[1] Tian Y Y, Deng M L, Gao H et al. Review of crowd counting algorithms based on deep learning[J]. Electron Meas Technol, 45, 152-159(2022).

[2] Xiong H P, Lu H, Liu C X et al. From open set to closed set: supervised spatial divide-and-conquer for object counting[J]. Int J Comput Vis, 131, 1722-1740(2023).

[3] Wu S K, Yang F Y. Boosting detection in crowd analysis via underutilized output features[C], 15609-15618(2023). https://doi.org/10.1109/CVPR52729.2023.01498

[4] Yu Y, Cai Z, Miao D Q et al. An interactive network based on transformer for multimodal crowd counting[J]. Appl Intell, 53, 22602-22614(2023).

[5] Wang M J, Zhou J, Cai H et al. CrowdMLP: weakly-supervised crowd counting via multi-granularity MLP[J]. Pattern Recognit, 144, 109830(2023).

[6] Lu Z K, Liu S, Zhong L et al. Survey on reaserch of crowd counting[J]. Comput Eng Appl, 58, 33-46(2022).

[7] Guo A X, Xia Y F, Wang D W et al. A multi-scale crowd counting algorithm with removing background interference[J]. Comput Eng, 48, 251-257(2022).

[8] Zhang Q M, Xu Y F, Zhang J et al. ViTAEv2: vision transformer advanced by exploring inductive bias for image recognition and beyond[J]. Int J Comput Vis, 131, 1141-1162(2023).

[9] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C], 6000-6010(2017).

[10] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth 16x16 words: transformers for image recognition at scale[C](2021).

[11] Lin H, Ma Z H, Ji R R et al. Boosting crowd counting via multifaceted attention[C], 19628-19637(2022). https://doi.org/10.1109/CVPR52688.2022.01901

[12] Liang D K, Xu W, Bai X. An end-to-end transformer model for crowd localization[C], 38-54(2022). https://doi.org/10.1007/978-3-031-19769-7_3

[13] Lin H, Ma Z H, Hong X P et al. Gramformer: learning crowd counting via graph-modulated transformer[C](2024). https://doi.org/10.1609/aaai.v38i4.28126

[14] Li B, Zhang Y, Xu H H et al. CCST: crowd counting with swin transformer[J]. Vis Comput, 39, 2671-2682(2023).

[15] Wang F S, Liu K, Long F et al. Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting[Z](2022). https://arxiv.org/abs/2203.06388

[16] Wang C A, Song Q Y, Zhang B S et al. Uniformity in heterogeneity: diving deep into count interval partition for crowd counting[C], 3234-3242(2021). https://doi.org/10.1109/ICCV48922.2021.00322

[17] Song Q Y, Wang C A, Jiang Z K et al. Rethinking counting and localization in crowds: a purely point-based framework[C], 3365-3374(2021). https://doi.org/10.1109/ICCV48922.2021.00335

[18] Shivapuja S V, Khamkar M P, Bajaj D et al. Wisdom of (binned) crowds: a bayesian stratification paradigm for crowd counting[C], 3574-3582(2021). https://doi.org/10.1145/3474085.3475522

[19] Wang X Y, Zhang B F, Yu L M et al. Hunting sparsity: density-guided contrastive learning for semi-supervised semantic segmentation[C], 3114-3123(2023). https://doi.org/10.1109/CVPR52729.2023.00304

[20] Lin W, Chan A B. Optimal transport minimization: crowd localization on density maps for semi-supervised counting[C], 21663-21673(2023). https://doi.org/10.1109/CVPR52729.2023.02075

[21] Gao H, Zhao W J, Zhang D X et al. Application of improved transformer based on weakly supervised in crowd localization and crowd counting[J]. Sci Rep, 13, 1144(2023).

[22] Liu Y T, Wang Z, Shi M J et al. Discovering regression-detection bi-knowledge transfer for unsupervised cross-domain crowd counting[J]. Neurocomputing, 494, 418-431(2022).

[23] Xu C F, Liang D K, Xu Y C et al. AutoScale: learning to scale for crowd counting[J]. Int J Comput Vis, 130, 405-434(2022).

[24] Liang D K, Xie J H, Zou Z K et al. CrowdCLIP: unsupervised crowd counting via vision-language model[C], 2893-2903(2023). https://doi.org/10.1109/CVPR52729.2023.00283

[25] Zhang C Y, Zhang Y, Li B et al. CrowdGraph: weakly supervised crowd counting via pure graph neural network[J]. ACM Trans Multimedia Comput,Commun Appl, 20, 135(2024).

