广西师范大学学报（自然科学版） ›› 2022, Vol. 40 ›› Issue (5): 90-103.doi: 10.16088/j.issn.1001-6600.2022022501

• 综述 • 上一篇    下一篇

基于孪生网络的目标跟踪算法研究进展

梁启花1, 胡现韬1, 钟必能1*, 于枫1,2, 李先贤1   

  1. 1.广西多源信息挖掘与安全重点实验室(广西师范大学), 广西 桂林 541004;
    2.东南大学 计算机网络和信息集成教育部重点实验室, 江苏 南京 211189
  • 收稿日期:2022-02-25 修回日期:2022-05-06 出版日期:2022-09-25 发布日期:2022-10-18
  • 通讯作者: 钟必能(1981—), 男(壮族), 广西宜州人, 广西师范大学教授, 博士, 博导。E-mail: bnzhong@gxnu.edu.cn
  • 基金资助:
    广西自然科学基金(2022GXNSFDA035079, GuiKeAD21075030, 2020GXNSFAA159109); 国家自然科学基金(61972167, 62062016); 东南大学计算机网络和信息集成教育部重点实验室开放课题(K93-9-2020-04); 广西高等教育本科教学改革工程项目(2020JGB123); 广西八桂学者创新研究团队项目; 广西多源信息集成与智能处理协同创新中心项目; 广西大数据智能与应用人才小高地项目

Research Progress of Target Tracking Algorithm Based on Siamese Network

LIANG Qihua1, HU Xiantao1, ZHONG Bineng1*, YU Feng1,2, LI Xianxian1   

  1. 1. Guangxi Key Laboratory of Multi-Source Information Mining and Security (Guangxi Normal University), Guilin Guangxi 541004, China;
    2. Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing Jiangsu 211189, China
  • Received:2022-02-25 Revised:2022-05-06 Online:2022-09-25 Published:2022-10-18

PDF (PC)

76

摘要： 目标跟踪是计算机视觉领域中最为核心的基础研究问题之一,其能够协同高层视频应用分析和研究,具有重要的理论价值、广泛的实用价值和多学科交叉性,成为学术界、工业界以及国家战略的关注焦点。由于跟踪场景复杂度高、干扰强,目标表观变化多样性以及多模态信息融合等因素,使得跟踪器需要均衡鲁棒性、准确性以及实时性等性能衡量指标。目前,已有很多工作从不同视角解决目标跟踪领域中的挑战,但是在多维度性能指标的衡量下,仍然不能很好地克服复杂场景下的跟踪问题。本文通过基于孪生网络的目标跟踪算法,回顾领域发展现状,探讨存在的挑战,展望未来值得关注的研究方向,为该领域未来的研究工作提供借鉴和参考。

关键词: 计算机视觉, 目标跟踪, 视频应用与分析, 多模态, 孪生网络

Abstract: Target tracking is one of the core basic research issues in the field of computer vision. Its performance can cooperate with the analysis and research of high-level video applications, which has important theoretical value, extensive practical value and interdisciplinary. Therefore, it has become the focus of academia, industry and national strategy. Due to the high complexity and strong interference of the tracking scene, the diversity of target apparent changes and multi-modal information fusion, the tracker needs to balance the performance measurement indicators such as robustness, accuracy and real-time. At present, a lot of work has been done to solve the challenges in the field of target tracking from different perspectives, but under the measurement of multi-dimensional performance indicators, it still can not solve the tracking problem in complex scenes. Through the research of target tracking algorithm based on Siamese network, this paper reviews the current development status of the field, discusses the existing challenges and looks forward to the research direction worthy of attention in the future, so as to provide a reference for the future research work in this field.

Key words: computer vision, target tracking, video application and analysis, multimodal, siamese network

中图分类号: 

