{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T18:39:33Z","timestamp":1775932773025,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066047,61966037"],"award-info":[{"award-number":["62066047,61966037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066047,61966037"],"award-info":[{"award-number":["62066047,61966037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066047,61966037"],"award-info":[{"award-number":["62066047,61966037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066047,61966037"],"award-info":[{"award-number":["62066047,61966037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066047,61966037"],"award-info":[{"award-number":["62066047,61966037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Practice Innovation Fund of Yunnan University","award":["ZC-23234092"],"award-info":[{"award-number":["ZC-23234092"]}]},{"name":"Practice Innovation Fund of Yunnan University","award":["ZC-23234092"],"award-info":[{"award-number":["ZC-23234092"]}]},{"name":"Practice Innovation Fund of Yunnan University","award":["ZC-23234092"],"award-info":[{"award-number":["ZC-23234092"]}]},{"name":"Practice Innovation Fund of Yunnan University","award":["ZC-23234092"],"award-info":[{"award-number":["ZC-23234092"]}]},{"name":"Practice Innovation Fund of Yunnan University","award":["ZC-23234092"],"award-info":[{"award-number":["ZC-23234092"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s11227-024-06443-9","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T16:02:16Z","timestamp":1723824136000},"page":"25888-25910","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SiamMGT: robust RGBT tracking via graph attention and reliable modality weight learning"],"prefix":"10.1007","volume":"80","author":[{"given":"Lizhi","family":"Geng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongming","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kerui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yisong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaixiang","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,16]]},"reference":[{"key":"6443_CR1","doi-asserted-by":"publisher","unstructured":"Zhang Z, Peng H, Fu J, Li B, Hu W (2020) Ocean: object-aware anchor-free tracking. In: Computer vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXI 16. Springer, pp 771\u2013787. https:\/\/doi.org\/10.1007\/978-3-030-58589-1_46","DOI":"10.1007\/978-3-030-58589-1_46"},{"issue":"12","key":"6443_CR2","doi-asserted-by":"publisher","first-page":"5743","DOI":"10.1109\/TIP.2016.2614135","volume":"25","author":"C Li","year":"2016","unstructured":"Li C, Cheng H, Hu S, Liu X, Tang J, Lin L (2016) Learning collaborative sparse representation for grayscale-thermal tracking. IEEE Trans Image Process 25(12):5743\u20135756. https:\/\/doi.org\/10.1109\/TIP.2016.2614135. (IEEE)","journal-title":"IEEE Trans Image Process"},{"key":"6443_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.106977","volume":"96","author":"C Li","year":"2019","unstructured":"Li C, Liang X, Lu Y, Zhao N, Tang J (2019) Rgb-t object tracking: benchmark and baseline. Pattern Recogn 96:106977. https:\/\/doi.org\/10.1016\/j.patcog.2019.106977. (Elsevier)","journal-title":"Pattern Recogn"},{"key":"6443_CR4","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/TIP.2021.3130533","volume":"31","author":"C Li","year":"2021","unstructured":"Li C, Xue W, Jia Y, Qu Z, Luo B, Tang J, Sun D (2021) Lasher: a large-scale high-diversity benchmark for rgbt tracking. IEEE Trans Image Process 31:392\u2013404. https:\/\/doi.org\/10.1109\/TIP.2021.3130533. (IEEE)","journal-title":"IEEE Trans Image Process"},{"key":"6443_CR5","doi-asserted-by":"crossref","unstructured":"Du D, Qi Y, Yu H, Yang Y, Duan K, Li G, Zhang W, Huang Q, Tian Q (2018) The unmanned aerial vehicle benchmark: Object detection and tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 370\u2013386","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"6443_CR6","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.inffus.2020.05.002","volume":"63","author":"X Zhang","year":"2020","unstructured":"Zhang X, Ye P, Leung H, Gong K, Xiao G (2020) Object fusion tracking based on visible and infrared images: a comprehensive review. Inf Fusion 63:166\u2013187 (Elsevier)","journal-title":"Inf Fusion"},{"key":"6443_CR7","doi-asserted-by":"publisher","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional siamese networks for object tracking. In: Computer vision\u2013ECCV 2016 workshops: Amsterdam, The Netherlands, October 8\u201310 and 15\u201316, 2016, proceedings, part II 14. Springer, pp. 