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Kang and A. Chan, \u201cCrowd counting by adaptively fusing predictions from an image pyramid,\u201d 2018, arXiv:1805.06115."},{"key":"ref2","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"5744","article-title":"Switching convolutional neural network for crowd counting","author":"Sam","year":"2017"},{"key":"ref3","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"1861","article-title":"Generating high-quality crowd density maps using contextual pyramid cnns","author":"Sindagi","year":"2017"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"3027","DOI":"10.1007\/s00530-021-00877-4","volume":"29","author":"Zhai","year":"2023","journal-title":"Multimed. Syst."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1007\/s10586-022-03749-2","article-title":"An attentive hierarchy ConvNet for crowd counting in smart city","volume":"26","author":"Zhai","year":"2023","journal-title":"Cluster Comput."},{"key":"ref6","series-title":"2016 IEEE Int. Conf. Image Process. (ICIP)","first-page":"3653","article-title":"Fast visual object counting via example-based density estimation","author":"Wang","year":"2016"},{"key":"ref7","series-title":"Comput. Vis.\u2013ECCV 2016: 14th Eur. Conf.","first-page":"660","article-title":"Learning to count with CNN boosting","author":"Walach","year":"2016"},{"key":"ref8","series-title":"Proc. of the 23th Int.Conf. on Neural Inf. Process. Sys.","first-page":"1324","article-title":"Learning to count objects in images","volume":"1","author":"Lempitsky","year":"2010"},{"key":"ref9","first-page":"1","article-title":"Cell counting via attentive recognition network","volume":"27","author":"Guo","year":"2024","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref10","series-title":"Comput. Vis.\u2013ECCV 2016: 14th Eur. Conf.","first-page":"483","article-title":"Counting in the wild","author":"Arteta","year":"2016"},{"key":"ref11","series-title":"Comput. Vis.\u2013ECCV 2016: 14th Eur. Conf.","first-page":"615","article-title":"Towards perspective-free object counting with deep learning","author":"Onoro-Rubio","year":"2016"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"15920","DOI":"10.1109\/TITS.2023.3296571","article-title":"Scale region recognition network for object counting in intelligent transportation system","volume":"24","author":"Guo","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref13","doi-asserted-by":"crossref","DOI":"10.1109\/TNNLS.2023.3336894","article-title":"Object counting via group and graph attention network","volume":"35","author":"Guo","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref14","doi-asserted-by":"crossref","first-page":"19199","DOI":"10.1007\/s10489-023-04499-3","article-title":"FPANet: Feature pyramid attention network for crowd counting","volume":"53","author":"Zhai","year":"2023","journal-title":"Appl. Intell."},{"key":"ref15","first-page":"1754","article-title":"Object counting in remote sensing via selective spatial-frequency pyramid network","volume":"54","author":"Chen","year":"2024","journal-title":"Softw.: Pract. Exp."},{"key":"ref16","series-title":"Proc. IEEE Int. Conf. Comput. Vis. Workshops","first-page":"2080","article-title":"Leaf counting with deep convolutional and deconvolutional networks","author":"Aich","year":"2017"},{"key":"ref17","series-title":"Proc. Comput. Vis. Probl. Plant Phenotyp. Workshop 2015","article-title":"Learning to count leaves in rosette plants","author":"Giuffrida","year":"2015"},{"key":"ref18","series-title":"Workshop Mach. Vis. Anim. Behav., MVAB'15","article-title":"Convolutional neural networks for counting fish in fisheries surveillance video","author":"French","year":"2015"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00138-008-0132-4","article-title":"Crowd analysis: A survey","volume":"19","author":"Zhan","year":"2008","journal-title":"Mach. Vis. Appl."},{"key":"ref20","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"4657","article-title":"Deeply learned attributes for crowded scene understanding","author":"Shao","year":"2015"},{"key":"ref21","series-title":"2012 IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"2871","article-title":"Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents","author":"Zhou","year":"2012"},{"key":"ref22","series-title":"2008 IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"1","article-title":"Privacy preserving crowd monitoring: Counting people without people models or tracking","author":"Chan","year":"2008"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2022.104597","article-title":"Revisiting crowd counting: State-of-the-art, trends, and future perspectives","volume":"129","author":"Khan","year":"2023, Art. no. 104597","journal-title":"Image Vis. Comput."