  • TP391.41
[1]陈云芳, 吴懿, 张伟. 基于孪生网络结构的目标跟踪算法综述[J]. 计算机工程与应用, 2020, 56(6): 10-18. DOI: 10.3778/j.issn.1002-8331.1911-0127.
[2]高文, 朱明, 贺柏根, 等. 目标跟踪技术综述[J]. 中国光学, 2014, 7(3): 365-375. DOI: 10.3788/CO.20140703.0365.
[3]葛宝义, 左宪章, 胡永江. 视觉目标跟踪方法研究综述[J]. 中国图象图形学报, 2018, 23(8): 1091-1107. DOI: 10.11834/jig.170604.
[4]朱强, 王超毅, 张吉庆, 等. 基于事件相机的无人机目标跟踪算法[J]. 浙江大学学报(理学版), 2022, 49(1): 10-18. DOI: 10.3785/j.issn.1008-9497.2022.01.002.
[5]管皓, 薛向阳, 安志勇. 深度学习在视频目标跟踪中的应用进展与展望[J]. 自动化学报, 2016, 42(6): 834-847. DOI: 10.16383/j.aas.2016.c150705.
[6]KRISTAN M, LEONARDIS A, MATAS J, et al. The sixth visual object tracking VOT2018 challenge results[C]// Computer Vision-ECCV 2018 Workshops: LNCS Volume 11129. Cham: Springer, 2019: 3-53. DOI: 10.1007/978-3-030-11009-3_1.
[7]WU Y, LIM J W, YANG M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848. DOI: 10.1109/TPAMI.2014.2388226.
[8]KRISTAN M, MATAS J, LEONARDIS A, et al. The seventh visual object tracking VOT2019 challenge results[C]// 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Los Alamitos, CA: IEEE Computer Society, 2019: 2206-2241. DOI: 10.1109/ICCVW.2019.00276.
[9]MUELLER M, SMITH N, GHANEM B. A benchmark and simulator for UAV tracking[C]// Computer Vision-ECCV 2016: LNCS Volume 11129. Cham: Springer, 2016: 445-461. DOI: 10.1007/978-3-319-46448-0_27.
[10]HUANG L H, ZHAO X, HUANG K Q. GOT-10k: a large high-diversity benchmark for generic object tracking in the wild[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(5): 1562-1577. DOI: 10.1109/TPAMI. 2019.2957464.
[11]FAN H, LIN L T, YANG F, et al.LaSOT: a high-quality benchmark for Large-Scale single object tracking[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2019: 5369-5378. DOI: 10.1109/CVPR.2019.00552.
[12]VALMADRE J, BERTINETTO L, HENRIQUES J F, et al. Long-term tracking in the wild: a benchmark[C]// Computer Vision-ECCV 2018: LNCS Volume 11207. Cham: Springer, 2018: 692-707. DOI: 10.1007/978-3-030-01219-9_41.
[13]MOUDGIL A, GANDHI V. Long-term visual object tracking benchmark[C]// Computer Vision-ACCV 2018: LNCS Volume 11362. Cham: Springer, 2019: 629-645. DOI: 10.1007/978-3-030-20890-5_40.
[14]REAL E, SHLENS J, MAZZOCCHI S, et al. YouTube-BoundingBoxes: a large high-precision human-annotated data set for object detection in video[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2017: 7464-7473. DOI: 10.1109/CVPR.2017.789.
[15]CHEN Z D, ZHONG B N, LI G R, et al. Siamese box adaptive network for visual tracking[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2020: 6667-6676. DOI: 10.1109/CVPR42600.2020.00670.
[16]XU Y D, WANG Z Y, LI Z X, et al.SiamFC++: towards robust and accurate visual tracking with target estimation guidelines[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12549-12556. DOI: 10.1609/aaai.v34i07.6944.
[17]GUO Q, FENG W, ZHOU C, et al. Learning dynamic Siamese network for visual object tracking[C]// 2017 IEEE International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2017: 1781-1789. DOI: 10.1109/ICCV.2017.196.
[18]BHAT G, DANELLJAN M, VAN GOOL L, et al. Learning discriminative model prediction for tracking[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2019: 6181-6190. DOI: 10.1109/ICCV.2019.00628.
[19]CHEN X, YAN B, ZHU J W, et al. Transformer tracking[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2021: 8122-8131. DOI: 10.1109/CVPR46437.2021.00803.
[20]YAN B, PENG H W, FU J L, et al. Learningspatio-temporal transformer for visual tracking[C]// 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2021: 10428-10437. DOI: 10.1109/ICCV48922.2021.01028.
