{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:25:07Z","timestamp":1767140707107,"version":"build-2238731810"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["21YJAZH077"],"award-info":[{"award-number":["21YJAZH077"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["21YJAZH077"],"award-info":[{"award-number":["21YJAZH077"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["21YJAZH077"],"award-info":[{"award-number":["21YJAZH077"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["21YJAZH077"],"award-info":[{"award-number":["21YJAZH077"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["21YJAZH077"],"award-info":[{"award-number":["21YJAZH077"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022ME091"],"award-info":[{"award-number":["ZR2022ME091"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022ME091"],"award-info":[{"award-number":["ZR2022ME091"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022ME091"],"award-info":[{"award-number":["ZR2022ME091"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022ME091"],"award-info":[{"award-number":["ZR2022ME091"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022ME091"],"award-info":[{"award-number":["ZR2022ME091"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00371-024-03572-3","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T09:02:29Z","timestamp":1722330149000},"page":"2857-2873","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive learning-enhanced lightweight network for real-time vehicle density estimation"],"prefix":"10.1007","volume":"41","author":[{"given":"Ling-Xiao","family":"Qin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong-Mei","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Meng","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng-Yue","family":"Che","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui-Sheng","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,30]]},"reference":[{"issue":"2","key":"3572_CR1","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s00371-023-02808-y","volume":"40","author":"M Liu","year":"2024","unstructured":"Liu, M., Wang, Y., Yi, H., Huang, X.: Vehicle object counting network based on feature pyramid split attention mechanism. Vis. Comput. 40(2), 663\u2013680 (2024). https:\/\/doi.org\/10.1007\/s00371-023-02808-y","journal-title":"Vis. Comput."},{"key":"3572_CR2","doi-asserted-by":"publisher","unstructured":"Li, Y., Zhang, X., Chen, D.: CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1091\u20131100 (2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00120","DOI":"10.1109\/cvpr.2018.00120"},{"key":"3572_CR3","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1007\/978-3-030-01228-1_45","volume":"2018","author":"X Cao","year":"2018","unstructured":"Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. Proceedings of the European Conference on Computer Vision, ECCV 2018, 734\u2013750 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_45","journal-title":"Proceedings of the European Conference on Computer Vision, ECCV"},{"key":"3572_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2023.3289290","author":"Z Du","year":"2023","unstructured":"Du, Z., Shi, M., Deng, J., Zafeiriou, S.: Redesigning multi-scale neural network for crowd counting. IEEE Trans. Image Process. (2023). https:\/\/doi.org\/10.1109\/tip.2023.3289290","journal-title":"IEEE Trans. Image Process."