{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T05:41:46Z","timestamp":1777182106813,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The special fund for basic scientific research operation expenses of Inner Mongolia University of Science and Technology","award":["2023QNJS046"],"award-info":[{"award-number":["2023QNJS046"]}]},{"DOI":"10.13039\/501100004763","name":"Natural Science Foundation of Inner Mongolia Autonomous Region","doi-asserted-by":"publisher","award":["2024MS06022"],"award-info":[{"award-number":["2024MS06022"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004763","name":"Natural Science Foundation of Inner Mongolia Autonomous Region","doi-asserted-by":"publisher","award":["2023MS06014"],"award-info":[{"award-number":["2023MS06014"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32460858"],"award-info":[{"award-number":["32460858"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Inner Mongolia Key Technology Research Project","award":["2021GG0224"],"award-info":[{"award-number":["2021GG0224"]}]},{"name":"Inner Mongolia Science and Technology Plan Project","award":["2023YFSW0014"],"award-info":[{"award-number":["2023YFSW0014"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s13042-025-02810-x","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T06:02:59Z","timestamp":1758520979000},"page":"10925-10938","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dual-stream modality prompt network for individual cattle identification"],"prefix":"10.1007","volume":"16","author":[{"given":"Mei","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianmin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingfang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangfang","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"key":"2810_CR1","doi-asserted-by":"crossref","unstructured":"Kumar S, Singh SK, Dutta T, Gupta HP (2016) A fast cattle recognition system using smart devices. In Proceedings of the 24th ACM international conference on multimedia, pp 742\u2013743","DOI":"10.1145\/2964284.2973829"},{"key":"2810_CR2","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1093\/jas\/skz258.378","volume":"97","author":"TA Mcallister","year":"2019","unstructured":"Mcallister TA, Basarab J, Guan L (2019) Strategies to improve the efficiency of beef cattle production. J Anim Sci 97:183\u2013183","journal-title":"J Anim Sci"},{"key":"2810_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.measurement.2017.10.064","volume":"116","author":"S Kumar","year":"2018","unstructured":"Kumar S, Amit Pandey K, Satwik SR, Kumar S, Singh SK, Singh AK, Mohan A (2018) Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement 116:1\u201317","journal-title":"Measurement"},{"issue":"4","key":"2810_CR4","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.patcog.2014.07.017","volume":"48","author":"J Duyck","year":"2015","unstructured":"Duyck J, Finn C, Hutcheon A, Vera P, Salas J, Ravela S (2015) Sloop: a pattern retrieval engine for individual animal identification. Pattern Recogn 48(4):1059\u20131073","journal-title":"Pattern Recogn"},{"issue":"11","key":"2810_CR5","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.3390\/agronomy11112365","volume":"11","author":"A Shojaeipour","year":"2021","unstructured":"Shojaeipour A, Falzon G, Kwan P, Hadavi N, Cowley FC, Paul D (2021) Automated muzzle detection and biometric identification via few-shot deep transfer learning of mixed breed cattle. Agronomy 11(11):2365","journal-title":"Agronomy"},{"key":"2810_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101549","volume":"68","author":"A Kaur","year":"2022","unstructured":"Kaur A, Kumar M, Jindal MK (2022) Shi-Tomasi corner detector for cattle identification from muzzle print image pattern. Eco Inform 68:101549","journal-title":"Eco Inform"},{"issue":"22","key":"2810_CR7","doi-asserted-by":"publisher","first-page":"4914","DOI":"10.3390\/app9224914","volume":"9","author":"AI Awad","year":"2019","unstructured":"Awad AI, Hassaballah M (2019) Bag-of-visual-words for cattle identification from muzzle print images. Appl Sci 9(22):4914","journal-title":"Appl Sci"},{"issue":"13","key":"2810_CR8","first-page":"135","volume":"14","author":"A Bustamin","year":"2020","unstructured":"Bustamin A, Rahmatullah M (2020) A portable cattle tagging based on muzzle pattern. iJIM 14(13):135","journal-title":"iJIM"},{"issue":"7","key":"2810_CR9","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1016\/j.tree.2013.02.013","volume":"28","author":"HS K\u00fchl","year":"2013","unstructured":"K\u00fchl HS, Burghardt T (2013) Animal biometrics: quantifying and detecting phenotypic appearance. Trends Ecol Evol 28(7):432\u2013441","journal-title":"Trends Ecol