检索结果-内蒙古大学图书馆

A Distributed Fixed-Time Neurodynamic Algorithm and Its Application in Multi-Autonomous Underwater Vehicle Collaborative Escorting

引用

IEEE Transactions on Network Science and Engineering 2025年

作者： Zhang, Peng He, Xing Yu, Junzhi Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Peking University Nanchang Innovation Institute Nanchang330224 China

In this paper, a distributed fixed-time neurodynamic algorithm (DFxTNA) is designed for solving distributed optimization problem with time-varying (TV) objective function and constraints. The DFxTNA consists of consensus part based on sliding-mode control technique and optimization part based on Hessian matrix and penalty function. In contrast to existing distributed TV algorithms, DFxTNA based on fixed-time sliding mode control can effectively improve the convergence performance of the algorithm and has better stability with the addition of TV disturbance. In addition, the consistency and fixed-time convergence of DFxTNA are proved by Lyapunov theory and fixed-time stability, respectively. The effectiveness of DFxTNA is verified by numerical experiments. Finally, a distributed TV optimization problem model is established for a multi-autonomous underwater vehicle (AUV) cooperative escort scenario, and DFxTNA is applied to solve the problem. Simulation results confirm the effectiveness and stability of DFxTNA in target tracking, formation control, and dynamic obstacle avoidance. © 2013 IEEE.

关键词： Lyapunov functions

来源：评论

学校读者我要写书评

暂无评论

Towards Combating Frequency Simplicity-biased Learning for Domain Generalization

arXiv

引用

arXiv 2024年

作者： He, Xilin Hu, Jingyu Lin, Qinliang Luo, Cheng Xie, Weicheng Song, Siyang Khan, Muhammad Haris Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models’ biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at AdvFrequency. © 2024, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

HCF-Net: Hybrid Coarse-to-Fine Network for Forgery Reconstruction

HCF-Net: Hybrid Coarse-to-Fine Network for Forgery Reconstru...

引用

2021 International Workshop on Safety and Security of Deep Learning, SSDL 2021

作者： Zhuo, Long Tan, Shunquan Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen Institute of Artificial Intelligence and Robotics for Society China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Due to the ubiquity of photo editing software, it is convenient and prevalent to create fake images which may cause terrible misunderstandings. To address this issue, we introduce forgery reconstruction, a novel image reconstruction that automatically reconstructs a forged image into its original appearance. To achieve this, we present a new two-phase system, including a forgery localization phase and a reconstruction phase, where we first localize forged regions and then inpaint the natural texture. The proposed approach, called HCF-Net, is a hybrid two coarse-to-fine networks. Unlike the existing solutions of image reconstruction that relies on the prior knowledge of the areas to be recovered, our proposed system is free from the mask information. To guide the reconstruction, we propose a new strategy for extracting noise features in the forgery localization phase, which leverages the relationships among the image channels and localizes the tampered regions more precisely. For better performance, we equip a dual attention module in the reconstruction phase. Our automatic system is applicable to localize the tampered areas and reconstruct the missing part in a single pass. The extensive experiments demonstrate that our system achieves state-of-the-art performance in forgery localization and generates higher-quality and more flexible results than traditional inpainting methods. © 2021 CEUR-WS. All rights reserved.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Precise segmentation of offshore farms in high-resolution SAR Images based on improved UNet++

Precise segmentation of offshore farms in high-resolution SA...

引用

2021 International Conference on Computer Application and information Security, ICCAIS 2021

作者： Yu, Chuang Liu, Yunpeng Xia, Xin Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510655218

The segmentation of the offshore farm area in the high-resolution SAR image is of great significance for the statistics of the farming area and the analysis of the rationality of the farming layout. However, the SAR images have the characteristics of a lot of noise and inconspicuous features. It is difficult to achieve precise segmentation by directly using non-learning image segmentation methods. Therefore, we propose a precise segmentation scheme for offshore farms in high-resolution SAR images based on improved UNet++. We first adopt a simulated annealing strategy for the update of the learning rate during the network training. By initializing the learning rate multiple times, we avoid the network from falling into a local optimum. Secondly, for the data set studied, we verify that the segmentation performance of resizing the image to 256×256 pixels is better than that of 512×512 pixels. Finally, we propose an improved UNet++, which uses SE-Net as the feature extraction network to enhance the feature learning ability. Extensive experimental results show that, compared to some state-of-the-art methods, the proposed scheme achieves superior performance with a Frequency Weighted Intersection over Union (FWIoU) of 0.9853 on the high-resolution SAR offshore farm data set. © The Authors.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Three-Way Decision-Based Co-Detection for Outliers

SSRN

引用

SSRN 2023年

作者： Tan, Xiaofeng Gao, Can Zhou, Jie Wen, Jiajun College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China

Outlier detection is an important research topic in data mining and machine learning. However, existing unsupervised outlier detection methods suffer from irrelevant and redundant attributes in high-dimensional data, and their performance is also limited by their outlier detection models that rely on only one view. In this study, we propose a three-way decision-based co-detection model for unsupervised outlier detection. Specifically, we first improve the local outlier factor (LOF) method by introducing the Gaussian kernel function to make the measure of local reachability density more accurate. Then, we introduce fuzzy rough sets to perform attribute reduction, which further reduces the negative effect of irrelevant and redundant attributes on the measure of sample similarity. Finally, we develop a co-detection model that is trained on the original view and the transformed view generated by principal component analysis and uses the strategy of the three- way decision to collaboratively detect outliers. The results of comparative experiments on the selected UCI datasets show that the proposed model outperforms state-of-the-art methods in terms of AUC-ROC index. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

A localizability estimation method for mobile robots based on 3D point cloud feature

A localizability estimation method for mobile robots based o...