},{"key":"3572_CR5","doi-asserted-by":"publisher","unstructured":"Liang, D., Xu, W., Bai, X.: An end-to-end transformer model for crowd localization. In: European Conference on Computer Vision, pp. 38\u201354 (2022). https:\/\/doi.org\/10.1007\/978-3-031-19769-7_3","DOI":"10.1007\/978-3-031-19769-7_3"},{"key":"3572_CR6","doi-asserted-by":"publisher","unstructured":"Hu, Y., Jiang, X., Liu, X., Zhang, B., Han, J., Cao, X., Doermann, D.: Nas-count: Counting-by-density with neural architecture search. In: European conference on computer vision, pp. 747\u2013766 (2020). https:\/\/doi.org\/10.1007\/978-3-030-58542-6_45","DOI":"10.1007\/978-3-030-58542-6_45"},{"key":"3572_CR7","doi-asserted-by":"publisher","first-page":"103853","DOI":"10.1016\/j.jvcir.2023.103853","volume":"94","author":"SS Savner","year":"2023","unstructured":"Savner, S.S., Kanhangad, V.: Crowdformer: Weakly-supervised crowd counting with improved generalizability. J. Vis. Commun. Image Represent. 94, 103853 (2023). https:\/\/doi.org\/10.1016\/j.jvcir.2023.103853","journal-title":"J. Vis. Commun. Image Represent."},{"key":"3572_CR8","doi-asserted-by":"publisher","unstructured":"Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5744\u20135752 (2017). https:\/\/doi.org\/10.1109\/cvpr.2017.429","DOI":"10.1109\/cvpr.2017.429"},{"issue":"6","key":"3572_CR9","doi-asserted-by":"publisher","first-page":"160104","DOI":"10.1007\/s11432-021-3445-y","volume":"65","author":"D Liang","year":"2022","unstructured":"Liang, D., Chen, X., Xu, W., Zhou, Y., Bai, X.: TransCrowd: weakly-supervised crowd counting with transformers. Sci. China Inf. Sci. 65(6), 160104 (2022). https:\/\/doi.org\/10.1007\/s11432-021-3445-y","journal-title":"Sci. China Inf. Sci."},{"issue":"22","key":"3572_CR10","doi-asserted-by":"publisher","first-page":"3792","DOI":"10.3390\/electronics11223792","volume":"11","author":"Y Jin","year":"2022","unstructured":"Jin, Y., Wu, J., Wang, W., Wang, Y., Yang, X., Zheng, J.: Dense vehicle counting estimation via a synergism attention network. Electron. 11(22), 3792 (2022). https:\/\/doi.org\/10.3390\/electronics11223792","journal-title":"Electron."},{"issue":"10","key":"3572_CR11","doi-asserted-by":"publisher","first-page":"3486","DOI":"10.1109\/tcsvt.2019.2919139","volume":"30","author":"J Gao","year":"2019","unstructured":"Gao, J., Wang, Q., Li, X.: PCC-Net: Perspective crowd counting via spatial convolutional network. IEEE Trans. Circuit. Syst. Video Technol. 30(10), 3486\u20133498 (2019). https:\/\/doi.org\/10.1109\/tcsvt.2019.2919139","journal-title":"IEEE Trans. Circuit. Syst. Video Technol."},{"key":"3572_CR12","doi-asserted-by":"publisher","unstructured":"Shi, X., Li, X., Wu, C., Kong, S., Yang, J., He, L.: A real-time deep network for crowd counting. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2328\u20132332 (2020). https:\/\/doi.org\/10.1109\/icassp40776.2020.9053780","DOI":"10.1109\/icassp40776.2020.9053780"},{"key":"3572_CR13","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.neucom.2020.05.056","volume":"407","author":"P Wang","year":"2020","unstructured":"Wang, P., Gao, C., Wang, Y., Li, H., Gao, Y.: MobileCount: An efficient encoder-decoder framework for real-time crowd counting. Neurocomputing 407, 292\u2013299 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2020.05.056","journal-title":"Neurocomputing"},{"key":"3572_CR14","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.neucom.2020.09.001","volume":"420","author":"J Chen","year":"2021","unstructured":"Chen, J., Xiu, S., Chen, X., Guo, H., Xie, X.: Flounder-Net: An efficient CNN for crowd counting by aerial photography. Neurocomputing 420, 82\u201389 (2021). https:\/\/doi.org\/10.1016\/j.neucom.2020.09.001","journal-title":"Neurocomputing"},{"key":"3572_CR15","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neucom.2021.11.099","volume":"472","author":"F Zhu","year":"2022","unstructured":"Zhu, F., Yan, H., Chen, X., Li, T.: Real-time crowd counting via lightweight scale-aware network. Neurocomputing 472, 54\u201367 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.11.099","journal-title":"Neurocomputing"},{"key":"3572_CR16","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.future.2023.05.013","volume":"147","author":"X Guo","year":"2023","unstructured":"Guo, X., Song, K., Gao, M., Zhai, W., Li, Q., Jeon, G.: Crowd counting in smart city via lightweight ghost attention pyramid network. Future Gener Comput. Syst. 147, 328\u2013338 (2023). https:\/\/doi.org\/10.1016\/j.future.2023.05.013","journal-title":"Future Gener Comput. Syst."