},{"key":"ref24","first-page":"296","article-title":"Visual crowd analysis: Open research problems","volume":"44","author":"Khan","year":"2023","journal-title":"AI Mag."},{"key":"ref25","unstructured":"A. Bochkovskiy, \u201cYOLOv4: Optimal speed and accuracy of object detection,\u201d 2020, arXiv:2004.10934."},{"key":"ref26","series-title":"2017 13th Int. Conf. Comput. Intell. Secur. (CIS)","first-page":"427","article-title":"Pedestrian detection method based on faster R-CNN","author":"Zhang","year":"2017"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","article-title":"Pedestrian detection: An evaluation of the state of the art","volume":"34","author":"Dollar","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref28","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"350","article-title":"HydraPlus-Net: Attentive deep features for pedestrian analysis","author":"Liu","year":"2017"},{"key":"ref29","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"589","article-title":"Single-image crowd counting via multi-column convolutional neural network","author":"Zhang","year":"2016"},{"key":"ref30","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"1091","article-title":"CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes","author":"Li","year":"2018"},{"key":"ref31","series-title":"2018 IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP)","first-page":"1942","article-title":"A deeply-recursive convolutional network for crowd counting","author":"Ding","year":"2018"},{"key":"ref32","series-title":"Proc. Eur. Conf. Comput. Vis. (ECCV)","first-page":"532","article-title":"Composition loss for counting, density map estimation and localization in dense crowds","author":"Idrees","year":"2018"},{"key":"ref33","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","first-page":"6142","article-title":"Bayesian loss for crowd count estimation with point supervision","author":"Ma","year":"2019"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"5186","DOI":"10.1109\/TCSVT.2023.3250946","article-title":"Frame-recurrent video crowd counting","volume":"33","author":"Hou","year":"2023","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"ref35","series-title":"2014 11th IEEE Int. Conf. Adv. Video Signal Based Surveill. (AVSS)","first-page":"313","article-title":"Counting people by clustering person detector outputs","author":"Topkaya","year":"2014"},{"key":"ref36","series-title":"2008 19th Int. Conf. Pattern Recognit.","first-page":"1","article-title":"Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection","author":"Li","year":"2008"},{"key":"ref37","series-title":"2005 IEEE Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR\u201905)","first-page":"878","article-title":"Pedestrian detection in crowded scenes","volume":"1","author":"Leibe","year":"2005"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1109\/TPAMI.2008.260","article-title":"Monocular pedestrian detection: Survey and experiments","volume":"31","author":"Enzweiler","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref39","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"1440","article-title":"Fast R-CNN","author":"Girshick","year":"2015"},{"key":"ref40","series-title":"Proc. of the 28th Int. Conf. on Neural Inf. Process. Sys.","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"1","author":"Ren","year":"2015"},{"key":"ref41","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"2961","article-title":"Mask R-CNN","author":"He","year":"2017"},{"key":"ref42","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"779","article-title":"You only look once: Unified, real-time object detection","author":"Redmon","year":"2016"},{"key":"ref43","series-title":"Comput. Vis.\u2013ECCV 2016: 14th Eur. Conf.","first-page":"21","article-title":"SSD: Single shot multibox detector","author":"Liu","year":"2016"},{"key":"ref44","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"2547","article-title":"Multi-source multi-scale counting in extremely dense crowd images","author":"Idrees","year":"2013"},{"key":"ref45","series-title":"2009 IEEE 12th Int. Conf. Comput. Vis.","first-page":"545","article-title":"Bayesian poisson regression for crowd counting","author":"Chan","year":"2009"},{"key":"ref46","author":"Chen","year":"2012","journal-title":"Proc. Brit. Mach. Vis. Conf."