{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T01:09:12Z","timestamp":1766106552989,"version":"3.48.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2022201013"],"award-info":[{"award-number":["F2022201013"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Startup Foundation for Advanced Talents of Hebei University","award":["No.521100221003"],"award-info":[{"award-number":["No.521100221003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s10489-025-06984-3","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T10:36:54Z","timestamp":1762857414000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Corner Sparse R-CNN for Pedestrian Detection in Dense Scenes"],"prefix":"10.1007","volume":"55","author":[{"given":"Jun","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaiqi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"key":"6984_CR1","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"6984_CR2","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"6984_CR3","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv:1804.02767"},{"key":"6984_CR4","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv:2004.10934"},{"key":"6984_CR5","unstructured":"Li C, Li L, Jiang H, Weng K, Geng Y, Li L, Ke Z, Li Q, Cheng M, Nie W, et\u00a0al (2022) Yolov6: A single-stage object detection framework for industrial applications. arXiv:2209.02976"},{"key":"6984_CR6","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2023) Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp. 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"1","key":"6984_CR7","doi-asserted-by":"publisher","first-page":"013006","DOI":"10.1117\/1.JEI.33.1.013006","volume":"33","author":"Q Song","year":"2024","unstructured":"Song Q, Hou M, Xue Y, Yu J (2024) Ma-yolo: a multi-attention object detection network for remote sensing images. J Electron Imaging 33(1):013006\u2013013006","journal-title":"J Electron Imaging"},{"issue":"1","key":"6984_CR8","doi-asserted-by":"publisher","first-page":"013023","DOI":"10.1117\/1.JEI.33.1.013023","volume":"33","author":"P Yan","year":"2024","unstructured":"Yan P, Liu Y, Lyu L, Xu X, Song B, Wang F (2024) Aiod-yolo: an algorithm for object detection in low-altitude aerial images. J Electron Imaging 33(1):013023\u2013013023","journal-title":"J Electron Imaging"},{"issue":"2","key":"6984_CR9","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/s11063-024-11558-4","volume":"56","author":"L Pan","year":"2024","unstructured":"Pan L, Diao J, Wang Z, Peng S, Zhao C (2024) Hf-yolo: Advanced pedestrian detection model with feature fusion and imbalance resolution. Neural Process Lett 56(2):90","journal-title":"Neural Process Lett"},{"issue":"2","key":"6984_CR10","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s11554-023-01287-7","volume":"20","author":"F Gao","year":"2023","unstructured":"Gao F, Cai C, Jia R, Hu X (2023) Improved yolox for pedestrian detection in crowded scenes. J Real-Time Image Proc 20(2):24","journal-title":"J Real-Time Image Proc"},{"key":"6984_CR11","doi-asserted-by":"crossref","unstructured":"Han R, Xu M, Pei S (2024) Crowded pedestrian detection with optimal bounding box relocation. Multimedia Tools and Applications 1\u201320","DOI":"10.1007\/s11042-023-18019-5"},{"issue":"15","key":"6984_CR12","doi-asserted-by":"publisher","first-page":"18171","DOI":"10.1007\/s10489-023-04456-0","volume":"53","author":"M Liu","year":"2023","unstructured":"Liu M, Wan L, Wang B, Wang T (2023) Se-yolov4: Shuffle expansion yolov4 for pedestrian detection based on pixelshuffle. Appl Intell 53(15):18171\u201318188","journal-title":"Appl Intell"},{"issue":"1","key":"6984_CR13","doi-asserted-by":"publisher","first-page":"013037","DOI":"10.1117\/1.JEI.34.1.013037","volume":"34","author":"G Li","year":"2025","unstructured":"Li G, Luo H, Huang H, Yu J, Huang C, Xu X, Cai J (2025) Rmtp-yolo: an improved dense pedestrian detection algorithm based on yolov8. J Electron Imaging 34(1):013037\u2013013037","journal-title":"J Electron Imaging"},{"issue":"5","key":"6984_CR14","doi-asserted-by":"publisher","first-page":"053022","DOI":"10.1117\/1.JEI.33.5.053022","volume":"33","author":"M Xu","year":"2024","unstructured":"Xu M, Sun J, Zhang J, Yan M, Cao W, Hou A (2024) Road target detection in harsh environments based on improved yolov8n. J Electron Imaging 33(5):053022\u2013053022","journal-title":"J Electron