},{"key":"3572_CR17","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network.\u00a0arXiv preprint arXiv:1503.02531 (2015). https:\/doi.org110.48550\/arxiv.1503.02531"},{"key":"3572_CR18","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition,\u00a0pp. 779\u2013788 (2016). https:\/\/doi.org\/10.1109\/cvpr.2016.91","DOI":"10.1109\/cvpr.2016.91"},{"key":"3572_CR19","doi-asserted-by":"publisher","unstructured":"Toropov, E., Gui, L., Zhang, S., Kottur, S., Moura, J.M.: Traffic flow from a low frame rate city camera. In:\u00a02015 IEEE International Conference on Image Processing (ICIP), pp. 3802\u20133806 (2015). https:\/\/doi.org\/10.1109\/icip.2015.7351516","DOI":"10.1109\/icip.2015.7351516"},{"issue":"10","key":"3572_CR20","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1109\/acpr.2013.33","volume":"11","author":"X Chen","year":"2014","unstructured":"Chen, X., Xiang, S., Liu, C.L., Pan, C.H.: Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 11(10), 1797\u20131801 (2014). https:\/\/doi.org\/10.1109\/acpr.2013.33","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"3572_CR21","doi-asserted-by":"publisher","unstructured":"Li, W., Wang, Z., Wu, X., Zhang, J., Peng, Q., Li, H.: CODAN: Counting-driven attention network for vehicle detection in congested scenes. In: Proceedings of the 28th ACM International Conference on Multimedia,\u00a0pp. 73\u201382 (2020). https:\/\/doi.org\/10.1145\/3394171.3413945","DOI":"10.1145\/3394171.3413945"},{"issue":"2","key":"3572_CR22","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MITS.2019.2903518","volume":"11","author":"H Wang","year":"2019","unstructured":"Wang, H., Yu, Y., Cai, Y., Chen, X., Chen, L., Liu, Q.: A comparative study of state-of-the-art deep learning algorithms for vehicle detection. IEEE Intell. Transp. Syst. Mag. 11(2), 82\u201395 (2019). https:\/\/doi.org\/10.1109\/MITS.2019.2903518","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"3572_CR23","doi-asserted-by":"publisher","unstructured":"Fan, Q., Brown, L., Smith, J.: A closer look at Faster R-CNN for vehicle detection. In: 2016 IEEE intelligent vehicles symposium (IV), pp. 124\u2013129 (2016). https:\/\/doi.org\/10.1109\/ivs.2016.7535375","DOI":"10.1109\/ivs.2016.7535375"},{"key":"3572_CR24","doi-asserted-by":"publisher","unstructured":"Mundhenk, T.N., Konjevod, G., Sakla, W.A., Boakye, K.: A large contextual dataset for classification, detection and counting of cars with deep learning. In\u00a0Computer Vision\u2013ECCV 2016: In: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part III 14. pp. 785\u2013800 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_48","DOI":"10.1007\/978-3-319-46487-9_48"},{"key":"3572_CR25","doi-asserted-by":"publisher","unstructured":"Marsden, M., McGuinness, K., Little, S., Keogh, C.E., O'Connor, N.E.: People, penguins and petri dishes: Adapting object counting models to new visual domains and object types without forgetting. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8070\u20138079 (2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00842","DOI":"10.1109\/cvpr.2018.00842"},{"key":"3572_CR26","doi-asserted-by":"publisher","unstructured":"L\u00f3pez, J.G., Agudo, A., Moreno-Noguer, F.: Vehicle pose estimation via regression of semantic points of interest. In: 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA),\u00a0pp. 209\u2013214 (2019). https:\/\/doi.org\/10.1109\/ispa.2019.8868508","DOI":"10.1109\/ispa.2019.8868508"},{"key":"3572_CR27","doi-asserted-by":"publisher","unstructured":"Walach, E., Wolf, L.: Learning to count with cnn boosting. In\u00a0Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part II 14.