{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:04:40Z","timestamp":1773486280543,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"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":"crossref","award":["No. 62101268"],"award-info":[{"award-number":["No. 62101268"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013088","name":"Qinglan Project of Jiangsu Province of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013088","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China Agriculture Research System of MOF and MARA","award":["No. CARS-21"],"award-info":[{"award-number":["No. CARS-21"]}]},{"name":"Jiangsu Province 333 High-level Talents Training Project"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s10044-026-01617-y","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:20:21Z","timestamp":1769718021000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-scale feature fusion for chrysanthemum classification using dual-view"],"prefix":"10.1007","volume":"29","author":[{"given":"Jian","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xichen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianshu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyuan","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"1617_CR1","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.sajb.2021.09.007","volume":"144","author":"H Hadizadeh","year":"2022","unstructured":"Hadizadeh H, Samiei L, Shakeri A (2022) Chrysanthemum, an ornamental genus with considerable medicinal value: a comprehensive review. S Afr J Bot 144:23\u201343","journal-title":"S Afr J Bot"},{"key":"1617_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.806711","volume":"13","author":"J Wang","year":"2022","unstructured":"Wang J, Tian Y, Zhang R, Liu Z, Tian Y, Dai S (2022) Multi-information model for large-flowered chrysanthemum cultivar recognition and classification. Front Plant Sci 13:806711","journal-title":"Front Plant Sci"},{"key":"1617_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodchem.2020.128733","volume":"344","author":"S Chen","year":"2021","unstructured":"Chen S, Liu J, Dong G, Zhang X, Liu Y, Sun W, Liu A (2021) Flavonoids and caffeoylquinic acids in chrysanthemum Morifolium Ramat flowers: a potentially rich source of bioactive compounds. Food Chem 344:128733","journal-title":"Food Chem"},{"key":"1617_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2020.107424","volume":"90","author":"SY Won","year":"2021","unstructured":"Won SY, Jung J-A, Kim JS (2021) Genome-wide analysis of the mads-box gene family in chrysanthemum. Comput Biol Chem 90:107424","journal-title":"Comput Biol Chem"},{"key":"1617_CR5","doi-asserted-by":"publisher","first-page":"1463113","DOI":"10.3389\/fpls.2024.1463113","volume":"15","author":"Y Chen","year":"2025","unstructured":"Chen Y, Yang X, Yan H, Liu J, Jiang J, Mao Z, Wang T (2025) Chrysanthemum classification method integrating deep visual features from both the front and back sides. Front Plant Sci 15:1463113","journal-title":"Front Plant Sci"},{"issue":"4","key":"1617_CR6","doi-asserted-by":"publisher","first-page":"1968","DOI":"10.1002\/fsn3.1484","volume":"8","author":"C Liu","year":"2020","unstructured":"Liu C, Lu W, Gao B, Kimura H, Li Y, Wang J (2020) Rapid identification of chrysanthemum teas by computer vision and deep learning. Food Sci Nutr 8(4):1968\u20131977","journal-title":"Food Sci Nutr"},{"key":"1617_CR7","unstructured":"Pandey D, Niwaria K, Chourasia B (2019) Machine learning algorithms: a review. Mach Learn 6(2):916\u2013922"},{"issue":"6","key":"1617_CR8","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1109\/TGRS.2004.827257","volume":"42","author":"GM Foody","year":"2004","unstructured":"Foody GM, Mathur A (2004) A relative evaluation of multiclass image classification by support vector machines. IEEE Trans Geosci Remote Sens 42(6):1335\u20131343","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1617_CR9","doi-asserted-by":"crossref","unstructured":"Bosch A, Zisserman A, Munoz X (2007) Image classification using random forests and ferns. In: 2007 IEEE 11th International Conference on Computer Vision, pp 1\u20138. IEEE","DOI":"10.1109\/ICCV.2007.4409066"},{"key":"1617_CR10","doi-asserted-by":"crossref","unstructured":"Cheng H (2025) Advancements in image classification: from machine learning to deep learning. In: ITM Web of Conferences, 70:02016. EDP Sciences","DOI":"10.1051\/itmconf\/20257002016"},{"issue":"4","key":"1617_CR11","first-page":"411","volume":"48","author":"P Ashwin","year":"2025","unstructured":"Ashwin P, Ansal K (2025) Pol-sar image classification using multifarious stratification stratagem in machine learning. J Intell Fuzzy Syst 48(4):411\u2013430","journal-title":"J Intell Fuzzy Syst"},{"key":"1617_CR12","doi-asserted-by":"publisher","unstructured":"Yadav A, Khatibi A, Shreenidhi H, Gupta SK, Jadhav A, Chohan MK, Raju GS, Alkhayyat A (2025) Handcrafted feature and deep features based image classification using machine learning models. Natl Acad Sci Lett 1\u20134 https:\/\/doi.org\/10.1007\/s40009-025-01616-3","DOI":"10.1007\/s40009-025-01616-3"},{"issue":"2","key":"1617_CR13","doi-asserted-by":"publisher","first-page":"120","DOI":"10.58602\/jics.v3i2.48","volume":"3","author":"F Saeed","year":"2025","unstructured":"Saeed F, Shiwlani A, Umar M, Jahangir Z, Tahir A, Shiwlani S (2025) Hepatocellular carcinoma prediction in hcv patients using machine learning and deep learning techniques. J Ilmiah Comput Sci 3(2):120\u2013134","journal-title":"J Ilmiah Comput Sci"},{"issue":"1","key":"1617_CR14","first-page":"145","volume":"3","author":"BSH Reddy","year":"2025","unstructured":"Reddy BSH (2025) Deep learning-based detection of hair and scalp diseases using cnn and image processing. Milestone Trans Med Technometrics 3(1):145\u2013155","journal-title":"Milestone Trans Med Technometrics"},{"key":"1617_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2017.05.015","volume":"71","author":"SH Lee","year":"2017","unstructured":"Lee SH, Chan CS, Mayo SJ, Remagnino P (2017) How deep learning extracts and learns leaf features for plant classification. Pattern Recogn 71:1\u201313","journal-title":"Pattern Recogn"},{"key":"1617_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106679","volume":"194","author":"P Yuan","year":"2022","unstructured":"Yuan P, Qian S, Zhai Z, Fern\u00e1nMart\u00ednez J, Xu H (2022) Study of chrysanthemum image phenotype on-line classification based on transfer learning and bilinear convolutional neural network. Comput Electron Agric 194:106679","journal-title":"Comput Electron Agric"},{"key":"1617_CR17","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.806711","volume":"13","author":"J Wang","year":"2022","unstructured":"Wang J, Tian Y, Zhang R, Liu Z, Tian Y, Dai S (2022) Multi-information model for large-flowered chrysanthemum cultivar recognition and classification. Front Plant Sci 13:806711","journal-title":"Front Plant Sci"},{"issue":"3","key":"1617_CR18","doi-asserted-by":"publisher","first-page":"2373","DOI":"10.1007\/s11600-024-01506-0","volume":"73","author":"Y Wang","year":"2025","unstructured":"Wang Y, Cui M, Xie B, Li Q, Li X, Wu Y, Xie R, Guo J (2025) Tight sandstone reservoir classification based on 1dcnn-blstm with conventional logging data. Acta Geophys 73(3):2373\u20132389","journal-title":"Acta Geophys"},{"key":"1617_CR19","doi-asserted-by":"crossref","unstructured":"Phan T-T-H (2024) Efficient dry bean varieties classification using a compact 1dcnn. In: International Conference on Applied Mathematics and Computer Science, pp 311\u2013321. Springer","DOI":"10.1007\/978-3-032-00267-9_28"},{"key":"1617_CR20","doi-asserted-by":"crossref","unstructured":"Gao X, Khan MH-M, Hui R, Tian Z, Qian Y, Gao A, Baichoo S (2022) Covid-vit: Classification of covid-19 from 3d ct chest images based on vision transformer model. In: 2022 3rd International Conference on Next Generation Computing Applications (NextComp), pp 1\u20134. IEEE","DOI":"10.1109\/NextComp55567.2022.9932246"},{"key":"1617_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108724","volume":"108","author":"G Li","year":"2023","unstructured":"Li G, Wang F, Zhou L, Jin S, Xie X, Ding C, Pan X, Zhang W (2023) Mcanet: multi-channel attention network with multi-color space encoder for underwater image classification. Comput Electr Eng 108:108724","journal-title":"Comput Electr Eng"},{"key":"1617_CR22","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.patrec.2024.11.015","volume":"187","author":"WA