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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Sirong Zheng, Huicheng School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783031781094

Much progress has been made in the field of person re-identification, but changes in clothing have hindered the practical application of long-term person re-identification. Cloth-changing person re-identification (CC-ReID) aims to address this problem, with the main challenge being the extraction of discriminative features unrelated to clothing. Existing methods, which mainly focus on introducing clothing-irrelevant cues such as key points, contours, and 3D shapes, require additional modules for feature extraction, resulting in increased complexity and potential inaccuracies due to dependence on the performance of external models. Few studies directly use the original RGB images to make the model constantly focus on clothing-independent information. In this paper, we propose a Confidence-Guided Feature Alignment Network (CGFA) for CC-ReID. Specifically, we design a confidence module that automatically learns to make confidence adjustments to fine-grained information, prompting the model to mine clothing-independent discriminative features without introducing other modal cues. By transferring knowledge, we encourage the model to learn discriminative identity features that are independent of clothing bias. As a result, the confidence module can be removed during the inference phase. The proposed simple but efficient method uses only RGB modality without additional cues, and can serve as a powerful baseline for CC-ReID to drive future research. Extensive experiments on the CC-ReID datasets demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature extraction

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Modification of Maxwell model for conductivity prediction of carbon nanotubes-filled polymer composites with tunneling effect

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 25-36页

作者： Jue ZHU Longyuan LI Ningtao ZHU Zhejiang Provincial Engineering Research Center for the Safety of Pressure Vessel and Pipeline Ningbo 315211Zhejiang ProvinceChina Key Laboratory of Impact and Safety Engineering(Ningbo University) Ministry of EducationNingbo 315211Zhejiang ProvinceChina School of Engineering Computing and MathematicsUniversity of PlymouthPlymouth PL48AAU.K. Xi'an Zhanshi Testing&Engineering Co.Ltd. Xi'an 710018China

Carbon nanotubes(CNTs)have garnered great attention in recent years due to their outstanding electrical,thermal,and mechanical *** incorporation of small amounts of CNTs in polymers can substantially improve the sensitivity of the polymer's electrical *** paper presents a modified Maxwell model to evaluate the electrical conductivity of CNTs-filled polymer composites by introducing a transition zone to account for the tunneling *** this modified Maxwell model,the CNTs-filled polymer composite is modeled as a three-phase composite,consisting of a matrix(polymer),inclusions(CNTs),and a transition zone(tunneling zone).The effective electrical conductivity(EEC)of the composite is calculated based on the volume fractions and electrical conductivities of the matrix,inclusions,and transition *** model's validity is confirmed through the use of available test data,which demonstrates its capability to accurately capture the nonlinear conductivity behavior observed in CNTs-polymer *** study offers valuable insights into the design of high-performance conductive polymer nanocomposites,and enhances the understanding of electrical conduction mechanisms in CNT-dispersed polymer composites.

关键词： carbon nanotube(CNT) polymer composite electrical conductivity tunneling Maxwell model

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Reflectance Recovery Guided Learning of Illumination-Invariant Features for Person Re-Identification 18th

Reflectance Recovery Guided Learning of Illumination-Invari...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Chen, Xianbiao Xie, Xiaohua School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9789819610679

In real-world scenarios, the changes of illumination are noticeable and can significantly impact the performance of re-identification (ReID) algorithms. However, the existing person ReID methods predominantly concentrate on tackling challenges in scenarios with minimal illumination fluctuations. The key to tackling this issue is to extract illumination-invariant features. So we propose a joint learning framework which combines the recovery of reflectance map and extraction of illumination-invariant feature. By sharing parameters, the proposed module, which can be seamlessly detached during the inference phase, contributes to a reduction in inference computation time. We also introduce an adversarial learning mechanism, utilizing illumination category and person identity to facilitate the extraction of features that are invariant to illumination. Besides, due to the lack of person ReID dataset containing images under diverse lighting conditions, we construct a real-world dataset called ICReID in which images have drastic illumination changes. Extensive experiments demonstrate the effectiveness of the proposed method, which achieves significant performance on both synthetic and real datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Redactable Blockchain From Decentralized Chameleon Hash Functions, Revisited