Imaging"},{"issue":"11","key":"6984_CR15","doi-asserted-by":"publisher","first-page":"118105","DOI":"10.1117\/1.OE.62.11.118105","volume":"62","author":"Y Liu","year":"2023","unstructured":"Liu Y, Yi F, Ma Y, Wang Y, Wang D (2023) Hold surrounding\u2019s key-you only look once version 7: a real-time pedestrian and vehicle detection algorithm in the low-signal-to-noise ratio infrared image. Opt Eng 62(11):118105\u2013118105","journal-title":"Opt Eng"},{"key":"6984_CR16","doi-asserted-by":"crossref","unstructured":"Neubeck A, Van\u00a0Gool L (2006) Efficient non-maximum suppression. In: 18th International Conference on Pattern Recognition (ICPR\u201906), vol. 3, pp. 850\u2013855. IEEE","DOI":"10.1109\/ICPR.2006.479"},{"key":"6984_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109614","volume":"227","author":"H Xing","year":"2024","unstructured":"Xing H, Wan Y, Zhong P, Lin J, Huang M, Yang R, Zang Y (2024) Design and experimental analysis of real-time detection system for the seeding accuracy of rice pneumatic seed metering device based on the improved yolov5n. Comput Electron Agric 227:109614","journal-title":"Comput Electron Agric"},{"key":"6984_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108213","volume":"241","author":"W Ma","year":"2022","unstructured":"Ma W, Zhou T, Qin J, Zhou Q, Cai Z (2022) Joint-attention feature fusion network and dual-adaptive nms for object detection. Knowl-Based Syst 241:108213","journal-title":"Knowl-Based Syst"},{"key":"6984_CR19","unstructured":"Zhang K, Xiong F, Sun P, Hu L, Li B, Yu G (2019) Double anchor r-cnn for human detection in a crowd. arXiv:1909.09998"},{"key":"6984_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107230","volume":"102","author":"C Zhang","year":"2025","unstructured":"Zhang C, Min X, Zhang P, Yang B, Zhang X, Xu S (2025) Detection of oesophageal cancer based on the js-detr model. Biomed Signal Process Control 102:107230","journal-title":"Biomed Signal Process Control"},{"key":"6984_CR21","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28"},{"key":"6984_CR22","doi-asserted-by":"crossref","unstructured":"Sun P, Zhang R, Jiang Y, Kong T, Xu C, Zhan W, Tomizuka M, Li L, Yuan Z, Wang C, et\u00a0al (2021) Sparse r-cnn: End-to-end object detection with learnable proposals. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp. 14454\u201314463","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"6984_CR23","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":"6984_CR24","doi-asserted-by":"crossref","unstructured":"Quan Y, Zhang D, Zhang L, Tang J (2023) Centralized feature pyramid for object detection. IEEE Trans Image Process","DOI":"10.1109\/TIP.2023.3297408"},{"key":"6984_CR25","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"6984_CR26","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings JR, Van De Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vision 104:154\u2013171","journal-title":"Int J Comput Vision"},{"issue":"4","key":"6984_CR27","doi-asserted-by":"publisher","first-page":"3462","DOI":"10.1109\/TEC.2021.3075897","volume":"36","author":"HS Dhiman","year":"2021","unstructured":"Dhiman HS, Deb D, Muyeen S, Kamwa I (2021) Wind turbine gearbox anomaly detection based on adaptive threshold and twin support vector machines. IEEE Trans Energy Convers 36(4):3462\u20133469","journal-title":"IEEE Trans Energy Convers"},{"key":"6984_CR28","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International conference on computer vision, pp. 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"6984_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.aquaeng.2022.102304","volume":"100","author":"H Hu","year":"2023","unstructured":"Hu H, Tang C, Shi C, Qian Y (2023) Detection of residual feed in aquaculture using yolo and mask rcnn. Aquacult Eng 100:102304","journal-title":"Aquacult Eng"},{"key":"6984_CR30","doi-asserted-by":"crossref","unstructured":"Lee SH (2022) A study on cascade r-cnn-based dangerous goods detection using x-ray image. Computers, Materials & Continua 73(2)","DOI":"10.32604\/cmc.2022.026012"},{"key":"6984_CR31","doi-asserted-by":"crossref","unstructured":"Ge Z, Jie Z, Huang X, Xu R, Yoshie O (2020) Ps-rcnn: Detecting secondary human instances in a crowd via primary object suppression. In: 2020 IEEE International conference on multimedia and expo (ICME), pp. 1\u20136. IEEE","DOI":"10.1109\/ICME46284.2020.9102793"},{"key":"6984_CR32","doi-asserted-by":"crossref","unstructured":"Chu X, Zheng A, Zhang X, Sun J (2020) Detection in crowded scenes: One proposal, multiple predictions. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp. 12214\u201312223","DOI":"10.1109\/CVPR42600.2020.01223"},{"key":"6984_CR33","unstructured":"Lin M, Li C, Bu X, Sun M, Lin C, Yan J, Ouyang W, Deng Z (2020) Detr for crowd pedestrian detection. arXiv:2012.06785"},{"key":"6984_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.107917","volume":"170","author":"Y Chen","year":"2024","unstructured":"Chen Y, Zhang C, Chen B, Huang Y, Sun Y, Wang C, Fu X, Dai Y, Qin F, Peng Y et al (2024) Accurate leukocyte detection based on deformable-detr and multi-level feature fusion for aiding diagnosis of blood diseases. Comput Biol Med 170:107917","journal-title":"Comput Biol Med"},{"issue":"4","key":"6984_CR35","doi-asserted-by":"publisher","first-page":"043004","DOI":"10.1117\/1.JEI.31.4.043004","volume":"31","author":"TM Qaid","year":"2022","unstructured":"Qaid TM, Loukil A, Kaddour El Boudadi L, Mohammed AA (2022) Fast and effective pedestrian detection based on low-level visual features combination. J Electron Imaging 31(4):043004\u2013043004","journal-title":"J Electron Imaging"},{"key":"6984_CR36","doi-asserted-by":"crossref","unstructured":"Li J, Wen Y, He L (2023) Scconv: Spatial and channel reconstruction convolution for feature redundancy. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp. 6153\u20136162","DOI":"10.1109\/CVPR52729.2023.00596"},{"key":"6984_CR37","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"6984_CR38","unstructured":"Wu Y, Johnson J (2021) Rethinking \u201cbatch\u201d in batchnorm. arXiv:2105.07576"},{"key":"6984_CR39","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807\u2013814"},{"key":"6984_CR40","doi-asserted-by":"crossref","unstructured":"Kadri R, Bouaziz B, Tmar M, Gargouri F (2022) Depthwise separable convolution resnet with attention mechanism for alzheimer\u2019s detection. In: 2022 International Conference on technology innovations for healthcare (ICTIH), pp. 47\u201352. IEEE","DOI":"10.1109\/ICTIH57289.2022.10112012"},{"key":"6984_CR41","doi-asserted-by":"crossref","unstructured":"Wu Y, He K (2018) Group normalization. In: Proceedings of the european conference on computer vision (ECCV), pp. 3\u201319","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"6984_CR42","doi-asserted-by":"crossref","unstructured":"Zhang H, Xu C, Cheng Y (2023) Document image object detection algorithm based on transformer and mixed-mlp network. In: 2023 IEEE 6th International Conference on electronic information and communication technology (ICEICT), pp. 45\u201350. IEEE","DOI":"10.1109\/ICEICT57916.2023.10245716"},{"key":"6984_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105863","volume":"148","author":"S Liu","year":"2022","unstructured":"Liu S, Wang A, Deng X, Yang C (2022) Mgnn: A multiscale grouped convolutional neural network for efficient atrial fibrillation detection. Comput Biol Med 148:105863","journal-title":"Comput Biol Med"},{"key":"6984_CR44","unstructured":"Shao S, Zhao Z, Li B, Xiao T, Yu G, Zhang X, Sun J (2018) Crowdhuman: A benchmark for detecting human in a crowd. arXiv:1805.00123"},{"key":"6984_CR45","doi-asserted-by":"crossref","unstructured":"Zhang S, Benenson R, Schiele B (2017) Citypersons: A diverse dataset for pedestrian detection. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 3213\u20133221","DOI":"10.1109\/CVPR.2017.474"},{"key":"6984_CR46","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp. 740\u2013755. Springer","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"4","key":"6984_CR47","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","volume":"34","author":"P Dollar","year":"2011","unstructured":"Dollar P, Wojek C, Schiele B, Perona P (2011) Pedestrian detection: An evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743\u2013761","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6984_CR48","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pp. 21\u201337. Springer","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"3","key":"6984_CR49","first-page":"25","volume":"7","author":"LK Meng","year":"2023","unstructured":"Meng LK, Xin LJ, Yi HH, Salam ZAA, Wei NB (2023) A machine learning approach for face mask detection system with adamw optimizer. J Appl Technol Innov 7(3):25","journal-title":"J Appl Technol Innov"},{"key":"6984_CR50","unstructured":"Wu Y, Kirillov A, Massa F, Lo WY, Girshick