\u00a0pp. 660\u2013676 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_41","DOI":"10.1007\/978-3-319-46475-6_41"},{"key":"3572_CR28","doi-asserted-by":"publisher","unstructured":"Pham, V.Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In:\u00a0Proceedings of the IEEE international conference on computer vision,\u00a0pp. 3253\u20133261 (2015). https:\/\/doi.org\/10.1109\/iccv.2015.372","DOI":"10.1109\/iccv.2015.372"},{"issue":"5","key":"3572_CR29","doi-asserted-by":"publisher","first-page":"492","DOI":"10.3390\/electronics8050492","volume":"8","author":"R Moreno","year":"2019","unstructured":"Moreno, R., Moreno-Salinas, D., Aranda, J.: Black-box marine vehicle identification with regression techniques for random manoeuvres. Electron. 8(5), 492 (2019). https:\/\/doi.org\/10.3390\/electronics8050492","journal-title":"Electron."},{"key":"3572_CR30","doi-asserted-by":"publisher","unstructured":"Shang, C., Ai, H., Bai, B.: End-to-end crowd counting via joint learning local and global count. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1215\u20131219 (2016). https:\/\/doi.org\/10.1109\/icip.2016.7532551","DOI":"10.1109\/icip.2016.7532551"},{"key":"3572_CR31","doi-asserted-by":"publisher","unstructured":"Lempitsky, V., Zisserman, A.: Learning to count objects in images.\u00a0Adv. Neural Inf. Process. Syst.\u00a023 (2010). https:\/\/doi.org\/10.1117\/12.2612729.6300254479001","DOI":"10.1117\/12.2612729.6300254479001"},{"key":"3572_CR32","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition,\u00a0pp. 589\u2013597 (2016). https:\/\/doi.org\/10.1109\/cvpr.2016.70","DOI":"10.1109\/cvpr.2016.70"},{"key":"3572_CR33","doi-asserted-by":"publisher","unstructured":"Hossain, M., Hosseinzadeh, M., Chanda, O., Wang, Y.: Crowd counting using scale-aware attention networks. In:\u00a02019 IEEE winter conference on applications of computer vision (WACV),\u00a0pp. 1280\u20131288 (2019). https:\/\/doi.org\/10.1109\/wacv.2019.00141","DOI":"10.1109\/wacv.2019.00141"},{"key":"3572_CR34","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/s10489-020-01842-w","volume":"51","author":"YB Liu","year":"2021","unstructured":"Liu, Y.B., Jia, R.S., Liu, Q.M., Zhang, X.L., Sun, H.M.: Crowd counting method based on the self-attention residual network. Appl. Intell. 51, 427\u2013440 (2021). https:\/\/doi.org\/10.1007\/s10489-020-01842-w","journal-title":"Appl. Intell."},{"key":"3572_CR35","doi-asserted-by":"publisher","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale.\u00a0arXiv preprint arXiv:2010.11929 (2020). https:\/doi.org\/https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"3572_CR36","unstructured":"Tian, Y., Chu, X., & Wang, H.: Cctrans: Simplifying and improving crowd counting with transformer.\u00a0arXiv preprint arXiv:2109.14483 (2021). https:\/doi.org110.48550\/arxiv2109.14483"},{"key":"3572_CR37","doi-asserted-by":"publisher","unstructured":"Lin, H., Ma, Z., Ji, R., Wang, Y., Hong, X.: Boosting crowd counting via multifaceted attention. In:\u00a0Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition,\u00a0pp. 19628\u201319637 (2022). https:\/\/doi.org\/10.1109\/cvpr52688.2022.01901","DOI":"10.1109\/cvpr52688.2022.01901"},{"issue":"7","key":"3572_CR38","doi-asserted-by":"publisher","first-page":"2671","DOI":"10.1007\/s00371-022-02485-3","volume":"39","author":"B Li","year":"2023","unstructured":"Li, B., Zhang, Y., Xu, H., Yin, B.: CCST: crowd counting with swin transformer. Vis. Comput. 39(7), 2671\u20132682 (2023). https:\/\/doi.org\/10.1007\/s00371-022-02485-3","journal-title":"Vis. Comput."