},{"key":"ref47","series-title":"2009 Digital Image Comput.: Tech. Appl.","first-page":"81","article-title":"Crowd counting using multiple local features","author":"Ryan","year":"2009"},{"key":"ref48","series-title":"Proc. Seventh IEEE Int. Conf. Comput. Vis.","first-page":"1150","article-title":"Object recognition from local scale-invariant features","volume":"2","author":"Lowe","year":"1999"},{"key":"ref49","series-title":"Comput. Vis.-ECCV 2000: 6th Eur. Conf. Comput. Vis.","first-page":"404","article-title":"Gray scale and rotation invariant texture classification with local binary patterns","author":"Ojala","year":"2000"},{"key":"ref50","series-title":"2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR\u201905)","first-page":"886","article-title":"Histograms of oriented gradients for human detection","volume":"1","author":"Dalal","year":"2005"},{"key":"ref51","series-title":"Proc. 2001 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. CVPR 2001","article-title":"A MRF-based approach for real-time subway monitoring","author":"Paragios","year":"2001"},{"key":"ref52","series-title":"Asian Conf. Comput. Vis.","first-page":"679","article-title":"Latent gaussian mixture regression for human pose estimation","author":"Tian","year":"2010"},{"key":"ref53","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"3253","article-title":"Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation","author":"Pham","year":"2015"},{"key":"ref54","first-page":"1","article-title":"CNN-based multi-object tracking networks with position correction and imm in intelligent transportation system","volume":"39","author":"Ren","year":"2023","journal-title":"M\u00e9todos num\u00e9ricos para c\u00e1lculo y dise\u00f1o en ingenier\u00eda: Revista internacional"},{"key":"ref55","series-title":"Proc. 23rd ACM Int. Conf. Multimed.","first-page":"1299","article-title":"Deep people counting in extremely dense crowds","author":"Wang","year":"2015"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.engappai.2015.04.006","article-title":"Fast crowd density estimation with convolutional neural networks","volume":"43","author":"Fu","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref57","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"833","article-title":"Cross-scene crowd counting via deep convolutional neural networks","author":"Zhang","year":"2015"},{"key":"ref58","series-title":"2006 IEEE Int. Conf. Robot. Biomimetics","first-page":"214","article-title":"Crowd density estimation using texture analysis and learning","author":"Wu","year":"2006"},{"key":"ref59","series-title":"2007 IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"1","article-title":"Face recognition using kernel ridge regression","author":"An","year":"2007"},{"key":"ref60","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"2467","article-title":"Cumulative attribute space for age and crowd density estimation","author":"Chen","year":"2013"},{"key":"ref61","series-title":"2017 14th IEEE Int. Conf. Adv. Video Signal Based Surveill. (AVSS)","first-page":"1","article-title":"CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting","author":"Sindagi","year":"2017"},{"key":"ref62","first-page":"2739","article-title":"Locate, size, and count: Accurately resolving people in dense crowds via detection","volume":"43","author":"Sam","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref63","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"4706","article-title":"Attention scaling for crowd counting","author":"Jiang","year":"2020"},{"key":"ref64","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","first-page":"3365","article-title":"Rethinking counting and localization in crowds: A purely point-based framework","author":"Song","year":"2021"},{"key":"ref65","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/9996232","article-title":"Multiscale aggregate networks with dense connections for crowd counting","volume":"2021","author":"Li","year":"2021, Art. no. 9996232","journal-title":"Comput. Intell. Neurosci."