检索结果-内蒙古大学图书馆

Learning to Learn Transferable Generative Attack for Person Re-Identification

IEEE Transactions on Image Processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance

arXiv

引用

arXiv 2025年

作者： Zhao, Jiayi Teng, Fei Luo, Kai Zhao, Guoqiang Li, Zhiyong Zheng, Xu Yang, Kailun The School of Robotics Hunan University China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China The AI Thrust Hong Kong University of Science and Technology Guangzhou China

The perception capability of robotic systems relies on the richness of the dataset. Although Segment Anything Model 2 (SAM2), trained on large datasets, demonstrates strong perception potential in perception tasks, its inherent training paradigm prevents it from being suitable for RGB-T tasks. To address these challenges, we propose SHIFNet, a novel SAM2-driven Hybrid Interaction Paradigm that unlocks the potential of SAM2 with linguistic guidance for efficient RGB-Thermal perception. Our framework consists of two key components: (1) Semantic-Aware Cross-modal Fusion (SACF) module that dynamically balances modality contributions through text-guided affinity learning, overcoming SAM2’s inherent RGB bias;(2) Heterogeneous Prompting Decoder (HPD) that enhances global semantic information through a semantic enhancement module and then combined with category embeddings to amplify cross-modal semantic consistency. With 32.27M trainable parameters, SHIFNet achieves state-of-the-art segmentation performance on public benchmarks, reaching 89.8% on PST900 and 67.8% on FMB, respectively. The framework facilitates the adaptation of pre-trained large models to RGB-T segmentation tasks, effectively mitigating the high costs associated with data collection while endowing robotic systems with comprehensive perception capabilities. The source code will be made publicly available at https://***/iAsakiT3T/SHIFNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Route Recommendation Method for Smart Internet-Connected Electric Vehicles Under the Coupling of Traffic and Electricity

An Intelligent Route Recommendation Method for Smart Interne...

引用

Chinese control Conference (CCC)

作者： Jinhuan Xie Dongqi Liu Zhenwu Yang School of Electrical and Information Engineering Changsha University of Science and Technology hunan China National Engineering Research Center for Robot Visual Perception and Control Technology hunan China

Aiming at the charging and navigation strategy of electric vehicles in the road-electricity coupling scenario, this paper proposes a hybrid planning travel scheme based on the pre-charging and charging warning model based on the road traffic congestion predicted by the big data of the Internet of Vehicles. This model integrally balances the interests of users and power grid in the coupled road-electric network, and solves the optimal path scheme under different travel scenarios based on collaborative filtering algorithm and user feedback, considering the influence of vehicle departure time and remaining power in short time scale. It is experimentally demonstrated that the proposed route hybrid recommendation model has better planning effect compared with the single travel model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Keypoint Affordance Representation for Functional Dexterous Grasping

arXiv

引用

arXiv 2025年

作者： Yang, Fan Luo, Dongsheng Chen, Wenrui Lin, Jiacheng Cai, Junjie Yang, Kailun Li, Zhiyong Wang, Yaonan School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China College of Computer Science and Electronic Engineering Hunan University Changsha China

Functional dexterous grasping requires precise hand-object interaction, going beyond simple gripping. Existing affordance-based methods primarily predict coarse interaction regions and cannot directly constrain the grasping posture, leading to a disconnection between visual perception and manipulation. To address this issue, we propose a multi-keypoint affordance representation for functional dexterous grasping, which directly encodes task-driven grasp configurations by localizing functional contact points. Our method introduces Contact-guided Multi-Keypoint Affordance (CMKA), leveraging human grasping experience images for weak supervision combined with Large Vision Models for fine affordance feature extraction, achieving generalization while avoiding manual keypoint annotations. Additionally, we present a Keypoint-based Grasp matrix Transformation (KGT) method, ensuring spatial consistency between hand keypoints and object contact points, thus providing a direct link between visual perception and dexterous grasping actions. Experiments on public real-world FAH datasets, IsaacGym simulation, and challenging robotic tasks demonstrate that our method significantly improves affordance localization accuracy, grasp consistency, and generalization to unseen tools and tasks, bridging the gap between visual affordance learning and dexterous robotic manipulation. The source code and demo videos will be publicly available at https://***/PopeyePxx/MKA. Copyright © 2025, The Authors. All rights reserved.