Alves","year":"2025","unstructured":"Alves WA, Campos WS, Gobber CF, Silva DJ, Hashimoto RF (2025) Multichannel image classification based on adaptive attribute profiles. Pattern Recogn Lett 187:107\u2013114","journal-title":"Pattern Recogn Lett"},{"issue":"21","key":"1617_CR23","doi-asserted-by":"publisher","first-page":"15511","DOI":"10.1007\/s00521-023-08544-7","volume":"35","author":"M Xu","year":"2023","unstructured":"Xu M, Gao J, Zhang Z, Guo X (2023) Multi-channel and multi-scale separable dilated convolutional neural network with attention mechanism for flue-cured tobacco classification. Neural Comput Appl 35(21):15511\u201315529","journal-title":"Neural Comput Appl"},{"key":"1617_CR24","doi-asserted-by":"crossref","unstructured":"Lin C, Yao H, Yu W, Tang W (2017) Multi-level semantic representation for flower classification. In: Pacific Rim Conference on Multimedia, pp 325\u2013335. Springer","DOI":"10.1007\/978-3-319-77380-3_31"},{"key":"1617_CR25","doi-asserted-by":"crossref","unstructured":"Nilsback M-E, Zisserman A (2008) Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, 722\u2013729. IEEE","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"1617_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107605","volume":"205","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Huang S, Zhou G, Hu Y, Li L (2023) Identification of tomato leaf diseases based on multi-channel automatic orientation recurrent attention network. Comput Electron Agric 205:107605","journal-title":"Comput Electron Agric"},{"key":"1617_CR27","doi-asserted-by":"publisher","first-page":"166488","DOI":"10.1109\/ACCESS.2021.3136567","volume":"9","author":"BH Ngo","year":"2021","unstructured":"Ngo BH, Kim JH, Chae YJ, Cho SI (2021) Multi-view collaborative learning for semi-supervised domain adaptation. IEEE Access 9:166488\u2013166501","journal-title":"IEEE Access"},{"issue":"1","key":"1617_CR28","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1002\/ima.22784","volume":"33","author":"S Tajjour","year":"2023","unstructured":"Tajjour S, Garg S, Chandel SS, Sharma D (2023) A novel hybrid artificial neural network technique for the early skin cancer diagnosis using color space conversions of original images. Int J Imaging Syst Technol 33(1):276\u2013286","journal-title":"Int J Imaging Syst Technol"},{"key":"1617_CR29","doi-asserted-by":"crossref","unstructured":"Ngo BH, Do-Tran N-T, Nguyen T-N, Jeon H-G, Choi TJ (2024) Learning cnn on vit: a hybrid model to explicitly class-specific boundaries for domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 28545\u201328554","DOI":"10.1109\/CVPR52733.2024.02697"},{"key":"1617_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.129580","volume":"626","author":"X Kui","year":"2025","unstructured":"Kui X, Jiang S, Li Q, Peng Y, Hu Z, Zou B (2025) Gl-mambanet: a global-local hybrid mamba network for medical image segmentation. Neurocomputing 626:129580","journal-title":"Neurocomputing"},{"key":"1617_CR31","doi-asserted-by":"crossref","unstructured":"Wang L, Zhan Y, Ma L, Tao D, Ding L, Gong C (2025) Splicemix: a cross-scale and semantic blending augmentation strategy for multi-label image classification. IEEE Trans Multimed 27:3251\u20133265","DOI":"10.1109\/TMM.2025.3535387"},{"issue":"3","key":"1617_CR32","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1049\/ipr2.12972","volume":"18","author":"L Wang","year":"2024","unstructured":"Wang L, Long C, Li X, Tang X, Bai Z, Gao H (2024) Csffnet: lightweight cross-scale feature fusion network for salient object detection in remote sensing images. IET Image Proc 18(3):602\u2013614","journal-title":"IET Image Proc"},{"key":"1617_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123939","volume":"250","author":"Y Li","year":"2024","unstructured":"Li Y, Yang X, Tang D, Zhou Z (2024) Rdtn: residual densely transformer network for hyperspectral image classification. Expert Syst Appl 250:123939","journal-title":"Expert Syst Appl"},{"key":"1617_CR34","doi-asserted-by":"publisher","first-page":"36267","DOI":"10.1109\/ACCESS.2024.3374105","volume":"12","author":"BH Ngo","year":"2024","unstructured":"Ngo BH, Lam BT, Nguyen TH, Dinh QV, Choi TJ (2024) Dual dynamic consistency regularization for semi-supervised domain adaptation. IEEE Access 