[21]YAN B, PENG H W, WU K, et al.LightTrack: finding lightweight neural networks for object tracking via one-shot architecture search[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2021: 15175-15184. DOI: 10.1109/CVPR46437.2021.01493.
[22]BORSUK V, VEI R, KUPYN O, et al. FEAR: fast, efficient, accurate and robust visualtracker[EB/OL]. (2021-12-15) [2022-02-25]. https://arxiv.org/abs/2112.07957. DOI: 10.48550/arXiv.2112.07957.
[23]FENG Q, ABLAVSKY V, BAI Q X, et al. Siamese natural language tracker: tracking by natural language descriptions with Siamese trackers[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2021: 5847-5856. DOI: 10.1109/CVPR46437.2021.00579.
[24]DAI K N, ZHANG Y H, WANG D, et al. High-performance long-term tracking with meta-updater[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2020: 6297-6306. DOI: 10.1109/CVPR42600.2020.00633.
[25]GUO D Y, WANG J, CUI Y, et al.SiamCAR: Siamese fully convolutional classification and regression for visual tracking[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2020: 6268-6276. DOI: 10.1109/CVPR42600.2020.00630.
[26]ZHANG Z P, PENG H W, FU J L, et al. Ocean: object-aware anchor-free tracking[C]// Computer Vision-ECCV 2020: LNCS Volume 12366. Cham: Springer, 2020: 771-787. DOI: 10.1007/978-3-030-58589-1_46.
[27]BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional Siamese networks for object tracking[C]// Computer Vision-ECCV 2016 Workshops: LNCS Volume 9914. Cham: Springer, 2016: 850-865. DOI: 10.1007/978-3-319-48881-3_56.
[28]VALMADRE J, BERTINETTO L, HENRIQUES J, et al. End-to-end representation learning for correlationfilter based tracking[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2017: 5000-5008. DOI: 10.1109/CVPR.2017.531.
[29]DANELLJAN M, BHAT G, KHAN F S, et al. ECO: efficient convolution operators for tracking[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2017: 6931-6939. DOI: 10.1109/CVPR.2017.733.
[30]LI B, YAN J J, WU W, et al. High performance visual tracking with Siamese region proposal network[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2018: 8971-8980. DOI: 10.1109/CVPR.2018.00935.
[31]LI B, WU W, WANG Q, et al.SiamRPN++: evolution of Siamese visual tracking with very deep networks[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2019: 4277-4286. DOI: 10.1109/CVPR.2019.00441.
[32]WANG G T, LUO C, XIONG Z W, et al. SPM-tracker: series-parallel matching for real-time visual object tracking[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2019: 3638-3647. DOI: 10.1109/CVPR.2019.00376.
[33]卢湖川, 李佩霞, 王栋. 目标跟踪算法综述[J]. 模式识别与人工智能, 2018, 31(1): 61-76. DOI: 10.16451/j.cnki. issn1003-6059.201801006.
[34]李玺, 查宇飞, 张天柱, 等. 深度学习的目标跟踪算法综述[J]. 中国图象图形学报, 2019, 24(12): 2057-2080.
[35]ZHANG L C, GONZALEZ-GARCIA A, VAN DE WEIJER J, et al. Learning the model update for Siamese trackers[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2019: 4009-4018. DOI: 10.1109/ICCV.2019.00411.
[36]ZHU Z, WANG Q, LI B, et al. Distractor-aware Siamese networks for visual object tracking[C]// Computer Vision-ECCV 2018: LNCS Volume 11213. Cham: Springer, 2018: 103-119. DOI: 10.1007/978-3-030-01240-3_7.
[37]THRUN S. Is learning then-th thing any easier than learning the first?[C]// Advances in Neural Information Processing Systems 8 (NIPS 1995). Cambridge, MA: MIT Press, 1995: 640-646.
[38]LI F F, FERGUS R, PERONA P. One-shot learning of object categories[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 594-611. DOI: 10.1109/TPAMI.2006.79.
[39]SANTORO A, BARTUNOV S, BOTVINICK M, et al. Meta-learning with memory-augmented neural networks[C]// Proceedings of the 33rd International Conference on International Conference on Machine Learning: PMLR Volume 48. New York, NY:PMLR, 2016: 1842-1850.