},{"key":"3572_CR39","doi-asserted-by":"publisher","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014). https:\/\/doi.org\/10.48550\/arXiv.1412.6550","DOI":"10.48550\/arXiv.1412.6550"},{"key":"3572_CR40","doi-asserted-by":"publisher","unstructured":"Liu, L., Chen, J., Wu, H., Chen, T., Li, G., Lin, L.: Efficient crowd counting via structured knowledge transfer. In: Proceedings of the 28th ACM international conference on multimedia, pp. 2645\u20132654 (2020). https:\/\/doi.org\/10.1145\/3394171.3413938","DOI":"10.1145\/3394171.3413938"},{"key":"3572_CR41","doi-asserted-by":"publisher","unstructured":"Sun, S., Cheng, Y., Gan, Z., & Liu, J.: Patient knowledge distillation for bert model compression. arXiv preprint arXiv:1908.09355 (2019). https:\/\/doi.org\/10.48550\/arXiv.1908.09355","DOI":"10.48550\/arXiv.1908.09355"},{"key":"3572_CR42","doi-asserted-by":"publisher","unstructured":"Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L., Liu, Q.: Tinybert: Distilling bert for natural language understanding. In: Proceedings of EMNLP, pp. 2645\u20132654 (2019). https:\/\/doi.org\/10.48550\/arXiv.1909.10351","DOI":"10.48550\/arXiv.1909.10351"},{"key":"3572_CR43","doi-asserted-by":"publisher","unstructured":"Wang, W., Bao, H., Huang, S., Dong, L., Wei, F.: Minilmv2: Multi-head self-attention relation distillation for compressing pretrained transformers. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2645\u20132654 (2020). https:\/\/doi.org\/10.18653\/v1\/2021","DOI":"10.18653\/v1\/2021"},{"key":"3572_CR44","doi-asserted-by":"publisher","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., J\u00e9gou, H.: Training data-efficient image transformers & distillation through attention. In International conference on machine learning, pp. 10347\u201310357 (2021). https:\/\/doi.org\/10.47611\/harp.320","DOI":"10.47611\/harp.320"},{"issue":"4","key":"3572_CR45","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1109\/tits.2010.2050881","volume":"11","author":"K Kwong","year":"2010","unstructured":"Kwong, K., Kavaler, R., Rajagopal, R., Varaiya, P.: Real-time measurement of link vehicle count and travel time in a road network. IEEE Trans. Intell. Transp. Syst. 11(4), 814\u2013825 (2010). https:\/\/doi.org\/10.1109\/tits.2010.2050881","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3572_CR46","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1061\/9780784483053.309","volume":"40","author":"JD Zhao","year":"2016","unstructured":"Zhao, J.D., Xu, F.F., Guo, Y.J., Gao, Y.: Traffic congestion detection based on pattern matching and correlation analysis. Adv Transp Stud 40, 27\u201340 (2016). https:\/\/doi.org\/10.1061\/9780784483053.309","journal-title":"Adv Transp Stud"},{"key":"3572_CR47","doi-asserted-by":"publisher","unstructured":"Voita, E., Talbot, D., Moiseev, F., Sennrich, R., Titov, I.: Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. arXiv preprint arXiv:1905.09418 (2019). https:\/\/doi.org\/10.18653\/v1\/p19-1580","DOI":"10.18653\/v1\/p19-1580"},{"issue":"2","key":"3572_CR48","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.jrtpm.2016.04.001","volume":"6","author":"D Horne","year":"2016","unstructured":"Horne, D., Findley, D.J., Coble, D.G., Rickabaugh, T.J., Martin, J.B.: Evaluation of radar vehicle detection at four quadrant gate rail crossings. J. Rail. Transp. Plan. Manag. 6(2), 149\u2013162 (2016). https:\/\/doi.org\/10.1016\/j.jrtpm.2016.04.001","journal-title":"J. Rail. Transp. Plan. Manag."},{"key":"3572_CR49","doi-asserted-by":"publisher","unstructured":"Guerrero-G\u00f3mez-Olmedo, R., Torre-Jim\u00e9nez, B., L\u00f3pez-Sastre, R., Maldonado-Basc\u00f3n, S., Onoro-Rubio, D.: Extremely overlapping vehicle counting. In: Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17\u201319, 2015, Proceedings 7, pp. 423\u2013431 (2015). https:\/\/doi.org\/10.1007\/978-3-319-19390-8_48","DOI":"10.1007\/978-3-319-19390-8_48"},{"key":"3572_CR50","doi-asserted-by":"publisher","unstructured":"Wen, L., Zhu, P., Du, D., Bian, X., Ling, H., Hu, Q., Tong, Z.: Visdrone-mot2019: The vision meets drone multiple object tracking challenge results. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp. 0\u20130 (2019). https:\/\/doi.org\/10.1109\/iccvw54120.2021.00318","DOI":"10.1109\/iccvw54120.2021.00318"},{"key":"3572_CR51","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/tmm.2021.3120873","volume":"25","author":"X Lin","year":"2021","unstructured":"Lin, X., Sun, S., Huang, W., Sheng, B., Li, P., Feng, D.D.: EAPT: efficient attention pyramid transformer for image processing. IEEE Trans. Multimed. 25, 50\u201361 (2021). https:\/\/doi.org\/10.1109\/tmm.2021.3120873","journal-title":"IEEE Trans. Multimed."},{"key":"3572_CR52","doi-asserted-by":"publisher","unstructured":"Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In:\u00a0Proceedings of the AAAI conference on artificial intelligence, pp. 2576\u20132583 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i3.16360","DOI":"10.1609\/aaai.v35i3.16360"},{"key":"3572_CR53","doi-asserted-by":"publisher","unstructured":"Wan, J., Liu, Z., Chan, AB.: A generalized loss function for crowd counting and localization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition,\u00a0pp. 1974\u20131983 (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.00201","DOI":"10.1109\/cvpr46437.2021.00201"},{"key":"3572_CR54","doi-asserted-by":"publisher","unstructured":"Wang, M., Cai, H., Han, X., Zhou, J., Gong, M.: STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting.\u00a0arXiv preprint arXiv:2012.10189 (2022). https:\/\/doi.org\/10.1109\/tmm.2022.3142398","DOI":"10.1109\/tmm.2022.3142398"},{"issue":"12","key":"3572_CR55","doi-asserted-by":"publisher","first-page":"2631","DOI":"10.3390\/electronics12122631","volume":"12","author":"Y Sun","year":"2023","unstructured":"Sun, Y., Li, M., Guo, H., Zhang, L.: MSGSA: Multi-Scale Guided Self-Attention Network for Crowd Counting. Electron 12(12), 2631 (2023). https:\/\/doi.org\/10.3390\/electronics12122631","journal-title":"Electron"},{"key":"3572_CR56","doi-asserted-by":"publisher","unstructured":"Yu, R., Xu, X., Shen, Y.: RHNet: Lightweight dilated convolutional networks for dense objects counting. In: 2019 Chinese Control Conference (CCC), pp. 8455\u20138459 (2019). https:\/\/doi.org\/10.23919\/ChiCC.2019.8866393","DOI":"10.23919\/ChiCC.2019.8866393"},{"key":"3572_CR57","doi-asserted-by":"publisher","unstructured":"Liu, L., Chen, J., Wu, H., et al.: Efficient crowd counting via structured knowledge transfer. In: Proceedings of the 28th ACM international conference on multimedia, pp. 2645\u20132654 (2020). https:\/\/doi.org\/10.1145\/3394171.3413938","DOI":"10.1145\/3394171.3413938"},{"key":"3572_CR58","doi-asserted-by":"publisher","unstructured":"Meng, Y., Zhang, H., Zhao, Y., et al.: Spatial uncertainty-aware semi-supervised crowd counting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15549\u201315559 (2021). https:\/\/doi.org\/10.1109\/iccv48922.2021.01526","DOI":"10.1109\/iccv48922.2021.01526"},{"issue":"10","key":"3572_CR59","doi-asserted-by":"publisher","first-page":"6821","DOI":"10.1109\/TCSVT.2022.3171235","volume":"32","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Cao, G., Shi, H., Hu, Y.: Lw-count: An effective lightweight encoding-decoding crowd counting network. Trans. Circuits Syst. Video Technol. 32(10), 6821\u20136834 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2022.3171235","journal-title":"Trans. Circuits Syst. Video Technol."},{"key":"3572_CR60","first-page":"15394","volume":"35","author":"W Cao","year":"2022","unstructured":"Cao, W., Zhang, Y., Gao, J., et al.: Pkd: General distillation framework for object detectors via pearson correlation coefficient. Adv. Neural. Inf. Process. Syst. 35, 15394\u201315406 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"2","key":"3572_CR61","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s11554-023-01286-8","volume":"20","author":"MA Khan","year":"2023","unstructured":"Khan, M.A., Menouar, H., Hamila, R.: LCDnet: a lightweight crowd density estimation model for real-time video surveillance. J. Real-Time Image Process. 