关键词： Vision

来源：评论

学校读者我要写书评

暂无评论

L₁ Adaptive control-Based Formation Tracking of Multiple Quadrotors Without Linear Velocity Feedback Under Unknown Disturbances

引用

IEEE Transactions on Automation Science and engineering 2024年 22卷 5804-5815页

作者： Yang Hu Zhiqiang Miao Yaonan Wang Haoming Tang Xiangke Wang Wei He College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Intelligence Science and Technology and the Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

This paper addresses the problem of formation control for a quadrotor swarm (QS) system with directed graph topology under external environmental disturbances and unreliable internal state acquisition. The proposed distributed robust control framework, based on a gemetric controller, incorporates ${\mathcal {L}}_control$ adaptive controllers and differentiator systems. First, the geometric formation controller is designed to implement the formation control of the nominal system. Then, ${\mathcal {L}}_control$ adaptive controllers are designed separately for each quadrotor’s position loop and attitude loop subsystems to address the effects of uncertainties such as external time-varying disturbances (matched and unmatched disturbances) and different mass variations of quadrotors. Furthermore, the differentiator system is devised to accurately estimate the higher-order derivatives of the non-directly-measurable velocity information and the virtual translation control signal, which enhances system accuracy while reducing computational complexity. The Lyapunov stability theory is employed to analyze the stability of the closed-loop system. Finally, the effectiveness and exceptional performance of this approach in QS formation control were validated through numerical simulation and experimental results. Note to Practitioners—The inspiration for this article comes from the issue of formation control in a cluster of quadrotor drones, which is also applicable to formation control in other types of drones. In this paper, a formation control algorithm based on ${\mathcal {L}}_control$ adaptive control strategy and arbitrary-order differentiation is designed. This algorithm can address not only the issue of time-varying wind disturbances frequently encountered during quadrotor drone flights but also the effects of unpredictable velocities and inconsistent masses of quadrotor drones. The disturbance rejection capability of this scheme enables quadrotor drones to be applied more safely and r

关键词： Quadrotors Formation control Adaptive control Topology Drones Autonomous aerial vehicles Uncertainty

来源：评论

学校读者我要写书评

暂无评论

A Flexible Framework for Universal Computational Aberration Correction via Automatic Lens Library Generation and Domain Adaptation

arXiv

引用

arXiv 2024年

作者： Jiang, Qi Gao, Yao Gao, Shaohua Yi, Zhonghua Sun, Lei Shi, Hao Yang, Kailun Wang, Kaiwei Bai, Jian State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou310027 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

Emerging universal Computational Aberration Correction (CAC) paradigms provide an inspiring solution to light-weight and high-quality imaging without repeated data preparation and model training to accommodate new lens designs. However, the training databases in these approaches, i.e., the lens libraries (LensLibs), suffer from their limited coverage of real-world aberration behaviors. Moreover, it is challenging to train a universal model for reliable results in a zero-shot manner, whose inflexible tuning pipeline is also confined to the lens-descriptions-known case. In this work, we set up an OmniLens framework for universal CAC, considering both the generalization ability and flexibility. OmniLens extends the idea of universal CAC to a broader concept, where a base model is trained as the pre-trained model for three cases, including zero-shot CAC with the pre-trained model, few-shot CAC with a little lens-specific data for fine-tuning, and domain adaptive CAC using domain adaptation for lens-descriptions-unknown lens. In terms of OmniLens’s data foundation, we first propose an Evolution-based Automatic Optical Design (EAOD) pipeline to construct the LensLib automatically, coined AODLib, whose diversity is enriched by an evolution framework, with comprehensive constraints and a hybrid optimization strategy for achieving realistic aberration behaviors. For network design, we introduce the guidance of high-quality codebook priors to facilitate both zero-shot CAC and few-shot CAC, which enhances the model’s generalization ability, while also boosting its convergence in a few-shot case. Furthermore, based on the statistical observation of dark channel priors in optical degradation, we design an unsupervised regularization term to adapt the base model to the target descriptions-unknown lens using its aberration images without ground truth. We validate the proposed OmniLens framework on 4 manually designed low-end lenses with various structures and aberration behaviors.

关键词： Aberrations

来源：评论

学校读者我要写书评

暂无评论

Adaptive extended state observer-based distributed fault detection and estimation for an unstable wave equation 36

Adaptive extended state observer-based distributed fault det...

引用

36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Feng, Yun Wang, Yaonan Kang, Wen Miao, Zhiqiang Hunan University National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha China University of Science and Technology School of Automation and Electrical Engineering Beijing China

ISBN: (纸本)9781665437127

In this paper, we study the fault detection along with the estimation problem for an unstable wave equation based on an adaptive extended state observer (ESO) using only boundary measurements. A fault detection filter (FDF) is proposed in the first place to detect fault based on a naive observer. Once an alarm has been raised by the FDF, a fault estimation filter is activated for fault identification by incorporating an adaptive law into the naive observer. The fault estimator along with the adaptive state observer consists of an adaptive ESO motivated by the intuitive idea of ESO. Numerical simulations on a faulty wave equation are presented to validate the fault detection and estimation performance. © 2021 IEEE.

