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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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Classification of Multisource Remote Sensing Data Using Slice Mamba

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： He, Yan Tu, Bing Jiang, Puzhao Liu, Bo Li, Jun Plaza, Antonio Institute of Optics and Electronics Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Faculty of Computer Science Wuhan430074 China University of Extremadura Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica Cáceres10003 Spain

The multisource remote sensing (RS) data have yielded promising results in target detection and classification tasks. However, most existing methods primarily focus on the spatial features inherent in spectral information, while the continuous spectral characteristics are generally neglected. This oversight leads to insufficient extraction of spectral information, thereby limiting detection performance. Recently, the Mamba architecture, based on state space models (SSMs), integrates the advantages of long-range sequence modeling and linear computational efficiency, demonstrating significant potential in low-dimensional scenarios. Inspired by this, we propose Slice Mamba for multisource RS data fusion classification. Specifically, we design two scanning methods: lateral slice scanning (LatSS) and longitudinal slice scanning (LonSS), which construct sequences from lateral and longitudinal perspectives to facilitate information interaction between pixels. In conjunction with the Mamba architecture, we develop the lateral slice Mamba block (LatSMB) and the longitudinal slice Mamba block (LonSMB) to capture continuous spatial-spectral features. Based on this, we establish the slice feature extraction (SFE) module for extracting spatial-spectral feature information and design the cross-information fusion (CIF) module to form a complementary structure for effectively modeling spatial-spectral features, thereby achieving the fusion and classification of multisource heterogeneous features. Experimental results on three benchmark datasets demonstrate that Slice Mamba outperforms existing advanced methods in fusion classification performance and exhibits greater robustness when applied to multispectral datasets. © 2025 IEEE.

关键词： information fusion

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Performer: A High-Performance Global-Local Model-Augmented with Dual Network Interaction Mechanism

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Tan, Dayu Hao, Rui Hua, Linfeng Xu, Qi Su, Yansen Zheng, Chunhou Zhong, Weimin Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

In deep learning, Convolutional Neural Networks (CNNs) focus on local information through convolutional kernels, while transformers attend to global information using self-attention mechanisms. The union of these distinct approaches enables a more comprehensive extraction of image features. However, the feature map dimensions of CNN and Transformer differ, leading to dimension mismatch issues when combining these architectures. Additionally, the parameter size of the hybrid model integrating both architectures remains large, making it difficult to train. To further augmenting the interpretation of complex image patterns, we present Performer, a dual-network architecture that seamlessly combines CNNs and transformers, resulting in a novel and efficient representation learning model. In the Performer model, we innovate by devising a unique interaction methodology for CNN and transformer architectures to enhance the image feature extraction capabilities mutually. To counteract issue of dimensionality mismatch, we also introduce a refined transformer block, a advancement over the transformer block of ViT. To validate the effectiveness of Performer, we conduct extensive experiments on both classification and segmentation tasks. Performer achieve an accuracy of 83.37% on the ImageNet-200 dataset. For semantic segmentation, Performer excels on the CamVid and Hippocampus datasets. On CamVid, our model achieves a mean Intersection over Union (mIoU) of 63.27% and Pixel Accuracy of 92.11%, demonstrating superior performance in capturing fine details and handling complex scenes effectively. The code is available at https://***/hlfthh/ Performer. © 2016 IEEE.

关键词： Semantic Segmentation

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A Deep Neural Network-Assisted Diagnosis Method for Cardiovascular Disease Based on the Optimization of the TAdam Algorithm

A Deep Neural Network-Assisted Diagnosis Method for Cardiova...

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2023 China Automation Congress, CAC 2023

作者： Hu, Yumei Li, Xing Zhang, Yang Zhu, Yuxuan Sun, Yuechao College of Computer Science and Technology Dongguan University of Technology Dongguan China School of Electrical Engineering & Intelligentization Dongguan University of Technology Dongguan China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China School of Mechanical Engineering of Dgut Dongguan University of Technology Dongguan China Northeastern University College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350303759