},{"key":"ref66","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"2893","article-title":"CrowdCLIP: Unsupervised crowd counting via vision-language model","author":"Liang","year":"2023"},{"key":"ref67","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"28025","article-title":"Single domain generalization for crowd counting","author":"Peng","year":"2024"},{"key":"ref68","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/3468.983420","article-title":"Estimation of number of people in crowded scenes using perspective transformation","volume":"31","author":"Lin","year":"2001","journal-title":"IEEE Trans. Syst., Man, Cybern.-Part A: Syst. Humans"},{"key":"ref69","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust real-time face detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref70","series-title":"Proc. Ninth IEEE Int. Conf. Comput. Vis.","first-page":"734","article-title":"Detecting pedestrians using patterns of motion and appearance","year":"2003"},{"key":"ref71","series-title":"Tenth IEEE Int. Conf. Comput. Vis. (ICCV\u201905)","first-page":"90","article-title":"Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors","volume":"1","author":"Wu","year":"2005"},{"key":"ref72","doi-asserted-by":"crossref","unstructured":"V. A. Sindagi, R. Yasarla, and V. M. Patel, \u201cJHU-CROWD++: Large-scale crowd counting dataset and a benchmark method,\u201d 2020. Accessed: Aug. 10, 2024. [Online]. Available: http:\/\/www.crowd-counting.com\/","DOI":"10.1109\/TPAMI.2020.3035969"},{"key":"ref73","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1109\/TPAMI.2020.3013269","article-title":"NWPU-Crowd: A large-scale benchmark for crowd counting and localization","volume":"43","author":"Wang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref74","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","first-page":"15549","article-title":"Spatial uncertainty-aware semi-supervised crowd counting","author":"Meng","year":"2021"},{"key":"ref75","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.1109\/TIP.2021.3055632","article-title":"A self-training approach for point-supervised object detection and counting in crowds","volume":"30","author":"Wang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref76","series-title":"Comput. Vis.\u2013ECCV 2020: 16th Eur. Conf.","first-page":"212","article-title":"Learning to count in the crowd from limited labeled data","author":"Sindagi","year":"2020"},{"key":"ref77","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106874","article-title":"DeepCorn: A semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation","volume":"218","author":"Khaki","year":"2021, Art. no. 106874","journal-title":"Knowl.-Based Syst."},{"key":"ref78","series-title":"Comput. Vis.\u2013ECCV 2020: 16th Eur. Conf.","first-page":"242","article-title":"Semi-supervised crowd counting via self-training on surrogate tasks","author":"Liu","year":"2020"},{"key":"ref79","series-title":"2020 25th Int. Conf. Pattern Recognit. (ICPR)","first-page":"843","article-title":"Learning error-driven curriculum for crowd counting","author":"Li","year":"2021"},{"key":"ref80","first-page":"3386","article-title":"Modeling noisy annotations for crowd counting","volume":"33","author":"Wan","year":"2020","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"ref81","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TIP.2019.2928634","article-title":"HA-CCN: Hierarchical attention-based crowd counting network","volume":"29","author":"Sindagi","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref82","series-title":"IEEE Conf. Comput. Vis. Pattern Recognit.: Learn. Imperfect Data Workshop","article-title":"Dense crowd counting convolutional neural networks with minimal data using semi-supervised dual-goal generative adversarial networks","author":"Olmschenk","year":"2019"},{"key":"ref83","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2019.06.004","article-title":"Generalizing semi-supervised generative adversarial networks to regression using feature contrasting","volume":"186","author":"Olmschenk","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref84","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107616","article-title":"Towards using count-level weak supervision for crowd counting","volume":"109","author":"Lei","year":"2021, Art. no. 107616","journal-title":"Pattern Recognit."},{"key":"ref85","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"8198","article-title":"Learning from synthetic data for crowd counting in the wild","author":"Wang","year":"2019"},{"key":"ref86","doi-asserted-by":"crossref","DOI":"10.1007\/s11432-021-3445-y","article-title":"Transcrowd: Weakly-supervised crowd counting with transformers","volume":"65","author":"Liang","year":"2022, Art. no. 160104","journal-title":"Sci. China Inf. Sci."