[1] Y Y Tian, M L Deng, H Gao et al. Review of crowd counting algorithms based on deep learning. Electron Meas Technol, 45, 152-159(2022).
[2] H P Xiong, H Lu, C X Liu et al. From open set to closed set: supervised spatial divide-and-conquer for object counting. Int J Comput Vis, 131, 1722-1740(2023).
[3] S K Wu, F Y Yang. Boosting detection in crowd analysis via underutilized output features, 15609-15618(2023). https://doi.org/10.1109/CVPR52729.2023.01498
[4] Y Yu, Z Cai, D Q Miao et al. An interactive network based on transformer for multimodal crowd counting. Appl Intell, 53, 22602-22614(2023).
[5] M J Wang, J Zhou, H Cai et al. CrowdMLP: weakly-supervised crowd counting via multi-granularity MLP. Pattern Recognit, 144, 109830(2023).
[6] Z K Lu, S Liu, L Zhong et al. Survey on reaserch of crowd counting. Comput Eng Appl, 58, 33-46(2022).
[7] A X Guo, Y F Xia, D W Wang et al. A multi-scale crowd counting algorithm with removing background interference. Comput Eng, 48, 251-257(2022).
[8] Q M Zhang, Y F Xu, J Zhang et al. ViTAEv2: vision transformer advanced by exploring inductive bias for image recognition and beyond. Int J Comput Vis, 131, 1141-1162(2023).
[9] A Vaswani, N Shazeer, N Parmar et al. Attention is all you need, 6000-6010(2017).
[10] A Dosovitskiy, L Beyer, A Kolesnikov et al. An image is worth 16x16 words: transformers for image recognition at scale(2021).
[11] H Lin, Z H Ma, R R Ji et al. Boosting crowd counting via multifaceted attention, 19628-19637(2022). https://doi.org/10.1109/CVPR52688.2022.01901
[12] D K Liang, W Xu, X Bai. An end-to-end transformer model for crowd localization, 38-54(2022). https://doi.org/10.1007/978-3-031-19769-7_3
[13] H Lin, Z H Ma, X P Hong et al. Gramformer: learning crowd counting via graph-modulated transformer(2024). https://doi.org/10.1609/aaai.v38i4.28126
[14] B Li, Y Zhang, H H Xu et al. CCST: crowd counting with swin transformer. Vis Comput, 39, 2671-2682(2023).
[15] F S Wang, K Liu, F Long et al. Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting(2022). https://arxiv.org/abs/2203.06388
[16] C A Wang, Q Y Song, B S Zhang et al. Uniformity in heterogeneity: diving deep into count interval partition for crowd counting, 3234-3242(2021). https://doi.org/10.1109/ICCV48922.2021.00322
[17] Q Y Song, C A Wang, Z K Jiang et al. Rethinking counting and localization in crowds: a purely point-based framework, 3365-3374(2021). https://doi.org/10.1109/ICCV48922.2021.00335
[18] S V Shivapuja, M P Khamkar, D Bajaj et al. Wisdom of (binned) crowds: a bayesian stratification paradigm for crowd counting, 3574-3582(2021). https://doi.org/10.1145/3474085.3475522
[19] X Y Wang, B F Zhang, L M Yu et al. Hunting sparsity: density-guided contrastive learning for semi-supervised semantic segmentation, 3114-3123(2023). https://doi.org/10.1109/CVPR52729.2023.00304
[20] W Lin, A B Chan. Optimal transport minimization: crowd localization on density maps for semi-supervised counting, 21663-21673(2023). https://doi.org/10.1109/CVPR52729.2023.02075
[21] H Gao, W J Zhao, D X Zhang et al. Application of improved transformer based on weakly supervised in crowd localization and crowd counting. Sci Rep, 13, 1144(2023).
[22] Y T Liu, Z Wang, M J Shi et al. Discovering regression-detection bi-knowledge transfer for unsupervised cross-domain crowd counting. Neurocomputing, 494, 418-431(2022).
[23] C F Xu, D K Liang, Y C Xu et al. AutoScale: learning to scale for crowd counting. Int J Comput Vis, 130, 405-434(2022).
[24] D K Liang, J H Xie, Z K Zou et al. CrowdCLIP: unsupervised crowd counting via vision-language model, 2893-2903(2023). https://doi.org/10.1109/CVPR52729.2023.00283
[25] C Y Zhang, Y Zhang, B Li et al. CrowdGraph: weakly supervised crowd counting via pure graph neural network. ACM Trans Multimedia Comput,Commun Appl, 20, 135(2024).