Cardiovascular disease is a sudden and extremely disabling and deadly disease that hinders the development of public health care in China and poses a serious threat to the health of users. Therefore, this paper proposes a deep neural network-assisted diagnosis of cardiovascular disease based on TAdam algorithm optimization under a distributed medical monitoring network to enable rapid identification of cardiovascular patients, timely detection and reporting of the condition. This paper addresses the problems that deep neural network models are susceptible to the influence of their structure and hyperparameters and poor generalization convergence, firstly, the mainstream optimization algorithms based on deep learning are sorted out, and finally the T Adam algorithm is used to optimize the model. By comparing the common gradient optimization algorithms SGD (Stochastic Gradient Descent), Adagrad (Adaptive Gradient Algorithm), RMSprop (Root Mean Square propagation), Adam (Adaptive Moment Estimation) and the improved TAd am optimization algorithm trained and tested on the same dataset, the experimental results show that the TAdam algorithm exhibits the fastest convergence speed and the best generalization performance on the cardiovascular disease dataset. The method proposed in this paper enables rapid diagnosis of the user's disease risk and feedback to the relevant personnel to maximise user safety. © 2023 IEEE.

关键词： Deep neural networks

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Classification-Detection of Medical Images by Visualizing Hidden-Layer Features of a Deep Learning Approach 12th

Classification-Detection of Medical Images by Visualizing Hi...

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Tong, Ziyu Deng, Xing Shao, Haijian Wang, Xia School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China School of Automation Key Laboratory of Measurement and Control for CSE Ministry of Education Southeast University Nanjing Jiangsu210096 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Deep learning algorithms, such as CNN, have been widely used on a variety of computer vision problems due to their powerful capacity to extract nonlinear features for medical picture analysis. However, due to the decreased identification rate for medical images, CNNs continue to fail to offer an interpretable depiction of the hidden-layers’ features. Furthermore, the enormous amount of redundant parameters from the convolutional to fully connected layers will surely add to the computational complexity. This work provides a set of fast deep learning architectures for visualizing and analyzing hidden-layer properties in medical images. The clustered feature map is used to simplify the model parameters and minimize redundancy parameters in order to meet the goal of a streamlined trade-off between model performance and architecture. The testing results on the medial images reveal that 41% of redundant channels may be removed while 100% of performance can be preserved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

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Neuroscience Bulletin 2023年第6期39卷 893-910页

作者： Feng Su Yangzhen Wang Mengping Wei Chong Wang Shaoli Wang Lei Yang Jianmin Li Peijiang Yuan Dong-Gen Luo Chen Zhang Department of Neurobiology School of Basic Medical SciencesBeijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing100069China Chinese Institute for Brain Research Beijing102206China State Key Laboratory of Translational Medicine and Innovative Drug Development Nanjing210000China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China School of Life Sciences Tsinghua UniversityBeijing100084China School of Biological Science and Medical Engineering Beihang UniversityBeijing100191China The Key Laboratory of Developmental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing210096JiangsuChina Institute for Artificial Intelligence the State Key Laboratory of Intelligence Technology and SystemsBeijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing100084China School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social *** tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being *** overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been ***,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction *** this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion *** system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing *** proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

关键词： Noninvasive tracking Deep learning Multi-camera Mouse group Social interaction

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Constraint-Pareto Dominance and Diversity Enhancement Strategy based Evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems

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IEEE Transactions on Evolutionary Computation 2025年

作者： Liu, Zhe Han, Fei Ling, Qinghua Han, Henry Jiang, Jing Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212013 China Hefei Comprehensive National Science Center Anhui Energy Laboratory Institute of Energy China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Zhenjiang212013 China Jiangsu University of Science and Technology School of Computer Science Zhenjiang212003 China Baylor University School of Engineering and Computer Science Waco76798 United States Anqing Normal University School of Computer and Information Anqing246011 China

The utilization of both constrained and unconstrained-based optimization for solving constrained multi-objective optimization problems (CMOPs) has become prevalent among recently proposed constrained multiobjective evolutionary algorithms (CMOEAs). However, the constrained-based optimization which adopted by many CMOEAs typically gives priority to feasible solutions over infeasible ones regardless of their objective values, potentially leading to degraded performance due to the elimination of promising infeasible solutions with strong convergence and diversity. Furthermore, many existing CMOEAs have difficulty in maintaining diversity while focusing on feasibility, thereby hindering their ability to effectively address CMOPs characterized by complex feasible regions. To tackle these challenges, a constraint-Pareto dominance relationship is proposed in this paper to evaluate solutions based on both objectives and feasibility, to improve the optimization potential by reduce the elimination probability of promising infeasible solutions. A diversity enhancement strategy is also designed to enable simultaneously focus on both diversity and feasibility, thus effectively ensuring the diversity of the feasible solutions obtained. Empirical results from benchmark suites and real-world problems demonstrate that our proposed algorithm surpasses state-of-the-art CMOEAs. © 1997-2012 IEEE.