},{"key":"ref87","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.1109\/TPAMI.2019.2899857","article-title":"Exploiting unlabeled data in CNNs by self-supervised learning to rank","volume":"41","author":"Liu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref88","doi-asserted-by":"crossref","first-page":"5220","DOI":"10.1109\/TIP.2023.3313490","article-title":"Semi-supervised crowd counting via multiple representation learning","volume":"32","author":"Wei","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref89","doi-asserted-by":"crossref","first-page":"8230","DOI":"10.1109\/TCSVT.2024.3392500","article-title":"Semi-supervised crowd counting with contextual modeling: Facilitating holistic understanding of crowd scenes","volume":"34","author":"Qian","year":"2024","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"ref90","doi-asserted-by":"crossref","DOI":"10.1002\/cav.2173","article-title":"Region feature smoothness assumption for weakly semi-supervised crowd counting","volume":"34","author":"Miao","year":"2023, Art. no. e2173","journal-title":"Comput. Animat. Virtual Worlds"},{"key":"ref91","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1109\/TITS.2018.2829987","article-title":"Crowd counting with limited labeling through submodular frame selection","volume":"20","author":"Zhou","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref92","doi-asserted-by":"crossref","DOI":"10.3390\/s20092624","article-title":"Large-scale crowd analysis through the use of passive radio sensing networks","volume":"20","author":"Denis","year":"2020, Art. no. 2624","journal-title":"Sensors"},{"key":"ref93","series-title":"2023 IEEE 20th Consum. Commun. Netw. Conf. (CCNC)","first-page":"455","article-title":"DroneNet: Crowd density estimation using self-onns for drones","author":"Khan","year":"2023"},{"key":"ref94","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s11554-023-01286-8","article-title":"LCDnet: A lightweight crowd density estimation model for real-time video surveillance","volume":"20","author":"Khan","year":"2023","journal-title":"J. Real Time Image Process."},{"key":"ref95","series-title":"2023 IEEE Int. Conf. Big Data Smart Comput. (BigComp)","first-page":"34","article-title":"CLIP: Train faster with less data","author":"Khan","year":"2023"},{"key":"ref96","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.csda.2017.01.006","article-title":"Online em for functional data","volume":"111","author":"Maire","year":"2017","journal-title":"Comput. Stat. Data Anal."},{"key":"ref97","author":"Patel","year":"2019","journal-title":"Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data"},{"key":"ref98","series-title":"Int. Conf. Mach. Learn.","first-page":"3897","article-title":"Safe deep semi-supervised learning for unseen-class unlabeled data","author":"Guo","year":"2020"},{"key":"ref99","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"8435","article-title":"SD-DiT: Unleashing the power of self-supervised discrimination in diffusion transformer","author":"Zhu","year":"2024"},{"key":"ref100","series-title":"Eur. Conf. Comput. Vis.","first-page":"186","article-title":"Completely self-supervised crowd counting via distribution matching","author":"Sam","year":"2022"},{"key":"ref101","series-title":"Proc. 28th ACM Int. Conf. Multimed.","first-page":"129","article-title":"Towards unsupervised crowd counting via regression-detection bi-knowledge transfer","author":"Liu","year":"2020"},{"key":"ref102","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"7661","article-title":"Leveraging unlabeled data for crowd counting by learning to rank","author":"Liu","year":"2018"},{"key":"ref103","first-page":"8868","article-title":"Almost unsupervised learning for dense crowd counting","volume":"33","author":"Sam","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref104","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.neucom.2022.04.107","article-title":"Discovering regression-detection bi-knowledge transfer for unsupervised cross-domain crowd counting","volume":"494","author":"Liu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref105","series-title":"2020 25th Int. Conf. Pattern Recognit. (ICPR)","first-page":"1067","article-title":"DAPC: Domain adaptation people counting via style-level transfer learning and scene-aware estimation","author":"Jiang","year":"2021"},{"key":"ref106","doi-asserted-by":"crossref","DOI":"10.3390\/app112412037","article-title":"Cross domain adaptation of crowd counting with model-agnostic meta-learning","volume":"11","author":"Hou","year":"2021, Art. no. 12037","journal-title":"Appl. Sci."