[40]FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]// Proceedings of the 34th International Conference on Machine Learning: PMLR Volume 70. Sydney:PMLR, 2017: 1126-1135.
[41]ORESHKIN B, RODRÍGUEZ LÓPEZ P, LACOSTE A. TADAM: task dependent adaptive metric for improved few-shot learning[C]// Advances in Neural Information Processing Systems 31 (NeurIPS 2018). Red Hook: Curran Associates, Inc., 2018: 721-731.
[42]SUNG F, YANG Y X, ZHANG L, et al. Learning tocompare: relation network for few-shot learning[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2018: 1199-1208. DOI: 10.1109/CVPR.2018.00131.
[43]HUANG L H, ZHAO X, HUANG K Q. Bridging the gap between detection and tracking: a unified approach[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2019: 3998-4008. DOI: 10.1109/ICCV.2019.00410.
[44]WANG G T, LUO C, SUN X Y, et al. Tracking by instance detection: a meta-learning approach[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2020: 6287-6296. DOI: 10.1109/CVPR42600.2020.00632.
[45]CHENG S Y, ZHONG B N, LI G R, et al. Learning to filter: Siamese relation network for robust tracking[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2021: 4419-4429. DOI: 10.1109/CVPR46437.2021.00440.
[46]WU Y, LIM J W, YANG M H. Online object tracking: a benchmark[C]// 2013 IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2013: 2411-2418. DOI: 10.1109/CVPR.2013.312.
[47]VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Advances in Neural Information Processing Systems 30 (NIPS 2017). Red Hook, NY: Curran Associates, Inc., 2017: 6000-6010.
[48]韩光, 刘旭辉, 刘佶鑫, 等. 基于并行空频注意力引导的任务感知目标跟踪算法[J]. 南京邮电大学学报(自然科学版), 2022, 42(1): 62-72. DOI: 10.14132/j.cnki.1673-5439.2022.01.009.
[49]冯琪堯, 张惊雷. 基于混合注意力机制的目标跟踪算法[J]. 计算机工程与科学, 2022, 44(2): 276-282. DOI: 10.3969/j.issn.1007-130X.2022.02.012.
[50]朱张莉, 饶元, 吴渊, 等. 注意力机制在深度学习中的研究进展[J]. 中文信息学报, 2019, 33(6): 1-11. DOI: 10.3969/j.issn.1003-0077.2019.06.001.
[51]WANG N, ZHOU W G, WANG J, et al. Transformer meets tracker: exploiting temporal context for robust visual tracking[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2021: 1571-1580. DOI: 10.1109/CVPR46437.2021.00162.
[52]ZHAO M J, OKADA K, INABA M.TrTr: visual tracking with transformer[EB/OL]. (2021-05-09)[2022-02-25]. https://arxiv.org/abs/2105.03817v1. DOI: 10.48550/arXiv.2105.03817.
[53]BLATTER P, KANAKIS M, DANELLJAN M, et al. Efficient visual tracking with exemplartransformers[EB/OL]. (2021-12-17)[2022-02-25]. https://arxiv.org/abs/2112.09686v1. DOI: 10.48550/arXiv.2112.09686.
[54]LI Z Y, TAO R, GAVVES E, et al. Tracking by natural language specification[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2017: 7350-7358. DOI: 10.1109/CVPR.2017.777.
[55]WANG X, LI C L, YANG R, et al. Describe and attend to track: learning natural language guided structural representation and visual attention for objecttracking[EB/OL]. (2018-11-27)[2022-02-25]. https://arxiv.org/abs/1811. 10014. DOI: 10.48550/arXiv.1811.10014.
[56]FENG Q, ABLAVSKY V, BAI Q X, et al. Robust visual object tracking with natural language region proposalnetwork[EB/OL]. (2019-12-04)[2022-02-25]. https://arxiv.org/abs/1912.02048v1. DOI: 10.48550/arXiv.1912.02048.
[57]WANG X, SHU X J, ZHANG Z P, et al. Towards more flexible and accurate object tracking with natural language: algorithms and benchmark[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2021: 13758-13768. DOI: 10.1109/CVPR46437.2021.01355.
[58]LUKEZIC A, ZAJC L C, VOJIR T, et al. Performance evaluation methodology for long-term single-object tracking[J]. IEEE Transactions on Cybernetics, 2021, 51(12): 6305-6318. DOI: 10.1109/TCYB.2020.2980618.