[26] Yang Y F, Li G R, Wu Z et al. Weakly-supervised crowd counting learns from sorting rather than locations[C], 1-17(2020). https://doi.org/10.1007/978-3-030-58598-3_1

[27] Liu Y T, Ren S C, Chai L Y et al. Reducing spatial labeling redundancy for active semi-supervised crowd counting[J]. IEEE Trans Pattern Anal Mach Intell, 45, 9248-9255(2022).

[28] Deng M F, Zhao H L, Gao M. CLFormer: a unified transformer-based framework for weakly supervised crowd counting and localization[J]. Vis Comput, 40, 1053-1067(2024).

[29] Liang D K, Chen X W, Xu W et al. TransCrowd: weakly-supervised crowd counting with transformers[J]. Sci China Inf Sci, 65, 160104(2022).

[30] Sun G L, Liu Y, Probst T et al. Rethinking global context in crowd counting[Z](2021). https://arxiv.org/abs/2105.10926

[31] Tian Y, Chu X X, Wang H P. CCTrans: simplifying and improving crowd counting with transformer[Z](2021). https://arxiv.org/abs/2109.14483

[32] Chen X S, Lu H T. Reinforcing local feature representation for weakly-supervised dense crowd counting[Z](2022). https://arxiv.org/abs/2202.10681v1

[33] Gao J Y, Gong M G, Li X L. Congested crowd instance localization with dilated convolutional swin transformer[J]. Neurocomputing, 513, 94-103(2022).

[34] Wang F S, Sang J, Wu Z Y et al. Hybrid attention network based on progressive embedding scale-context for crowd counting[J]. Inf Sci, 591, 306-318(2022).

[35] Yang S P, Guo W Y, Ren Y H. CrowdFormer: an overlap patching vision transformer for top-down crowd counting[C], 23-29(2022). https://doi.org/10.24963/ijcai.2022/215

[36] Zhang Y Y, Zhou D S, Chen S Q et al. Single-image crowd counting via multi-column convolutional neural network[C], 589-597(2016). https://doi.org/10.1109/CVPR.2016.70

[37] Idrees H, Tayyab M, Athrey K et al. Composition loss for counting,density map estimation and localization in dense crowds[C], 532-546(2018). https://doi.org/10.1007/978-3-030-01216-8_33

[38] Idrees H, Saleemi I, Seibert C et al. Multi-source multi-scale counting in extremely dense crowd images[C], 2547-2554(2013). https://doi.org/10.1109/CVPR.2013.329

[39] Patwal A, Diwakar M, Tripathi V et al. Crowd counting analysis using deep learning: a critical review[J]. Proc Comput Sci, 218, 2448-2458(2023).

[40] Chen Y Q, Zhao H L, Gao M et al. A weakly supervised hybrid lightweight network for efficient crowd counting[J]. Electronics, 13, 723(2024).

[41] Lei Y J, Liu Y, Zhang P P et al. Towards using count-level weak supervision for crowd counting[J]. Pattern Recognit, 109, 107616(2021).

[42] Meng Y D, Zhang H R, Zhao Y T et al. Spatial uncertainty-aware semi-supervised crowd counting[C], 15549-15559(2021). https://doi.org/10.1109/ICCV48922.2021.01526

[43] Liang L J, Zhao H L, Zhou F B et al. PDDNet: lightweight congested crowd counting via pyramid depth-wise dilated convolution[J]. Appl Intell, 53, 10472-10484(2023).

[44] Sindagi V A, Yasarla R, Babu D S et al. Learning to count in the crowd from limited labeled data[C], 212-229(2020). https://doi.org/10.1007/978-3-030-58621-8_13

[45] Wang Q, Gao J Y, Lin W et al. Learning from synthetic data for crowd counting in the wild[C], 8198-8207(2019). https://doi.org/10.1109/CVPR.2019.00839

[46] Liu W Z, Durasov N, Fua P. Leveraging self-supervision for cross-domain crowd counting[C], 5341-5352(2022). https://doi.org/10.1109/CVPR52688.2022.00527

[47] Savner S S, Kanhangad V. CrowdFormer: weakly-supervised crowd counting with improved generalizability[J]. J Vis Commun Image Representation, 94, 103853(2023).

[48] Li Y C, Jia R S, Hu Y X et al. A weakly-supervised crowd density estimation method based on two-stage linear feature calibration[J]. IEEE/CAA J Autom Sin, 11, 965-981(2024).