12:36267\u201336279","journal-title":"IEEE Access"},{"key":"1617_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2024.104883","volume":"157","author":"F Sui","year":"2025","unstructured":"Sui F, Wang H, Zhang F (2025) Cross-scale informative priors network for medical image segmentation. Digital Signal Process 157:104883","journal-title":"Digital Signal Process"},{"key":"1617_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106316","volume":"95","author":"B Nagachandrika","year":"2024","unstructured":"Nagachandrika B, Prasath R, Joe IP (2024) An automatic classification framework for identifying type of plant leaf diseases using multi-scale feature fusion-based adaptive deep network. Biomed Signal Process Control 95:106316","journal-title":"Biomed Signal Process Control"},{"key":"1617_CR37","doi-asserted-by":"crossref","unstructured":"Al-Gaashani MS, Alkanhel R, Ali MAS, Muthanna MSA, Aziz A, Muthanna A (2025) Mscpnet: a multi-scale convolutional pooling network for maize disease classification. IEEE Access 13:11423\u201311446","DOI":"10.1109\/ACCESS.2024.3524729"},{"issue":"4","key":"1617_CR38","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.1007\/s00521-023-09158-9","volume":"36","author":"H F\u0131rat","year":"2024","unstructured":"F\u0131rat H (2024) Classification of microscopic peripheral blood cell images using multibranch lightweight cnn-based model. Neural Comput Appl 36(4):1599\u20131620","journal-title":"Neural Comput Appl"},{"issue":"1","key":"1617_CR39","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1038\/s41598-024-51329-8","volume":"14","author":"ON Oyelade","year":"2024","unstructured":"Oyelade ON, Irunokhai EA, Wang H (2024) A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification. Sci Rep 14(1):692","journal-title":"Sci Rep"},{"key":"1617_CR40","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale. ICLR"},{"key":"1617_CR41","doi-asserted-by":"crossref","unstructured":"Huo Y, Jin K, Cai J, Xiong H, Pang J (2023) Vision transformer (vit)-based applications in image classification. In: 2023 IEEE 9th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), pp 135\u2013140. IEEE","DOI":"10.1109\/BigDataSecurity-HPSC-IDS58521.2023.00033"},{"key":"1617_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109507","volume":"185","author":"C Chen","year":"2025","unstructured":"Chen C, Isa NAM, Liu X (2025) A review of convolutional neural network based methods for medical image classification. Comput Biol Med 185:109507","journal-title":"Comput Biol Med"},{"key":"1617_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107486","volume":"203","author":"S Fang","year":"2022","unstructured":"Fang S, Wang Y, Zhou G, Chen A, Cai W, Wang Q, Hu Y, Li L (2022) Multi-channel feature fusion networks with hard coordinate attention mechanism for maize disease identification under complex backgrounds. Comput Electron Agric 203:107486","journal-title":"Comput Electron Agric"},{"key":"1617_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.triboint.2022.107656","volume":"173","author":"F Xie","year":"2022","unstructured":"Xie F, Wei H (2022) Research on controllable deep learning of multi-channel image coding technology in ferrographic image fault classification. Tribol Int 173:107656","journal-title":"Tribol Int"},{"key":"1617_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105534","volume":"87","author":"X Huo","year":"2024","unstructured":"Huo X, Sun G, Tian S, Wang Y, Yu L, Long J, Zhang W, Li A (2024) Hifuse: hierarchical multi-scale feature fusion network for medical image classification. Biomed Signal Process Control 87:105534","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"1617_CR46","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s00521-023-09062-2","volume":"36","author":"E Elfatimi","year":"2024","unstructured":"Elfatimi E, Eryi\u011fit R, Elfatimi L (2024) Deep multi-scale convolutional neural networks for automated classification of multi-class leaf diseases in tomatoes. Neural Comput Appl 36(2):803\u2013822","journal-title":"Neural Comput Appl"},{"issue":"1","key":"1617_CR47","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1186\/s13007-019-0532-7","volume":"15","author":"Z Liu","year":"2019","unstructured":"Liu Z, Wang J, Tian Y, Dai S (2019) Deep learning