R (2025) Detectron2. https:\/\/github.com\/facebookresearch\/detectron2 (Accessed: 2025-09-10)"},{"key":"6984_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109530","volume":"227","author":"SS Lee","year":"2024","unstructured":"Lee SS, Lim LG, Palaiahnakote S, Cheong JX, Lock SSM, Ayub MNB (2024) Oil palm tree detection in uav imagery using an enhanced retinanet. Comput Electron Agric 227:109530","journal-title":"Comput Electron Agric"},{"key":"6984_CR52","doi-asserted-by":"crossref","unstructured":"Mo W, Ke Y, Huo Q, Cao R, Song G, Zhang W (2022) Adn: Atss-based deep network for pedestrian detection on high-resolution images. In: 2022 International conference on networking and network applications (NaNA), pp. 454\u2013458. IEEE","DOI":"10.1109\/NaNA56854.2022.00084"},{"key":"6984_CR53","doi-asserted-by":"crossref","unstructured":"Mao Y, Shu M, Liu Q (2024) Pbn: Progressive batch normalization for dnn training on edge device. In: 2024 IEEE International symposium on circuits and systems (ISCAS), pp. 1\u20135. IEEE","DOI":"10.1109\/ISCAS58744.2024.10558569"},{"key":"6984_CR54","doi-asserted-by":"crossref","unstructured":"Zhou S, Du G, Fu R (2023) Fcos-gb-an improved uav aerial images detection method based on fcos model. In: 2023 9th International conference on computer and communications (ICCC), pp. 1755\u20131759. IEEE","DOI":"10.1109\/ICCC59590.2023.10507646"},{"key":"6984_CR55","doi-asserted-by":"crossref","unstructured":"Wang J, Song L, Li Z, Sun H, Sun J, Zheng N (2021) End-to-end object detection with fully convolutional network. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp. 15849\u201315858","DOI":"10.1109\/CVPR46437.2021.01559"},{"key":"6984_CR56","doi-asserted-by":"crossref","unstructured":"Li J, Chen T, Ji K, Li Q (2024) Oadb-net: An occlusion-aware dual-branch network for pedestrian detection. IEEE Transactions on Intelligent Transportation Systems","DOI":"10.1109\/TITS.2024.3495814"},{"key":"6984_CR57","doi-asserted-by":"crossref","unstructured":"Ci Y, Wang Y, Chen M, Tang S, Bai L, Zhu F, Zhao R, Yu F, Qi D, Ouyang W (2023) Unihcp: A unified model for human-centric perceptions. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 17840\u201317852","DOI":"10.1109\/CVPR52729.2023.01711"},{"key":"6984_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123324","volume":"248","author":"J Zhang","year":"2024","unstructured":"Zhang J, Xia K, Huang Z, Wang S, Akindele RG (2024) Obhunter: An ensemble spectral-angular based transformer network for occlusion detection. Expert Syst Appl 248:123324","journal-title":"Expert Syst Appl"},{"key":"6984_CR59","doi-asserted-by":"publisher","first-page":"78623","DOI":"10.1109\/ACCESS.2023.3293532","volume":"11","author":"HK Choi","year":"2023","unstructured":"Choi HK, Paik CK, Ko HW, Park M-C, Kim HJ (2023) Recurrent detr: transformer-based object detection for crowded scenes. IEEe Access 11:78623\u201378643","journal-title":"IEEe Access"},{"key":"6984_CR60","doi-asserted-by":"crossref","unstructured":"Zheng A, Zhang Y, Zhang X, Qi X, Sun J (2022) Progressive end-to-end object detection in crowded scenes. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 857\u2013866","DOI":"10.1109\/CVPR52688.2022.00093"},{"key":"6984_CR61","doi-asserted-by":"crossref","unstructured":"Gao Z, Wang L, Han B, Guo S (2022) Adamixer: A fast-converging query-based object detector. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5364\u20135373","DOI":"10.1109\/CVPR52688.2022.00529"},{"key":"6984_CR62","unstructured":"Xie Z, Lin Y, Yao Z, Zhang Z, Dai Q, Cao Y, Hu H (2025) Self-supervised learning with swin transformers. arxiv 2021. arXiv:2105.04553"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06984-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06984-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06984-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T01:05:08Z","timestamp":1766106308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06984-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":62,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["6984"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06984-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"18 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 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 that they 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":"Conflict of interest"}}],"article-number":"1096"}}