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A comprehensive survey of visible infrared person re-identification from an application perspective

Multimedia Tools and Applications 2024年第42期83卷 90243-90270页

作者： Chang, Hua Xu, Xin Liu, Wei Lu, Lingyi Li, Weigang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430065 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

Person re-identification (ReID) is a significant issue in computer vision, aiming to match the same pedestrian across various cameras. Recent research on this issue has successfully reached a satisfactory performance level in the daytime scenario. However, the performance of ReID in low illumination scenarios suffers a dramatic degradation due to the insufficient personal information captured by visible cameras in low or no light scenarios. To address this challenge, the adoption of novel camera technologies for more effective pedestrian data is imperative. Owing to the constraints of distance and usage scenarios in deploying depth cameras, practical infrared cameras are frequently utilized, spurring research in visible infrared person re-identification (VI-ReID). Significant progress has been made in the field of VI-ReID in recent years, but only a few reviews have categorized this problem based on methodology. This article offers a novel perspective by classifying VI-ReID according to application scenarios. Following our categorization scheme, the main advancements in deep learning for VI-ReID over the past five years have been grouped into three categories as follows: retrieval of infrared images using visible images (V→IR), retrieval of visible images using infrared images (IR→V), and joint retrieval of infrared and visible images (V→IR and IR→V). Next, this survey outlines the available datasets and evaluation metrics. After that, we categorize diverse methodologies under the proposed classification and assess their performance metrics across common datasets. Finally, we present two prospective avenues for future research in this domain, informed by the current state of advancement. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Coplanar waveguide multiband antenna based on open resonant ring

Coplanar waveguide multiband antenna based on open resonant ...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Li, Minquan Qu, Xin Xiao, Shuang Li, Chen Zhang, Zufeng Zhang, Weiqiang Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Liaoyang Province China

ISBN: (纸本)9781665452366

A compact coplanar waveguide multiband antenna based on an open resonant ring is proposed in this paper. The proposed antenna is fabricated on FR-4 substrate with a dielectric constant of 4.4 and a small size of 23 × 23 × 1.6mm3. In the design process of the antenna, the deformed circular split ring metamaterial (RSRR) is introduced to realize the broadband and multi frequency characteristics of the antenna. The two frequency bands of the antenna are 2.91-3.63GHz (relative bandwidth 22%), 4.19-9.32GHz (relative bandwidth 75.9%), covering WiMax (3.4/5.5GHz), lower C-band (4.41GHz), upper C-band (6.9GHz), WLAN (5.4/5.8GHz), lower X-band (8.7GHz) wireless standards, S11 ≤ -10dB. High frequency structure simulator (HFSS) is used to simulate the antenna structure, which has uniform radiation characteristics. © 2022 IEEE.

关键词： Coplanar waveguides

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Graph Prompt Clustering

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5794-5805页

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530219 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Beijing100816 China South China Technology Commercialization Center Guangzhou510006 China Guangdong University of Technology School of Information Engineering Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510033 China Ministry of Education Beijing100044 China

Due to the wide existence of unlabeled graph-structured data (e.g., molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Graph embeddings

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A cross-domain person re-identification algorithm based on distribution-consistency and multi-label collaborative learning

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Multimedia Tools and Applications 2024年第38期83卷 85939-85962页

作者： Zhang, Baohua Hao, Chen Lv, Xiaoqi Gu, Yu Wang, Yueming Liu, Xin Ren, Yan Li, Jianjun School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014010 China School of Information Engineering Mongolia Industrial University Inner Mongolia Huhehaote010051 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia Baotou014010 China

To decrease domain shift in cross-domain person re-identification, existing methods generate pseudo labels for training models, however, the inherent distribution between source domain data and the hard quantization loss is ignored. Therefore, a cross-domain person re-identification method based on distribution consistency and multi-label collaborative learning is proposed. Firstly, a soft binary cross-entropy loss function is constructed to constrain the inter-sample relationship of cross-domain transformation, which can ensure the consistency of appearance features and sample distribution, and achieving feature cross-domain alignment. On this basis, in order to suppress the noise of hard pseudo labels, a multi-label collaborative learning network is constructed. The soft pseudo labels are generated by using the collaborative foreground features and global features to guide the network training, making the model adapt to the target domain. The experimental results show that the proposed method has better performance than that of recent representative methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Federated learning

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A beamforming microstrip array antenna for K-band applications

A beamforming microstrip array antenna for K-band applicatio...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Yu, Zijian Feng, Wenwen Song, Xiaodi Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781665452366

A beamforming microstrip array antenna for K-band is proposed. The antenna array is a 4∗4 array, the array elements are composed of slotted rectangular microstrip antennas, and the feed network is a combination of series and parallel connections. The beamforming is achieved by using a microstrip phase shift network. The design results show that the VSWR of the antenna array is less than 1.8dB in the operating frequency bandwidth of 24.0 GHz-24.5 GHz. This antenna array can achieve a gain of more than 7.6dB in the beam range of 22.5° azimuth and 30° pitch. © 2022 IEEE.