},{"key":"ref107","unstructured":"A. D\u2019Alessandro, A. Mahdavi-Amiri, and G. Hamarneh, \u201cSYRAC: Synthesize, rank, and count,\u201d 2023, arXiv:2310.01662."},{"key":"ref108","series-title":"Int. Conf. Mach. Learn.","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"ref109","series-title":"Int. Conf. Mach. Learn.","first-page":"12888","article-title":"BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation","author":"Li","year":"2022"},{"key":"ref110","series-title":"2017 Int. Conf. Secur., Pattern Anal., Cybern. (SPAC)","first-page":"219","article-title":"An unsupervised abnormal crowd behavior detection algorithm","author":"Xu","year":"2017"},{"key":"ref111","series-title":"2017 8th IEEE Annu. Inform. Technol., Electron. Mobile Commun. Conf. (IEMCON)","first-page":"640","article-title":"UrbanCount: Mobile crowd counting in urban environments","author":"Danielis","year":"2017"},{"key":"ref112","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2024.101167","article-title":"Privacy-aware crowd counting by decentralized learning with parallel transformers","volume":"26","author":"Chen","year":"2024, Art. no. 101167","journal-title":"Internet of Things"},{"key":"ref113","doi-asserted-by":"crossref","DOI":"10.1145\/3653454","article-title":"Scale attentive aggregation network for crowd counting and localization in smart city","author":"Zhai","year":"2024","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref114","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.future.2023.05.013","article-title":"Crowd counting in smart city via lightweight ghost attention pyramid network","volume":"147","author":"Guo","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref115","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/CVPR.2019.00131","article-title":"Recurrent attentive zooming for joint crowd counting and precise localization","author":"Liu","year":"2019","journal-title":"2019 IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"key":"ref116","article-title":"Crowd counting via attention and multi-feature fused network","volume":"13","author":"Guo","year":"2023","journal-title":"Human-Centric Comput. Inform. Sci."},{"key":"ref117","series-title":"Comput. Vis.\u2013ECCV 2020: 16th Eur. Conf.","first-page":"747","article-title":"NAS-Count: Counting-by-density with neural architecture search","author":"Hu","year":"2020"},{"key":"ref118","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"19618","article-title":"Crowd counting in the frequency domain","author":"Shu","year":"2022"},{"key":"ref119","doi-asserted-by":"crossref","first-page":"3664","DOI":"10.1109\/TIP.2023.3289290","article-title":"Redesigning multi-scale neural network for crowd counting","volume":"32","author":"Du","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref120","series-title":"Eur. Conf. Comput. Vis.","first-page":"38","article-title":"An end-to-end transformer model for crowd localization","author":"Liang","year":"2022"},{"key":"ref121","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","first-page":"1676","article-title":"Point-query quadtree for crowd counting, localization, and more","author":"Liu","year":"2023"},{"key":"ref122","doi-asserted-by":"crossref","unstructured":"I. Chen et al., \u201cImproving point-based crowd counting and localization based on auxiliary point guidance,\u201d 2024, arXiv:2405.10589.","DOI":"10.1007\/978-3-031-72691-0_24"},{"key":"ref123","series-title":"Proc. 30th ACM Int. Conf. Multimed.","first-page":"1416","article-title":"Semi-supervised crowd counting via density agency","author":"Lin","year":"2022"},{"key":"ref124","first-page":"1","article-title":"Deep rank-consistent pyramid model for enhanced crowd counting","author":"Gao","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref125","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","first-page":"15570","article-title":"Crowd counting with partial annotations in an image","author":"Xu","year":"2021"},{"key":"ref126","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"21663","article-title":"Optimal transport minimization: Crowd localization on density maps for semi-supervised counting","author":"Lin","year":"2023"},{"key":"ref127","unstructured":"Y. Liu et al., \u201cReducing spatial labeling redundancy for semi-supervised crowd counting,\u201d arXiv preprint arXiv:2108.02970, 2021."