{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:41:20Z","timestamp":1775324480037,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533923","type":"print"},{"value":"9789819533930","type":"electronic"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3393-0_39","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T09:51:20Z","timestamp":1761904280000},"page":"479-491","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GramFormer-Based Crowd Counting with\u00a0Learnable Fourier Encoding and\u00a0Attention Mechanism"],"prefix":"10.1007","author":[{"given":"Wenjie","family":"Xia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yehao","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohua","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"39_CR1","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.neunet.2022.01.015","volume":"148","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Yang, J., Zhang, D., Zhang, K., Chen, B., Du, S.: Region-aware network: model human\u2019s top-down visual perception mechanism for crowd counting. Neural Netw. 148, 219\u2013231 (2022)","journal-title":"Neural Netw."},{"key":"39_CR2","doi-asserted-by":"crossref","unstructured":"Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the CVPR, pp. 2547\u20132554 (2013)","DOI":"10.1109\/CVPR.2013.329"},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Jiang, S., Cai, J., Zhang, H., Liu, Y., Liu, Q.: Compare and focus: multi-scale view aggregation for crowd counting. IEEE Trans. Intell. Transp. Syst. (2024)","DOI":"10.1109\/TITS.2024.3432789"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Jiang, X., et al.: Density-aware multi-task learning for crowd counting. IEEE Trans. Multimedia 23, 443\u2013453 (2020)","DOI":"10.1109\/TMM.2020.2980945"},{"key":"39_CR5","unstructured":"Lee, J., Toutanova, K.: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.048053(8) (2018)"},{"key":"39_CR6","unstructured":"Li, Y., Si, S., Li, G., Hsieh, C.J., Bengio, S.: Learnable Fourier features for multi-dimensional spatial positional encoding. In: NeurIPS, vol. 34, pp. 15816\u201315829 (2021)"},{"key":"39_CR7","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, X., Chen, D.: CSRNet: dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of CVPR, pp. 1091\u20131100 (2018)","DOI":"10.1109\/CVPR.2018.00120"},{"key":"39_CR8","doi-asserted-by":"crossref","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)","DOI":"10.1007\/s11432-021-3445-y"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Lin, H., et al.: Direct measure matching for crowd counting. arXiv preprint arXiv:2107.01558 (2021)","DOI":"10.24963\/ijcai.2021\/116"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Lin, H., Ma, Z., Hong, X., Shangguan, Q., Meng, D.: Gramformer: learning crowd counting via graph-modulated transformer. In: Proceedings of the AAAI, vol.\u00a038, pp. 3395\u20133403 (2024)","DOI":"10.1609\/aaai.v38i4.28126"},{"key":"39_CR11","doi-asserted-by":"crossref","unstructured":"Liu, C., Hu, G., Li, Y., Gao, Y., Shi, L.: GTL-ASENet: global to local adaptive spatial encoder network for crowd counting. Multimedia Tools Appl. 83(22), 61697\u201361714 (2024)","DOI":"10.1007\/s11042-023-14330-3"},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of CVPR, pp. 5099\u20135108 (2019)","DOI":"10.1109\/CVPR.2019.00524"},{"key":"39_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Hu, Y., Cao, G., Wang, J.: SENet: super-resolution enhancement network for crowd counting. Pattern Recogn. 162, 111420 (2025)","DOI":"10.1016\/j.patcog.2025.111420"},{"key":"39_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of ICCV, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"39_CR15","doi-asserted-by":"crossref","unstructured":"Luo, A., et al.: Hybrid graph neural networks for crowd counting. In: Proceedings of AAAI, vol.\u00a034, pp. 11693\u201311700 (2020)","DOI":"10.1609\/aaai.v34i07.6839"},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Ma, Z., Wei, X., Hong, X., Gong, Y.: Bayesian loss for crowd count estimation with point supervision. In: Proceedings of ICCV, pp. 6142\u20136151 (2019)","DOI":"10.1109\/ICCV.2019.00624"},{"key":"39_CR17","doi-asserted-by":"crossref","unstructured":"Ma, Z., Wei, X., Hong, X., Lin, H., Qiu, Y., Gong, Y.: Learning to count via unbalanced optimal transport. In: Proceedings of AAAI, vol.