{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T20:40:42Z","timestamp":1735418442235,"version":"3.32.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"42","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18869-7","type":"journal-article","created":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T07:02:05Z","timestamp":1712386925000},"page":"90153-90175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accurate target estimation with image contents for visual tracking"],"prefix":"10.1007","volume":"83","author":[{"given":"Sheng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,6]]},"reference":[{"key":"18869_CR1","doi-asserted-by":"publisher","first-page":"102820","DOI":"10.1016\/j.jvcir.2020.102820","volume":"70","author":"D Elayaperumal","year":"2020","unstructured":"Elayaperumal D, Joo YH (2020) Visual object tracking using sparse context-aware spatio-temporal correlation filter. J Vis Commun Image Represent 70:102820","journal-title":"J Vis Commun Image Represent"},{"key":"18869_CR2","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Khan FS, Felsberg M (2019) ATOM: accurate tracking by overlap maximization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00479"},{"key":"18869_CR3","doi-asserted-by":"crossref","unstructured":"Zhang J, Xie X, Zheng Z, Kuang L-D, Zhang Y (2022) Siamoa: siamese offset-aware object tracking. Neural Comput Appl:1\u201317","DOI":"10.1007\/s00521-022-07684-6"},{"key":"18869_CR4","doi-asserted-by":"publisher","first-page":"103107","DOI":"10.1016\/j.jvcir.2021.103107","volume":"77","author":"Z Li","year":"2021","unstructured":"Li Z, Hu C, Nai K, Yuan J (2021) Siamese target estimation network with aiou loss for real-time visual tracking. J Vis Commun Image Represent 77:103107","journal-title":"J Vis Commun Image Represent"},{"key":"18869_CR5","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional siamese networks for object tracking. In: European conference on computer vision, Springer, pp 850\u2013865","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"18869_CR6","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":"18869_CR7","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Advan Neural Inform Process Syst 28"},{"key":"18869_CR8","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2019) FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF International conference on computer vision, pp 9627\u20139636","DOI":"10.1109\/ICCV.2019.00972"},{"key":"18869_CR9","doi-asserted-by":"publisher","first-page":"12549","DOI":"10.1609\/aaai.v34i07.6944","volume":"34","author":"Y Xu","year":"2020","unstructured":"Xu Y, Wang Z, Li Z, Yuan Y, Yu G (2020) SiamFC++: towards robust and accurate visual tracking with target estimation guidelines. Proc AAAI Conference Artif Intell 34:12549\u201312556","journal-title":"Proc AAAI Conference Artif Intell"},{"key":"18869_CR10","doi-asserted-by":"publisher","first-page":"103911","DOI":"10.1016\/j.imavis.2020.103911","volume":"97","author":"S Wu","year":"2020","unstructured":"Wu S, Li X, Wang X (2020) IoU-aware single-stage object detector for accurate localization. Image Vis Comput 97:103911","journal-title":"Image Vis Comput"},{"key":"18869_CR11","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2020) Fcos: a simple and strong anchor-free object detector. IEEE transactions on pattern analysis and machine intelligence","DOI":"10.1109\/TPAMI.2020.3032166"},{"key":"18869_CR12","doi-asserted-by":"crossref","unstructured":"Zhang H, Wang Y, Dayoub F, Sunderhauf N (2021) VarifocalNet: an iou-aware dense object detector. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8514\u20138523","DOI":"10.1109\/CVPR46437.2021.00841"},{"issue":"4","key":"18869_CR13","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"18869_CR14","doi-asserted-by":"crossref","unstructured":"Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer society conference on computer vision and pattern recognition, IEEE, pp 2544\u20132550","DOI":"10.1109\/CVPR.2010.5539960"},{"issue":"3","key":"18869_CR15","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2014","unstructured":"Henriques JF, Caseiro R, Martins P, Batista J (2014) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583\u2013596","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18869_CR16","doi-asserted-by":"crossref","unstructured":"Danelljan M, Robinson A, Shahbaz\u00a0Khan F, Felsberg