关键词： Beamforming

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A Conflict-Priority-Based Variable Neighborhood Tabu Search Method for Multi-Satellite Scheduling

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Advances in Astronautics Science and Technology 2024年第2期7卷 163-176页

作者： Chen, Xiaoyu Gao, Qi Peng, Shengren Song, Shihui Liu, Yufei Dai, Guangming Wang, Maocai Zhang, Changxing School of Computer China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Lumo Road Wuhan430074 China Qian Xuesen Laboratory China Academy of Space Technology Youyi Road Beijing100094 China

With the increasing number and capabilities of orbital satellites and their onboard sensors, satellites scheduling plays an important role in growing demands for Earth observation tasks. A conflict-priority-based variable neighborhood tabu search method is proposed in this paper. By analyzing the flexibility of resources, the conflict-priority of tasks, and the visible time window features, the indicators of the impact, the conflict, the flexibility, and the bad impact are first established. On this basis, a first-come first-served greedy algorithm and a minimum bad impact greedy algorithm are designed for the generation of the initial solution. some rule-based heuristic strategies are also adopted for optimizing. Additionally, the algorithm incorporates three types of neighborhood structures including insertion, swapping and rearrangement, and deletion, using a tabu list to avoid local optima. Experimental results indicate the validity and efficiency of the proposed method. All solutions are within 10% of the optimal solution, with some even within 5%.The proposed method is effective for obtaining optimum solutions or solutions with a very good quality in various multi-satellite scheduling scenarios of realistic planning. © Chinese Society of Astronautics 2024.

关键词： Tabu search

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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A single notch ultra-wideband filter with wide Stopband

A single notch ultra-wideband filter with wide Stopband

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Li, Minquan Li, Chen Qu, Xin Xiao, Shuang Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781665452366

An ultra-wideband filter with wide stopband performance with single notch using E-shaped resonator is proposed. Based on a U-shaped MMR structure loaded by an open-circuited stub connected at its center, the loaded basic open-stub structure can generate transmission zeros on both sides of the passband to improve the in-band selectivity. Meanwhile, the symmetrical U-shaped open stub introduced not only strengthens the out-of-band suppression ability, but also optimizes the lower stopband. To suppress the in-band interference phenomenon, the proposed UWB BPF used E-shaped resonator coupled to the main transmission line of the basic UWB BPF to generate a notch band at 8GHz (Satellite communications X band). The filter has passband from 2.74 GHz to 11.5 GHz, the insertion loss is lower than 0.6dB and the filter has a relative high selectivity (30dB skirt factor is 0.891), Meanwhile, the filter has a wide stopband of up to 20.8GHz (S21 © 2022 IEEE.

关键词： Ultra-wideband (UWB)

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A One-to-Two Filter Power Divider Based on LTCC 12

A One-to-Two Filter Power Divider Based on LTCC

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12th International Conference on Electronics, Communications and Networks, CECNet 2022

作者： Li, Yuanyuan Wu, Xianliang Hu, Yunfeng Dong, Mengying Ma, Yue Geng, Xiaolei Fang, Rongzhen Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230001 China

ISBN: (纸本)9781643683683

In order to miniaturize microwave devices in RF systems, a power divider with filtering performance is designed. The filter power divider is based on the LTCC process, and miniaturization is achieved by cascading a bandpass filter and a one-to-two power divider. The band-pass filter adopts a fourth-order comb-line structure, and the one-to-two power divider adopts an LC lumped structure. The size of the one-to-two filter power divider designed is 10mm×4mm×5mm, which is working in 3.3GHz-3.6GHz. The simulation results show that the insertion loss(S21 and S31) are less than or equal to 4 dB, the return loss(S11) is greater than 15 dB, the isolation(S23) is greater than 17 dB, and the out-of-band suppression is greater than 18 dB in 3.8-4.3GHz and 22 dB in 0-3.2GHz and 4.3-6GHz. © 2022 The authors and IOS Press.

关键词： Bandpass filters

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Globally Consistent Vertical Federated Graph Autoencoder for Privacy-Preserving Community Detection 17th

Globally Consistent Vertical Federated Graph Autoencoder for...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Fang, Yutong Huang, Qingqing Ye, Enjie Guo, Wenzhong Guo, Kun Chen, Xiaoqi Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Community detection is a trendy area in research on complex network analysis and has a wide range of real-world applications, like advertising. As people become increasingly concerned about privacy, protecting participants’ privacy in distributed community detection has become a new challenge. When applied to not identically and independently distributed (Non-IID) data, most federated graph algorithms suffer from the weight divergence problem caused by diversified training of local and global models, resulting in accuracy degradation. Furthermore, the privacy protection approaches based on anonymization, such as differential privacy (DP), and cryptography, such as homomorphic encryption (HE), incur accuracy loss and high time consumption, respectively. In this paper, we propose a globally consistent vertical federated graph autoencoder (GCVFGAE) algorithm, which builds a globally consistent model among the coordinator and all participants to solve the Non-IID graph data problem. As well, an attribute blinding strategy based on security aggregation is developed to protect the network privacy of each participant without losing accuracy. Both real-world and artificial networks’ experiments show that our algorithm reaches higher accuracy than the existing vertical federated graph neural networks (GNNs) and the simple distributed graph autoencoder without federated learning and detects communities identical to those found by the standard graph autoencoder (GAE). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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