Computer Science ›› 2024, Vol. 51 ›› Issue (1): 72-83.doi: 10.11896/jsjkx.230700101
• Special Issue on the 58th Anniversary of Computer Science • Previous Articles Next Articles
JING Yeyiran1, YU Zeng1,2, SHI Yunxiao1, LI Tianrui1,2
CLC Number:
[1]ZHENG L,YANG Y,HAUPTMANN A G.Person Re-Identification:Past,Present and Future[J].arXiv:1610.02984,2016. [2]LIN Y,ZHENG L,ZHENG Z,et al.Improving Person Re-Identification by Attribute and Identity Learning[J].Pattern Recognition,2019,95:151-161. [3]LI W,ZHU X,GONG S.Harmonious Attention Network forPerson Re-Identification[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2018:2285-2294. [4]YE M,SHEN J,LIN G,et al.Deep Learning for Person Re-Iden-tification:A Survey and Outlook[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,44(6):2872-2893. [5]ZHENG Z,ZHENG L,YANG Y.Unlabeled Samples Generated by Gan Improve the Person Re-Identification Baseline in Vitro[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:3754-3762. [6]XIAO W.Research on domain adaptive pedestrian re-identification based on deep learning [D].Wuhan:Huazhong University of Science and Technology,2021. [7]WANG M,DENG W.Deep Visual Domain Adaptation:A Survey[J].Neurocomputing,2018,312:135-153. [8]LONG M,ZHU H,WANG J,et al.Unsupervised Domain Adaptation with Residual Transfer Networks[J].Advances in Neural Information Processing Systems,2016,31:3073-3086. [9]GANIN Y,LEMPITSKY V.Unsupervised Domain Adaptationby Backpropagation[C]//International Conference on Machine Learning.2015:1180-1189. [10]SAITO K,WATANABE K,USHIKU Y,et al.Maximum Clas-sifier Discrepancy for Unsupervised Domain Adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:3723-3732. [11]ZHAN F,ZHANG C.Spatial-Aware Gan for Unsupervised Person Re-Identification[C]//2020 25th International Conference on Pattern Recognition(ICPR),2021:6889-6896. [12]TANG H,ZHAO Y,LU H.Unsupervised Person Re-Identificationwith Iterative Self-Supervised Domain Adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019:1536-1543. [13]ZHOU S,KE M,LUO P.Multi-Camera Transfer Gan for Person Re-Identification[J].Journal of Visual Communication and Image Representation,2019,59:393-400. [14]DENG W,ZHENG L,YE Q,et al.Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-Identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:994-1003. [15]JIANG Y,CHEN W,SUN X,et al.Exploring the Quality of Gan Generated Images for Person Re-Identification[C]//Proceedings of the 29th ACM International Conference on Multimedia.2021:4146-4155. [16]VERMA A,SUBRAMANYAM A,WANG Z,et al.Unsuper-vised Domain Adaptation for Person Re-Identification Via Individual-Preserving and Environmental-Switching Cyclic Generation[J].IEEE Transactions on Multimedia,2021:364-377. [17]HOFFMAN J,TZENG E,PARK T,et al.Cycada:Cycle-Cons-istent Adversarial Domain Adaptation[C]//International Conference on Machine Learning.2018:1989-1998. [18]BOUSMALIS K,SILBERMAN N,DOHAN D,et al.Unsupervised Pixel-Level Domain Adaptation with Generative Adversa-rial Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:3722-3731. [19]WEI L,ZHANG S,GAO W,et al.Person Transfer Gan toBridge Domain Gap for Person Re-Identification[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:79-88. [20]ZHONG Z,ZHENG L,LI S,et al.Generalizing a Person Retrieval Model Hetero-and Homogeneously[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:172-188. [21]ZHU M,MING Z Q,YAN J R,et al.A Review of Research on Person Re-Identification Methods Based on Generative Adversarial Networks [J].Journal of Computer-Aided Design & Computer Graphics/Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao,2022,34(2):163-179. [22]LUO