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Neighborhood Structure Consistency for Point Cloud Registration With Severe Outliers

IEEE Sensors Journal 2025年

作者： Ding, Junfeng An, Pei Xu, Yunbiao Ma, Jie Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology School of Electronic Information and Communication Wuhan430074 China

Point cloud registration is a fundamental task in 3D sensor technology and computer vision, aiming to estimate the rigid pose between two point clouds and align them. Correspondence-based methods generate and verify hypothetical poses by sampling consistent correspondences. However, the presence of severe outliers in feature correspondences poses substantial challenges during the sampling process. It may result in misjudgment of inlier consistency relationships, leading to incorrect sampling and causing point cloud registration failure. In this paper, we introduce the Neighborhood Structure Consistency (NSC) metric to guide consensus excavation. It incorporates tighter global consistency constraints rather than local consistency, yielding a more distinct inlier cluster and significantly mitigating the impact of outliers. After that, we propose a Neighborhood Structure Consistency based-Graph guided Sampling Consensus (NSC-GSAC) algorithm, which performs the sampling process on a consistency graph based on the NSC metric. This graph provides a dense representation of connections among inlier correspondences, narrowing the search space and facilitating cleaner consensus subsets sampling, thus improving the registration robustness and efficiency. Additionally, to further improve efficiency, we introduce a Pruning Redundancy Hypothesis Selection (PRHS) strategy to prioritize highly promising representative hypothetical poses, thus accelerating the registration process. Extensive experiments on real-world indoor and outdoor datasets demonstrate that our method achieves superior registration accuracy and efficiency compared to state-of-the-art methods, including both traditional and learning-based methods. Furthermore, experimental results also indicate that our method exhibits more robust performance in the presence of severe outliers, outperforming all other methods. © 2001-2012 IEEE.

关键词： Three dimensional computer graphics

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Differentially Private Federated Stochastic Primal-Dual Learning for Internet of Vehicles

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IEEE Internet of Things Journal 2025年

作者： Li, Yiwei Wang, Shuai Chang, Tsung-Hui Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

Federated learning (FL) has the potential to empower Internet-of-Vehicles (IoV) networks by enabling smart vehicles (SVs) to participate in the learning process under the orchestration of a vehicular service provider while keeping data locally. In this paper, we propose a novel federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP) to ensure robust privacy protection for FL based IoV (FL-IoV) systems. The FedSPD-DP algorithm leverages multiple steps of local stochastic gradient descent (SGD) and partial client participation (PCP) to improve communication efficiency while incorporating differential privacy (DP) to ensure privacy protection. Our theoretical analysis explores the impact of these strategies on learning performance. Specifically, we demonstrate that the data sampling strategy and PCP enhance data privacy, while a larger number of local SGD steps may increase privacy leakage, revealing a non-trivial tradeoff between communication efficiency and privacy protection. Extensive experiments on real-world data validate the effectiveness of the proposed algorithm, showing superior performance compared to state-of-the-art FL algorithms, and confirming the analytical results and properties. © 2014 IEEE.

关键词： Federated learning

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Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge Computing

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IEEE Transactions on Vehicular technology 2025年

作者： Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China

Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collaborative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space

arXiv

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arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Big-Moe: Bypassing Isolated Gating For Generalized multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIG-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction Security Speech processing Overfitting

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CNS/Pulsar Integrated Navigation Using Two-level Filter

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电子学报(英文版) 2010年第2期19卷 265-269页

作者： LIU Jin MA Jie TIAN Jinwen Key Laboratory of National Defense Science and Technology for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan China

To improve the position accuracy, a CNS (Celestial navigation system)/pulsar integrated navigation system with clock drift is proposed. In the pulsar navigation subsystem, the epoch-difference method is adopted to eliminate clock drift, but it induces colored noise. The two-level filter, which is composed of an UKF (Unscented Kalman filter) and an H_∞ filter in series connected, is proposed for fusing the data from the celestial navigation and pulsar navigation subsystems. The celestial navigation subsystem adopts the UKF because of its good performance in nonlinear estimation, while the pulsar navigation subsystem utilizes the H_∞ filter due to its robustness to colored noise. Compared with CNS and two CNS/pulsar integrated navigation methods without two-level filter, the proposed method can provide higher navigation accuracy. Simulation results show that the position estimation accuracy of the proposed method can achieve 140m in the presence of clock drift.