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IEEE Transactions on Computers 2025年第6期74卷 1911-1920页

作者： Li, Cong Shen, Qingni Wu, Zhonghai Peking University School of Software and Microelectronics Beijing100871 China Peking University National Engineering Research Center for Software Engineering Beijing100871 China Peking University Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing100871 China Peking University PKU-OCTA Laboratory for Blockchain and Privacy Computing Beijing100871 China

Recently, redactable blockchains have attracted attention owing to enabling the contents of blocks to be re-written. The existing redactable blockchain solutions can be classified as two categories, the centralized one and decentralized one. In centralized solutions, a single blockchain node possessing the trapdoor conducts redaction operations. However, they suffer from the issue of single point of failure. In decentralized solutions, redaction operations are performed by numerous blockchain nodes cooperatively. But there also exists the issue of inefficiency or requiring a trusted party in these solutions. Subsequently, Jia et al. proposed a redactable blockchain solution from a decentralized chameleon hash function (DCH) they designed, which supports the threshold redaction, traceability and consistency check. Nevertheless, after carefully analyzing their solution, we find that it fails to achieve the security they claimed by presenting a concrete attack. To resolve this security issue, we propose a novel chameleon hash function scheme that achieves strong collision-resistant security while maintaining simple and efficient as the building block. Based on it, we then present an improved DCH scheme with sufficient security, so that the redactable blockchain from it can resist the presented attack. Theoretical and experimental analyses demonstrate that improved DCH achieves efficiency comparable to DCH. © 1968-2012 IEEE.

关键词： Decentralized control

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Fairness is essential for robustness:fair adversarial training by identifying and augmenting hard examples

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Frontiers of Computer Science 2025年第3期19卷 1-13页

作者： Ningping MOU Xinli YUE Lingchen ZHAO Qian WANG Key Laboratory of Aerospace Information Security and Trusted Computing(Ministry of Education) School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Adversarial training has been widely considered the most effective defense against adversarial ***,recent studies have demonstrated that a large discrepancy exists in the class-wise robustness of adversarial training,leading to two potential issues:firstly,the overall robustness of a model is compromised due to the weakest class;and secondly,ethical concerns arising from unequal protection and biases,where certain societal demographic groups receive less robustness in defense *** these issues,solutions to address the discrepancy remain largely *** this paper,we advance beyond existing methods that focus on class-level *** investigation reveals that hard examples,identified by higher cross-entropy values,can provide more fine-grained information about the ***,we find that enhancing the diversity of hard examples can effectively reduce the robustness gap between *** by these observations,we propose Fair Adversarial Training(FairAT)to mitigate the discrepancy of class-wise *** experiments on various benchmark datasets and adversarial attacks demonstrate that FairAT outperforms state-of-the-art methods in terms of both overall robustness and *** a WRN-28-10 model trained on CIFAR10,FairAT improves the average and worst-class robustness by 2.13%and 4.50%,respectively.

关键词： robust fairness adversarial training hard example data augmentation

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EB-SNN: An Ensemble Binary Spiking Neural Network for Visual Recognition 27th

EB-SNN: An Ensemble Binary Spiking Neural Network for Visua...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Xinjie Tang, Jianxiong Lai, Jianhuang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangzhou510555 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783031781858

In recent years, spiking neural networks (SNNs) have gained significant attention in visual recognition tasks due to the low computational energy. However, most SNNs have a large number of parameters, which limits their use on resource-limited devices. In this paper, we propose an Ensemble Binary Spiking Neural Network (EB-SNN) for accurate and memory-friendly visual recognition. The EB-SNN is modeled by Ensemble Binary Weights (EBW) module, which integrates multiple binary weights for lightweight SNN modeling. Meanwhile, we propose Knowledge Alignment Strategy to ensure that the EB-SNN can approximate a well-trained SNN for good performance. Experimental results show that the EB-SNN can achieve accuracy of 95.39% on CIFAR10, using 9.3% memory of full-precision SNN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Local binary pattern

