检索结果-内蒙古大学图书馆

Visual Prompt Flexible-Modal Face Anti-Spoofing

IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2597-2606页

作者： Yu, Zitong Cai, Rizhao Cui, Yawen Liu, Ajian Chen, Changsheng Great Bay University School of Computing and Information Technology Dongguan523000 China Nanyang Technological University ROSE Lab School of EEE 639798 Singapore Hong Kong Polytechnic University Kowloon Hong Kong Chinese Academy of Sciences University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Beijing100190 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security College of Electronics and Information Engineering Shenzhen518060 China

Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data collected from the real world is often imperfect due to missing modalities from various imaging sensors. Recently, flexible-modal FAS (Yu et al. 2023) has attracted more attention, which aims to develop a unified multimodal FAS model using complete multimodal face data but is insensitive to test-time missing modalities. In this paper, we tackle one main challenge in flexible-modal FAS, i.e., when missing modality occurs either during training or testing in real-world situations. Inspired by the recent success of the prompt learning in language models, we propose Visual Prompt flexible-modal FAS (VP-FAS), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to downstream flexible-modal FAS task. Specifically, both vanilla visual prompts and residual contextual prompts are plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 4% learnable parameters compared to training the entire model. Furthermore, missing-modality regularization is proposed to force models to learn consistent multimodal feature embeddings when missing partial modalities. Extensive experiments conducted on two multimodal FAS benchmark datasets demonstrate the effectiveness of our VP-FAS framework that improves the performance under various missing-modality cases while alleviating the requirement of heavy model re-training. © 2004-2012 IEEE.

关键词： Alignment

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Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1452-1463页

作者： Mei Liu Xiaoyan Zhang Mingsheng Shang Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810016China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Shenzhen Research Institute Lanzhou UniversityShenzhen 518063China

Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)*** obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA ***,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ***,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.

关键词： Consensus filter k-winners-take-all(kWTA) multirobot coordination noise resistance

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Patch-based Cross-domain Adaptation for Few-shot Remote Sensing Scene Recognition 2

Patch-based Cross-domain Adaptation for Few-shot Remote Sens...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： He, Chengzhi Liu, Yu Jiang, Zhizhuo Li, Yaowen Yan, Chenggang Zheng, Bolun Tsinghua University Shenzhen International Graduate School Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China Hangzhou Dianzi University Intelligent Information Processing Laboratory Hangzhou China

ISBN: (纸本)9798331515669

Recognizing new classes from a few labeled remote sensing images presents a significant challenge. Existing methodologies assume that training and testing datasets come from identical domains. Collecting training datasets of remote sensing images from each domain is practically unfeasible. To tackle this challenge, we propose a strategy that leverages image patch feature embedding and cross-adaptation mechanism for cross-domain few-shot classification. Initially, the supplied image is segmented into patches of the same size. Subsequently, feature embeddings are extracted and reinforced with prototypical triplet loss to enhance robustness. A unified module then facilitates cross-adaptation between the support and query sets, producing classification features that effectively represent the relationship between them, thus better adapting to few-shot remote sensing classification challenges with domain shifts. Our proposed method's excellence and generality are confirmed by comprehensive experiments. © 2024 IEEE.

关键词： Jurassic

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A super resolution target separation and reconstruction approach for single channel sar against deceptive jamming

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Defence Technology（防务技术） 2023年第3期21卷 164-175页

作者： Shi-qi Liu Bing Li Bo Zhao Lei Huang Yue-zhou Wu Wei-min Bao Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information EngineeringShenzhen UniversityShenzhen518060China School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China School of Computer Science Civil Aviation Flight University of ChinaGuanghan618307China China Aerospace Science and Technology Corporation Beijing100048China

The excellent remote sensing ability of synthetic aperture radar(SAR)will be misled seriously when it encounters deceptive jamming which possesses high fidelity and *** this paper,the dynamic synthetic aperture(DSA)scheme is used to extract the difference between the true and false targets.A simultaneous deceptive jamming suppression and target reconstruction method is proposed for a single channel SAR system to guarantee remote sensing *** system model is formulated as a sparse signal recovery problem with an unknown parametric dictionary to be *** iterative reweighted method is employed to jointly handle the dictionary parameter learning and target reconstruction problem in an majorization-minimization framework,where a surrogate function majorizing the Gaussian entropy in the objective function is introduced to circumvent its *** dictionary parameter learning,the grid mismatching problem in a fixed grid based method is ***,the proposed method can reap a super resolution ***,a simple yet effective DSA section scheme is developed for the SAR data excerpting,in which only two DSAs are *** results about location error and reconstruction power error reveal that the proposed method is able to achieve a good performance in deceptive jamming suppression.

关键词： channel resolution separation

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An EEG Source Imaging-based Feature Extraction Method for Motor Imagery Classification

An EEG Source Imaging-based Feature Extraction Method for Mo...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Li, Junhan Zheng, Nengheng Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China Pengcheng Laboratory Shenzhen China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

This paper presents a new feature extraction method for Electroencephalogram (EEG)-based motor imagery (MI) classification. Current researches mostly classify different MIs by detecting the event-related desynchronization (ERD) phenomenon from the EEG signals. Due to the poor spatial resolution of the MI-EEG signals, the cortical area (source) activating the MI cannot be located accurately with the EEG (sensor) signal, which might degrade the classification accuracy. This study adopts the EEG source imaging (ESI) technique to estimate the cortical area where source ERD happens from the EEG signal. An improved ESI method based on the linearly constrained minimum variance (LCMV) algorithm, in which an average LCMV filter and an average baseline covariance are constructed for the ESI, is proposed to locate the activated cortical area from the noisy EEG signals. The source ERD features are then extracted. Analytical results show that, for subjects with obvious average source ERD phenomenon, their activated cortical area in a single-trial MI can be well located. MI classification results also support the feasibility of the proposed method for MI-EEG signal processing. © 2022 IEEE.