关键词：组合导航系统中枢神经系统脉冲星过滤器 Unscented卡尔曼滤波天文导航系统双级有色噪声

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Unsupervised Domain Adaptation with Hierarchical Masked Dual-adversarial Network for End-to-end Classification of multisource Remote Sensing Data

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Hu, Wen-Shuai Li, Wei Li, Heng-Chao Zhao, Xudong Zhang, Mengmeng Tao, Ran Beijing Institute of Technology School of Information and Electronics Beijing Key Laboratory of Fractional Signals and Systems National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Beijing Institute of Technology Guangdong Zhuhai519088 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China

Although unsupervised domain adaptation (UDA) has been successfully applied for cross-scene classification of multisource remote sensing (MSRS) data, there are still some tough issues: 1) The vast majority of them are patch-based, requiring pixel by pixel processing at high complexity and ignoring the roles of unlabeled data between different domains. 2) Traditional masked autoencoder (MAE)-based methods lack effective multiscale analysis and require pre-training, ignoring the roles of low-level representations. As such, a hierarchical masked dual-adversarial DA network (HMDA-DANet) is proposed for cross-domain end-to-end classification of MSRS data. Firstly, a hierarchical asymmetric MAE (HAMAE) without pre-training is designed, containing a frequency dynamic large-scale convolutional (FDLConv) block to enhance important structural information in the frequency domain, and an intramodality enhancement and intermodality interaction (IAEIEI) block to embed some additional information beyond the domain distribution by expanding the cross-modal reconstruction space. Representative multimodal multiscale features can be extracted, while to some extent improving their generalization to the target domain. Then, a multimodal multiscale feature fusion (MMFF) block is built to model the spatial and scale dependencies for feature fusion and reduce the layer by layer transmission of redundancy or interference information. Finally, a dual-discriminator-based DA (DDA) block is designed for class-specific semantic feature and global structural alignments in both spatial and prediction spaces. It will enable HAMAE to model the cross-modal, cross-scale, and cross-domain associations, yielding more representative domain-invariant multimodal fusion features. Extensive experiments on five cross-domain MSRS datasets verify the superiority of the proposed HMDA-DANet over other state-of-the-art methods. © 1980-2012 IEEE.

关键词： Frequency domain analysis

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Robust key point descriptor for multi-spectral image matching

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Journal of Systems Engineering and Electronics 2014年第4期25卷 681-687页

作者： Yueming Qin Zhiguo Cao Wen Zhuo Zhenghong Yu National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of AutomationHuazhong University of Science and Technology Yichang Testing Technique R&D Institute

Histogram of collinear gradient-enhanced coding （HCGEC）, a robust key point descriptor for multi-spectral image matching, is proposed. The HCGEC mainly encodes rough structures within an image and suppresses detailed textural information, which is desirable in multi-spectral image matching. Experiments on two multi-spectral data sets demonstrate that the proposed descriptor can yield significantly better results than some state-of- the-art descriptors.

关键词： collinear gradient-enhanced coding (CGEC) key pointdescriptor multi-spectral image matching.

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Learning to Learn Transferable Generative Attack for Person Re-Identification

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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

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Rate-Splitting multiple Access Enabled Green Probabilistic Semantic Communication over Wireless Networks

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Xu, Ruopeng Yang, Zhaohui Mao, Yijie Huang, Chongwen Yang, Qianqian Xu, Lexi Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering China Hangzhou310027 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Zhejiang Prov. Key Lab. of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China China United Network Communications Corporation Research Institute Beijing100048 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China

In this paper, we propose a multi-user green probabilistic semantic communication (PSC) system for semantic communication (SemCom) facilitated by probabilistic knowledge graphs (PKGs), which are knowledge graphs (KGs) integrated with probability to represent semantic information. In the considered model, semantic information of different users is represented by PKGs. On this basis, we design a semantic compression model for multi-user downlink task-oriented SemCom, utilizing the semantic compression ratio (SCR) as a parameter to connect the computation and communication processes of information transmission. Utilizing rate-splitting multiple access (RSMA) technology, the transmitted messages are splitted into shared and private ones. Considering the limited wireless resources and constraint in semantic communication, an optimization problem with the goal of minimizing system energy consumption comprehensively considering the computation and communication process is formulated. In order to address the optimization problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design with successive convex approximation (SCA) method, semantic compression ratio optimization with variable substitution method, and computation capacity allocation with obtaining closed-form optimal solution. Simulation results verify the effectiveness of the proposed algorithm. © 2017 IEEE.

关键词： Knowledge graph

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