850\u2013865. https:\/\/doi.org\/10.1007\/978-3-319-48881-3_56","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"6443_CR8","doi-asserted-by":"crossref","unstructured":"Guo Q, Feng W, Zhou C, Huang R, Wan L, Wang S (2017) Learning dynamic siamese network for visual object tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1763\u20131771","DOI":"10.1109\/ICCV.2017.196"},{"key":"6443_CR9","doi-asserted-by":"crossref","unstructured":"Li B, Yan J, Wu W, Zhu Z, Hu X (2018) High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8971\u20138980","DOI":"10.1109\/CVPR.2018.00935"},{"key":"6443_CR10","doi-asserted-by":"crossref","unstructured":"Li B, Wu W, Wang Q, Zhang F, Xing J, Yan J (2019) Siamrpn++: evolution of siamese visual tracking with very deep networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4282\u20134291","DOI":"10.1109\/CVPR.2019.00441"},{"key":"6443_CR11","doi-asserted-by":"crossref","unstructured":"Guo D, Wang J, Cui Y, Wang Z, Chen S (2020) Siamcar: siamese fully convolutional classification and regression for visual tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6269\u20136277","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"6443_CR12","doi-asserted-by":"crossref","unstructured":"Guo D, Shao Y, Cui Y, Wang Z, Zhang L, Shen C (2021) Graph attention tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9543\u20139552","DOI":"10.1109\/CVPR46437.2021.00942"},{"key":"6443_CR13","doi-asserted-by":"publisher","first-page":"9152","DOI":"10.1109\/TIP.2020.3023621","volume":"29","author":"Y Qi","year":"2020","unstructured":"Qi Y, Zhang S, Jiang F, Zhou H, Tao D, Li X (2020) Siamese local and global networks for robust face tracking. IEEE Trans Image Process 29:9152\u20139164. https:\/\/doi.org\/10.1109\/TIP.2020.3023621","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"6443_CR14","doi-asserted-by":"publisher","first-page":"8049","DOI":"10.1109\/TPAMI.2022.3230064","volume":"45","author":"X Dong","year":"2023","unstructured":"Dong X, Shen J, Porikli F, Luo J, Shao L (2023) Adaptive siamese tracking with a compact latent network. IEEE Trans Pattern Anal Mach Intell 45(7):8049\u20138062. https:\/\/doi.org\/10.1109\/TPAMI.2022.3230064","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6443_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3400873","author":"W Han","year":"2024","unstructured":"Han W, Dong X, Zhang Y, Crandall D, Xu C-Z, Shen J (2024) Asymmetric convolution: an efficient and generalized method to fuse feature maps in multiple vision tasks. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2024.3400873","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6443_CR16","doi-asserted-by":"crossref","unstructured":"Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4293\u20134302","DOI":"10.1109\/CVPR.2016.465"},{"key":"6443_CR17","unstructured":"Long\u00a0Li C, Lu A, Hua\u00a0Zheng A, Tu Z, Tang J (2019) Multi-adapter rgbt tracking. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp 2262\u20132270"},{"key":"6443_CR18","doi-asserted-by":"publisher","first-page":"5613","DOI":"10.1109\/TIP.2021.3087341","volume":"30","author":"A Lu","year":"2021","unstructured":"Lu A, Li C, Yan Y, Tang J, Luo B (2021) Rgbt tracking via multi-adapter network with hierarchical divergence loss. IEEE Trans Image Process 30:5613\u20135625. https:\/\/doi.org\/10.1109\/TIP.2021.3087341. (IEEE)","journal-title":"IEEE Trans Image Process"},{"key":"6443_CR19","doi-asserted-by":"crossref","unstructured":"Gao Y, Li C, Zhu Y, Tang J, He T, Wang F (2019) Deep adaptive fusion network for high performance rgbt tracking. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp 91\u201399","DOI":"10.1109\/ICCVW.2019.00017"},{"issue":"2","key":"6443_CR20","doi-asserted-by":"publisher","first-page":"393","DOI":"10.3390\/s20020393","volume":"20","author":"H Zhang","year":"2020","unstructured":"Zhang H, Zhang L, Zhuo L, Zhang J (2020) Object tracking in rgb-t videos using modal-aware attention network and competitive learning. Sensors 20(2):393. https:\/\/doi.org\/10.3390\/s20020393. (MDPI)","journal-title":"Sensors"},{"key":"6443_CR21","doi-asserted-by":"publisher","unstructured":"Hou R, Ren T, Wu G (2022) Mirnet: A robust rgbt tracking jointly with multi-modal interaction and refinement. In: 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICME52920.2022.9860018","DOI":"10.1109\/ICME52920.2022.9860018"},{"key":"6443_CR22","doi-asserted-by":"publisher","first-page":"4348","DOI":"10.1109\/TMM.2022.3174341","volume":"4335","author":"X Wang","year":"2022","unstructured":"Wang X, Shu X, Zhang S, Jiang B, Wang Y, Tian Y, Wu F (2022) Mfgnet: dynamic modality-aware filter generation for rgb-t tracking. IEEE Trans