M (2016) Beyond correlation filters: learning continuous convolution operators for visual tracking. In: European conference on computer vision, Springer, pp 472\u2013488","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"18869_CR17","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Shahbaz\u00a0Khan F, Felsberg M (2017) ECO: efficient convolution operators for tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2017.733"},{"key":"18869_CR18","doi-asserted-by":"crossref","unstructured":"Kiani\u00a0Galoogahi H, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 1135\u20131143","DOI":"10.1109\/ICCV.2017.129"},{"key":"18869_CR19","doi-asserted-by":"crossref","unstructured":"Sun Y, Sun C, Wang D, He Y, Lu H (2019) Roi pooled correlation filters for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5783\u20135791","DOI":"10.1109\/CVPR.2019.00593"},{"key":"18869_CR20","doi-asserted-by":"crossref","unstructured":"Bhat G, Johnander J, Danelljan M, Khan FS, Felsberg M (2018) Unveiling the power of deep tracking. In: Proceedings of the european conference on computer vision (ECCV), pp 483\u2013498","DOI":"10.1007\/978-3-030-01216-8_30"},{"key":"18869_CR21","doi-asserted-by":"crossref","unstructured":"Danelljan M, H\u00e4ger G, Khan F, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: British machine vision conference, Nottingham, Bmva Press, Sept 1-5, 2014","DOI":"10.5244\/C.28.65"},{"key":"18869_CR22","doi-asserted-by":"crossref","unstructured":"Dai K, Wang D, Lu H, Sun C, Li J (2019) Visual tracking via adaptive spatially-regularized correlation filters. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4670\u20134679","DOI":"10.1109\/CVPR.2019.00480"},{"issue":"19","key":"18869_CR23","doi-asserted-by":"publisher","first-page":"27879","DOI":"10.1007\/s11042-022-12760-z","volume":"81","author":"F Wang","year":"2022","unstructured":"Wang F, Yin S, Mbelwa JT, Sun F (2022) Context and saliency aware correlation filter for visual tracking. Multimed Tools Appl 81(19):27879\u201327893","journal-title":"Multimed Tools Appl"},{"key":"18869_CR24","doi-asserted-by":"crossref","unstructured":"Zhao Z, Zhu Z, Yan M, Wu B, Zhao Z (2023) Robust object tracking based on power-law probability map and ridge regression. Multimedia Tool Appl:1\u201319","DOI":"10.1007\/s11042-023-16339-0"},{"key":"18869_CR25","doi-asserted-by":"crossref","unstructured":"Wang Q, Zhang L, Bertinetto L, Hu W, Torr PHS (2019) Fast online object tracking and segmentation: a unifying approach. In: IEEE conference on computer vision and pattern recognition, CVPR 2019, Long Beach, CA, USA, Computer Vision Foundation \/ IEEE, June 16-20, 2019, pp 1328\u20131338","DOI":"10.1109\/CVPR.2019.00142"},{"key":"18869_CR26","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":"18869_CR27","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhong B, Li G, Zhang S, Ji R (2020) Siamese box adaptive network for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6668\u20136677","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"18869_CR28","doi-asserted-by":"crossref","unstructured":"Zhu Z, Wang Q, Li B, Wu W, Yan J, Hu W (2018) Distractor-aware siamese networks for visual object tracking. In: Proceedings of the european conference on computer vision (ECCV), pp 101\u2013117","DOI":"10.1007\/978-3-030-01240-3_7"},{"key":"18869_CR29","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"},{"issue":"1","key":"18869_CR30","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11042-022-12008-w","volume":"82","author":"Q-h Sheng","year":"2023","unstructured":"Sheng Q-h, Huang J, Li Z, Zhou C-y, Yin H-b (2023) SiamDAG: Siamese dynamic receptive field and global context modeling network for visual tracking. Multimed Tools Appl 82(1):681\u2013701","journal-title":"Multimed Tools Appl"},{"key":"18869_CR31","doi-asserted-by":"crossref","unstructured":"Zhang J, Huang H, Jin X, Kuang L-D, Zhang J (2023) Siamese visual tracking based on criss-cross attention and improved head network. Multimedia Tool Appl:1\u201327","DOI":"10.1007\/s11042-023-15429-3"},{"key":"18869_CR32","doi-asserted-by":"crossref","unstructured":"Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Torr PH (2017) End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2805\u20132813","DOI":"10.1109\/CVPR.2017.531"},{"key":"18869_CR33","doi-asserted-by":"crossref","unstructured":"Zhang Z, Peng H (2019) Deeper