20(2), 29 (2023). https:\/\/doi.org\/10.1007\/s11554-023-01286-8","journal-title":"J. Real-Time Image Process."},{"key":"3572_CR62","doi-asserted-by":"publisher","unstructured":"Hu, J., Han, H.: NeXtCrowd: Lightweight And Efficient Network Design for Dense Crowd Counting. In:\u00a02023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC\/DSS\/SmartCity\/DependSys), pp. 90\u201397(2023). IEEE. https:\/\/doi.org\/10.1109\/HPCC-DSS-SmartCity-DependSys60770.2023.00022","DOI":"10.1109\/HPCC-DSS-SmartCity-DependSys60770.2023.00022"},{"key":"3572_CR63","doi-asserted-by":"publisher","unstructured":"Guo, Z., Yan, H., Li, H., et al.: Class attention transfer based knowledge distillation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11868\u201311877 (2023). https:\/\/doi.org\/10.1109\/cvpr52729.2023.01142","DOI":"10.1109\/cvpr52729.2023.01142"},{"key":"3572_CR64","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TIP.2023.3343609","volume":"33","author":"R Wang","year":"2023","unstructured":"Wang, R., Hao, Y., Hu, L., et al.: Efficient crowd counting via dual knowledge distillation. IEEE Trans. Image Process. 33, 569\u2013583 (2023). https:\/\/doi.org\/10.1109\/TIP.2023.3343609","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"3572_CR65","doi-asserted-by":"publisher","first-page":"3082","DOI":"10.1109\/JIOT.2023.3294727","volume":"11","author":"J Yi","year":"2024","unstructured":"Yi, J., Chen, F., Shen, Z., Xiang, Y., Xiao, S., Zhou, W.: An effective lightweight crowd counting method based on an encoder\u2013decoder network for internet of video things. IEEE Internet Things J. 11(2), 3082\u20133094 (2024). https:\/\/doi.org\/10.1109\/JIOT.2023.3294727","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"3572_CR66","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1109\/tmi.2022.3223683","volume":"42","author":"R Liu","year":"2022","unstructured":"Liu, R., Wang, T., Li, H., Zhang, P., Li, J., Yang, X., Sheng, B.: TMM-Nets: transferred multi-to mono-modal generation for lupus retinopathy diagnosis. IEEE Trans. Med. Imaging 42(4), 1083\u20131094 (2022). https:\/\/doi.org\/10.1109\/tmi.2022.3223683","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3572_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119038","volume":"213","author":"H Li","year":"2023","unstructured":"Li, H., Zhang, J., Kong, W., Shen, J., Shao, Y.: CSA-Net: cross-modal scale-aware attention-aggregated network for RGB-T crowd counting. Expert Syst. Appl. 213, 119038 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2022.119038","journal-title":"Expert Syst. Appl."}],"updated-by":[{"DOI":"10.1007\/s00371-024-03662-2","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000}}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03572-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03572-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03572-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T06:30:19Z","timestamp":1740983419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03572-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,30]]},"references-count":67,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3572"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03572-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,30]]},"assertion":[{"value":"6 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2024","order":3,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":4,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00371-024-03662-2","URL":"https:\/\/doi.org\/10.1007\/s00371-024-03662-2","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}