for image-based large-flowered chrysanthemum cultivar recognition. Plant Methods 15(1):146","journal-title":"Plant Methods"},{"issue":"11","key":"1617_CR48","doi-asserted-by":"publisher","first-page":"2831","DOI":"10.3390\/molecules23112831","volume":"23","author":"N Wu","year":"2018","unstructured":"Wu N, Zhang C, Bai X, Du X, He Y (2018) Discrimination of chrysanthemum varieties using hyperspectral imaging combined with a deep convolutional neural network. Molecules 23(11):2831","journal-title":"Molecules"},{"key":"1617_CR49","doi-asserted-by":"crossref","unstructured":"Liu X, He Y (2022) Realization of chrysanthemum harvesting recognition system based on cnn. In: 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA), pp 368\u2013371. IEEE","DOI":"10.1109\/CVIDLICCEA56201.2022.9824157"},{"issue":"4","key":"1617_CR50","doi-asserted-by":"publisher","first-page":"2865","DOI":"10.11591\/eei.v13i4.6203","volume":"13","author":"Q Aini","year":"2024","unstructured":"Aini Q, Zulfiandri Z, Firmansyah R, Arif YM (2024) Applying convolutional neural network and Nadam optimization in flower classification. Bull Electric Eng Inform 13(4):2865\u20132877","journal-title":"Bull Electric Eng Inform"},{"key":"1617_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109834","volume":"230","author":"S Gupta","year":"2025","unstructured":"Gupta S, Tripathi AK (2025) Flora-net: Integrating dual coordinate attention with adaptive kernel based convolution network for medicinal flower identification. Comput Electron Agric 230:109834","journal-title":"Comput Electron Agric"},{"issue":"1","key":"1617_CR52","doi-asserted-by":"publisher","first-page":"70234","DOI":"10.1111\/cns.70234","volume":"31","author":"B Li","year":"2025","unstructured":"Li B, Xu X-M, Wu Y-Q, Feng Y, Chen Y-C, Salvi R, Xu J-J, Qi J-W (2025) Disrupted cross-scale network associated with cognitive-emotional disorders in sudden sensorineural hearing loss. CNS Neurosci Therapeutics 31(1):70234","journal-title":"CNS Neurosci Therapeutics"},{"key":"1617_CR53","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, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1617_CR54","doi-asserted-by":"crossref","unstructured":"Ding X, Zhang X, Ma N, Han J, Ding G, Sun J (2021) Repvgg: making vgg-style convnets great again. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 13733\u201313742","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"1617_CR55","doi-asserted-by":"crossref","unstructured":"Li Y, Wu C-Y, Fan H, Mangalam K, Xiong B, Malik J, Feichtenhofer C (2022) Mvitv2: improved multiscale vision transformers for classification and detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4804\u20134814","DOI":"10.1109\/CVPR52688.2022.00476"},{"key":"1617_CR56","doi-asserted-by":"crossref","unstructured":"Touvron H, Cord M, Sablayrolles A, Synnaeve G, J\u00e9gou H (2021) Going deeper with image transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 32\u201342","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"1617_CR57","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1617_CR58","doi-asserted-by":"crossref","unstructured":"Yu W, Luo M, Zhou P, Si C, Zhou Y, Wang X, Feng J, Yan S (2022) Metaformer is actually what you need for vision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 10819\u201310829","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"1617_CR59","doi-asserted-by":"crossref","unstructured":"Kim D, Heo B, Han D (2024) Densenets reloaded: paradigm shift beyond resnets and vits. In: European Conference on Computer Vision, pp 395\u2013415. Springer","DOI":"10.1007\/978-3-031-72646-0_23"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01617-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-026-01617-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01617-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T10:38:25Z","timestamp":1773484705000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-026-01617-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["1617"],"URL":"https:\/\/doi.org\/10.1007\/s10044-026-01617-y","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]},"assertion":[{"value":"3 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2026","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"33"}}