},{"key":"ref128","series-title":"2023 IEEE\/CVF Int. Conf. Comput. Vis. (ICCV)","first-page":"16685","article-title":"Calibrating uncertainty for semi-supervised crowd counting","author":"Li","year":"2023"},{"key":"ref129","doi-asserted-by":"crossref","first-page":"4665","DOI":"10.1109\/TMM.2022.3180222","article-title":"Crowd counting via unsupervised cross-domain feature adaptation","volume":"25","author":"Ding","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref130","doi-asserted-by":"crossref","first-page":"6686","DOI":"10.1109\/TCSVT.2022.3179824","article-title":"Cross-domain attention network for unsupervised domain adaptation crowd counting","volume":"32","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"ref131","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"5341","article-title":"Leveraging self-supervision for cross-domain crowd counting","author":"Liu","year":"2022"},{"key":"ref132","series-title":"2020 IEEE Int. Conf. Multimed. Expo (ICME)","first-page":"1","article-title":"Scale-aware rolling fusion network for crowd counting","author":"Chen","year":"2020"},{"key":"ref133","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"5197","article-title":"Decidenet: Counting varying density crowds through attention guided detection and density estimation","author":"Liu","year":"2018"},{"key":"ref134","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1109\/TIP.2020.3043122","article-title":"Embedding perspective analysis into multi-column convolutional neural network for crowd counting","volume":"30","author":"Yang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref135","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1089\/big.2022.0039","article-title":"Spatial-frequency attention network for crowd counting","volume":"10","author":"Guo","year":"2022","journal-title":"Big Data"},{"key":"ref136","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"6133","article-title":"Crowd counting and density estimation by trellis encoder-decoder networks","author":"Jiang","year":"2019"},{"key":"ref137","doi-asserted-by":"crossref","first-page":"18930","DOI":"10.1109\/JIOT.2023.3268226","article-title":"Scale-context perceptive network for crowd counting and localization in smart city system","volume":"10","author":"Zhai","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref138","unstructured":"M. Mirza and S. Osindero, \u201cConditional generative adversarial nets,\u201d 2014, arXiv:1411.1784."},{"key":"ref139","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"2830","article-title":"Robust optimization for deep regression","author":"Belagiannis","year":"2015"},{"key":"ref140","series-title":"2022 Asia Conf. Algorithms, Comput. Mach. Learn. (CACML)","first-page":"749","article-title":"Semi-supervised crowd counting based on patch crowds statistics","author":"Peng","year":"2022"},{"key":"ref141","series-title":"2022 10th Int. Conf. Inform. Syst. Comput. Technol. (ISCTech)","first-page":"386","article-title":"Semi-supervised dense object counting via mutual consistency learning","author":"Li","year":"2022"},{"key":"ref142","doi-asserted-by":"crossref","first-page":"10394","DOI":"10.1109\/TNNLS.2023.3241211","article-title":"Multi-task credible pseudo-label learning for semi-supervised crowd counting","volume":"35","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref143","first-page":"1","article-title":"Semi-supervised crowd counting based on hard pseudo-labels","author":"Li","year":"2024","journal-title":"2024 Int. Joint Conf. on Neural Networks (IJCNN)"},{"key":"ref144","unstructured":"E. Tu and J. Yang, \u201cA review of semi supervised learning theories and recent advances,\u201d 2019, arXiv:1905.11590."}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/cmc\/v81n3\/59064\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T06:13:14Z","timestamp":1741327994000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v81n3\/59064"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":144,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.058637","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2024-09-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-19","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-19","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}