Evol"},{"key":"2810_CR10","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.compag.2015.10.023","volume":"119","author":"A Nasirahmadi","year":"2015","unstructured":"Nasirahmadi A (2015) Using machine vision for investigation of changes in pig group lying patterns. Comput Electron Agric 119:184\u2013190","journal-title":"Comput Electron Agric"},{"issue":"2","key":"2810_CR11","first-page":"84","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 25(2):84\u201390","journal-title":"Adv Neural Inform Process Syst"},{"issue":"1","key":"2810_CR12","doi-asserted-by":"publisher","first-page":"313","DOI":"10.13031\/2013.22395","volume":"50","author":"GP Corkery","year":"2007","unstructured":"Corkery GP, Gonzales-Barron UA, Butler F, Donnell KM, Ward S (2007) A preliminary investigation on face recognition as a biometric identifier of sheep. Trans ASABE 50(1):313\u2013320","journal-title":"Trans ASABE"},{"key":"2810_CR13","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/853\/1\/012051","volume":"853","author":"C Sian","year":"2020","unstructured":"Sian C, Jiye W, Ru Z, Lizhi Z (2020) Cattle identification using muzzle print images based on feature fusion. IOP Conf Ser Mater Sci Eng 853:012051","journal-title":"IOP Conf Ser Mater Sci Eng"},{"issue":"2","key":"2810_CR14","first-page":"458","volume":"32","author":"MO Incetas","year":"2019","unstructured":"Incetas MO, Dem\u0131rc\u0131 R, Guclu Yavuzcan H (2019) Automatic color edge detection with similarity transformation. Gazi Univ J Sci 32(2):458\u2013469","journal-title":"Gazi Univ J Sci"},{"issue":"12","key":"2810_CR15","doi-asserted-by":"publisher","first-page":"2056007","DOI":"10.1142\/S0218001420560078","volume":"34","author":"W Kusakunniran","year":"2020","unstructured":"Kusakunniran W, Wiratsudakul A, Chuachan U, Kanchanapreechakorn S, Imaromkul T, Suksriupatham N, Thongkanchorn K (2020) Biometric for cattle identification using muzzle patterns. Int J Pattern Recognit Artif Intell 34(12):2056007","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"2810_CR16","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1007\/s10766-017-0550-x","volume":"46","author":"S Kumar","year":"2018","unstructured":"Kumar S, Singh SK, Abidi AI, Datta D, Sangaiah AK (2018) Group sparse representation approach for recognition of cattle on muzzle point images. Int J Parallel Prog 46:812\u2013837","journal-title":"Int J Parallel Prog"},{"issue":"10","key":"2810_CR17","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1049\/iet-ipr.2016.0799","volume":"11","author":"S Kumar","year":"2017","unstructured":"Kumar S, Singh SK, Singh AK (2017) Muzzle point pattern based techniques for individual cattle identification. IET Image Proc 11(10):805\u2013814","journal-title":"IET Image Proc"},{"key":"2810_CR18","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.compag.2015.12.022","volume":"122","author":"T Gaber","year":"2016","unstructured":"Gaber T, Tharwat A, Hassanien AE, Snasel V (2016) Biometric cattle identification approach based on weber\u2019s local descriptor and adaboost classifier. Comput Electron Agric 122:55\u201366","journal-title":"Comput Electron Agric"},{"issue":"6","key":"2810_CR19","doi-asserted-by":"publisher","first-page":"5746","DOI":"10.3168\/jds.2019-17804","volume":"103","author":"A Beaver","year":"2020","unstructured":"Beaver A, Proudfoot KL, Keyserlingk MAG (2020) Symposium review: considerations for the future of dairy cattle housing: an animal welfare perspective. J Dairy Sci 103(6):5746\u20135758","journal-title":"J Dairy Sci"},{"key":"2810_CR20","doi-asserted-by":"crossref","unstructured":"Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3476\u20133483","DOI":"10.1109\/CVPR.2013.446"},{"issue":"3","key":"2810_CR21","first-page":"139","volume":"6","author":"S Kumar","year":"2017","unstructured":"Kumar S, Singh SK (2017) Visual animal biometrics: survey. Iet. Biometrics 6(3):139\u2013156","journal-title":"Biometrics"},{"issue":"7","key":"2810_CR22","doi-asserted-by":"publisher","first-page":"2244","DOI":"10.1109\/TNNLS.2018.2874657","volume":"30","author":"P Tang","year":"2019","unstructured":"Tang P, Wang X, Shi B, Bai X, Liu W, Zhuowen T (2019) Deep fishernet for image classification. IEEE Trans Neural Netw Learn Syst 30(7):2244\u20132250. https:\/\/doi.org\/10.1109\/TNNLS.2018.2874657 (https:\/\/ieeexplore.ieee.org\/document\/8522050\/)","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2810_CR23","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"key":"2810_CR24","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929"},{"key":"2810_CR25","unstructured":"Chen J, Zhu D, Shen X, Li X, Liu Z, Zhang P, Krishnamoorthi R, Chandra V, Xiong Y, Elhoseiny M (2023) Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. arXiv:2310.09478"},{"key":"2810_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120391","volume":"228","author":"SU Amin","year":"2023","unstructured":"Amin SU, Hussain A, Kim B, Seo S (2023) Deep learning based active learning technique for data annotation and improve the overall performance of classification models. Expert Syst Appl 228:120391","journal-title":"Expert Syst Appl"},{"issue":"9","key":"2810_CR27","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.3390\/math10091555","volume":"10","author":"SU Amin","year":"2022","unstructured":"Amin SU, Ullah M, Sajjad M, Cheikh FA, Hijji M, Hijji A, Muhammad K (2022) Eadn: an efficient deep learning model for anomaly detection in videos. Mathematics 10(9):1555","journal-title":"Mathematics"},{"key":"2810_CR28","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1453\/1\/012054","volume":"1453","author":"H Wang","year":"2020","unstructured":"Wang H, Qin J, Hou Q, Gong S (2020) Cattle face recognition method based on parameter transfer and deep learning. J Phys Conf Ser 1453:012054","journal-title":"J Phys Conf Ser"},{"key":"2810_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106133","volume":"185","author":"W Andrew","year":"2021","unstructured":"Andrew W, Gao J, Mullan S, Campbell N, Dowsey AW, Burghardt T (2021) Visual identification of individual Holstein-Friesian cattle via deep metric learning. Comput Electron Agric 185:106133","journal-title":"Comput Electron Agric"},{"key":"2810_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106738","volume":"194","author":"J Xiao","year":"2022","unstructured":"Xiao J, Liu G, Wang K, Si Y (2022) Cow identification in free-stall barns based on an improved mask r-cnn and an svm. Comput Electron Agric 194:106738","journal-title":"Comput Electron Agric"},{"issue":"2","key":"2810_CR31","doi-asserted-by":"publisher","first-page":"2351","DOI":"10.1109\/JIOT.2023.3294944","volume":"11","author":"I Bakhshayeshi","year":"2023","unstructured":"Bakhshayeshi I, Erfani E, Taghikhah FR, Elbourn S, Beheshti A, Asadnia M (2023) An intelligence cattle reidentification system over transport by Siamese neural networks and yolo. IEEE Internet Things J 11(2):2351\u20132363","journal-title":"IEEE Internet Things J"},{"issue":"7","key":"2810_CR32","doi-asserted-by":"publisher","first-page":"2300706","DOI":"10.1002\/aisy.202300706","volume":"6","author":"SU Amin","year":"2024","unstructured":"Amin SU, Kim B, Jung Y, Seo S, Park S (2024) Video anomaly detection utilizing efficient spatiotemporal feature fusion with 3d convolutions and long short-term memory modules. Adv Intell Syst 6(7):2300706","journal-title":"Adv Intell Syst"},{"key":"2810_CR33","doi-asserted-by":"publisher","first-page":"2110","DOI":"10.3934\/mbe.2023098","volume":"20","author":"K Jin","year":"2023","unstructured":"Jin K, Zhai J, Gao Y (2023) Twinsreid: person re-identification based on twins transformer\u2019s multi-level features. Math Biosci Eng 20:2110\u20132130","journal-title":"Math Biosci Eng"},{"issue":"10","key":"2810_CR34","doi-asserted-by":"publisher","first-page":"7015","DOI":"10.1007\/s00371-024-03286-6","volume":"40","author":"L Shenglin","year":"2024","unstructured":"Shenglin L, Fung S, Pan W, Wickramasinghe N, Xuequan L (2024) Veintr: robust end-to-end full-hand vein identification with transformer. Vis Comput 40(10):7015\u20137023","journal-title":"Vis Comput"},{"key":"2810_CR35","unstructured":"Mehta S, Rastegari M (2021) Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv:2110.02178"},{"key":"2810_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.108703","volume":"218","author":"X Zhihao","year":"2024","unstructured":"Zhihao X, Zhao Y, Yin Z, Qiuping Yu (2024) Optimized bottlenet transformer model with graph sampling and counterfactual attention for cow individual identification. Comput Electron Agric 218:108703","journal-title":"Comput Electron Agric"},{"issue":"1","key":"2810_CR37","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/TBIOM.2023.3317303","volume":"6","author":"SA Grosz","year":"2023","unstructured":"Grosz SA, Jain AK (2023) Afr-net: attention-driven fingerprint recognition network. IEEE Trans Biomet Behav Ident Sci 6(1):30\u201342","journal-title":"IEEE Trans Biomet Behav Ident Sci"},{"key":"2810_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110362","volume":"150","author":"Y Li","year":"2024","unstructured":"Li Y, Miao D, Zhang H, Zhou J, Zhao C (2024) Multi-granularity cross transformer network for person re-identification. Pattern Recogn 150:110362","journal-title":"Pattern Recogn"},{"key":"2810_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106871","volume":"196","author":"Z Weng","year":"2022","unstructured":"Weng Z, Meng F, Liu S, Zhang