关键词： Optimization algorithms

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ADS Modeling and Safety Verification in Extreme Driving Scenarios

ADS Modeling and Safety Verification in Extreme Driving Scen...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Ying Zhao Yi Zhu Li Zhao Zexin Li Chan Yin School of Computer Science and Technology Jiangsu Normal University Xuzhou China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Autonomous vehicles play a vital role in improving traffic efficiency and reducing traffic accidents. The spatio-temporal data in driving scenarios is complex and unpredictable, with uncertain interaction behavior. Despite efforts by developers to model road dynamics for evaluating Autonomous Driving Systems (ADS), safety concerns under accidental and catastrophic road conditions have been overlooked. Firstly, to tackle this, this paper proposes extending feature variables for catastrophic scenarios using Domain-Specific Modeling Language (DSML). Introducing the Accidental and Catastrophic Automatic Driving Scenario Modeling Language (AC-ADSML) aims to accurately model ADS behavior in accidental and catastrophic road conditions. The AC-ADSML meta-model categorizes scenario variables and specifies abstract and concrete syntax along with operational semantics. Secondly, it is then transformed into Networks of Stochastic Hybrid Automata (NSHA) following ATL rules. The CARLA simulator is employed to replicate urban driving scenarios during natural disasters with PCTL scenario specifications. Model security can be verified using UPP AAL-SMC. Finally, an autonomous vehicle with adaptive cruise control is used as an example to verify the safety of the proposed model under different road condition parameters.

关键词： Analytical models Adaptation models Computational modeling Roads Disasters Semantics Syntactics Security Autonomous vehicles Meteorology

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Real-Time Student Behavior Analysis via YOLOv5 with Coordinate Attention 9th

Real-Time Student Behavior Analysis via YOLOv5 with Coordina...

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9th International Symposium on Artificial Intelligence and Robotics, ISAIR 2024

作者： Ma, Shuai Chang, Heyou Yang, Xinru Chen, Quan Han, Jian Key Laboratory of Intelligent Information Processing Nanjing XiaoZhuang University Nanjing China School of Information and Engineering Nanjing XiaoZhuang University Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9789819629138

The detection and recognition of student behavior play a pivotal role in the context of smart classrooms. However, conventional methods often encounter performance degradation due to challenges such as occlusion, data diversity, and complexity. To effectively addresses these issues, we propose a method that integrates YOLOv5 with coordinate attention mechanism and GhostConv for analyzing students' behavior. Specifically, the coordinate attention module enhances the localization ability of the proposed method by addressing problems related to target scale variations and uneven distribution. Additionally, we employ GhostConv to replace conventional convolutional layers in YOLOv5, reducing parameters while improving computational efficiency. To verify the validity of our method, a self-built challenging dataset for real teaching scenarios and Citypersons dataset are selected for the experiment. In comparison to the original YOLOv5, our method has achieved a 14.8% and 5.7% increase in mean average precision rates, with a 19.0% improvement in frames per second. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Invulnerability Optimization of Communication Network Based on Analog Attack Strategy 11th

Invulnerability Optimization of Communication Network Based ...

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11th International Conference on computer Engineering and Networks, CENet2021

作者： Xia, Lingling Ni, Xueli Jing, Zhengjun Liu, Jiayin Zhang, Yan Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China College of Computer Engineering Jiangsu University of Technology Changzhou213001 China Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center Nanjing210031 China Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department Nanjing210031 China

ISBN: (纸本)9789811665530

Invulnerability of communication networks directly related to security and reliability of networks has received increasing attention in recent years. In order to enhance the destruction resisting capacity of communication network, we propose a new invulnerability optimization method based on analog attack strategy and present an improved invulnerability measure with consideration of network connectivity and construction cost. The experimental simulations are conducted to construct four different optimized communication networks using analog attack strategy with different indicators of node centrality on the initial network respectively. Results demonstrate that the improved metric used to evaluate the economic benefits of network invulnerability is reasonable, and the proposed topology optimization method can improve the invulnerability of communication network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Structural optimization

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