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Breaking Information Silos: Global Guided Task Prediction for Class-Incremental Learning 27th

Breaking Information Silos: Global Guided Task Prediction fo...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hu, Chaoshun Ye, Biaohua Chen, Zixuan Lai, Jian-Huang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangzhou510555 China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou China

ISBN: (纸本)9783031781889

Class-incremental learning (CIL) aims to learn a series of tasks sequentially, each introducing several new categories. Because providing the task labels during inference can significantly increase accuracy, many approaches attempt to predict task labels in CIL. However, existing works focus on learning local information and overlook the importance of global information. The absence of global guidance leads to the formation of information silos across disparate tasks, resulting in potential inter-task interference. To break information silos, we propose a method called Global Guided Task Prediction (GGTP) to introduce global information. Our method consists of two modules. The local de-redundant module aims to reduce information redundancy across different tasks from a global perspective. We combine dual encoders and feature decorrelation loss to effectively reduce redundancy while minimizing catastrophic forgetting. The global information module explicitly extracts global information to serve as auxiliary information guiding task prediction. We extract the most important information from all local information through a global encoder and then aggregate them to form global information. Extensive experiments validate the effectiveness of our method, which achieves state-of-the-art results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Redundancy

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Model Recovery in Federated Unlearning With Restricted Server Data Resources

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IEEE Internet of Things Journal 2025年第11期12卷 17920-17935页

作者： Zhang, Jianxin Zhao, Mengda Wang, Zhenwei Su, Weijian Wang, Pengfei Dalian Minzu University School of Computer Science and Engineering Dalian116600 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China

Recent model recovery methods in federated unlearning (FUL) either rely on additional communication with the remaining clients or require large amounts of high-quality data from the server for training, overlooking scenarios with limited data resources. Currently, Contrastive Language-Image Pretraining (CLIP) has demonstrated remarkable performance across a wide range of tasks, particularly excelling in few-shot learning scenarios. In this paper, inspired by CLIP, we explore the scenario of few-shot knowledge distillation and propose CLIP-Guided Few-shot Knowledge Distillation (CGKD) for Model Recovery in FUL. CGKD mainly consists of three components: 1) The unlearning module constructs the unlearning model by erasing all historical contributions of the target client, and this model is treated as the student model;2) Fine-tuning the pre-trained CLIP model using few-shot data from the server side to obtain a more robust teacher model (CLIP*);and 3) Model recovery is achieved through knowledge distillation, leveraging the rich visual and semantic knowledge of CLIP∗ to enhance the student model's understanding of image semantic context, thereby improving the performance of the unlearning model. Extensive experimental results demonstrate that CGKD outperforms the compared FUL method in recovery performance across four standard datasets, validating the effectiveness of our approach. © 2014 IEEE.

关键词： Federated learning

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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A Novel Construction of Two-Dimensional Z-Complementary Array Code Sets Based on Extended Boolean Functions

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7821-7834页

作者： Tian, Liying Song, Xiaoshi Li, Yubo Zhang, Haijun Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Two-dimensional (2-D) array sets with good 2-D correlation properties have received considerable attention in wireless communication systems. This paper focuses on 2-D Z-complementary array code sets (ZCACSs), which have ideal 2-D correlation properties within a rectangular zone around the origin. By developing a theory linking 2-D extended Boolean functions with 2-D arrays, we establish an efficient construction of 2-D ZCACSs. In contrast to previous work, the proposed construction is capable of producing 2-D ZCACSs with new flexible parameters. Constructions of three-dimensional (3-D) and higher-dimensional ZCACSs are further investigated. We remark that 2-D and higher-dimensional complete complementary codes (CCCs) can also be generated from the proposed constructions. © 1967-2012 IEEE.

关键词： Codes (symbols)

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