Y, Zheng Z, Gong C (2022) Cattle face recognition based on a two-branch convolutional neural network. Comput Electron Agric 196:106871","journal-title":"Comput Electron Agric"},{"key":"2810_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105408","volume":"87","author":"SU Amin","year":"2024","unstructured":"Amin SU, Taj S, Hussain A, Seo S (2024) An automated chest x-ray analysis for covid-19, tuberculosis, and pneumonia employing ensemble learning approach. Biomed Signal Process Control 87:105408","journal-title":"Biomed Signal Process Control"},{"key":"2810_CR41","doi-asserted-by":"crossref","unstructured":"Lu Y, Wu Y, Liu B, Zhang T, Li B, Chu Q, Yu N (2020) Cross-modality person re-identification with shared-specific feature transfer. In Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pages 13379\u201313389,","DOI":"10.1109\/CVPR42600.2020.01339"},{"key":"2810_CR42","doi-asserted-by":"crossref","unstructured":"Yu W, Zhou P, Yan S, Wang X (2024) Inceptionnext: when inception meets convnext. In Proceedings of the IEEE\/cvf Conference on computer vision and pattern recognition, pages 5672\u20135683,","DOI":"10.1109\/CVPR52733.2024.00542"},{"key":"2810_CR43","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, et\u00a0al (2021) Learning transferable visual models from natural language supervision. In: International Conference on machine learning, pages 8748\u20138763. PmLR,"},{"key":"2810_CR44","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pages 4700\u20134708,","DOI":"10.1109\/CVPR.2017.243"},{"key":"2810_CR45","unstructured":"Mingxing T, Le Quoc V (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv:1905.11946, 1,"},{"key":"2810_CR46","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, pages 1\u20139,","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2810_CR47","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on computer vision and pattern recognition, pages 2818\u20132826,","DOI":"10.1109\/CVPR.2016.308"},{"key":"2810_CR48","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI Conference on artificial intelligence, volume\u00a031,","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"2810_CR49","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on computer vision and pattern recognition, pages 4510\u20134520,","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2810_CR50","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V, et\u00a0al (2019) Searching for mobilenetv3. In Proceedings of the IEEE\/CVF International Conference on computer vision, pages 1314\u20131324,","DOI":"10.1109\/ICCV.2019.00140"},{"key":"2810_CR51","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE Conference on computer vision and pattern recognition, pages 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2810_CR52","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng HT, Sun J (2018) Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European Conference on computer vision (ECCV), pages 116\u2013131,","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"2810_CR53","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556,"},{"key":"2810_CR54","first-page":"30662","volume":"36","author":"A Conti","year":"2023","unstructured":"Conti A, Fini E, Mancini M, Rota P, Wang Y, Ricci E (2023) Vocabulary-free image classification. Adv Neural Inf Process Syst 36:30662\u201330680","journal-title":"Adv Neural Inf Process Syst"},{"key":"2810_CR55","doi-asserted-by":"crossref","unstructured":"Yang Y, Panagopoulou A, Zhou S, Jin D, Callison-Burch C, Yatskar M (2023) Language in a bottle: language model guided concept bottlenecks for interpretable image classification. In Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pages 19187\u201319197,","DOI":"10.1109\/CVPR52729.2023.01839"},{"key":"2810_CR56","doi-asserted-by":"crossref","unstructured":"Liu X, Peng H, Zheng N, Yang Y, Hu H, Yuan Y (2023) Efficientvit: memory efficient vision transformer with cascaded group attention. In Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pages 14420\u201314430","DOI":"10.1109\/CVPR52729.2023.01386"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02810-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02810-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02810-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:41:38Z","timestamp":1765618898000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02810-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,22]]},"references-count":56,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2810"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02810-x","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,22]]},"assertion":[{"value":"22 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}