VTON significantly outperforms all state-of-the-art methods both qualitatively and quantitatively, showing promising virtual try-on performances.

关键词： Shape Three-dimensional displays Generators Hair Task analysis Face Gallium nitride

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NAS-Count: Counting-by-Density with Neural Architecture Search 16th

NAS-Count: Counting-by-Density with Neural Architecture Sear...

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16th European Conference on Computer Vision, ECCV 2020

作者： Hu, Yutao Jiang, Xiaolong Liu, Xuhui Zhang, Baochang Han, Jungong Cao, Xianbin Doermann, David School of Electronic and Information Engineering Beihang University Beijing China Key Laboratory of Advanced Technologies for Near Space Information Systems Ministry of Industry and Information Technology Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing China YouKu Cognitive and Intelligent Lab Alibaba Group Hangzhou China Beihang University Beijing China Computer Science Department Aberystwyth University AberystwythSY23 3FL United Kingdom Department of Computer Science and Engineering University at Buffalo New York United States

ISBN: (纸本)9783030585419

Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation problem, but at the expense of demanding design efforts. In this work, we automate the design of counting models with Neural Architecture Search (NAS) and introduce an end-to-end searched encoder-decoder architecture, Automatic Multi-Scale Network (AMSNet). Specifically, we utilize a counting-specific two-level search space. The encoder and decoder in AMSNet are composed of different cells discovered from micro-level search, while the multi-path architecture is explored through macro-level search. To solve the pixel-level isolation issue in MSE loss, AMSNet is optimized with an auto-searched Scale Pyramid Pooling Loss (SPPLoss) that supervises the multi-scale structural information. Extensive experiments on four datasets show AMSNet produces state-of-the-art results that outperform hand-designed models, fully demonstrating the efficacy of NAS-Count. © 2020, Springer Nature Switzerland AG.

关键词： Decoding

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Coordination Evaluation Research of Railway Collection and Distribution System in Port Areas Based on Cloud Model

Coordination Evaluation Research of Railway Collection and D...

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作者： Kai Zeng Haihong Xie Liuyi Wu Yongbin Xu Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Institute of Computing Technology China Academy of Railway Sciences

The railway is of great significance for port cargo to achieve collection and distribution. It is necessary to evaluate the coordination of the railway collection and distribution system scientifically in port areas. Based on the research that analyses factors that affecting the coordination of railway collection and distribution system in port areas and constructs a comprehensive evaluation indicator system, this paper innovates a comprehensive evaluation model suitable for studies of the coordination of railway collection and distribution system in port areas by combining the cloud model algorithm with the traditional expert scoring method. Then, this paper will verify the applicability and operability of the model we proposed by representing a case study of the coordination of the railway collection and distribution system in Caofeidian Port Area.

关键词： Railway in port areas collection and distribution system coordination evaluation cloud model

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Corrigendum to "TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology" [Comput Struct Biotechnol J 20 (2022) 5680-5689]

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Computational and structural biotechnology journal 2023年 21卷 3540页

作者： Yufan Luo Liu Liu Zihao He Shanshan Zhang Peipei Huo Zhihao Wang Qin Jiaxin Lianhe Zhao Yang Wu Dongdong Zhang Dechao Bu Runsheng Chen Yi Zhao Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China. University of Chinese Academy of Sciences Beijing 100049 China. Luoyang Zhongke Information Industry Research Institute Luoyang China. Key Laboratory of RNA Biology Center for Big Data Research in Health Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China. Hwa Mei Hospital University of Chinese Academy of Sciences China. Shenzhen Institute of Nucleic Acid Drug Research Shenzhen Bay Laboratory Pingshan Translational Medicine Center Shenzhen 510800 China.

[This corrects the article DOI: 10.1016/***.2023.103115.].

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Local averaging type a posteriori error estimates for the nonlinear steady-state Poisson-Nernst-Planck equations

arXiv

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arXiv 2020年

作者： Yang, Ying Shen, Ruigang Fang, Mingjuan Shu, Shi School of Mathematics and Computational Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan411105 China School of Mathematics and Computational Science Guilin University of Electronic Technology GuilinGuangxi541004 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China

The a posteriori error estimates are studied for a class of nonlinear stead-state Poisson-Nernst-Planck equations, which are a coupled system consisting of the Nernst-Planck equation and the Poisson equation. Both the global upper bounds and the local lower bounds of the error estimators are obtained by using a local averaging operator. Numerical experiments are given to confirm the reliability and efficiency of the error estimators. Copyright © 2020, The Authors. All rights reserved.

