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Towards Fully Decoupled End-to-End Person Search

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Towards Fully Decoupled End-to-End Person Search

Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Pengcheng Zhang Xiao Bai Jin Zheng Xin Ning School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for personsearch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search.

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm

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An Auto-Parallel Method for Deep Learning Models Based on Ge...

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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Ensemble Learning-based Traffic Classification with Small-Scale Datasets for Wireless Networks

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Ensemble Learning-based Traffic Classification with Small-Sc...

IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xiaorong Wang Wenting Wei Xiaoshan Yu Danyang Zheng Neeraj Kuma Lei Liu State Key Laboratory of Integrated Service Networks Xidian University Xi'an China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology India Guangzhou Institute of Technology Xidian University Xi'an China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Traffic classification is an essential part of intelligent network management in Fifth generation (5G) networks, from quality of service guarantees to security monitoring. With the evolution of diverse services and applications over 5G networks, traffic patterns and their implications also becoming more complicated, especially in wireless networks. How to dynamically manage the growing traffic demand and satisfy the quality of service (QoS) needs of new applications is particularly critical. The biggest challenge is how to identify traffic types accurately without application information and manual features. However, the existing deep learning (DL)-based classifiers heavily rely on extensive and high-quality samples, which proves impractical to obtain in wireless networks. To overcome this limitation, we propose an ensemble Learning based traffic classification model to provide accurate identification with small-scale datasets, where Attention-based Long Short-Term Memory Networks are bagged to train this classifier. The ensemble learning approach employs a combinatorial design involving five base models to construct more accurate and robust learning models. This enables effective feature absorption, making it feasible to train classifiers on limited sample sets. Comprehensive experiments are conducted using a real-world dataset covering 16 applications. Results demonstrate that, even with a mere 10% of training samples, our proposed model achieves a classification accuracy of 95.57% and a precision of 95.83%, surpassing multiple cutting-edge methods.

关键词： Training Accuracy 5G mobile communication Wireless networks Computational modeling Quality of service Telecommunication traffic

Towards Fully Decoupled End-to-End Person Search

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arXiv 2023年

作者： Zhang, Pengcheng Zheng, Jin Bai, Xiao Ning, Xin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for persons earch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems

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arXiv 2024年

作者： Qu, Qianxin Xiong, Yijin Zhang, Guipeng Wu, Xin Gao, Xiaohan Gao, Xin Li, Hanyu Guo, Shichun Zhang, Guoying The School of Artificial Intelligence China University of Mining and Technology -Beijing Beijing100083 China The State Key Laboratory of Automotive Safety and Energy School of Vehicle and Mobility Tsinghua University Beijing100084 China Computer Science and Technology in Institute of Computing Technology Chinese Academy of Sciences Beijing China

Cooperative LiDAR systems integrating vehicles and road infrastructure, termed V2I calibration, exhibit substantial potential, yet their deployment encounters numerous challenges. A pivotal aspect of ensuring data accuracy and consistency across such systems involves the calibration of LiDAR units across heterogeneous vehicular and infrastructural endpoints. This necessitates the development of calibration methods that are both real-time and robust, particularly those that can ensure robust performance in urban canyon scenarios without relying on initial positioning values. Accordingly, this paper introduces a novel approach to V2I calibration, leveraging spatial association information among perceived objects. Central to this method is the innovative Overall Intersection over Union (oIoU) metric, which quantifies the correlation between targets identified by vehicle and infrastructure systems, thereby facilitating the real-time monitoring of calibration results. Our approach involves identifying common targets within the perception results of vehicle and infrastructure LiDAR systems through the construction of an affinity matrix. These common targets then form the basis for the calculation and optimization of extrinsic parameters. Comparative and ablation studies conducted using the DAIR-V2X dataset substantiate the superiority of our approach. For further insights and resources, our project repository is accessible at https://***/MassimoQu/v2i-calib. Copyright © 2024, The Authors. All rights reserved.

关键词： Calibration

HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection

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arXiv 2024年

作者： Xu, Shibiao Zheng, ShuChen Xu, Wenhao Xu, Rongtao Wang, Changwei Zhang, Jiguang Teng, Xiaoqiang Li, Ao Guo, Li Artificial Intelligence Beijing University of Posts and Telecommunications China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China School of Computer Science and Technology Harbin University of Science and Technology China

Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared single-frame image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://***/zhengshuchen/HCFNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

Critical grid method: An extensible Smoothed Particle Hydrodynamics fluid general interpolation method for Fluid-Structure Interaction surface coupling based on preCICE

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arXiv 2024年

作者： Long, Sifan Guo, Xiaowei Fan, Xiaokang Yang, Canqun College of Computer Central South University Changsha410083 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha410073 China National Supercomputing Center in Tianjin Tianjin300457 China

Solving Fluid-Structure Interaction (FSI) problems using traditional methods is a big challenge in the field of numerical simulation. As a powerful multi-physical field coupled library, preCICE has a bright application prospect for solving FSI, which supports many open/closed source software and commercial CFD solvers to solve FSI problems in the form of a black box. However, this library currently only supports mesh-based coupling schemes. This paper proposes a critical grid (mesh) as an intermediate medium for the particle method to connect a bidirectional coupling tool named preCICE. The particle and critical mesh are used to interpolate the displacement and force so that the pure Lagrangian Smoothed Particle Hydrodynamic (SPH) method can also solve the FSI problem. This method is called the particle mesh coupling (PMC) method, which theoretically solves the mesh mismatch problem based on the particle method to connect preCICE. In addition, we conduct experiments to verify the performance of the PMC method, in which the fluid and the structure is discretized by SPH and the Finite Element Method (FEM), respectively. The results show that the PMC method given in this paper is effective for solving FSI problems. Finally, our source code for the SPH fluid adapter is open-source and available on GitHuba for further developing preCICE compatibility with more meshless methods. © 2024, CC0.

关键词： Fluid structure interaction

Defending against a laser-seeding attack on continuous-variable quantum key distribution using an improved optical power limiter

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Physical Review A 2023年第5期108卷 052616-052616页

作者： Qingquan Peng Binwu Gao Dongyang Wang Qin Liao Zhiyue Zuo Hai Zhong Anqi Huang Ying Guo School of Automation Central South University Changsha 410083 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China School of Computer Science Beijing University of Posts and Telecommunications Beijing 100876 China

Laser-seeding attack enables an attacker to control the source output and eavesdrops on the key information, compromising the security of the continuous-variable (CV) quantum key distribution (QKD) system. In this paper, we suggest an improved optical power limiter (OPL) to defend against a laser-seeding attack on CV QKD systems. The proposed OPL utilizes a temperature monitor to detect Eve's attacks through temperature changes, providing an effective optical component for improving the security and reliability of the CV QKD system in practical environments, which is called an OPL-T scheme. In particular, we consider the security threats of a laser-seeding attack on two common CV QKD systems and prove their theoretical security degradation. Experiments are conducted to verify the effective OPL-T, and the results show that this defense scheme has an advantage in resisting the laser-seeding attack. Numerical simulations demonstrate the security analysis on CV QKD equipped with an OPL-T, which further confirms the effectiveness of our scheme. This work provides a practical solution for the security enhancements of continuous-variable quantum communication in practical applications.

关键词： Quantum communication Quantum communication, protocols & technology