关键词： Electroencephalography

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Group-Oriented Multi-Signature supporting Monotonic Endorse Policies in Hyperledger Fabric 6

Group-Oriented Multi-Signature supporting Monotonic Endorse ...

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6th IEEE International Conference on Blockchain, Blockchain 2023

作者： Zhang, Peng Huang, Yongwen Ge, Fa Liu, Yuhong Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Electronics and Information Engineering Shenzhen China Santa Clara University Department of Computer Science and Engineering Santa Clara United States

ISBN: (纸本)9798350319293

As multi-signature can produce one joint signature on the same message by an interactive protocol among a group of signers, it is widely used in all kinds of blockchain platforms (e.g., Bitcoin, Hypherledger Fabric, Algorand) to reduce the proportion of signatures in blocks, so that the platform scalability is improved. However, as existing multi-signature schemes mainly focus on 'AND' relationship among multiple signers, they cannot adequately support the Fabric endorsement process that generally requires monotonic endorse policy with both 'AND' and 'OR' operations. Therefore, a Group-oriented Multi-Signature scheme (GMS) is proposed, which can aggregate the signatures of multiple signers from any group into one. Furthermore, GMS is used in the process of Fabric endorsement instead of ECDSA signature, so that the transaction protocol is improved. Analysis shows that, by aggregating multiple endorsement signatures according to the monotonic endorse policies, the proposed GMS has the security features of unforgeability, anonymity, revocability and traceability, and the improved transaction protocol has advantages in terms of smaller block size and faster verification time. In addition, it can protect the endorsement policies and the identities of the endorsement nodes anonymity. © 2023 IEEE.

关键词： Authentication

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Source Localization for Extremely Large-Scale Antenna Arrays with Spatial Non-Stationarity 48

Source Localization for Extremely Large-Scale Antenna Arrays...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Wu, Xiaohuan Sun, Ji Jia, Xiaoyuan Wang, Shuxin Nanjing University of Posts and Telecommunications School of Telecommunication and Information Engineering Nanjing210003 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China

ISBN: (纸本)9781728163277

Extremely large-scale antenna array (ELAA) is a promising technique in 6G and autonomous driving thanks to its high spatial resolution. However, due to the extremely large array aperture, the sources may only "see"a portion of the array, called visibility region (VR). Since the information of VR is usually unknown a priori, traditional methods may encounter performance degradation. In this paper, we show that under the exact steering vector model of ELAA, the eigenvectors of the signal subspace and the steering vectors are approximately collinear in most scenarios. Thus, the angle and range can be easily estimated by using the eigenvectors of the signal subspace. Moreover, the VRs of each source can be also estimated from the eigenvectors of signal subspace. Numerical results show that the estimation performance of our method is comparable to MUSIC with known VR information. © 2023 IEEE.

关键词： collinear exact steering vector extremely largescale antenna array (ELAA) Source localization spatial non-stationarity

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Unsupervised Feature Selection Based on 3-D Feature Decision Graph for High-dimensional Data 5

Unsupervised Feature Selection Based on 3-D Feature Decision...

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5th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2022

作者： Jia, Hong Weng, Yubin Guangdong Key Laboratory of Intelligent Information Processing China and College of Electronics and Information Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781450398343

Feature selection is aimed at reducing the dimensionality of data sets and obtaining a feature subset with better performance for the target learner. Unsupervised feature selection is more challenging because of the lack of label information. In this paper, the idea of decision graph is applied to unsupervised feature selection. Specifically, a 3-D decision graph model of features is proposed to reveal the characteristics of each feature and the relationship between them. Then, a movable hyperplane is constructed to select specified number of features from the original feature space to form the final feature subset. Experiments in comparison with both traditional and novel algorithms on benchmark data sets reveal that the proposed method is able to select feature subset with both of lower redundancy and higher performance. © 2022 ACM.

关键词： Feature Selection

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Multi-view Graph Learning with Fuzzy Linear Discriminant Analysis 5

Multi-view Graph Learning with Fuzzy Linear Discriminant Ana...

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5th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2022

作者： Jia, Hong Huang, Jian Guangdong Key Laboratory of Intelligent Information Processing China and College of Electronics and Information Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781450398343

The multi-view clustering method based on graph learning has been extensively studied because of its good clustering effect. However, most of the graph learning methods are based on the original data features, which often contain noise and outliers, and using them directly may lead to learning suboptimal graph. To address the above problems, we propose a multi-view graph learning method based on linear discriminant analysis. On the one hand, the manifold structure obtained using the global graph guides the learning of the discriminative projections for each view, and on the other hand, the common graph is learned using the projection data of each view. Then, an efficient solution is proposed by dividing the problem into two sub-problems. Finally, experiments are conducted on some public multi-view datasets to verify the effectiveness of the method. © 2022 ACM.

关键词： Learning systems

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