关键词： Finite element method

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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

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Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

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Computers, Materials & Continua 2019年第9期61卷 1117-1128页

作者： Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&SafetyControl Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry Transport Beijing100088China Department of Computer Science and Technology Tsinghua UniversityHaidian DistrictBeijing100088China

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes *** by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected *** at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making *** addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access *** paper proposes a novel framework where the bio-inspired mechanism is combined with *** a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global *** experimental results confirm that the proposed method performs better when compared with conventional schemes.

关键词： Revised attraction selection model TOPSIS VANETs QoS

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An auto code generator for stencil on SW26010 21

An auto code generator for stencil on SW26010

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhu, Xiaomin Zeng, Yunhui Wei, Yanjie Feng, Shengzhong Liu, Weiguo Balaji, Pavan Joint Engineering Research Center Health Big Data Intelligent Analysis Technology Center for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shandong Computer Science Center National SuperComputer Center in Jinan Jinan China Sclool of Software Shandong University Jinan China Argonne National Laboratory United States

ISBN: (纸本)9781728120584

Stencil is a basic building block widely used in many HPC areas and applications. It generally dominates the time cost and is critical to the overall performance. Given that heterogeneous many-core is frequently adopted to build super computers, porting and optimizing stencil codes on modern accelerator-based architectures is important. Porting is non-trivial, and optimization is more difficult as it requires better understanding of the underlying architecture. As a result, auto tuning targeting on accelerators such as GPU becomes a hot research topic. In this paper, we focus on tuning stencil automatically on SW26010, which is a heterogeneous many-core CPU equipped in Sunway TaihuLight SuperComputer, and mainly work on 2D stencil variants code in ROMS (Regional Ocean Modeling System). The code generator can generate codes running on slave cores for 50+ different FORTRAN stencil-alike loops in ROMS in seconds, and optimization methods are integrated into the generator as well. The performance is better than the directive based method (OpenACC) and the hand-drafted code by a junior programmer, and it is comparable compared with the code written by a senior programmer with several years of programming experience. © 2019 IEEE.

关键词： Supercomputers

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Online learning with sparse labels

Online learning with sparse labels

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作者： He, Wenwu Zou, Fumin Liang, Quan School of Mathematics and Physics Fujian University of Technology Fujian China Fujian Collaborative Innovation Center for Beidou Navigation and Intelligent Traffic Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China School of Information Science and Engineering Fujian University of Technology Fujian China

In this paper, we consider an online learning scenario where the instances arrive sequentially with partly revealed labels. We assume that the labels of instances are revealed randomly according to some distribution, eg, Bernoulli distribution. Three specific algorithms based on different inspirations are developed. The first one performs the idea of Estimated gradient for which a strict high-probability regret guarantee in scale of (Formula presented.) can be derived, when the distributing parameter p is revealed. An empirical version is also developed for cases where the learner has to learn the parameter p when it is not revealed. Experiments on several benchmark data sets show the feasibility of the proposed method. To further improve the performance, two kinds of aggressive algorithms are presented. The first one is based on the idea of instances recalling, which tries to get the full use of the labeled instances. The second one is based on the idea of labels learning, and it tries to learn the labels for unlabeled instances. In particular, it includes the step of online co-learning, which aims to learn the labels, and the step of weighted voting, which aims to make the final decision. Empirical results confirm the positive effects of the two aggressive algorithms. © 2018 John Wiley & Sons, Ltd.

关键词： E-learning

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Anomaly detection on attributed networks via contrastive self-supervised learning

arXiv

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arXiv 2021年

作者： Liu, Yixin Li, Zhao Pan, Shirui Gong, Chen Zhou, Chuan Karypis, George The Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia The Alibaba Group Hangzhou310000 China The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100093 China The Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets. © 2021, CC BY.

关键词： Anomaly detection

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