引用

2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Liu, Ying Wang, Jingchuan Huang, Yi Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China Nanjing Kingyoung Intelligent Technology CO. LTD.

ISBN: (纸本)9781665436786

This paper introduces the theoretical analysis and derivation of localizability estimation for mobile robots based on point cloud observation. Combined with the observation model, the pre-established point cloud map is firstly clustered and segmented with a newly proposed method considering multiple constraints on depth map. Then localizability is set to be equal to the strength of the constraints associated with 3D point cloud features. In addition, based on the method of using information matrix theory, this paper integrates the Fisher's information matrix and point cloud features to estimate localizability. © 2021 IEEE.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery

PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust ...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wendi Yang Zihang Jiang Shang Zhao S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

With the recent advancements in single-image-based 3D human pose and shape estimation (3DHPSE), there is a growing amount of works that can achieve good results on standard benchmarks but struggle to yield accurate hu-man mesh in extreme scenarios like occlusion. Previous works propose to leverage 2D poses to help 3D HPSE model improve performance under occlusion, but usually rely on manual design to integrate 2D poses and only aim for specific kinds of occlusion. In this paper, we present PostoMETRO (Pose token enhanced MEsh TRansfOrmer), which integrates 2D pose prior knowledge as tokens into transformers to improve model's performance under occlusion. Using a VQ- VAE-based pose tokenizer; we efficiently represent 2D poses as tokens andfeed them to transformers together with image tokens. Subsequently, these tokens are queried by vertex andJoint tokens to decode 3D coordinates of mesh vertices and human Joints. Our proposed 2D poses integration strategy is manual-design-free and suitable for various kinds of occlusion. Experiments on both standard and occlusion-specific benchmarks demonstrate the effectiveness of PostoMETRO. Code will be made available 1 1 https://***/PostoMETRO/PostoMETRO-Paper.

关键词： Solid modeling Computer vision Three-dimensional displays Shape Estimation Manuals Benchmark testing Transformers Robustness Standards

来源：评论

学校读者我要写书评

暂无评论

Improving Referring Expression Comprehension by Suppressing Expression-unrelated Proposals 3

Improving Referring Expression Comprehension by Suppressing ...

引用

3rd International Conference on Electronic information Engineering and Computer, EIECT 2023

作者： Wang, Qun Zhu, Feng Li, Xiang Ye, Ruida Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Faculty of Computer Science and Engineering Shenyang110016 China Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China

ISBN: (纸本)9798350357707

The prevailing referring expression comprehension (REC) method is based on two stages: 1) detecting multiple candidate proposals using an object detector and 2) aligning proposals with text descriptions. Existing two-stage models have achieved significant improvements with excellent alignment modules. However, their alignment modules treat each proposal equally without filtering expression-unrelated proposals. These expression-unrelated proposals may interfere with the alignment modules' ability to accurately reason about the target proposal. These proposals also increase the difficulty of training alignment modules. To solve this problem, we propose a new framework, that can suppress expression-unrelated proposals for alignment modules. Specifically, we first design a suppression module, which calculates the relevance scores between the proposals and the expression by using the semantics in the expression. Then, these scores are integrated into three basic alignment modules, including subject, location, and relationship modules, to guide each module to focus on the expression-related proposals. We conduct extensive experiments on three benchmark datasets, and the results show the effectiveness of our suppression module and performance improvement. © 2023 IEEE.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

Point Cloud Saliency Maps Based on Non-Contribution Factors 22

Point Cloud Saliency Maps Based on Non-Contribution Factors

引用

3rd International Conference on Control, robotics and intelligent System, CCRIS 2022

作者： Liang, Ao Zhang, Hao Hua, Haiyang Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Hunnan Qu Shenyang Shi China Shenyang Institute of Automation Chinese Academy of Sciences Hunnan Qu Shenyang Shi China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Hunnan Qu Shenyang Shi China University of Chinese Academy of Sciences Beijing Shi China

ISBN: (纸本)9781450396851

The special properties of the point cloud structure lead to difficulties in interpreting the features learned from Deep Neural Networks. In this paper, a method for obtaining point cloud saliency maps in point cloud target recognition models is proposed. Several free factors are randomly released in the target space and fed into the model. Then the pooled features output from the backbone of the model are made to deviate as much as possible from the pooled features output from the target point cloud during recognition process. The iterative factors contribute "zero"to the prediction of the model, so that moving any point in the target to these factors has exactly the same effect on the recognition result as dropping the point. Since the movement of the point is differentiated, saliency maps can be obtained based on the gradient information. We generate saliency maps on PointNet, and the saliency maps are evaluated to be of good generality. © 2022 Owner/Author.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency

arXiv

引用

arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：