关键词： Wave equations

来源：评论

学校读者我要写书评

暂无评论

Resource-Efficient Affordance Grounding with Complementary Depth and Semantic Prompts

arXiv

引用

arXiv 2025年

作者： Huang, Yizhou Yang, Fan Zhu, Guoliang Li, Gen Shi, Hao Zuo, Yukun Chen, Wenrui Li, Zhiyong Yang, Kailun School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China School of Informatics University of Edinburgh United Kingdom State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University China

Affordance refers to the functional properties that an agent perceives and utilizes from its environment, and is key perceptual information required for robots to perform actions. This information is rich and multimodal in nature. Existing multimodal affordance methods face limitations in extracting useful information, mainly due to simple structural designs, basic fusion methods, and large model parameters, making it difficult to meet the performance requirements for practical deployment. To address these issues, this paper proposes the BiT-Align image-depth-text affordance mapping framework. The framework includes a Bypass Prompt Module (BPM) and a Text Feature Guidance (TFG) attention selection mechanism. BPM integrates the auxiliary modality depth image directly as a prompt to the primary modality RGB image, embedding it into the primary modality encoder without introducing additional encoders. This reduces the model’s parameter count and effectively improves functional region localization accuracy. The TFG mechanism guides the selection and enhancement of attention heads in the image encoder using textual features, improving the understanding of affordance characteristics. Experimental results demonstrate that the proposed method achieves significant performance improvements on public AGD20K and HICO-IIF datasets. On the AGD20K dataset, compared with the current state-of-the-art method, we achieve a 6.0% improvement in the KLD metric, while reducing model parameters by 88.8%, demonstrating practical application values. The source code will be made publicly available at https://***/DAWDSE/BiT-Align. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

来源：评论

学校读者我要写书评

暂无评论

Forecasting of the Incoming Dustcarts of a Waste Transfer Station Based on SARIMA Model

Forecasting of the Incoming Dustcarts of a Waste Transfer St...

引用

Chinese control and Decision Conference, CCDC

作者： Jiakang Zhou Qiu Fang Han Zhu Haojie Song Yaonan Wang National Engineering Reserch Center for Robot Visual Perception and Control Technology Hunan University Changsha China Shanghai Chengtou Environment (Group) Co.

The quantity forecast of incoming dustcarts in the waste transfer station is essential to enhancing the operational efficiency of smart sanitation, because it is helpful for the station management and the planning of resources. In this study, a seasonal autoregressive integrated moving average (SARIMA) model is suggested to forecast the incoming dustcarts of a waste transfer station. The dataset utilized contains both the hourly-sampled quantity and proportion of residual waste dustcarts. The outcomes of single step and multi-step forecasting are examined with different performance measures in order to confirm SARIMA’s effectiveness. The experimental results show that the SARIMA model has better prediction results compared with the LSTM model. In addition, SARIMA model has high accuracy in both single step and multi-step forecasting, but multi-step forecasting is more effective for solving real-world issues because of its less time-consuming.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Distributed Resilient Estimator for Networked Systems Under Deception Attacks

Distributed Resilient Estimator for Networked Systems Under ...

引用

International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Weiwei Zhan Zhiqiang Miao Yang Hu Yizong Chen Yanjie Chen Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center of Robot Visual Perception and Control Technology Changsha China School of Mechanical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Deception attacks are employed to compromise cyber-physical systems through fake data injection. This paper concentrates on the distributed resilient estimation issue of multi-sensor networked systems under deception attacks. In order to detect deception attacks, we utilize Kullback-Leibler(K-L) divergence as a criterion to distinguish the discrepancy between the deceived information and the estimated information. When the attack does not exist, the transmitted information can be restored to ensure the resilient estimation performance. Based on the extended Kalman filter design method, a distributed resilient estimation with a dual-gain mechanism is developed. This advanced approach dynamically adjusts the weighting balance between the predictive model and sensor data inputs, achieving the optimal estimation during the shutdown and activation of spoofing attacks. Finally, numerical simulations are provided to further illustrate the results.

关键词： Measurement Fault tolerance Design methodology Buildings Estimation Predictive models Cyber-physical systems robot sensing systems Numerical simulation Kalman filters

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：