Multimedia 4335:4348. https:\/\/doi.org\/10.1109\/TMM.2022.3174341","journal-title":"IEEE Trans Multimedia"},{"issue":"7","key":"6443_CR23","doi-asserted-by":"publisher","first-page":"7301","DOI":"10.1109\/JSEN.2023.3244834","volume":"23","author":"J Mei","year":"2023","unstructured":"Mei J, Zhou D, Cao J, Nie R, He K (2023) Differential reinforcement and global collaboration network for rgbt tracking. IEEE Sens J 23(7):7301\u20137311. https:\/\/doi.org\/10.1109\/JSEN.2023.3244834. (IEEE)","journal-title":"IEEE Sens J"},{"key":"6443_CR24","doi-asserted-by":"crossref","unstructured":"Zhang P, Zhao J, Wang D, Lu H, Ruan X (2022) Visible-thermal uav tracking: a large-scale benchmark and new baseline. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 8886\u20138895","DOI":"10.1109\/CVPR52688.2022.00868"},{"key":"6443_CR25","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1109\/TIP.2024.3371355","volume":"33","author":"L Liu","year":"2024","unstructured":"Liu L, Li C, Xiao Y, Ruan R, Fan M (2024) Rgbt tracking via challenge-based appearance disentanglement and interaction. IEEE Trans Image Process 33:1753\u20131767. https:\/\/doi.org\/10.1109\/TIP.2024.3371355","journal-title":"IEEE Trans Image Process"},{"key":"6443_CR26","doi-asserted-by":"publisher","first-page":"122122","DOI":"10.1109\/ACCESS.2019.2936914","volume":"7","author":"X Zhang","year":"2019","unstructured":"Zhang X, Ye P, Peng S, Liu J, Gong K, Xiao G (2019) Siamft: an rgb-infrared fusion tracking method via fully convolutional siamese networks. IEEE Access 7:122122\u2013122133. https:\/\/doi.org\/10.1109\/ACCESS.2019.2936914. (IEEE)","journal-title":"IEEE Access"},{"issue":"3","key":"6443_CR27","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1109\/TCSVT.2021.3072207","volume":"32","author":"T Zhang","year":"2021","unstructured":"Zhang T, Liu X, Zhang Q, Han J (2021) Siamcda: complementarity and distractor-aware rgb-t tracking based on siamese network. IEEE Trans Circuits Syst Video Technol 32(3):1403\u20131417. https:\/\/doi.org\/10.1109\/TCSVT.2021.3072207. (IEEE)","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"6443_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3310295","author":"M Feng","year":"2023","unstructured":"Feng M, Su J (2023) Learning multi-layer attention aggregation siamese network for robust rgbt tracking. IEEE Trans Multimedia. https:\/\/doi.org\/10.1109\/TMM.2023.3310295","journal-title":"IEEE Trans Multimedia"},{"key":"6443_CR29","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1109\/TIP.2024.3393298","volume":"33","author":"H Fan","year":"2024","unstructured":"Fan H, Yu Z, Wang Q, Fan B, Tang Y (2024) Querytrack: joint-modality query fusion network for rgbt tracking. IEEE Trans Image Process 33:3187\u20133199. https:\/\/doi.org\/10.1109\/TIP.2024.3393298","journal-title":"IEEE Trans Image Process"},{"key":"6443_CR30","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.patrec.2018.10.002","volume":"130","author":"X Lan","year":"2020","unstructured":"Lan X, Ye M, Zhang S, Zhou H, Yuen PC (2020) Modality-correlation-aware sparse representation for rgb-infrared object tracking. Pattern Recogn Lett 130:12\u201320 (Elsevier)","journal-title":"Pattern Recogn Lett"},{"key":"6443_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3288853","author":"J Liu","year":"2023","unstructured":"Liu J, Luo Z, Xiong X (2023) Online learning samples and adaptive recovery for robust rgb-t tracking. IEEE Trans Circuits Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2023.3288853","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"6443_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2022.104509","volume":"128","author":"Y Huang","year":"2023","unstructured":"Huang Y, Li X, Lu R (2023) Qi N Rgb-t object tracking via sparse response-consistency discriminative correlation filters. Infrared Phys Technol 128:104509 (Elsevier)","journal-title":"Infrared Phys Technol"},{"key":"6443_CR33","doi-asserted-by":"crossref","unstructured":"Zhang L, Danelljan M, Gonzalez-Garcia A, Van De\u00a0Weijer J, Shahbaz\u00a0Khan F (2019) Multi-modal fusion for end-to-end rgb-t tracking. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops","DOI":"10.1109\/ICCVW.2019.00278"},{"key":"6443_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2020.102881","volume":"72","author":"M Feng","year":"2020","unstructured":"Feng M, Song K, Wang Y, Liu J, Yan Y (2020) Learning discriminative update adaptive spatial-temporal regularized correlation filter for rgb-t tracking. J Vis Commun Image Represent 72:102881","journal-title":"J Vis Commun Image Represent"},{"key":"6443_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101881","volume":"99","author":"Z Tang","year":"2023","unstructured":"Tang Z, Xu T, Li H, Wu X-J, Zhu X, Kittler J (2023) Exploring fusion strategies for accurate rgbt visual object tracking. Inf Fusion 99:101881","journal-title":"Inf Fusion"},{"key":"6443_CR36","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"7","key":"6443_CR37","doi-asserted-by":"publisher","first-page":"2555","DOI":"10.1007\/s00371-021-02131-4","volume":"38","author":"C Guo","year":"2022","unstructured":"Guo C, Yang D, Li C, Song P (2022) Dual siamese network for rgbt tracking via fusing predicted position maps. Vis Comput 38(7):2555\u20132567","journal-title":"Vis Comput"},{"issue":"13","key":"6443_CR38","doi-asserted-by":"publisher","first-page":"3252","DOI":"10.3390\/rs15133252","volume":"15","author":"Y Xue","year":"2023","unstructured":"Xue Y, Zhang J, Lin Z, Li C, Huo B, Zhang Y (2023) Siamcaf: complementary attention fusion-based siamese network for rgbt tracking. Remote Sens 15(13):3252","journal-title":"Remote Sens"},{"issue":"11","key":"6443_CR39","doi-asserted-by":"publisher","first-page":"18520","DOI":"10.1109\/JSEN.2024.3386772","volume":"24","author":"Y Liu","year":"2024","unstructured":"Liu Y, Zhou D, Cao J, Yan K, Geng L (2024) Specific and collaborative representations siamese network for rgbt tracking. IEEE Sens J 24(11):18520\u201318534. https:\/\/doi.org\/10.1109\/JSEN.2024.3386772","journal-title":"IEEE Sens J"},{"key":"6443_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/TICPS.2023.3307340","author":"G Wang","year":"2023","unstructured":"Wang G, Jiang Q, Jin X, Lin Y, Wang Y, Zhou W (2023) Siamtdr: time-efficient rgbt tracking via disentangled representations. IEEE Trans Ind Cyber Phys Syst. https:\/\/doi.org\/10.1109\/TICPS.2023.3307340","journal-title":"IEEE Trans Ind Cyber Phys Syst"},{"issue":"1","key":"6443_CR41","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TIV.2020.2980735","volume":"6","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Li C, Tang J, Luo B (2020) Quality-aware feature aggregation network for robust rgbt tracking. IEEE Trans Intell Veh 6(1):121\u2013130. https:\/\/doi.org\/10.1109\/TIV.2020.2980735. (IEEE)","journal-title":"IEEE Trans Intell Veh"},{"key":"6443_CR42","doi-asserted-by":"publisher","first-page":"3335","DOI":"10.1109\/TIP.2021.3060862","volume":"30","author":"P Zhang","year":"2021","unstructured":"Zhang P, Zhao J, Bo C, Wang D, Lu H, Yang X (2021) Jointly modeling motion and appearance cues for robust rgb-t tracking. IEEE Trans Image Process 30:3335\u20133347. https:\/\/doi.org\/10.1109\/TIP.2021.3060862. (IEEE)","journal-title":"IEEE Trans Image Process"},{"key":"6443_CR43","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TIP.2021.3125504","volume":"31","author":"Z Tu","year":"2021","unstructured":"Tu Z, Lin C, Zhao W, Li C, Tang J (2021) M 5 l: multi-modal multi-margin metric learning for rgbt tracking. IEEE Trans Image Process 31:85\u201398. https:\/\/doi.org\/10.1109\/TIP.2021.3125504. (IEEE)","journal-title":"IEEE Trans Image Process"},{"key":"6443_CR44","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/TNNLS.2022.3157594","volume":"1","author":"A Lu","year":"2022","unstructured":"Lu A, Qian C, Li C, Tang J, Wang L (2022) Duality-gated mutual condition network for rgbt tracking. IEEE Trans Neural Netw Learn Syst 1:14. https:\/\/doi.org\/10.1109\/TNNLS.2022.3157594","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6443_CR45","doi-asserted-by":"crossref","unstructured":"Xiao Y, Yang M, Li C, Liu L, Tang J (2022) Attribute-based progressive fusion network for rgbt tracking. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 36, pp 2831\u20132838","DOI":"10.1609\/aaai.v36i3.20187"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06443-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06443-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06443-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T20:41:09Z","timestamp":1726778469000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06443-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,16]]},"references-count":45,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["6443"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06443-9","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,16]]},"assertion":[{"value":"9 August 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"To the best of our knowledge, the named authors have no Conflict of interest, financial or otherwise.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No human or animal experiments are involved in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}