C,SONG C,ZHANG Z.Generalizing Person Re-Identification by Camera-Aware Invariance Learning and Cross-Domain Mixup[C]//Computer Vision-ECCV 2020:16th European Conference,Glasgow,UK,Part XV 16.2020:224-241. [23]ZHONG Z,ZHENG L,LUO Z,et al.Invariance Matters:Exemplar Memory for Domain Adaptive Person Re-Identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:598-607. [24]LEE D H.Pseudo-Label:The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks[C]//Workshop on Challenges in Representation Learning,ICML.2013:896. [25]HE K,ZHANG X,REN S,et al.Deep Residual Learning forImage Recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [26]LIN X,REN P,YEH C H,et al.Unsupervised Person Re-Identification:A Systematic Survey of Challenges and Solutions[J].arxiv:2109.06057,2021. [27]ZOU G F,FU J X,GAO M L,et al.Research Progress on Me-tric Learning Methods in Person Re-Identification [J].Control and Decision,2021,36(7):1547-1557. [28]GROSSMANN V,SCHMARJE L,KOCH R.Beyond Hard Labels:Investigating Data Label Distributions[J].arXiv:2207.06224,2022. [29]XIE Q,LUONG M T,HOVY E,et al.Self-Training with Noisy Student Improves Imagenet Classification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:10687-10698. [30]ZOU Y,YU Z,LIU X,et al.Confidence Regularized Self-Trai-ning[C]//Proceedings ofthe IEEE/CVF International Conference on Computer Vision.2019:5982-5991. [31]SOHN K,BERTHELOT D,CARLINI N,et al.Fixmatch:Simplifying Semi-Supervised Learning with Consistency and Confidence[J].Advances in Neural Information Processing Systems,2020,33:596-608. [32]WENG W,WEI B,KE W,et al.Learning Label-Specific Features with Global and Local Label Correlation for Multi-Label Classification[J].Applied Intelligence,2023,53(3):3017-3033. [33]ZHAO T,ZHANG Y,PEDRYCZ W.Robust Multi-Label Classification with Enhanced Global and Local Label Correlation[J].Mathematics,2022,10(11):1871. [34]ZHU Y,KWOK J T,ZHOU Z H.Multi-Label Learning withGlobal and Local Label Correlation[J].IEEE Transactions on Knowledge and Data Engineering,2017,30(6):1081-1094. [35]SUN W,SONG Y,CHEN C,et al.Face Spoofing DetectionBased on Local Ternary Label Supervision in Fully Convolu-tional Networks[J].IEEE Transactions on Information Forensics and Security,2020,15:3181-3196. [36]SUN G J,LIU J,LIU L Y.Research on Clustering Algorithms[J].Journal of Software,2008,19(1):48-61. [37]MADHULATHA T S.An Overview on Clustering Methods[J].arXiv:1205.1117,2012. [38]OMRAN M G,ENGELBRECHT A P,SALMAN A.An Overview of Clustering Methods[J].Intelligent Data Analysis,2007,11(6):583-605. [39]ESTER M,KRIEGEL H P,SANDER J,et al.A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//KDD.1996:226-231. [40]LIN Y,DONG X,ZHENG L,et al.A Bottom-up Clustering Approach to Unsupervised Person Re-Identification[C]//Procee-dings of the AAAI Conference on Artificial Intelligence.2019:8738-8745. [41]MOHANTY A,BANERJEE B,VELMURUGAN R.Ssmtreid-Net:Multi-Target Unsupervised Domain Adaptation for Person Re-Identification[J].Pattern Recognition Letters,2022,163:40-46. [42]REMIGEREAU F,MEKHAZNI D,ABDOLI S,et al.Know-ledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification[C]//2022 IEEE International Conference on Image Processing(ICIP).2022:3853-3557. [43]SONG L,WANG C,ZHANG L,et al.Unsupervised DomainAdaptive Re-Identification:Theory and Practice[J].Pattern Recognition,2020,102:107173. [44]TANG C,XUE D,CHEN D.Multi-Level Mutual Supervisionfor Cross-Domain Person Re-Identification[J].Journal of Visual Communication and Image Representation,2022,89:103674. [45]ZHANG X,CAO J,SHEN C,et al.Self-Training with Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:8222-8231. [46]MACQUEEN J.Some Methods for Classification and Analysis of Multivariate Observations[C]//Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probabi-lity.1967:281-297. [47]FAN H,ZHENG L,YAN C,et al.Unsupervised Person Re-Identification:Clustering and Fine-Tuning[J].ACM Transactions on Multimedia Computing,Communications,and Applications(TOMM),2018,14(4):1-18. [48]WU J,LIAO S,WANG X,et al.Clustering and Dynamic Sampling Based Unsupervised Domain Adaptation for Person Re-Identification[C]//2019 IEEE International Conference on Multimedia and Expo(ICME).2019:886-891. [49]WANG R,YAN J,YANG X.Graduated Assignment for JointMulti-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning[J].Advances in Neural Information Processing Systems,2020,33:19908-19919. [50]GAO S,WANG J,LU H,et al.Pose-Guided Visible Part Ma-tching for Occluded Person Reid[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:11744-11752. [51]GUO W,ZHANG L,TU S,et al.Self-Supervised Bidirectional Learning for Graph Matching[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023:7784-7792. [52]YE M,MA A J,ZHENG L,et al.Dynamic Label Graph Ma-tching for Unsupervised Video Re-Identification[C]//Procee-dings of the IEEE International Conference on Computer Vision.2017:5142-5150. [53]CAETANO T S,MCAULEY J J,CHENG L,et al.Learning Graph Matching[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(6):1048-1058. [54]LIVI L,RIZZI A.The Graph Matching Problem[J].PatternAnalysis and Applications,2013,16:253-283. [55]XU K,HUW,LESKOVEC J,et al.How Powerful Are Graph Neural Networks?[J].arXiv:1810.00826,2018. [56]ZHOU J,CUI G,HU S,et al.Graph Neural Networks:A Review of Methods and Applications[J].AI Open,2020,1:57-81. [57]SCARSELLI F,GORI M,TSOI A C,et al.The Graph Neural Network Model[J].IEEE Transactions on Neural Networks,2008,20(1):61-80. [58]FENG H,CHEN M,HU J,et al.Complementary Pseudo Labels for Unsupervised Domain Adaptation on Person Re-Identification[J].IEEE Transactions on Image Processing,2021,30:2898-2907. [59]ZHONG Z,ZHENG L,LUO Z,et al.Learning to Adapt Inva-riance in Memory for Person Re-Identification[J].IEEETran-sactions on Pattern Analysis and Machine Intelligence,2020,43(8):2723-2738. [60]FU Y,WEI Y,WANG G,et al.Self-Similarity Grouping:A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-Identification[C]//proceedings of the IEEE/CVF International Conference on Computer Vision.2019:6112-6121. [61]YANG F,LI K,ZHONG Z,et al.Asymmetric Co-Teachingfor Unsupervised Cross-Domain Person Re-Identification[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:12597-12604. [62]ZHAI Y,LU S,YE Q,et al.Ad-Cluster:Augmented Discriminative Clustering for Domain Adaptive Person Re-Identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:9021-9030. [63]HUANG Y,PENG P,JIN Y,et al.Domain Adaptive Attention Model for Unsupervised Cross-Domain Person Re-Identification[J].arXiv:1905.10529,2019. [64]ZHAO F,LIAO S,XIE G S,et al.Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-Identification[C]//Computer Vision-ECCV 2020:16th European Conference.Glasgow,UK,2020:526-544. [65]GE Y,CHEN D,LI H.Mutual Mean-Teaching:Pseudo LabelRefinery for Unsupervised Domain Adaptation on Person Re-Identification[J].arXiv:2001.01526,2020. [66]CHEN S,FAN Z,YIN J.Pseudo Label Based on Multiple Clustering for Unsupervised Cross-Domain Person Re-Identification[J].IEEE Signal Processing Letters,2020,27:1460-1464. [67]LI J,ZHANG S.Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification[C]//Computer Vision-ECCV 2020:16th European Conference.Glasgow,UK,2020:483-499. [68]ZHENG K,LIU W,HE L,et al.Group-Aware Label Transfer for Domain Adaptive Person Re-Identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:5310-5319. [69]LIU X,ZHANG S.Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification[J].arXiv:2105.04776,2021. [70]LI Y,YAO H,XU C.Test:Triplet Ensemble Student-Teacher Model for Unsupervised Person Re-Identification[J].IEEE Transactionson Image Processing,2021,30:7952-7963. [71]HUANG D,ZHANG L,DIAO Q,et al.Asymmetric MutualLearning for Unsupervised Cross-Domain Person Re-Identification[C]//Trends in Artificial Intelligence:18th Pacific Rim International Conference on Artificial Intelligence(PRICAI 2021).Hanoi,Vietnam,2021:124-137. [72]ZHENG K,LAN C,ZENG W,et al.Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2021:3538-3546. [73]HOU H,ZHOU Y,ZHAO J,et al.Unsupervised Cross-Domain Person Re-Identification with Self-Attention and Joint-Flexible Optimization[J].Image and Vision Computing,2021,111:104191. [74]YU S,WANG S.Consistency Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification[C]//2022 the 5th International Conference on Image and Graphics Processing(ICIGP).2022:159-166. [75]WANG W,ZHAO F,LIAO S,et al.Attentive Waveblock:Complementarity-Enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-Identification and Beyond[J].IEEE Transactions on Image Processing,2022,31:1532-1544. [76]TAO Y,ZHANG J,HONG J,et al.Dreamt:Diversity Enlarged Mutual Teaching for Unsupervised Domain Adaptive Person Re-Identifcation[J].IEEE Transactions on Multimedia,2022,25:4586-4597. [77]HE T,SHEN L,GUO Y,et al.Secret:Self-Consistent Pseudo Label Refinement for Unsupervised Domain Adaptive Person Re-Identification[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:879-887. [78]CHEN Z,CUI Z,ZHANG C,et al.Dual Clustering Co-Teaching with Consistent Sample Mining for Unsupervised Person Re-Identification[J].IEEE Transactions on Circuits and Systems for Video Technology,2023,33:5908-5920. [79]WANG H,YANG M,LIU J,et al.Pseudo-Label Noise Prevention,Suppression and Softening for Unsupervised Person Re-Identification[J].IEEE Transactions on Information Forensics and Security,2023,18:3222-3237. [80]WANG X L.A Review of Label Noise Learning Algorithms[J].Computer System Applications,2021,30(1):10-18. [81]HINTON G,VINYALS O,DEAN J.Distilling the Knowledgein a Neural Network[J].arXiv:1503.02531,2015. [82]CHEN P,LIU S,ZHAO H,et al.Distilling Knowledge ViaKnowledge Review[C]//Proceedings of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition.2021:5008-5017. [83]KUMAR M,PACKER B,KOLLER D.Self-Paced Learning for Latent Variable Models[J].Advances in Neural Information Processing Systems,2010,23:1-9. [84]WANG G,YUAN Y,CHEN X,et al.Learning Discriminative Features with Multiple Granularities for Person Re-Identification[C]//Proceedings of the 26th ACM InternationalConfe-rence on Multimedia.2018:274-282. [85]YANG W,ZHANG D.Unsupervised Person Re-Identification by Part-Compensated Soft Multi-Label Learning[J].IET Image Processing,2022,16(7):2012-2024. [86]TAY C P,YAP K H.Collaborative Learning Mutual Network for Domain Adaptation in Person Re-Identification[J].Neural Computing and Applications,2022,34(14):12211-12222. [87]TU Y.Domain Camera Adaptation and Collaborative MultipleFeature Clustering for Unsupervised Person Re-Id[C]//Proceedings of the 3rd International Workshop on Human-Centric Multimedia Analysis.2022:51-59. [88]ZHENG L,SHEN L,TIAN L,et al.Scalable Person Re-Identification:A Benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision,2015:1116-1124. [89]SANTINI S,JAIN R.Similarity Measures[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(9):871-883. [90]CHENG D,LI J,KOU Q,et al.H-Net:Unsupervised Domain Adaptation Person Re-Identification Network Based on Hierarchy[J].Image and Vision Computing,2022,124:104493. [91]CHEN G,LU Y,LU J,et al.Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-Identification[C]//Computer Vision-ECCV 2020:16th European Confe-rence.Glasgow,UK,2020:643-659. [92]WANG D,ZHANG S.Unsupervised Person Re-IdentificationVia Multi-Label Classification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:10981-10990. [93]FELZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Object Detection with Discriminatively Trained Part-Based Models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,32(9):1627-1645. [94]ZHONG Z,ZHENG L,CAO D,et al.Re-Ranking Person Re-Identification with K-Reciprocal Encoding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1318-1327. [95]REN S,HE K,GIRSHICK R,et al.Faster R-Cnn:TowardsReal-Time Object Detection with Region Proposal Networks[J].Advances in Neural Information Processing Systems,2015,28(39):1137-1149. |
[1] | GE Huibin, WANG Dexin, ZHENG Tao, ZHANG Ting, XIONG Deyi. Study on Model Migration of Natural Language Processing for Domestic Deep Learning Platform [J]. Computer Science, 2024, 51(1): 50-59. |
[2] | JIN Yu, CHEN Hongmei, LUO Chuan. Interest Capturing Recommendation Based on Knowledge Graph [J]. Computer Science, 2024, 51(1): 133-142. |
[3] | SUN Shukui, FAN Jing, SUN Zhongqing, QU Jinshuai, DAI Tingting. Survey of Image Data Augmentation Techniques Based on Deep Learning [J]. Computer Science, 2024, 51(1): 150-167. |
[4] | WANG Weijia, XIONG Wenzhuo, ZHU Shengjie, SONG Ce, SUN He, SONG Yulong. Method of Infrared Small Target Detection Based on Multi-depth Feature Connection [J]. Computer Science, 2024, 51(1): 175-183. |
[5] | CHEN Tianyi, XUE Wen, QUAN Yuhui, XU Yong. Raindrop In-Situ Captured Benchmark Image Dataset and Evaluation [J]. Computer Science, 2024, 51(1): 190-197. |
[6] | SHI Dianxi, LIU Yangyang, SONG Linna, TAN Jiefu, ZHOU Chenlei, ZHANG Yi. FeaEM:Feature Enhancement-based Method for Weakly Supervised Salient Object Detection via Multiple Pseudo Labels [J]. Computer Science, 2024, 51(1): 233-242. |
[7] | ZHOU Wenhao, HU Hongtao, CHEN Xu, ZHAO Chunhui. Weakly Supervised Video Anomaly Detection Based on Dual Dynamic Memory Network [J]. Computer Science, 2024, 51(1): 243-251. |
[8] | HOU Jing, DENG Xiaomei, HAN Pengwu. Survey on Domain Limited Relation Extraction [J]. Computer Science, 2024, 51(1): 252-265. |
[9] | YAN Zhihao, ZHOU Zhangbing, LI Xiaocui. Survey on Generative Diffusion Model [J]. Computer Science, 2024, 51(1): 273-283. |
[10] | ZHAO Mingmin, YANG Qiuhui, HONG Mei, CAI Chuang. Smart Contract Fuzzing Based on Deep Learning and Information Feedback [J]. Computer Science, 2023, 50(9): 117-122. |
[11] | XU Jie, WANG Lisong. Contrastive Clustering with Consistent Structural Relations [J]. Computer Science, 2023, 50(9): 123-129. |
[12] | LI Haiming, ZHU Zhiheng, LIU Lei, GUO Chenkai. Multi-task Graph-embedding Deep Prediction Model for Mobile App Rating Recommendation [J]. Computer Science, 2023, 50(9): 160-167. |
[13] | HUANG Hanqiang, XING Yunbing, SHEN Jianfei, FAN Feiyi. Sign Language Animation Splicing Model Based on LpTransformer Network [J]. Computer Science, 2023, 50(9): 184-191. |
[14] | ZHU Ye, HAO Yingguang, WANG Hongyu. Deep Learning Based Salient Object Detection in Infrared Video [J]. Computer Science, 2023, 50(9): 227-234. |
[15] | ZHANG Yian, YANG Ying, REN Gang, WANG Gang. Study on Multimodal Online Reviews Helpfulness Prediction Based on Attention Mechanism [J]. Computer Science, 2023, 50(8): 37-44. |
|