and wider siamese networks for real-time visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4591\u20134600","DOI":"10.1109\/CVPR.2019.00472"},{"key":"18869_CR34","doi-asserted-by":"crossref","unstructured":"He A, Luo C, Tian X, Zeng W (2018) A twofold siamese network for real-time object tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4834\u20134843","DOI":"10.1109\/CVPR.2018.00508"},{"key":"18869_CR35","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"18869_CR36","doi-asserted-by":"crossref","unstructured":"Yu J, Jiang Y, Wang Z, Cao Z, Huang T (2016) Unitbox: an advanced object detection network. In: Proceedings of the 24th ACM international conference on multimedia, pp 516\u2013520","DOI":"10.1145\/2964284.2967274"},{"key":"18869_CR37","doi-asserted-by":"crossref","unstructured":"Jiang B, Luo R, Mao J, Xiao T, Jiang Y (2018) Acquisition of localization confidence for accurate object detection. In: Proceedings of the european conference on computer vision (ECCV), pp 784\u2013799","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"18869_CR38","first-page":"21002","volume":"33","author":"X Li","year":"2020","unstructured":"Li X, Wang W, Wu L, Chen S, Hu X, Li J, Tang J, Yang J (2020) Generalized Focal Loss: learning qualified and distributed bounding boxes for dense object detection. Adv Neural Inf Process Syst 33:21002\u201321012","journal-title":"Adv Neural Inf Process Syst"},{"key":"18869_CR39","unstructured":"Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2013) OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229"},{"key":"18869_CR40","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"},{"key":"18869_CR41","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advan Neural Inform Process Syst 25"},{"issue":"3","key":"18869_CR42","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"18869_CR43","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft COCO: common objects in context. In: European conference on computer vision, Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"18869_CR44","doi-asserted-by":"crossref","unstructured":"Fan H, Lin L, Yang F, Chu P, Deng G, Yu S, Bai H, Xu Y, Liao C, Ling H (2019) LaSOT: a high-quality benchmark for large-scale single object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5374\u20135383","DOI":"10.1109\/CVPR.2019.00552"},{"issue":"5","key":"18869_CR45","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TPAMI.2019.2957464","volume":"43","author":"L Huang","year":"2019","unstructured":"Huang L, Zhao X, Huang K (2019) GOT-10k: a large high-diversity benchmark for generic object tracking in the wild. IEEE Trans Pattern Anal Mach Intell 43(5):1562\u20131577","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18869_CR46","doi-asserted-by":"crossref","unstructured":"Muller M, Bibi A, Giancola S, Alsubaihi S, Ghanem B (2018) TrackingNet: a large-scale dataset and benchmark for object tracking in the wild. In: Proceedings of the european conference on computer vision (ECCV), pp 300\u2013317","DOI":"10.1007\/978-3-030-01246-5_19"},{"issue":"9","key":"18869_CR47","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu Y, Lim J, Yang M-H (2015) Object tracking benchmark. IEEE Trans Pattern Analysis Mach Intell 37(9):1834\u20131848","journal-title":"IEEE Trans Pattern Analysis Mach Intell"},{"key":"18869_CR48","doi-asserted-by":"crossref","unstructured":"Li X, Ma C, Wu B, He Z, Yang M-H (2019) Target-aware deep tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1369\u20131378","DOI":"10.1109\/CVPR.2019.00146"},{"key":"18869_CR49","doi-asserted-by":"crossref","unstructured":"Li P, Chen B, Ouyang W, Wang D, Yang X, Lu H (2019) GradNet: gradient-guided network for visual object tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6162\u20136171","DOI":"10.1109\/ICCV.2019.00626"},{"key":"18869_CR50","doi-asserted-by":"crossref","unstructured":"Zheng J, Ma C, Peng H, Yang X (2021) Learning to track objects from unlabeled videos. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 13546\u201313555","DOI":"10.1109\/ICCV48922.2021.01329"},{"key":"18869_CR51","doi-asserted-by":"crossref","unstructured":"Wang N, Song Y, Ma C, Zhou W, Liu W, Li H (2019) Unsupervised deep tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1308\u20131317","DOI":"10.1109\/CVPR.2019.00140"},{"issue":"3","key":"18869_CR52","first-page":"3072","volume":"45","author":"W Hu","year":"2023","unstructured":"Hu W, Wang Q, Zhang L, Bertinetto L, Torr PH (2023) SiamMask: a framework for fast online object tracking and segmentation. IEEE Trans Pattern Anal Mach Intell 45(3):3072\u20133089","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"18869_CR53","doi-asserted-by":"publisher","first-page":"9742","DOI":"10.1109\/TPAMI.2021.3137933","volume":"44","author":"A Luke\u017ei\u010d","year":"2021","unstructured":"Luke\u017ei\u010d A, Matas J, Kristan M (2021) A discriminative single-shot segmentation network for visual object tracking. IEEE Trans Pattern Anal Mach Intell 44(12):9742\u20139755","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18869_CR54","doi-asserted-by":"crossref","unstructured":"Zhang Z, Peng H, Fu J, Li B, Hu W (2020) Ocean: object-aware anchor-free tracking. In: European conference on computer vision, Springer, pp 771\u2013787","DOI":"10.1007\/978-3-030-58589-1_46"},{"key":"18869_CR55","doi-asserted-by":"crossref","unstructured":"Zhao A, Zhang Y (2023) Evota: an enhanced visual object tracking network with attention mechanism. Multimedia Tool Appl:1\u201322","DOI":"10.1007\/s11042-023-16149-4"},{"key":"18869_CR56","doi-asserted-by":"crossref","unstructured":"Chen X, Peng H, Wang D, Lu H, Hu H (2023) SeqTrack: sequence to sequence learning for visual object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14572\u201314581","DOI":"10.1109\/CVPR52729.2023.01400"},{"key":"18869_CR57","doi-asserted-by":"crossref","unstructured":"Xie F, Chu L, Li J, Lu Y, Ma C (2023) VideoTrack: learning to track objects via video transformer. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 22826\u201322835","DOI":"10.1109\/CVPR52729.2023.02186"},{"key":"18869_CR58","doi-asserted-by":"crossref","unstructured":"Yang Y, Gu X (2023) Joint correlation and attention based feature fusion network for accurate visual tracking. IEEE Trans Image Process","DOI":"10.1109\/TIP.2023.3251027"},{"key":"18869_CR59","doi-asserted-by":"crossref","unstructured":"Peng J, Jiang Z, Gu Y, Wu Y, Wang Y, Tai Y, Wang C, Lin W (2021) Siamrcr: reciprocal classification and regression for visual object tracking. arXiv:2105.11237","DOI":"10.24963\/ijcai.2021\/132"},{"key":"18869_CR60","doi-asserted-by":"crossref","unstructured":"Bhat G, Danelljan M, Gool LV, Timofte R (2019) Learning discriminative model prediction for tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6182\u20136191","DOI":"10.1109\/ICCV.2019.00628"},{"key":"18869_CR61","first-page":"11037","volume":"34","author":"L Huang","year":"2020","unstructured":"Huang L, Zhao X, Huang K (2020) GlobalTrack: a simple and strong baseline for long-term tracking. Proc AAAI Conf on Artif Intell 34:11037\u201311044","journal-title":"Proc AAAI Conf on Artif Intell"},{"key":"18869_CR62","doi-asserted-by":"crossref","unstructured":"Yu B, Tang M, Zheng L, Zhu G, Wang J, Feng H, Feng X, Lu H (2021) High-performance discriminative tracking with transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9856\u20139865","DOI":"10.1109\/ICCV48922.2021.00971"},{"issue":"2","key":"18869_CR63","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/s11263-020-01357-4","volume":"129","author":"N Wang","year":"2021","unstructured":"Wang N, Zhou W, Song Y, Ma C, Liu W, Li H (2021) Unsupervised deep representation learning for real-time tracking. Int J Comput Vis 129(2):400\u2013418","journal-title":"Int J Comput Vis"},{"key":"18869_CR64","doi-asserted-by":"crossref","unstructured":"Han W, Dong X, Khan FS, Shao L, Shen J (2021) Learning to fuse asymmetric feature maps in siamese trackers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16570\u201316580","DOI":"10.1109\/CVPR46437.2021.01630"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18869-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18869-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18869-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T20:05:58Z","timestamp":1735416358000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18869-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,6]]},"references-count":64,"journal-issue":{"issue":"42","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["18869"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18869-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,4,6]]},"assertion":[{"value":"20 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}