[59]JUNG I, SON J, BAEK M, et al. Real-timeMDNet[C]// Computer Vision-ECCV 2018: LNCS Volume 11208. Cham: Springer, 2018: 89-104. DOI: 10.1007/978-3-030-01225-0_6.
[60]ZHANG Z P, PENG H W. Deeper and wider Siamese networks for real-time visual tracking[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2019: 4586-4595. DOI: 10.1109/CVPR.2019.00472.
[61]ZHANG Y H, WANG D, WANG L J, et al. Learning regression and verification networks for long-term visual tracking [EB/OL]. (2018-11-19)[2022-02-25]. https://arxiv.org/abs/1809.04320. DOI: 10.48550/arXiv.1809.04320.
[62]YAN B, ZHAO H J, WANG D, et al. ‘Skimming-perusal’ tracking: a framework for real-time and robust long-term tracking[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2019: 2385-2393. DOI: 10.1109/ICCV.2019.00247.
[63]HUANG L H, ZHAO X, HUANG K Q.GlobalTrack: a simple and strong baseline for long-term tracking[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 11037-11044. DOI: 10.1609/aaai.v34i07.6758.
[64]ZHANG Z K, ZHONG B N, ZHANG S P, et al. Distractor-aware fast tracking via dynamic convolutions and MOT philosophy[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2021: 1024-1033. DOI: 10.1109/CVPR46437.2021.00108.
[65]DU X Z, LIN T Y, JIN P C, et al.SpineNet: learning scale-permuted backbone for recognition and localization[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2020: 11589-11598. DOI: 10.1109/CVPR42600.2020.01161.
[66]GHIASI G, LIN T Y, LE Q V. NAS-FPN: learning scalable feature pyramid architecture for object detection[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2019: 7029-7038. DOI: 10.1109/CVPR.2019.00720.
[67]XU H, YAO L W, ZHANG W, et al. Auto-FPN: automatic network architecture adaptation for object detection beyond classification[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2019: 6648-6657. DOI: 10.1109/ICCV.2019.00675.
[1] 杜锦丰, 王海荣, 梁焕, 王栋. 基于表示学习的跨模态检索方法研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(3): 1-12.
[2] 晁睿, 张坤丽, 王佳佳, 胡斌, 张维聪, 韩英杰, 昝红英. 中文多模态知识库构建[J]. 广西师范大学学报（自然科学版）, 2022, 40(3): 31-39.
[3] 马新娜, 赵猛, 祁琳. 基于卷积脉冲神经网络的故障诊断方法研究[J]. 广西师范大学学报（自然科学版）, 2022, 40(3): 112-120.
[4] 薛其威, 伍锡如. 基于多模态特征融合的无人驾驶系统车辆检测[J]. 广西师范大学学报（自然科学版）, 2022, 40(2): 37-48.
[5] 张灿龙, 李燕茹, 李志欣, 王智文. 基于核相关滤波与特征融合的分块跟踪算法[J]. 广西师范大学学报（自然科学版）, 2020, 38(5): 12-23.
[6] 马先兵, 孙水发, 覃音诗, 郭青, 夏平. 基于粒子滤波的on-line boosting目标跟踪算法[J]. 广西师范大学学报（自然科学版）, 2013, 31(3): 100-105.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 张喜龙, 韩萌, 陈志强, 武红鑫, 李慕航. 面向复杂数据流的集成分类综述[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 1 -21 .
[2] 童凌晨, 李强, 岳鹏鹏. 基于CiteSpace的喀斯特土壤有机碳研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 22 -34 .
[3] 帖军, 隆娟娟, 郑禄, 牛悦, 宋衍霖. 基于SK-EfficientNet的番茄叶片病害识别模型[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 104 -114 .
[4] 翁烨, 邵德盛, 甘淑. 等式约束病态最小二乘的主成分Liu估计解法[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 115 -125 .
[5] 覃城阜, 莫芬梅. C3-和C4-临界连通图的结构[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 145 -153 .
[6] 贺青, 刘剑, 韦联福. 微弱电磁信号的物理极限检测:单光子探测器及其研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 1 -23 .
[7] 田芮谦, 宋树祥, 刘振宇, 岑明灿, 蒋品群, 蔡超波. 逐次逼近型模数转换器研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 24 -35 .
[8] 张师超, 李佳烨. 知识矩阵表示[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 36 -48 .
[9] 梁钰婷, 罗玉玲, 张顺生. 基于压缩感知的混沌图像加密研究综述[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 49 -58 .
[10] 郝雅茹, 董力, 许可, 李先贤. 预训练语言模型的可解释性研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 59 -71 .
版权所有 © 广西师范大学学报（自然科学版）编辑部
地址：广西桂林市三里店育才路15号　邮编：541004
电话：0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发