[26] Y F Yang, G R Li, Z Wu et al. Weakly-supervised crowd counting learns from sorting rather than locations, 1-17(2020). https://doi.org/10.1007/978-3-030-58598-3_1
[27] Y T Liu, S C Ren, L Y Chai et al. Reducing spatial labeling redundancy for active semi-supervised crowd counting. IEEE Trans Pattern Anal Mach Intell, 45, 9248-9255(2022).
[28] M F Deng, H L Zhao, M Gao. CLFormer: a unified transformer-based framework for weakly supervised crowd counting and localization. Vis Comput, 40, 1053-1067(2024).
[29] D K Liang, X W Chen, W Xu et al. TransCrowd: weakly-supervised crowd counting with transformers. Sci China Inf Sci, 65, 160104(2022).
[30] G L Sun, Y Liu, T Probst et al. Rethinking global context in crowd counting(2021). https://arxiv.org/abs/2105.10926
[31] Y Tian, X X Chu, H P Wang. CCTrans: simplifying and improving crowd counting with transformer(2021). https://arxiv.org/abs/2109.14483
[32] X S Chen, H T Lu. Reinforcing local feature representation for weakly-supervised dense crowd counting(2022). https://arxiv.org/abs/2202.10681v1
[33] J Y Gao, M G Gong, X L Li. Congested crowd instance localization with dilated convolutional swin transformer. Neurocomputing, 513, 94-103(2022).
[34] F S Wang, J Sang, Z Y Wu et al. Hybrid attention network based on progressive embedding scale-context for crowd counting. Inf Sci, 591, 306-318(2022).
[35] S P Yang, W Y Guo, Y H Ren. CrowdFormer: an overlap patching vision transformer for top-down crowd counting, 23-29(2022). https://doi.org/10.24963/ijcai.2022/215
[36] Y Y Zhang, D S Zhou, S Q Chen et al. Single-image crowd counting via multi-column convolutional neural network, 589-597(2016). https://doi.org/10.1109/CVPR.2016.70
[37] H Idrees, M Tayyab, K Athrey et al. Composition loss for counting,density map estimation and localization in dense crowds, 532-546(2018). https://doi.org/10.1007/978-3-030-01216-8_33
[38] H Idrees, I Saleemi, C Seibert et al. Multi-source multi-scale counting in extremely dense crowd images, 2547-2554(2013). https://doi.org/10.1109/CVPR.2013.329
[39] A Patwal, M Diwakar, V Tripathi et al. Crowd counting analysis using deep learning: a critical review. Proc Comput Sci, 218, 2448-2458(2023).
[40] Y Q Chen, H L Zhao, M Gao et al. A weakly supervised hybrid lightweight network for efficient crowd counting. Electronics, 13, 723(2024).
[41] Y J Lei, Y Liu, P P Zhang et al. Towards using count-level weak supervision for crowd counting. Pattern Recognit, 109, 107616(2021).
[42] Y D Meng, H R Zhang, Y T Zhao et al. Spatial uncertainty-aware semi-supervised crowd counting, 15549-15559(2021). https://doi.org/10.1109/ICCV48922.2021.01526
[43] L J Liang, H L Zhao, F B Zhou et al. PDDNet: lightweight congested crowd counting via pyramid depth-wise dilated convolution. Appl Intell, 53, 10472-10484(2023).
[44] V A Sindagi, R Yasarla, D S Babu et al. Learning to count in the crowd from limited labeled data, 212-229(2020). https://doi.org/10.1007/978-3-030-58621-8_13
[45] Q Wang, J Y Gao, W Lin et al. Learning from synthetic data for crowd counting in the wild, 8198-8207(2019). https://doi.org/10.1109/CVPR.2019.00839
[46] W Z Liu, N Durasov, P Fua. Leveraging self-supervision for cross-domain crowd counting, 5341-5352(2022). https://doi.org/10.1109/CVPR52688.2022.00527
[47] S S Savner, V Kanhangad. CrowdFormer: weakly-supervised crowd counting with improved generalizability. J Vis Commun Image Representation, 94, 103853(2023).
[48] Y C Li, R S Jia, Y X Hu et al. A weakly-supervised crowd density estimation method based on two-stage linear feature calibration. IEEE/CAA J Autom Sin, 11, 965-981(2024).