\u00a035, pp. 2319\u20132327 (2021)","DOI":"10.1609\/aaai.v35i3.16332"},{"key":"39_CR18","doi-asserted-by":"crossref","unstructured":"Miao, Y., Lin, Z., Ding, G., Han, J.: Shallow feature based dense attention network for crowd counting. In: Proceedings of AAAI, vol.\u00a034, pp. 11765\u201311772 (2020)","DOI":"10.1609\/aaai.v34i07.6848"},{"key":"39_CR19","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Yasarla, R., Patel, V.M.: JHU-CROWD++: large-scale crowd counting dataset and a benchmark method. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2594\u20132609 (2020)","DOI":"10.1109\/TPAMI.2020.3035969"},{"key":"39_CR20","doi-asserted-by":"crossref","unstructured":"Wan, J., Liu, Z., Chan, A.B.: A generalized loss function for crowd counting and localization. In: Proceedings of CVPR, pp. 1974\u20131983 (2021)","DOI":"10.1109\/CVPR46437.2021.00201"},{"issue":"6","key":"39_CR21","doi-asserted-by":"publisher","first-page":"2141","DOI":"10.1109\/TPAMI.2020.3013269","volume":"43","author":"Q Wang","year":"2020","unstructured":"Wang, Q., Gao, J., Lin, W., Li, X.: Nwpu-crowd: a large-scale benchmark for crowd counting and localization. IEEE Trans. Pattern Anal. Mach. Intell. 43(6), 2141\u20132149 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"39_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, F., Huang, D.: Dual-branch counting method for dense crowd based on self-attention mechanism. Exp. Syst. Appl. 236, 121272 (2024)","DOI":"10.1016\/j.eswa.2023.121272"},{"issue":"1","key":"39_CR23","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1109\/TCSVT.2022.3187194","volume":"33","author":"Z Wu","year":"2022","unstructured":"Wu, Z., Zhang, X., Tian, G., Wang, Y., Huang, Q.: Spatial-temporal graph network for video crowd counting. IEEE Trans. Circ. Syst. Video Technol. 33(1), 228\u2013241 (2022)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"issue":"1","key":"39_CR24","doi-asserted-by":"publisher","first-page":"2329859","DOI":"10.1080\/08839514.2024.2329859","volume":"38","author":"Z Xu","year":"2024","unstructured":"Xu, Z., Lin, H., Chen, Y., Li, Y.: Label noise robust crowd counting with loss filtering factor. Appl. Artif. Intell. 38(1), 2329859 (2024)","journal-title":"Appl. Artif. Intell."},{"key":"39_CR25","doi-asserted-by":"crossref","unstructured":"Yang, S., Guo, W., Ren, Y.: CrowdFormer: an overlap patching vision transformer for top-down crowd counting. In: IJCAI, vol.\u00a01, p.\u00a02 (2022)","DOI":"10.24963\/ijcai.2022\/215"},{"issue":"5","key":"39_CR26","first-page":"1","volume":"20","author":"C Zhang","year":"2024","unstructured":"Zhang, C., Zhang, Y., Li, B., Piao, X., Yin, B.: Crowdgraph: weakly supervised crowd counting via pure graph neural network. ACM Trans. Multimed. Comput. Commun. Appl. 20(5), 1\u201323 (2024)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"39_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of CVPR, pp. 589\u2013597 (2016)","DOI":"10.1109\/CVPR.2016.70"},{"key":"39_CR28","doi-asserted-by":"crossref","unstructured":"Zheng, C., Gao, X., Chen, Z., Lyu, L.: Dual backbone multi-attention hierarchical fusion and feature enhancement network for crowd counting. IEEE Trans. Consum. Electron. (2025)","DOI":"10.1109\/TCE.2025.3557449"},{"key":"39_CR29","doi-asserted-by":"publisher","first-page":"1794","DOI":"10.1109\/LSP.2022.3198371","volume":"29","author":"X Zhong","year":"2022","unstructured":"Zhong, X., Yan, Z., Qin, J., Zuo, W., Lu, W.: An improved normed-deformable convolution for crowd counting. IEEE Sig. Process. Lett. 29, 1794\u20131798 (2022)","journal-title":"IEEE Sig. Process. Lett."},{"key":"39_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, J., et al.: Confusion region mining for crowd counting. IEEE Trans. Neural Netw. Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3311020"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3393-0_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T16:31:03Z","timestamp":1775320263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3393-0_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819533923","9789819533930"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3393-0_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xuzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icig.csig.org.cn\/2025\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}